Reviewerly/exHarmony huggingface dataset:
Unnecessarily Identifiable: Quantifying the fingerprintability of browser extensions due to bloat

In this paper, we investigate to what extent the page modifications that make browser extensions fingerprintable are necessary for their operation. We characterize page modifications that are completely unnecessary for the extension's functionality as extension bloat. By analyzing 58,034 extensions from the Google Chrome store, we discovered that 5.7% of them were unnecessarily identifiable because of extension bloat. To protect users against unnecessary extension fingerprinting due to bloat, we describe the design and implementation of an in-browser mechanism that provides coarse-grained access control for extensions on all websites. The proposed mechanism and its built-in policies, does not only protect users from fingerprinting, but also offers additional protection against malicious extensions exfiltrating user data from sensitive websites.
Research on Topic Recognition of Network Sensitive Information Based on SW-LDA Model

The mining of network sensitive information is of great significance for understanding the social stability of the network.Obtaining the network public opinion of sensitive information is helpful to master Internet users' attitudes toward important social events.The related artificial intelligence technology can achieve the topics from the network texts.At present, the current topic recognition model has a low recognition rate for sensitive information and usually generates some inaccurate topic keywords.In this paper, a topic recognition method of the network sensitive information based on a sensitive word weighted-latent Dirichlet allocation (LDA) model is proposed.First, the basic sensitive word vocabulary is constructed by manual collection, and the embedding representation of the word is obtained through the training of a large amount of network corpus based on Word2vec.The semantic similarity between the word embedding is calculated to extend the basic sensitive word vocabulary.Second, the extended sensitive word vocabulary is embedded in the LDA model.On the one hand, it can improve the semantic understanding and the recognition ability of LDA for the sensitive topic words and promote the quality of the generated topic words.On the other hand, it can also improve the relevance of the topic keywords and the related topics and find more fine-grained keywords.The experimental results show that the sensitive word weighted-LDA model can effectively improve the topic recognition quantity and quality of sensitive information.This paper is helpful to the development of artificial intelligence.The generated corpus in this paper is meaningful to the research of text classification, clustering and information retrieval, and so on.
Empirical study on character level neural network classifier for Chinese text

Character level models are drawing attention recently. A number of these models have been proposed and shown successful in Natural Language Processing tasks. While most of the models are experimented mainly on English, or other alphabetic languages, a number of problems arise when they applied these models to non-alphabetic language such as Chinese. In this study, we investigated the problems encountered when transferring these models to the Chinese and put forward some solutions. We propose a double embedding neural network model that is also character level and consists of both CNN and RNN with two separate embeddings. The model is applied to a fundamental Natural Language Processing task, text classification. Experiment results conducted on the Chinese corpus demonstrated that our character level neural network model performs just as well as or better than those word level classification models. Our model is able to reach 95.9% accuracy on a Chinese Fudan news dataset, which outperforms the state-of-the-art models.
Deep neural network-aided Gaussian message passing detection for ultra-reliable low-latency communications

Ultra-reliable low-latency communications (URLLC) is a key technology in 5G supporting real-time multimedia services, which requires a low-cost signal recovery technology in the physical layer. A kind of well-known low-complexity signal detection is message passing algorithm (MPA) based on factor graph. However, reliability and robustness of MPA are deteriorated when there are cycles in factor graph. To address this issue, we propose two novel Gaussian message passing (GMP) algorithms with the aid of deep neural network (DNN), in which the network architectures consist of two DNNs associated with detections for mean and variance of the signal. Particularly, the network architecture is constructed by transforming the factor graph and message update functions of the original GMP algorithm from node-type into edge-type. Then, weights and bias parameters are assigned in the network architecture. With the aid of deep learning methods, the optimal weights and bias parameters are obtained. Numerical results demonstrate that two proposed DNN-aided GMP algorithms can significantly improve the convergence of original GMP algorithm and also achieve robust performances in the cases without prior information.
Copyright Protection for Holographic Video Using Spatiotemporal Consistent Embedding Strategy

