Reviewerly/exHarmony huggingface dataset:
Discovering Authorship of Vulnerabilities in Open Source Software

Software vulnerabilities are, often the outcome of poor programming practices in the software developmental process. Elimination of bugs increases the expenses of the software. Bugs are created during software development process by programmers. Mitigation of flawed code in the programs can be achieved by training programmers to adopt secure code practices. Therefore, identification of flawed code created by authors becomes critical. In this work, a novel network theoretic approach to understand the relationships between programmers and flawed code in open source software projects is created. Experiments are conducted on multiple open source software to evaluate and validate the connections with authors and flaws, using multiple metrics motivated by concepts from natural language processing.
Machine Learning in Computer Vision

Computer vision is a research field in computer science that provides the computer the ability of human perceptions. The goal of computer vision is to understand the image and its contents. Computer vision has evolved from simple pattern recognition to solving various complex real-world problems. Nowadays, computer vision has its application to the various domains of scientific areas not limited to Computer Science. It is widely used for medical science, biology, physics, and chemistry as well. The recent boost in computer vision is the due influence of machine learning (ML). ML is a subfield of artificial intelligence. Machine learning is a method that makes a computer learn from the provided data and improve its performance with time. Computer vision problems are complex, and they require a correct choice of algorithms. In this chapter, some machine learning algorithms that are widely used in computer vision will be discussed. The theoretical concepts are related to real-world computer vision problems such as human skin detection.
Sentiment Analysis of Science Fiction Movie Reviews Based on Deep Learning

In recent years, the information technology and film industry have developed rapidly. Online film reviews can greatly reflect the opinions of the movie audience and can extract valuable information from the online data by using advanced information processing technology. In this article, we applied TextCNN to achieve the emotional analysis of the audience’s comments on science fiction movies: Inception and The Wondering Earth. The reviews of the movies were collected on MTime. Experimental results show that TextCNN has high accuracy in sentiment analysis of film reviews.
A Heuristic Approach using Block Chain to Fight Novel COVID-19 During an Election

India is a highly densely populated country. India is facing a very serious challenge due to the novel COVID-I9 outbreak. In this situation, the economic state of the people is very unstable. Blockchain technology proves helpful to subdue the situation. A voting system with ballot papers is quite tuff now. It is very costly for the government. The pandemic may also spread very quickly. The system of developing ledger serves the idea of developments of a new e-voting system that is economical, easy to use, and highly secure. This paper proposes ways and solutions to design an e-voting system using blockchain technology. The paper's primary goal is to develop a highly secure e-voting technique using which people can cast their valuable votes from their home, working place - anywhere from the world. This e-voting technique is least costly, highly secure, and able to prevent further spreading of any infectious disease in the near future. In this paper, an e-voting system using blockchain has been designed to overcome all these difficulties mentioned above during traditional voting using ballot. The significance of the proposed work is to design of a highly safe and least costly e- voting system using blockchain. Using this system the people can cast their vote easily, securely and without wasting any time. Casting vote will be now just from few clicks away and from any place with a stable internet connection.
Detection of Microcalcifications in Mammograms Based on Hyper Faster R-CNN

Microcalcifications are one of the important indicators of breast cancer. Accurate breast microcalcification detection can effectively assist radiologists in early diagnosis. The Hyper Faster R-CNN network is a deep neural network specially proposed for the detection of microcalcifications in mammograms. The network uses ResNeSt as the feature extraction network that can extract richer semantic features from the image, and uses the FPN feature fusion mechanism to fuse high-resolution features at low levels and high-semantic features at high levels, thereby improving the network's detection accuracy of microcalcifications. Focal Loss is adopted as the loss function to alleviate the problem of imbalanced microcalcification samples. To evaluate the defection effect of the proposed network on microcalcifications, the DDSM database is used to generate the microcalcification image dataset required for experiments, and the obtained AP value is up to 90.75%. In the end, the HF R-CNN network is compared with other state-of-the-art object detection networks. The experimental results show that the proposed HF R-CNN network can detect microcalcifications in mammograms more effectively.
An Effective Graphical Password Authentication Method in Health Care Sectors

