Collaborative filtering is being used within organizations and in community contexts for knowledge management and decision support as well as the facilitation of interactions among individuals. This article analyzes rhetorical and technical efforts to establish trust in the constructions of individual opinions, reputations, and tastes provided by these systems. These initiatives have some important parallels with early efforts to support quantitative opinion polling and construct the notion of “public opinion.” The article explores specific ways to increase trust in (...) these systems, albeit a “guarded trust” in which individuals actively seek information about system foibles and analyze the reputations of participants. (shrink)

This research work proposes an implementation of adept content-based music retrieval technique that attempts to address the demands of the rising availability of digital music. The primary objective of this research work is to balance the perilous impact of non-relevant songs through progressive filtering for query by humming music information retrieval system. The PF is a technique of searching in manifolds for problem solving through reduced search space. A new strategy for empirical mode decomposition analysis is adopted, and outcomes (...) are propelled as a significant source of information for multiresolution histogram representations. Finally, the concept of PF is realized through MRH to retrieve the desired song by humming. The proposed multiresolution QBH system is evaluated on a music database of 1200 fragments, and the experimental results effectively locate the humming patterns and recite the eminence of EMD analysis for retrieving the desired song through humming. (shrink)

This report brings out a simple observation on the close connection of lters with algebraic closure systems. In [1], Orrin Frink gave a general denition of ideals in ordered sets. Here, we use the dual notion of lter and apply it to preordered sets. When referring to nite sets fc1; : : : ; ckg we often omit the brackets. The symbol ; denotes the empty set and, X f Y means that X is a nite subset of Y.

This note calls attention to the fact that the natural extensions of standard logics appear to be an application of the method of inductive generation of logics . One can generalize our observations beyond the range of algebraic logics under suitable conditions on cardinals of certain involved sets.

We show that a finitely generated protoalgebraic strict universal Horn class that is filter-distributive is finitely based. Equivalently, every protoalgebraic and filter-distributive multidimensional deductive system determined by a finite set of finite matrices can be presented by finitely many axioms and rules.

We show that a finitely generated protoalgebraic strict universal Horn class that is filter-distributive is finitely based. Equivalently, every protoalgebraic and filter-distributive multidimensional deductive system determined by a finite set of finite matrices can be presented by finitely many axioms and rules.

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated (...) class='Hi'>filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction. (shrink)

For unmanned aerial vehicle, object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network is proposed in this paper for (...) image recognition system in the UAV. A novel building block, namely, multiscale feature filtering module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks. (shrink)

Airports are crucial channels of mobility for the global citizens of the twenty‐first century. They are points of entry and exit for tourists, business persons, workers, students and of course, for some refugees as well. The scale of operations is huge ‐ international passenger travel increased twelve‐fold in the second half of the twentieth century and the vast majority of this is accounted for in air travel. In the USA alone there are two million daily airtravelers on 20,000 flights. Airports (...) are ‘placeless’ sites of temporary sojourn, air‐lock chambers for nomadic executives or sun‐seekers. But they have profound social and political significance, particularly in personal data handling. (shrink)

In this paper we introduce the notion of filter on the neutrosophic crisp set, then we consider a generalization of the filter’s studies. Afterwards, we present the important neutrosophic crisp filters. We also study several relations between different neutrosophic crisp filters and neutrosophic topologies. Possible applications to database systems are touched upon.

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated (...) class='Hi'>filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction. (shrink)

The present paper is thought as a formal study of distributive closure systems which arise in the domain of sentential logics. Special stress is laid on the notion of a C-filter, playing the role analogous to that of a congruence in universal algebra. A sentential logic C is called filter distributive if the lattice of C-filters in every algebra similar to the language of C is distributive. Theorem IV.2 in Section IV gives a method of axiomatization of those filter (...) distributive logics for which the class Matr (C) prime of C-prime matrices (models) is axiomatizable. In Section V, the attention is focused on axiomatic strengthenings of filter distributive logics. The theorems placed there may be regarded, to some extent, as the matrix counterparts of Baker's well-known theorem from universal algebra [9, § 62, Theorem 2]. (shrink)

In this paper we introduce the notion of filter on the neutrosophic crisp set, then we consider a generalization of the filter’s studies. Afterwards, we present the important neutrosophic crisp filters. We also study several relations between different neutrosophic crisp filters and neutrosophic topologies. Possible applications to database systems are touched upon.

We characterize (both from a syntactic and an algebraic point of view) the normal K4-logics for which unification is filtering. We also give a sufficient semantic criterion for existence of most general unifiers, covering natural extensions of K4.2⁺ (i.e., of the modal system obtained from K4 by adding to it, as a further axiom schemata, the modal translation of the weak excluded middle principle).

