This paper studies the information of big data in the digital publishing industry chain and adopts advanced algorithms for its fusion calculation. The basic theory of digital publishing ecological chain is dissected, the construction requirements, construction methods, and construction paths of digital publishing ecological chain are analysed, and feasible construction measures are proposed. It also defines the connotation of the fusion of knowledge services between publishing institutions and libraries in the digital era; then analyses the characteristics and (...) principles of the fusion of knowledge services between publishing institutions and libraries in the digital era; and finally sorts out the theoretical foundations such as synergy theory, information integration service theory, and game theory. Meanwhile, this paper also studies the flow of digital publishing resources and empirically analyses the eco-efficiency of digital publishing eco-chain by using metabolic network analysis, population analysis, and life cycle analysis, and it finds the eco-efficiency problems of the existing e-book eco-chain from its analysis. Finally, the imbalance in the digital publishing ecological chain and its hazards are analysed, and specific regulation and optimization measures are proposed. The research in this paper makes up for the deficiencies of related studies and can well solve the problems that exist in the previous and provide theoretical support for the healthy development of digital publishing enterprises. (shrink)

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book (...) presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. (shrink)

Aiming at the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, an English teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the author uses the idea of K-means clustering to analyze the collected original error data, such as teacher level, teaching facility investment, and policy relevance level, removes the data that the algorithm considers unreliable, uses the remaining valid data to calculate (...) the weighting factor of the modified fuzzy logic algorithm, and evaluates the weighted average with the node measurement data and gets the final fusion value. Secondly, the author integrates the big data information fusion and K-means clustering algorithm, realizes the clustering and integration of the index parameters of English teaching ability, compiles the corresponding English teaching resource allocation plan, and realizes the evaluation of English teaching ability. Finally, the results show that using this method to evaluate English teaching ability has better information fusion analysis ability, which improves the accuracy of teaching ability evaluation and the efficiency of teaching resources application. (shrink)

To obtain effective fusion results of multi source evidences with different importance, an evidence fusion method with importance discounting factors based on neutrosopic probability analysis in DSmT framework is proposed. First, the reasonable evidence sources are selected out based on the statistical analysis of the pignistic probability functions of single focal elements. Secondly, the neutrosophic probability analysis is conducted based on the similarities of the pignistic probability functions from the prior evidence knowledge of the reasonable evidence sources. Thirdly, (...) the importance discounting factors of the reasonable evidence sources are obtained based on the neutrosophic probability analysis and the reliability discounting factors of the real-time evidences are calculated based on probabilistic-based distances. Fourthly, the real-time evidences are discounted by the importance discounting factors and then the evidences with the mass assignments of neutrosophic empty sets are discounted by the reliability discounting factors. Finally, DSmT+PCR5 of importance discounted evidences is applied. Experimental examples show that the decision results based on the proposed fusion method are different from the results based on the existed fusion methods. Simulation experiments of recognition fusion are performed and the superiority of proposed method is testified well by the simulation results. (shrink)

Fusion validity assessments employ structural equation models to investigate whether an existing scale functions in accordance with theory. Fusion validity parallels criterion validity by depending on correlations with non-scale variables but differs from criterion validity because it requires at least one theorized effect of the scale, and because both the scale and scaled-items are included in the model. Fusion validity, like construct validity, will be most informative if the scale is embedded in as full a substantive context (...) as theory permits. Appropriate scale functioning in a comprehensive theoretical context greatly enhances a scale’s validity. Inappropriate scale functioning questions the scale but the scale’s theoretical embedding encourages detailed diagnostic investigations potentially challenging specific items, the procedure used to calculate scale values, or aspects of the theory, but also possibly recommends incorporating additional items into the scale. The scaled items should have survived prior content and methodological assessments but the items may or may not reflect a common factor because items having diverse causal backgrounds can sometimes fuse to form a unidimensional entity. Though items reflecting a common cause can be assessed for fusion validity, we illustrate fusion validity in the more challenging context of a scale comprised of diverse items and embedded in a complicated theory. Specifically we consider the Leadership scale from the Alberta Context Tool with care aides working in Canadian long-term care homes. (shrink)

We introduce a new operator – belief fusion– which aggregates the beliefs of two agents, each informed by a subset of sources ranked by reliability. In the process we definepedigreed belief states, which enrich standard belief states with the source of each piece of information. We note that the fusion operator satisfies the invariants of idempotence, associativity, and commutativity. As a result, it can be iterated without difficulty. We also define belief diffusion; whereas fusion generally produces (...) a belief state with more information than is possessed by either of its two arguments, diffusion produces a state with less information. Fusion and diffusion are symmetric operators, and together define a distributive lattice. Finally, we show that AGM revision can be viewed as fusion between a novice and an expert. (shrink)

