Reviewerly/exHarmony huggingface dataset:
Radar Signal Recognition Based on Deep Neural Networks

The article presents the problem of recognizing radar sources using multilayer artificial neural networks on the basis of radar parameters such as pulse repetition interval, pulse duration, radio frequency, and inter-pulse temporal structure of signals generated by these sources. Parameters of real radar signals were used as a set of training data. The correctness of the operation was tested using a simulation environment that verified the effectiveness of operation in the conditions of the presence of many radar signals that interfere with each other and in the environment of noise and interferences.
The Survey of Global Software Design Processes

The researches results on applying of hybrid approaches to development of large software projects are discussed in the paper. These approaches include traditional, planned and agile methods of development. The analysis of empirical studding of applying frequency for different technologies and their combinations are shown. The cons and pros of hybrid technologies are examined. The problems of their effectiveness increase are discussed as well by means of architecture design automation and software products quality assurance processes are discussed too.
The Study of the Semi-Global Block Matching Algorithm Implementing Parallel Calculation with GPU

A Block Matching Algorithm is a way of locating matching macroblocks in a sequence of digital video frames for the purposes of motion estimation. The underlying supposition behind motion estimation is that the patterns corresponding to objects and background in a frame of video sequence move within the frame to form corresponding objects on the subsequent frame. This can be used to discover temporal redundancy in the video sequence, increasing the effectiveness of inter-frame video compression by defining the contents of a macroblock by reference to the contents of a known macroblock which is minimally different. This article discusses the fundamental principles of local and global stereo image matching algorithms. The operation of the Semi-Global Block Matching algorithm is described in more detail. Its implementation is also affected by transferring the calculation of the depth map to a graphics processor (GPU) to speed up processing. The results of the construction of the depth map, as well as the dependence of the time of the algorithm on the size of the input images are shown.
Encryption Based on the Group of the Hermitian Function Field and Homomorphic Encryption

The article proposes a general approach to the implementation of encryption schemes based on the group of automorphisms of the Hermitian functional field. The three-parameter group is used with logarithmic captions outside the center of the group. This time we applied for an encryption scheme based on a Hermitian function field with homomorphic encryption. The use of homomorphic encryption is an advantage of this implementation. The complexity of the attack and the size of the encrypted message depends on the strength of the group.
Recommendation Engines: Traditional vs Deep Learning Approaches

With digitlzation growing by leap and bounds, websites are now overloaded with products and information density, making it challenging for customers to choose between a variety of products. The Recommendation engine enables companies to deliver their customers more personalized and customized products or information efficiently. As a result, the recommendation engine eventually helps in boosting different business performance metrics such as revenues, Click- Through Rates (CTRs), conversions, etc., for these companies significantly. And in the long run, it also optimizes user experience, thus translating to higher customer satisfaction and retention. In this paper, we will highlight some of the contemporary algorithms used in building high-performance recommendation engines, illustrate use cases from Tech Giants that can be applied in the context of the banking and financial services domain and identify major challenges that are open for further research work. Also, in this paper, we will be using recommendation engine and recommender system alternatively as both are synonymous.
A Big Data Perspective of Individual Privacy Protection Approaches

The evolution of social media has made big data more and more accessible to the public along with the availability of diverse datasets which may lead to the rise of privacy concerns. These datasets may contain Personally Identifiable Information which is meant for a specific purpose can lead to the violation of the user&#x0027;s privacy if misused. The existing data protection mechanisms don&#x0027;t scale up with the characteristics of big data namely Volume, Velocity, Variety, Veracity, and Value and therefore there is a need to redefine privacy-preserving techniques to address the issues related to the characteristics that are associated with big data. In this work, we assess and analyze the capability of traditional privacy-preserving techniques and the relevance of these techniques in the present scenario.
Computational Analysis of Online Hate Content using Cognitive -AI

As the hate content is expressed based on the identity of an individual, it is influencing communal violence in society. The inappropriate content can be detected and analyzed using automated AI systems. The purpose of this paper is to study the role of AI systems in the detection of online hate content and how the cognitive processes affect the behavior of an individual. Therefore, using related keywords in the research domain, the published articles are searched through different search engines. The information from the associated articles and sources is reported in the paper. The literature review revealed that social media platforms could use AI systems to detect and analyze online hate content. It has been observed that the cognitive process affects both the perpetrators and the victims of online hate content. The paper also highlights some of the challenges in controlling online hate content. The paper concludes with a note that the hate content can be reduced by building robust AI systems and healthier cognitive processes of an individual.
An end-to-end interpolated Automatic speech recognition system with punctuated transcripts for the Hindi language