Frequency selective surface -based intelligent spatial filters are capturing the eyes of the researchers by offering a dynamic behavior when exposed to the electromagnetic radiations. In this manuscript, a concept of creating complementary structures which stems from Babinet’s principle is illustrated. A hybrid complementary pair of FSS comprising double square loop FSS and double square slot FSS on either side of the dielectric substrate is proposed. DSLFSS offers band-pass behavior and can be placed as a superstrate, whereas DSSFSS behaves as (...) a band-stop intelligent spatial filter that blocks the radiations falling on it, thus making them applicable for use as a substrate. The technique utilized for analyzing DSLFSS and DSSFSS structures is based on the equivalent circuit modeling and transmission line methodology. The CPFSS structure offers the design simplicity, hence, suitable for placing them with the printed patch antenna radiators in wireless networking devices operating in sub-6 GHz 5G spectrum. DSLFSS offers band-pass behavior ranging from 2.99 to 5.56 GHz, whereas DSSFSS offers band-stop behavior ranging from 2.85 to 5.42 GHz covering all n77, n78, and n79 bands of FR1 spectrum of sub-6 GHz 5G range. The passband and the stopband offered by the two structures of CPFSS geometry are stable to oblique angles of incidence and the proposed design also offers polarization-independent behavior. The thickness of the dielectric region existing within the pair of designed structures is critical for the location of the passbands and the stopbands. The impact of the overall thickness of the dielectric substrate on the passbands and stopbands is also reported in this article. (shrink)

In an earlier paper [Rational choice and AGM belief revision, _Artificial Intelligence_, 2009] a correspondence was established between the set-theoretic structures of revealed-preference theory (developed in economics) and the syntactic belief revision functions of the AGM theory (developed in philosophy and computer science). In this paper we extend the re-interpretation of those structures in terms of one-shot belief revision by relating them to the trichotomous attitude towards information studied in Garapa (Rev Symb Logic, 1–21, 2020) where information may be either (...) (1) fully accepted or (2) rejected or (3) taken seriously but not fully accepted. We begin by introducing the syntactic notion of _filtered belief revision_ and providing a characterization of it in terms of a mixture of both AGM revision and contraction. We then establish a correspondence between the proposed notion of filtered belief revision and the above-mentioned set-theoretic structures, interpreted as semantic _partial_ belief revision structures. We also provide an interpretation of the trichotomous attitude towards information in terms of the degree of implausibility of the information. (shrink)

When confronting the unexplained, it is helpful to consider it from many different points of view. In an essay published in 2004, entitled "Sensors, Filters, and the Source of Reality," Robert Jahn and Brenda Dunne of Princeton University's PEAR laboratory proposed that consciousness constructs its reality by ordering the information it derives from the external world through an array of physiological, psychological, and cultural filters. This thesis has now been considered by nineteen distinguished scholars who here present their commentaries from (...) a broad spectrum of professional and personal perspectives. Drawn from such diverse backgrounds as art, Buddhism, evolutionary biology, fantasy, out-of-body experiences, philosophy, physics, psychology, semiotics, and systems engineering, among others, these contributions offer an assortment of unique and fascinating glimpses of how our experiences and their styles of representation are reflected through these filters of consciousness. (shrink)

It is often claimed that social media accelerate political extremism by employing personalization algorithms that filter users into groups with homogenous beliefs. While an intuitive position, recent research has shown that social media users exhibit self-filtering tendencies. In this paper, I apply Hannah Arendt’s theory of political judgment to hypothesize a cause for self-filtering on social media. According to Arendt, a crucial step in political judgment is the imagination of a general standpoint of distinct yet equal perspectives, against (...) which individuals compare their own judgments in order to test their defensibility. I argue that social media inhibit this step by gamifying the pursuit of social status, which encourages users to consider the perspectives of others not for the sake of a general standpoint but for the sake of improving their social status, resulting in self-filtering. Consequently, ameliorating political extremism on social media requires not just reforming the algorithms that deliver content to users, but the interfaces on which users present their social identities. (shrink)

In this essay, I explore ethical considerations that might arise from the use of collaborative filtering algorithms on dating apps. Collaborative filtering algorithms can predict the preferences of a target user by looking at the past behavior of similar users. By recommending products through this process, they can influence the news we read, the movies we watch, and more. They are extremely powerful and effective on platforms like Amazon and Google. Recommender systems on dating apps are likely (...) to group people by race, since they exhibit similar patterns of behavior: users on dating platforms seem to segregate themselves based on race, exclude certain races from romantic and sexual consideration, and generally show a preference for white men and women. As collaborative filtering algorithms learn from these patterns to predict preferences and build recommendations, they can homogenize the behavior of dating app users and exacerbate biased sexual and romantic behavior. (shrink)