In this paper, we formulate some approaches to belief fusion and revision using epistemic logic semantics. Fusion operators considered in this paper are majority merging, arbitration, and general merging. Some modalities corresponding to belief fusion and revision operators are incorporated into epistemic logics. The Kripke semantics of these extended logics are presented. While most existing approaches treat belief fusion and revision operators as meta-level constructs, we directly incorporate these operators into our object logic language. By doing (...) so, we both extend the expressive power of epistemic logic and enhance the techniques of information fusion. (shrink)

We introduce a new operator – belief fusion– which aggregates the beliefs of two agents, each informed by a subset of sources ranked by reliability. In the process we definepedigreed belief states, which enrich standard belief states with the source of each piece of information. We note that the fusion operator satisfies the invariants of idempotence, associativity, and commutativity. As a result, it can be iterated without difficulty. We also define belief diffusion; whereas fusion generally produces a (...) belief state with more information than is possessed by either of its two arguments, diffusion produces a state with less information. Fusion and diffusion are symmetric operators, and together define a distributive lattice. Finally, we show that AGM revision can be viewed as fusion between a novice and an expert. (shrink)

A NewsFusion System is a logic-based system for merging heterogeneous structured news reports. Structured news reports are XML documents, where the textentries are restricted to individual words or simple phrases, such as names and domain-specific terminology, and numbers and units. We assume structured news reports do not require natural language processing. In previous papers, we have presented aspects of a logic-based framework for merging structured news reports based on fusion rules. Fusion rules are a form of scripting language (...) that define how structured news reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured news reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. It is expected that a set of fusion rules is defined for any given application. In this paper, we present a framework for aggregation predicates that can be used in fusion rules for context-dependent aggregation of structured news reports. We also present a case study that uses aggregation predicates to merge weather reports that have been obtained from a selection of websites. (shrink)

If certain versions of the correspondence theory of truth are correct, then truth can be informatively defined as correspondence to facts; facts would be truth-makers, and we could explain truth in terms of truth-bearers, correspondence, and truth-makers. I explain how slingshot arguments work generally, as collapsing arguments (regardless of their targets). Working through the slingshots of Davidson, and Gödel, I claim that Davidson’s slingshot involves dubitable premises, but that Gödel’s slingshot is terminal to certain versions of the correspondence theory, as (...) it poses a fatal dilemma between what I call fact fission and fact fusion. My claim is that it is impossible to informatively define truth in terms of correspondence to facts. (shrink)

We examined the influence of identity fusion and status of evaluator on willingness to fight for one’s group after group-descriptive or not group-descriptive feedback. The valence of evaluative information was varied as well: feedback either support negative group-stereotype or contradict negative group-stereotype. In two studies we partially replicated previous findings on self-verification. Individuals fused with one’s group were more prone than non fused to fight for group members after receiving, challenging, not group-describing feedback, but only when evaluator’s status (...) was high. When the evaluator’ status was low, fused participants engaged in self-enhancement strategies: that is they endorse radical behavior after negative, even if group-describing, feedback. We discuss the importance of identity fusion and evaluator’s status for determining when self-enhancement and self-verification will and will not be evoke. (shrink)

The vital data about the electrical activities of the brain are carried by the electroencephalography signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG (...) studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model with Singular Value Decomposition Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset. (shrink)

We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and representing new kinds (...) of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

Combining information from two or possibly several blocks of data is gaining increased attention and importance in several areas of science and industry. Typical examples can be found in chemistry, spectroscopy, metabolomics, genomics, systems biology and sensory science. Many methods and procedures have been proposed and used in practice. The area goes under different names: data integration, data fusion, multiblock analyses, multiset analyses and a few more. This book is an attempt to give an up-to-date treatment of the (...) most used and important methods within an important branch of the area; namely methods based on so-called components or latent variables. These methods have already obtained an enormous attention in for instance chemometrics, bioinformatics, machine learning and sensometrics and have proved to be important both for prediction and interpretation. The book is primarily a description of methodologies, but most of the methods will be illustrated by examples from the above-mentioned areas. The book is written such that both users of the methods as well as method developers will hopefully find sections of interest. In the end of the book there is a description of a software package developed particularly for the book. This package is freely available in R and covers many of the methods discussed. To distinguish the different type of methods from each other, the book is divided into five parts. Part I is introduction and preliminary concepts. Part II is the core of the book containing the main unsupervised and supervised methods. Part III deals with more complex structures and, finally, Part IV discusses alternative unsupervised and supervised methods. The book ends with Part V discussing the available software. Our recommendation for reading the book are as follows. A minimum read of the book would involve chapters 1, 2, 3, 5 and 7. Chapters 4, 6 and 8 are more specialized and chapters 9 and 10 contain methods we think are more advanced or less obvious to use. We feel privileged to have so many friendly colleagues who were willing to spend their time on helping us to improve the book by reading separate chapters. We would like to express our thanks to: Rasmus Bro, Margriet Hendriks, Ulf Indahl, Henk Kiers, Ingrid MaÌ⁽ge, Federico Marini, AÌ⁽smund Rinnan, Rosaria Romano, Lars Erik Solberg, Marieke Timmerman, Oliver Tomic, Johan Westerhuis and Barry Wise. Of course, the correctness of the final text is fully our responsibility! (shrink)