The interest in a three-dimensional (3-D) holographic display is increasing daily because it can generate full depth cue without utilizing any special glasses. However, the content of 3-D holographic videos currently without any protection can be illegally distributed and maliciously manipulated. Therefore, there is an urgent need to protect the copyright of holographic videos against malicious copying. Since watermarking is an effective way to protect the ownership of holographic sequence, this paper proposes a spatiotemporal consistent embedding algorithm for the holographic video watermarking. Imperceptibility requirement in the holographic video watermarking is more challenging compared with static holograms because of the temporal dimension existing in videos. The embedding algorithm should not only consider spatially embedding strength for each frame of the video, but also take the temporal dimension into account in order to guarantee the visual quality of the moving object. Before embedding, to defend the imperceptibility of the watermark from the holographic moving object, the embedding parameters are evaluated by the salient object from the interframe and intraframe. Different from the previous video watermarking algorithm, in order to ensure the robustness, in our paper, instead of two-dimensional watermark, 3-D watermark converted data (QR code) are embedded in the cellular automata (CA) domains using 3-D CA filters. Finally, the QR codes can be extracted from the watermarked holographic frames, and the final 3-D watermark can be digitally reconstructed with different depths’ cue using the computational integral imaging reconstruction algorithm. The experimental results demonstrate that the proposed method exhibits superior performance compared to several methods in the literature, especially the robustness against additive noise and compression attacks.
Using machine learning for communication classification

Abstract The present study explores the value of machine learning techniques in the classification of communication content in experiments. Previously human-coded datasets are used to both train and test algorithm-generated models that relate word counts to categories. For various games, the computer models of the classification are able to match out-of-sample the human classification to a considerable extent. The analysis raises hope that the substantial effort going into such studies can be reduced by using computer algorithms for classification. This would enable a quick and replicable analysis of large-scale datasets at reasonable costs and widen the applicability of such approaches. The paper gives an easily accessible technical introduction into the computational method.
The mixed model for the analysis of a repeated‐measurement multivariate count data

Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. In addition, the effects of covariates on the multivariate counts distribution need to be assessed. To fulfill these requirements, a regression model based on the Dirichlet‐multinomial distribution for association between covariates and the categorical counts is extended by using random effects to deal with the additional clustering. This model is the Dirichlet‐multinomial mixed regression model. Alternatively, a negative binomial regression mixed model can be deployed where the corresponding likelihood is conditioned on the total count. It appears that these two approaches are equivalent when the total count is fixed and independent of the random effects. We consider both subject‐specific and categorical‐specific random effects. However, the latter has a larger computational burden when the number of categories increases. Our work is motivated by microbiome data sets obtained by sequencing of the amplicon of the bacterial 16S rRNA gene. These data have a compositional structure and are typically overdispersed. The microbiome data set is from an epidemiological study carried out in a helminth‐endemic area in Indonesia. The conclusions are as follows: time has no statistically significant effect on microbiome composition, the correlation between subjects is statistically significant, and treatment has a significant effect on the microbiome composition only in infected subjects who remained infected.
Think Outside the Dataset

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews.
Multi-Aspect Review-Team Assignment using Latent Research Areas

Reviewer assignment is an important task in many research-related activities, such as conference organization and grant-proposal adjudication. The goal is to assign each submitted artifact to a set of reviewers who can thoroughly evaluate all aspects of the artifact's content, while, at the same time, balancing the workload of the reviewers. In this paper, we focus on textual artifacts such as conference papers, where both (aspects of) the submitted papers and (expertise areas of) the reviewers can be described with terms and/or topics extracted from the text. We propose a method for automatically assigning a team of reviewers to each submitted paper, based on the clusters of the reviewers' publications as latent research areas. Our method extends the definition of the relevance score between reviewers and papers using the latent research areas information to find a team of reviewers for each paper, such that each individual reviewer and the team as a whole cover as many paper aspects as possible. To solve the constrained problem where each reviewer has a limited reviewing capacity, we utilize a greedy algorithm that starts with a group of reviewers for each paper and iteratively evolves it to improve the coverage of the papers' topics by the reviewers' expertise. We experimentally demonstrate that our method outperforms state-of-the-art approaches w.r.t several standard quality measures.
Dynamics of the entanglement and teleportation of thermal state of a spin chain with multiple interactions