We introduce a computational theory of situated language understanding in which the meaning of words and utterances depends on the physical environment and the goals and plans of communication partners. According to the theory, concepts that ground linguistic meaning are neither internal nor external to language users, but instead span the objective‐subjective boundary. To model the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be used for prediction (...) at multiple levels of abstraction. Language understanding is treated as a process of filtering perceived affordances. The theory accounts for many aspects of the situated nature of human language use and provides a unified solution to a number of demands on any theory of language understanding including conceptual combination, prototypicality effects, and the generative nature of lexical items.To support the theory, we describe an implemented system that understands verbal commands situated in a virtual gaming environment. The implementation uses probabilistic hierarchical plan recognition to generate perceived affordances. The system has been evaluated on its ability to correctly interpret free‐form spontaneous verbal commands recorded from unrehearsed game play between human players. The system is able to “step into the shoes” of human players and correctly respond to a broad range of verbal commands in which linguistic meaning depends on social and physical context. We quantitatively compare the system's predictions in response to direct player commands with the actions taken by human players and show generalization to unseen data across a range of situations and verbal constructions. (shrink)

In response to the continued expansion of “red flag” laws allowing broader classes of people to petition a court for the removal of firearms from individuals who exhibit dangerous conduct, this paper argues that state laws should adopt a double-filter provision that balances individual rights and government public safety interests. The main component of such a provision is a special statutory category — “reporting party” — that enables a broader social network, such as co-workers or school administrators, to request that (...) a law enforcement officer file a petition for an Extreme Risk Protection Order. A double-filter provision would not give reporting parties a right to file a court petition directly. Instead, parties would file a request for petition with law enforcement officers, who must seek an ERPO from the court if they find the reporting party's information credible. That information is then transmitted to the court as a sworn affidavit of the reporting party. The goal is to facilitate a balanced policy model that widens the reporting circle in order to feed more potentially life-saving information into the system, mitigates the risk of erroneous deprivation of constitutionally protected due process and Second Amendment rights. (shrink)

Neutrosophic set is a new mathematical tool for handling problems involving imprecise, indetermi nacy and inconsistent data. Pseudo-BCI algebra is a kind of non-classical logic algebra in close connection with various non-commutative fuzzy logics. Recently, we applied neutrosophic set theory to pseudo-BCI al gebras. In this paper, we study neutrosophic filters in pseudo-BCI algebras. The concepts of neutrosophic regular filter, neutrosophic closed filter and fuzzy regular filter in pseudo-BCI algebras are introduced, and some basic properties are discussed. Moreover, the relationships (...) among neutrosophic regular filter, fuzzy filters and anti-grouped neutrosophic filters are prese nted, and the results are proved: a neutrosophic filter (fuzzy filter) is a neutrosophic regular filter (fuzzy regular filter), if and only if it is both a neutrosophic closed filter (fuzzy closed filter) and an anti-grouped neutrosophic filter (fuzzy anti-grouped filter). (shrink)

This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing (...) measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results. (shrink)

This paper is one of several contributions in a series, illustrating the application of a specific deductive methodology to explain diversity of form. The methodology facilitates the explanation of feeding morphologies in various ducks as a transformation of the mallard's feeding design maximized for specific proportions of performance that are contributed by pecking and filter feeding mechanisms.The earlier described anatomy and formal analyses of the three mechanisms in the mallard served as the initial conditions used in simulation models. Four elements (...) of the feeding system were chosen that play a major role in all three mechanisms. For each element, the main parameter was selected: storage capacity of the rostral mouth cavity, transport capacity of the rostral mouth tube, storage capacity of the caudal mouth cavity and transport capacity of the caudal mouth tube. The boundary conditions for the simulation were determined from internal organismic constraints. The total food uptake of the mallard was regarded as the function to be maximized. This object function is the summation of the food uptake by one second of pecking and one second of filter feeding. The drinking mechanism was shown not to interfere, since that mechanism operates sufficiently whenever the pumping mechanism works properly. (shrink)

The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities (...) of light reflected from surfaces over a range of wavelengths. Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals. For some mineral classes, carbonates for example, specific short spectral intervals are known to carry a distinctive signature. Finding similar distinctive spectral ranges for other mineral classes is not an easy problem. We propose and evaluate data-driven techniques in two stages: first, evaluating algorithms to identify which components are probably present in a given rock; second, trying to improve this classification by automatically searching for spectral ranges optimized for specific classes of minerals. In one set of studies, we partition the whole interval of wavelengths available in our data into sub-intervals, or bins, and use a genetic algorithm to evaluate a candidate selection of subintervals. As an alternative to these computationally expensive search techniques, we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes. Results are presented for four different classes, showing reasonable improvements in identifying some, but not all, of the mineral classes tested. (shrink)

In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial intelligence filters (...) carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs. (shrink)

We give some restrictions for the search for a model of the club principle with no Souslin trees. We show that ${\diamondsuit(2^\omega, [\omega]^\omega}$ , is almost constant on) together with CH and “all Aronszajn trees are special” is consistent relative to ZFC. This implies the analogous result for a double weakening of the club principle.