The Automatic Speech Recognition System (ASR) produces transcripts that often are misinterpreted and confuses the reader due to lack of context and punctuations. The presence of punctuation in the text improves readability and helps in better cognitive understanding. A wide variety of work has been done on English but Hindi which is the third-largest spoken language in the world, after English and Mandarin, still remains in the shadows. This paper aims to extend the technology to a wider section and introduces an end-to-end system, interpolating an automatic speech recognition system and natural language processing to produce high-quality punctuated transcriptions for the Hindi language. An ASR is implemented using the Kaldi toolkit leveraging the hybrid deep neural networks and the punctuation restoration is done with Bidirectional RNNs with an attention network.
Generate Adversarial Examples Combined with Image Entropy Distribution

With the rapid development of artificial intelligence technology, artificial intelligence-based systems and applications have shown explosive growth, bringing great convenience to people&#x0027;s lives. As a representative of artificial intelligence, deep learning, whose excellent performance in the classification task has been proved, can obtain high-precision classification models by effectively training from large amounts of data. However, recent studies have shown that deep neural networks are vulnerable to attacks. By carefully constructing input data, Deep Neural Networks can export the output results expected by the attacker. In general, it is difficult to detect the difference between the carefully constructed data and the original data, but it can induce the neural network to output wrong results. This kind of attack is called adversarial example attack, and the carefully constructed data used to deceive deep learning is called adversarial examples. This paper proposes a generation method of adversarial example that combines the distribution of image entropy. The areas with high entropy values in the image tend to have complex tones, so the added perturbation cannot be noticed by naked eyes easily, and experiments have proved that the adversarial examples generated by adding perturbations in this area are more aggressive. This paper proposes that the greater the entropy in a region of an image, the greater the weight assigned to the position of the Adversarial Perturbation; the smaller the entropy in a region of the image, the lower the weight is assigned to the position of the Adversarial Perturbation, so that an adversarial example with lower disturbance and less noticeable is generated, and at the same time, the adversarial example is more aggressive.
Improving the Transferability of Adversarial Examples with Advanced Diversity-Ensemble Method

Recent trend on transferable adversarial attacks focuses on applying different transformations on input images to improve input diversity. DEM, the recently proposed state-of-the-art method, adopts a diversity-ensemble method that contains multiple resize-padding-resize transformation branches to improve transferability. Despite its impressive attacking performance, we observe that the input diversity of DEM is still limited. In light of this, we propose an Advanced Diversity-Ensemble Method (ADEM) to further improve the transferability of adversarial examples. Specifically, we enlarge the range of image proportion and eliminate the overlap between different diversity scales existing in DEM to further improve the input diversity. Extensive experiments demonstrate the effectiveness of the proposed method.
MilKBQA: A Complex Knowledge Base Question Answering Dataset on Chinese Military Field

Facts in military field tend to involve elements of time, space, quantity, status, and so on. Existing methods of representing knowledge in the form of triples fail to adequately express these facts, and also cause obstacles to knowledge storage and updating. Furthermore, question answering on these facts introduces new complexity dimension, which are complicated to be supported by existing corpus. Thus, we construct a Chinese knowledge base for military field covering entities and events centric knowledge, referred as MilKB. It consists of 965 entities and 3,017 facts. Moreover, we classify the natural questions into 26 types and construct a complex question answering dataset derived from MilKB, referred as MilKBQA. It consists of 2,829 question-answer pairs, in which 600 are event-centric ones. Experiments with three recent strong baseline models demonstrate that MilKBQA requires further research.
PTMT: Multi-Target Stance Detection with PTM-enhanced Multi-Task Learning

Stance detection, as a sub-task of sentiment analysis, is becoming an essential tool in the field of online public opinion analysis with the rapid development of social media. At the moment, the overwhelming majority of stance detection methods are only focused on a single target. However, in an electoral scene, those single-target methods may lose some interrelated information in multi-target sentences. This paper proposes a pre-trained model based multi-target stance detection approach to automatically dig out the implicit targets&#x0027; interrelated information and words of interest via the attention mechanism under the multi-task learning framework. And to involve the hashtags&#x0027; semantic information, some specific dataset preprocessing methods are also applied here. By comparing various methods, we show that our model achieves state-of-the-art results in a benchmark dataset.
A Multilevel Non-interference Vulnerability Analysis Method for Information Leakage Problem