The problem of merging multiple sources information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, expert opinion pooling, etc. Recently, several approaches have been proposed to merge classical propositional bases, or sets of (non-prioritized) goals. These approaches are in general semantically defined. Like in belief revision, they use priorities, generally based on Dalal's distance, for merging the classical bases and return a new classical base as a result. An immediate consequence (...) of the generation of a classical base is the impossibility of iterating the fusion process in a coherent way w.r.t. priorities since the underlying ordering is lost. This paper presents a general approach for fusing prioritized bases, both semantically and syntactically, when priorities are represented in the possibilistic logic framework. Different classes of merging operators are considered including conjunctive, disjunctive, reinforcement and adaptive operators. We show that the approaches which have been recently proposed for merging classical propositional bases can be embedded in this setting. The result is then a prioritized base, and hence the process can be coherently iterated. Moreover, we also provide a syntactic counterpart for the fusion of classical bases. (shrink)

In nature, gregarious animals, insects, or bacteria usually exhibit paradoxical behaviors in the form of group fission and fusion, which exerts an important influence on group’s pattern formation, information transfer, and epidemiology. However, the fission-fusion dynamics have received little attention compared to other flocking behavior. In this paper, an intermittent selective interaction based control algorithm for the self-organized fission-fusion behavior of flocking system is proposed, which bridges the gap between the two conflicting behaviors in a unified (...) fashion. Specifically, a hybrid velocity coordination strategy that includes both the egalitarian and selective interactions is proposed, where the egalitarian interaction is to maintain the flock’s order and achieve the fusion behavior while the selective interaction strategy is for the response to external stimulus information and generates the fission behavior. Numerical simulations demonstrate that the proposed control algorithm can realize the self-organized fission-fusion behavior of flocking system under a unified framework. The influences of the main control parameters on the performance of the fission-fusion behavior are also discussed. In particular, the trade-off parameterαbalances the exploration and exploitation behaviors of flocking system and significantly enhances its movement flexibility and environmental adaptivity. (shrink)

Nowadays, innovation and entrepreneurship courses occupy a very important place in universities and colleges and have also become an important teaching position in the process of building a new science. Colleges and universities actively respond to the challenge of “mass entrepreneurship and innovation” and define the goals and specifications of the talent training mechanism based on data fusion algorithms to cultivate as much high-quality applied talent as possible. In view of some shortcomings and problems in the current talent training (...) mechanism in universities and colleges, this paper proposes a data fusion algorithm based on information fusion theory and proof theory. The aim is to verify the feasibility of establishing a talent training mechanism for innovation and entrepreneurship education in universities and colleges. And this paper analyzes and explores the data fusion algorithm and the elements of innovation and entrepreneurial talent training, and forms an operating mechanism for entrepreneurial talent training according to social needs. Among them, the efficiency of the data fusion algorithm used by the GM model plays a significant role in the final result, and the minimum relative error value is 3.2%. Finally, it is concluded that we should focus on establishing a perfect talent training system for college students’ innovation and entrepreneurship education to improve students’ own comprehensive quality and various abilities, and to solve some social problems that are difficult to find employment in essence. (shrink)