We investigate the effect of Calogero-Moser (CM) type interactions on the thermal entanglement and teleportation in an anisotropic two-qubit Heisenberg XYZ system with Dzyaloshinskii-Moriya (Dz) interaction in the presence of the external magnetic field. In this regard, the system is designed and analytically solved. The final state density of the system is obtained, and the impact of the system parameters is investigated. The negativity is used to quantify the entanglement dynamics over the system. The generated entangled channels are examined to exchange the information between the system qubits using the standard teleportation protocol; then, the fidelity of the transmitted information is quantified. The effect of the system parameters (Dzyaloshinskii-Moriya, Spin-orbit coupling, CM type interaction, and magnetic field) in the dynamics of the entanglement and state transfer is investigated. Our results indicate that the strength coupling, Dzyaloshinskii-Moriya (Dz) interaction, and magnetic field have a great effect on the dynamics of the entanglement and, consequently, the quality of the generated channels to exchange the information. In addition, we observe that the entanglement of sudden death exists when the distance between the spins is increased.
Collective Anomalies Detection for Sensing Series of Spacecraft Telemetry with the Fusion of Probability Prediction and Markov Chain Model

Telemetry series, generally acquired from sensors, are the only basis for the ground management system to judge the working performance and health status of orbiting spacecraft. In particular, anomalies within telemetry can reflect sensor failure, transmission errors, and the major faults of the related subsystem. Therefore, anomaly detection for telemetry series has drawn great attention from the aerospace area, where probability prediction methods, e.g., Gaussian process regression and relevance vector machine, have an inherent advantage for anomaly detection in time series with uncertainty presentation. However, labelling a single point with probability prediction faces many isolated false alarms, as well as a lower detection rate for collective anomalies that significantly limits its practical application. Simple sliding window fusion can decrease the false positives, but the support number of anomalies within the sliding window is difficult to set effectively for different series. Therefore, in this work, fused with the probability prediction-based method, the Markov chain is designed to compute the support probability of each testing series to realize the improvement on collective anomaly mode. The experiments on simulated data sets and the actual telemetry series validated the effectiveness and applicability of our proposed method.
Automatic Generation of Pattern-controlled Product Description in E-commerce

Nowadays, online shoppers have paid more and more attention to detailed product descriptions, since a well-written description is a huge factor in making online sales. However, for a website with billions of product data like Alibaba, the writing efficiency of human copywriters cannot match the growth rate of new products. To address this issue, we propose a novel pointer-generator neural network to generate product description. In particular, coordinate encoders and a pattern-controlled decoder are utilized to improve generation quality with an attention mechanism. The coordinate encoders equipped with a Transformer and a gated convolutional unit is introduced to learn the source input representations. In the decoding phase, a pattern controlled decoder is proposed to control the output description pattern (such as category, length, and style) to ensure the quality of the description. For evaluation, we build a substantial collection of real-world products along with human-written descriptions. An extensive set of experiments with both human annotated data demonstrate the advantage of the proposed method for generation qualities. Finally, an online deployment shows significant benefits of our model in a real online shopping scenario, as measured by the click-through rate.
Experienced technology teachers’ teaching practices

Teachers’ teaching practice plays a key role in the learning process of pupils, and for teaching to be successful, teachers must have knowledge in many different fields. This obviously also applies to teaching the subject technology. However, lower secondary school technology education in Sweden has reportedly been described in terms of teaching not following the curriculum along with widespread uncertainty among teachers regarding how to design their teaching practices. To address this national challenge, we need to understand the existing technology teaching practice. The purpose of this study is therefore to explore the considerations experienced technology teachers make. The study is based on interviews with technology teachers who work in lower secondary school (13–15-year-old pupils). The collected data consist of teacher’s statements regarding their own expertise and teaching practice. To visualize the described teaching practice we have analysed collected data through the lens of pedagogical content knowledge (PCK). The results show both similarities and differences in the teachers’ descriptions. Speaking in terms of PCK, the purpose and teaching focus expressed by the respondents, framed within the category ‘Orientations to teach technology’, vary considerably. However, regarding ‘instructional strategies’, the consensus among those experienced teachers is striking. Experienced technology teachers’ teaching practices are proven to provide valuable information about the subject’s potential, and the findings offer a basis for the future development of the subject of technology as well as future teacher education and professional development courses.
Musical Instruments for Novices: Comparing NIME, HCI and Crowdfunding Approaches