Security vulnerabilities caused by information leakage are a critical aspect of the security for information systems. Such vulnerabilities can be identified by non-interference analysis. Strong nondeterministic non-interference (SNNI) and bisimulation SNNI (BSNNI) are the typical properties of non-interference. Existing works related to non-interference analysis are mainly based on the setting with only two security levels. Such setting is coarse-grained compared to the real system. In this paper, we analyze the non-interference with multiple security levels by integer linear programming. Obviously, the non-interference with multiple security levels is more fine-grained but more complex than that with only two security levels. On the perspective of complexity, we provide the general and simplified non-interference analyses in turn for multilevel SNNI and BSNNI which are defined on the concepts of upper sets. The general analysis is derived from the definitions of multilevel SNNI and BSNNI, and solve the identification problem of multilevel SNNI and BSNNI directly. By analyzing and removing the impact of upper sets&#x0027; coverability, the simplified analysis reduces the redundancy and improves the efficiency in the analysis process.
$^{\prime}\mathbf{R}$: Towards Detecting and Understanding Code-Document Violations in Rust

Documentation and comments are important for any software project. Although documentation is not executed, it is useful for many purposes, such as code comprehension, reuse, and maintenance. As a project evolves, the code and documentation can easily grow out-of-sync, and inconsistencies are introduced, which can mislead developers and introduce new bugs in subsequent developments. Recent studies have shown it is promising to use natural language processing and machine learning to detect inconsistencies between code and documentation. However, it&#x0027;s challenging to apply existing techniques to detect code-document inconsistency in Rust programs, as Rustdoc supports advanced document features like document testing, which makes existing solutions inapplicable. This paper presents the first software tool prototype, &#x0027;R, to detect and understand code-document inconsistencies in Rust. To perform such analysis, &#x0027;R leverages static program analysis, not only on Rust source code, but also on document testing code, to detect inconsistency indicating either bugs or bad documentation. To evaluate the effectiveness of &#x0027;R, we applied it to 37 open source Rust projects from 9 domains, with a total of 6,192,251 lines of Rust source code (with 322,330 lines of comments). The results of the analysis give interesting insights, for example: the cryptocurrency domain has the highest documentation ratio (58.23&#x0025;), documentation testing is rarely used (ratio 2.30&#x0025; on average) in real-world Rust projects in all domains, etc. Based on these findings, we propose recommendations to guide the construction of better Rust documentation, better Rust documentation quality detection tools, and boarder adoption of the language.
Sampled Data Debugging via Fuzzy C-Means

People usually assumed that the more data, the better the prediction ability of the machine learning model. However, if irrelevant and redundant features in the data sets are included, it will undoubtedly increase the burden during the learning process and produce over-fitting effects, failing to obtain good performance in subsequent tasks. Inspired from this, the paper here studies the use of data debugging technique based on clustering algorithm given limited sample data. It can often boost the prediction accuracy of the algorithm, by selecting a subset of key features from the initial attribute, while removing irrelevant ones can reduce interference noise and enhance the generalization of the model. Our evaluation result shows that even on the basis of a small amount of sampled data (1&#x0025;, 3&#x0025;, 5&#x0025; &#x0026; 10&#x0025;), through the help of feature selection, it can still reach or exceed the implementation of predicting readmission rate based on the original full data, with an improvement from original AUC of 0.661 to data debugged AUC of 0.748.
Attack-words Guided Sentence Generation for Textual Adversarial Attack

Deep neural networks are vulnerable to carefully crafted adversarial examples and many adversarial attack methods have been proposed in computer vision tasks, such as image classification, object detection, etc. Generating adversarial examples for textual tasks is more challenging since the lexical correctness, grammatical correctness and semantics similarity should be maintained. In this paper, we introduce an attack-words guided sentence generation (AGSG) method to attack text classification models. We first determine words&#x0027; attack ability by the ensemble strategy, then we add perturbation by inserting a short attack sentence. We conduct extensive experiments on two popular datasets IMDB and Amazon Comments against TextCNN, LSTM and RCNN models. The results show that the AGSG method greatly reduces the classification accuracy with a low word substitution rate. Specifically, the accuracy is reduced by 94.5&#x0025; and 90.1&#x0025; when disturbance rate is 13.3&#x0025; and 25.1&#x0025; for IMDB and Amazon Comments respectively. The similarity evaluation study shows that our adversarial attack method guarantees semantic similarity and grammatical correctness. Compared with two baseline adversarial attack methods, the AGSG method can generate adversarial texts that are harder for humans to perceive.
Mixed Strategy PSO for Global Optimization