On the basis of ecological footprint theory and tourism ecological footprint theory, the sustainable development indexes such as ecological footprint, ecological carrying capacity, ecological deficit, and ecological surplus of the research area were calculated and the long-term change pattern of each index was analyzed. This paper shows that the ecological footprint of the research area increases year by year, but the ecological footprint is always smaller than the ecological carrying capacity, indicating that the area is still in the state of (...) sustainable development. However, the per capita ecological surplus shows a decreasing trend year by year, indicating that the sustainable development of the region is getting worse. This paper proposes a reordering method of tourist attractions based on heterogeneous information fusion, and realizes the retrieval and reordering of tourist attractions based on user query and fusion of heterogeneous information, so as to help users make travel decisions. In view of the shortage of tourism commercial websites to passively provide scenic spot information, this paper puts forward a scenic spot retrieval method based on query words to enable users to obtain scenic spot information according to their needs, and constructs a tourist consumer data analysis system. The preprocessing methods and methods adopted by the data preprocessing module are analyzed in detail, and the algorithms used in the travel route analysis and consumer spending ability analysis are described in detail. The data of tourism consumers are analyzed by this system, and the results are evaluated. (shrink)

Some experiments are risky in that they cannot repeatedly produce certain phenomenon at will for study because the scientific knowledge of the process generating the uncertain phenomenon is poorly understood or may directly contradict with existing scientific knowledge. These experiments may have great impact not just to the scientific community but to mankind in general. Banning them from study may incur societies a great opportunity cost but accepting them runs the risk that scientists are doing junk science. How to make (...) an informed decision to accept/reject such study scientifically for the mainstream scientific community is of great importance to mankind. Here, we propose a statistical methodology to handle the situation. Specifically, we consider the likelihood of not observing the phenomenon after n trails so that it is statistically significant to have nil result. Consequently, we reject the hypothesis that there is some probability that we observe the phenomenon. (shrink)

In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise (...) ratio, and 12 statistical features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification. (shrink)

State spaces are, in the most general sense, sets of entities that contain information. Examples include states of dynamical systems, processes of observations, or possible worlds. We use domain theory to describe the structure of positive and negative information in state spaces. We present examples ranging from the space of trajectories of a dynamical system, over Dunn’s aboutness interpretation of fde, to the space of open sets of a spectral space. We show that these information structures induce (...) so-called hype models which were recently developed by Leitgeb. Conversely, we prove a representation theorem: roughly, hype models can be represented as induced by an information structure. Thus, the well-behaved logic hype is a sound and complete logic for reasoning about information in state spaces. As application of this framework, we investigate information fusion. We motivate two kinds of fusion. We define a groundedness and a separation property that allow a hype model to be closed under the two kinds of fusion. This involves a Dedekind–MacNeille completion and a fiber-space like construction. The proof-techniques come from pointless topology and universal algebra. (shrink)

This paper discusses the emergence of the concepts of fusion as an energy source and of the controlled fusion reactor. These concepts are shown to have arisen from the bringing together of several different branches of physics, notably nuclear physics, astrophysics, and gas discharge physics, in the period between the two world wars. By the late 1930s, enough information had become available for the possibility of a controlled fusion device to be explored, and a number of (...) physicists seem to have recognized this independently. Such a possibility was the subject of discussion at Los Alamos during and immediately after the war, but the first detailed proposals to be recorded seem to be those of G. P. Thomson and P. C. Thonemann, both working in Britain in the middle to late 1940s. These proposals, which led to the setting up in Britain of the first coherent programme of controlled fusion research, are outlined and discussed. (shrink)

The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, (...) an efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images. (shrink)

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and (...) word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method. (shrink)

The impact of new technologies on the transformation of information into knowledge is not clear. Especially problematic is the degree to which electronic communication can replace traditional forums in which information is judged and social consensus about its value is achieved. This article uses electronic bulletin boards active during the cold fusion saga that began in 1989 to explore these issues. Dividing the contents of the bulletin boards into big ideas and little ideas, the article suggests that (...) only about half of all messages on the boards were big ideas, and only half of those were on technical issues. The study suggests that the substantial volume of irrelevant material and the difficulty of applying extratextual cues to the judgment of information made the bulletin boards an ineffective tool for creating knowledge in this case. (shrink)

Image similarity metric, also known as metric learning in computer vision, is a significant step in various advanced image tasks. Nevertheless, existing well-performing approaches for image similarity measurement only focus on the image itself without utilizing the information of other modalities, while pictures always appear with the described text. Furthermore, those methods need human supervision, yet most images are unlabeled in the real world. Considering the above problems comprehensively, we present a novel visual similarity metric model named PTF-SimCM. It (...) adopts a self-supervised contrastive structure like SimSiam and incorporates a multimodal fusion module to utilize textual modality correlated to the image. We apply a cross-modal model for text modality rather than a standard unimodal text encoder to improve late fusion productivity. In addition, the proposed model employs Sentence PIE-Net to solve the issue caused by polysemous sentences. For simplicity and efficiency, our model learns a specific embedding space where distances directly correspond to the similarity. Experimental results on MSCOCO, Flickr 30k, and Pascal Sentence datasets show that our model overall outperforms all the compared methods in this work, which illustrates that the model can effectively address the issues faced and enhance the performances on unsupervised visual similarity measuring relatively. (shrink)

We consider a computational model comparing the possible roles of and in phonetic decoding, demonstrating that these two routes can contain similar information in some communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve some sort of fusion of association and simulation in the human brain.

Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster–Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive model taxonomy. This gives a number of new insights and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete (...) class='Hi'>information. (shrink)

Performing an inference involves irreducibly dynamic cognitive procedures. The article proposes that a non-associative information frame, corresponding to a residuated pogroupoid, underpins the information structure involved. The argument proceeds by expounding the informational turn in logic, before outlining the cognitive actions at work in deductive inference. The structural rules of Weakening, Contraction, Commutation, and Association are rejected on the grounds that they cause us to lose track of the information flow in inferential procedures. By taking the operation (...) of information application as the primary operation, the fusion connective is retained, with commutative failure generating a double implication. The other connectives are rejected. (shrink)

A lot of research has been done on the detection of mental workload using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of measurement modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify MWL. The goal of this research was to classify perceived mental workload using a deep neural network that flexibly makes use of multiple modalities, in order to allow for feature sharing (...) between modalities. To achieve this goal, an experiment was conducted in which MWL was simulated with the help of verbal logic puzzles. The puzzles came in five levels of difficulty and were presented in a random order. Participants had 1 h to solve as many puzzles as they could. Between puzzles, they gave a difficulty rating between 1 and 7, seven being the highest difficulty. Galvanic skin response, photoplethysmograms, functional near-infrared spectrograms and eye movements were collected simultaneously using LabStreamingLayer. Marker information from the puzzles was also streamed on LSL. We designed and evaluated a novel intermediate fusion multimodal DNN for the classification of PMWL using the aforementioned four modalities. Two main criteria that guided the design and implementation of our DNN are modularity and generalisability. We were able to classify PMWL within-level accurate on a seven-level workload scale using the aforementioned modalities. The model architecture allows for easy addition and removal of modalities without major structural implications because of the modular nature of the design. Furthermore, we showed that our neural network performed better when using multiple modalities, as opposed to a single modality. The dataset and code used in this paper are openly available. (shrink)

The integrated information theory (IIT) is an ambitious theory of consciousness that aims to provide both a neuroscientific and a metaphysical account of consciousness by identifying consciousness with integrated information. In the philosophical literature, IIT is often associated with a panpsychist worldview. In this paper, I show that IIT can be considered, instead, as a form of emergentism that is incompatible with panpsychism. First, I show that the panpsychist interpretation of IIT is based on two properties of integrated (...) information: intrinsicality and fundamentality. I show that the way IIT deals with these two properties, however, aligns better with emergentism than panpsychism. Then, after plugging some anti-panpsychist assumptions into IIT’s structure, I analyse different philosophical options for interpreting the formal result of causal emergence of integrated information in terms of dependence on and autonomy from its physical substrate. The upshot is that integrated information can be seen as dependent upon the fusion of the cause-effect powers of a physical substrate, and as autonomous in virtue of global-to-local determination. According to this interpretation, consciousness is the constraining power of the system as a whole upon itself, when this power emerges from the fusion on the cause-effect powers of the system’s components. (shrink)

Heterogeneous information networks can naturally simulate complex objects, and they can enrich recommendation systems according to the connections between different types of objects. At present, a large number of recommendation algorithms based on heterogeneous information networks have been proposed. However, the existing algorithms cannot extract and combine the structural features in heterogeneous information networks. Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to (...) automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. Furthermore, by fully considering the scoring relationship between nodes, a calculation strategy combining heterogeneous information and a scoring information fusion strategy is proposed to solve the scoring between nodes, which makes the prediction scoring more accurate. Finally, by updating the nodes, the training scale is reduced, and the calculation efficiency is improved. The study conducted a large number of experiments on three real data sets with millions of edges. The results of the experiments show that compared with PMF, SemRec, and other algorithms, the proposed algorithm improves the recommendation accuracy MAE by approximately 3% and the RMSE by approximately 8% and reduces the time consumption significantly. (shrink)

The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the (...) case of a stratified experiment with a binary outcome and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend existing results to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as a regret minimization problem subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a concave maximization and solved using conventional methods. Finally, we demonstrate the practical utility of our methods using data from the Women’s Health Initiative. (shrink)

The Editors express their gratitude and appreciation to the indi-viduals listed below who served as referees for Informal Logic for Volumes 31 (2011) and 32 (2012).