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the (...) basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP. The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model (...) and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

In this paper, we will re-elaborate the notions of filter bubble and of echo chamber by considering human cognitive systems’ limitations in everyday interactions and how they experience digital technologies. Researchers who applied the concept of filter bubble and echo chambers in empirical investigations see them as forms of algorithmically-caused systems that seclude the users of digital technologies from viewpoints and opinions that oppose theirs. However, a significant majority of empirical research has shown that users do find and (...) interact with opposing views. Furthermore, we argue that the notion of filter bubble overestimates the social impact of digital technologies in explaining social and political developments without considering the not-only-technological circumstances of online behavior and interaction. This provides us with motivation to reconsider this notion’s validity and re-elaborate it in light of existing epistemological theories that deal with the discomfort people experience when dealing with what they do not know. Therefore, we will survey a series of philosophical reflections regarding the epistemic limitations of human cognitive systems. In particular, we will discuss how knowledge and mere belief are phenomenologically indistinguishable and how people’s experience of having their beliefs challenged is cause of epistemic discomfort. We will then go on to argue, in contrast with Pariser’s assumptions, that digital media users might tend to conform to their held viewpoints because of the “immediate” way they experience opposing viewpoints. Since online people experience others and their viewpoints as material features of digital environments, we maintain that this modality of confronting oneself with contrasting opinions prompts users to reinforce their preexisting beliefs and attitudes. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...) the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (shrink)

The common law system is a legal system that values precedent, or previous court decisions, in the resolution of current cases. As the availability of legal documents in digital form has increased, it has become more difficult for legal professionals to manually identify relevant past cases due to the vast amount of data. Researchers have developed automated systems for determining the similarity between legal documents to address this issue. Our research explores various representations of a legal document and discusses (...) a novel paragraph filtering process to identify key paragraphs using legal citation information to remove unnecessary text paragraphs without disturbing the concept of the legal document. State-of-the-art techniques like TF-IDF, BERT, Legal Bert, Doc2Vec, and Legal-longformer are used for the performance analysis of the proposed approach with document comparison. It has been shown that a model trained on the proposed filtered paragraphs can achieve better results than a model trained on the complete text and can also shorten the document by over 40%. The proposed filtering strategy could be helpful for models like BERT, where the maximum token length is fixed. (shrink)

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By (...) improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online. (shrink)

The strap-down inertial navigation system is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter (...) and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm. (shrink)

. A new spatial-frequency image enhancement algorithm via wavelet homomorphic filtering transform is proposed to enhance the contrast of an image. The wavelet analysis coefficients are processed using a high-pass filter to amplify the high spatial frequencies and attenuate the low spatial frequencies. So the object features can be emphasized while the undesired contributions within the image due to light source nonuniformity can be reduced. Experimental results show that this new algorithm can gain better performance in enhancing the local (...) contrast of an image while maintaining its global appearance, in contrast to some representative algorithms. (shrink)

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the (...) recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future. (shrink)

Background As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method. Methods The method used to detect the pulse noise points in the image, and then the modified mean method used to filter out the detected noise points. Patents on metallographic image processing have discussed for the development of the proposed methodology. Results It is shown that filter window can be (...) filtered in comparison with the conventional 3×3, 5×5 and 7×7 filt window to reduce noise detection and reduce the complexity of the weight calculation. Conclusion It can be concluded that this method can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image. (shrink)

Short circuit is a key factor which drastically affects the efficiency of metal electrorefining. Infrared image of the intercell busbar region is used to perform short circuit detection. To cope with the high thermal background, a two-level short circuit detection method is designed. Firstly, with background subtraction, high intensity short circuit electrodes, as well as the background, are removed, and normal working electrodes are preserved. In the second stage, suspicious short circuit areas are sifted out by normal electrode detecting and (...) texture period estimation. Gaussian difference filter which is based on the human visual system is improved to match the target gray distribution. A comparative experiment indicates that the proposed orthogonal DoG outperforms the original DoG and top-hat in the accuracy of normal electrode detection. The two-level detection method in this paper is applied in a copper electrolysis plant and exhibits superiority in locating short circuits and avoiding miss detection. (shrink)