Designing musical instruments to make performance accessible to novice musicians is a goal which long predates digital technology. However, just in the space of the past 6 years, dozens of instrument designs have been introduced in various academic venues and in commercial crowdfunding campaigns. In this paper, we draw comparisons in design, evaluation and marketing across four domains: crowdfunding campaigns on Kickstarter and Indiegogo; the New Interfaces for Musical Expression (NIME) conference; conferences in human-computer interaction (HCI); and researchers creating accessible instruments for children and adults with disabilities. We observe striking differences in approach between commercial and academic projects, with less pronounced differences between each of the academic communities. The paper concludes with general reflections on the identity and purpose of instruments for novice musicians, with suggestions for future exploration.
The Interrelation between Cognitive Styles and Copying Strategies among Student Youth

The article deals with the problem of studying the issues of creative thinking, cognitive styles and coping strategies among students. The importance of this problem is determined by contemporary society and its social demand, requiring that a young specialist should be creative, he should have a non-standard approach, he could generate new ideas that can help him cope with difficulties and resolve stressful situations. An empirical study was conducted to analyze the relationship between the indicators of creative thinking, the cognitive styles of "Field Dependence-Field independence" and students' coping strategies. Fifty-two people participated in it, among them 26 students of the Faculty of Humanities of Ulyanovsk State University and 26 students of the Institute of Mathematics and Mechanics of Kazan Federal University. The interpretation of this study results allowed us to draw the major conclusions: students of humanities programs with developed creative thinking are focused on external factors, do not strive to avoid problem resolution, but when they encounter a problem situation, the primary negative reaction prevents them from resolving it quickly, thereby enabling them to correctly plan and find optimal ways out of this stressful situation; creative students-mathematicians are less susceptible to external factors, have a high control of emotions, they are capable of generating new solutions, which allows them to take conscious steps to solve the problem.
Improving the Hybrid A* method for a non-holonomic wheeled robot

This article proposes and completely describes a modification of the Hybrid A* method used for navigation of a non-holonomic mobile wheeled robot. Our modification allows straightforward multi-criterial adjustment of the algorithm according to the desired behavior considering not only traveled distance but also time, changing of direction, stopping, going backwards while avoiding obstacles. The obstacle avoidance algorithm evaluates the danger of collision smoothly (not-binarily) using danger fields. Such behavior reflects human-like sensing of danger—the closer to the obstacle the robot is, the higher is the danger of collision. A modified uniform state expansion method has been used to cover the state space of the robot more uniformly providing the possibility of precise near-target navigation. A greed factor has been introduced to decrease the computational time and improve the real-time performance of the algorithm.
Development of new ontological solution for an energy intelligent management in Adrar city

Currently, the growth in building energy consumption presents raises concerns with greenhouse gases and environmental pollution. The building occupants face significant difficulties to control the consumed power due to several instabilities such as climate change and human behavior. Therefore, it is necessary to have a decision tool that optimizes the consumed energy and ensures an acceptable comfort for the occupants. This article presents an intelligent solution based on an ontology that presents knowledge about the internal and external environment of a residence, as well as the occupants’ behavior and activities. The system openness with the external environment, the knowledge presentation flexibility built on Web Ontology Language (OWL) and the possibility of intelligent reasoning using Semantic Web Rule Language (SWRL) are the main reasons for choosing the ontological approach. The proposed ontology is applied to a real home located in the Adrar city, in the Algerian Sahara. A comparison between two scenarios (with and without the proposed system, Ontological Solution for Energy Intelligent Management (OSEIM) revealed the effectiveness of the proposed solution. The obtained results present a significant energy saving of 4.58%.
A novel secret image sharing with steganography scheme utilizing Optimal Asymmetric Encryption Padding and Information Dispersal Algorithms

Secret image sharing schemes are employed to safeguard the confidentiality and availability of critical commercial or military images. However, many of the existing image sharing schemes have some security flaws that can reveal key elements of the shared secret image. In this paper, to overcome these weaknesses, we propose a new secret image sharing scheme which utilizes Optimal Asymmetric Encryption Padding (OAEP) and Information Dispersal Algorithms (IDA). The proposed scheme provides computational security, small shadow size, and better performance in share generation and secret reconstruction. We also analyze the security of the proposed scheme formally and prove that the confidentiality of the secret image is guaranteed in the face of computationally bounded adversaries. Additionally, we introduce an edge-based steganography method to conceal the participants' shares in cover images. The proposed technique achieves higher visual quality and better resistance against steganalysis methods. Finally, experimental results confirm the efficiency and effectiveness of the proposed scheme.