Particle Swarm Optimization (PSO) is an evolutionary algorithm for solving global optimization problems. In recent two decades, a lot of researches have been working on improving the performance of PSO and numerous PSO variants have been presented. According to a recent theory, the hardest problem to one evolutionary algorithm may be the easiest to another algorithm, and vice versa. Thus, mixed strategy PSO algorithms might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) was proposed which integrates five different PSO variants. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 variables. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than those of comparative algorithms.
A Building Network Security Monitoring and Management System Based on the Internet of Things

The aim of the study is to design a network security monitoring and management system based on the Internet of Things and to realize the automate management. The system is made up of three layers which are sensor layer, transmission layer and application layer, respectively. The modular structure is adopted to develop router node which have flexible structures and strong versatility. The node connects with sensors through standard analog interface. Internet of things can monitoring network security information and transmit the data to the remote server management system reliably. Experiments show that the network security monitoring and management system is stable, safe and provides basis for the scientific management.
Information Hiding using Bucket Steganography

This paper describes bucket steganography-a data hiding technique in which secret message may be extracted from the carrier with standard software preinstalled in operating system, and with inconspicuous graphical applications, like GIMP, Paint or Photoshop. First, the carrier file should be presented in chosen base, for example hexadecimal, octal, etc. Then the user takes the screenshot of the listing and fill its background with bucket tool. In this way the secret message appears and may be read from unfilled regions of different color.
LoginSoEasy: a System Enabling both Authentication and Protection of Personal Information based on Trusted User Agent

An ecosystem based on LoginSoEasy, which enables both authentication and protection of personal information is proposed. It works as a hub connecting all application servers on the one hand, and all information/verification services on the other hand. In such ecosystem, a user&#x0027;s personal information is stored and managed by his trusted user agent only, and could be verified by an authoritative information/verification service only after authorized by the user. The user agent, which could be a mobile app, stores all personal information the user wishes to store, or otherwise obtains instant input from the user on demand. The trusted user agent is also used to help the user to automatically register for an anonymous and unlinkable account ID with a strong password on each application server. Any other personal information necessary for a business can be obtained on demand. The application server no longer needs to store other personal information for the user, and therefore would never be blamed for any leakage, while no business would be impeded. Analyses have been conducted to show its effectiveness to prevent vast amount breaches of personal information.
MC-FGSM: Black-box Adversarial Attack for Deep Learning System

Deep learning (DL) technology has been widely applied in the safety-critical area, for instance, autopilot system in which the misbehavior will have a huge influence. Hence the reliability of DL system should be tested thoroughly. DL reliability testing is mainly achieved via adversarial attack, however, the existing attack methods lack mathematical proof whether the convergence of the attack can be guaranteed. This paper proposes a novel adversarial attack method, i.e., Monte Carlo-Fast Gradient Sign Method (MC-FGSM) to test the DL robustness. This method does not require any knowledge of the victim DL system. Specifically, this method first approximates the gradient of the input variable via Monte Carlo sampling technique, and then the gradient-based method is applied to generate adversarial attacks. Moreover, a strict mathematical proof has shown the gradient estimation is unbiased and the time complexity is <tex>$\boldsymbol{O}(1)$</tex>, while the existing method is <tex>$\boldsymbol{O}(N)$</tex>. The effectiveness of the proposed method is demonstrated by numerical experiments. This method can work as the reliability evaluation tool of the autopilot system.
TCABRS: An Efficient Traceable Constant-Size Attribute-Based Ring Signature Scheme for Electronic Health Record System

With the flourishing of the e-healthcare industry, electronic health record (EHR) systems are being widely deployed, bringing new challenges in fine-grained authentication and tracing. Malicious publishers might upload improper files to the system, causing horrible harm to patients. Consequently, it is quite important to make sure that file publishers in EHR system can be easily authenticated and traced. Identity-based signatures provide coarse-grained authentication which only briefly tells identities. But attribute-based signatures can tell more details, making fine-grained authentication possible. In this paper, we propose an efficient traceable constant-size attribute-based ring signature scheme for electronic health record system, focusing on fine-grained authentication and traceability of file publishers. We give formal definitions of syntax and the security model and then propose a concrete scheme with a pair of link flags to achieve traceability. The security of our scheme is eventually proved under Diffie-Hellman assumptions in the standard model.