Graphical passwords are commonly used to provide an alternative way for users to access their accounts. Current challenges in facilitating patients’ access of their medical records, health data, etc. using graphical elements are yet to be explored. This study proposes the design of a webpage to allow user register and login to their account using graphical password by extending the current features of PassFaces. A system was designed and developed to allow users to register by selecting a set of picture related to doctors along. A total of 10 students undertaking master were used to evaluate the proposed system. The evaluation result showed that users’ success rates to access their accounts were promising.
BSS-ITS: Blockchain Scaling Scheme with Sharding for Intelligent Transportation System

With the development of modern technologies, Intelligent Transportation System (ITS) has made great progress and brought convenience to every aspect of people's lives. ITS has become more complicated and uncertain due to the existing security and privacy problems. Blockchain, being a decentralized and tamper-resistant technology, can provide an environment for ITS, allowing ITS entities to perform transactions securely and trustfully. However, the inefficiency problem of blockchain, severely restricts its development and application. Studies only combine traditional blockchain with ITS, but do not consider scaling blockchain to improve performance. This paper devotes to scale blockchain that adapted to ITS, improving the efficiency of the blockchain without sacrificing the decentralization and security. A sharding scheme is proposed, which takes network sharding, data sharding and state sharding into account. Nodes are divided into several shards. Different shards can process data in parallel and store related data in their own ledger. Backup shard is put forward in this paper to enhance the shard security. And network will reshuffle when one or more shards break down. Some experiments are performed using Hyperledger Fabric and network simulator 3. Throghput, storage and delay are tested under sharding scheme and non sharding solution. The results show that sharding method is effective in scaling blockchain for ITS.
An Unmanned Aerial Vehicle Video Object Tracking Algorithm Based on Siamese Attention Network

Unmanned Aerial Vehicle (UAV) has been widely used in military and civilian fields, and object tracking is one of the critical technologies in UAV application. For addressing deformation, occlusion, small object, and other UAV object tracking problems, an UAV video object tracking algorithm based on Siamese Attention Network (SANet) is proposed in this paper. Initially, we designed a lightweight network as an extractor to extract features. After that, the attention mechanism module is constructed to screen out the feature map's semantic attributes, and the corresponding weights are re-assigned to different channels and spatial features. Finally, three Regional Proposal Networks (RPNs) are introduced to hierarchical fusion to obtain the tracking results. Our proposed algorithm in this paper has experimented on the UAV123 dataset and self-built dataset. The results show that the algorithm has a good tracking effect, the average accuracy is improved to 0.815, and the success rate is 0.619.
Blockchain-based Supply Chain Financial Services Using Smart Contract

Supply chain finance (SCF), a proposal to address financing problems of small and medium enterprises (SMEs), aims to make working capital optimized by utilizing credit and transaction information through cooperation between upstream and downstream in the supply chain. But some demerits such as information asymmetry and the lack of trust in financing scenarios could still be witnessed in supply chain finance. Blockchain technology, with the characteristics of asymmetric encryption, decentralization, nonrepudiation, and conducting transactions without the supervision of a trusted third party, has revolved multiple stages of development. Smart contracts are regarded as an important derivative technology of blockchain, which are automatically executed by the code between multiple parties through the blockchain without relying on a trusted institution. In this paper, we propose a blockchain-based supply chain financial system combining smart contracts and main methods of supply chain financing to solve the disadvantages of SCF. We select three represented financing modes to design financing platforms with the introduction of smart contracts, and present our schemes in the form of specific pseudo code. All financing processes of supply chain are conducted on the basis of ethereum blockchain. This allows SMEs which have difficulty in capital turnover in the short term, or have insufficient credit to get financial loans more easily compared with traditional financing approaches due to merits of blockchain, and also this could facilitate supply chain to improve financing efficiency for SMEs.
The emotions of upper secondary level students in a virtual learning environment

In recent years and derived from the pandemic that is experienced today, online education has been advancing by leaps and bounds. It has demanded that educational actors face and adapt to a series of changes that have been generated by the implementation of digital environments in the teaching-learning process. It is in this scenario where the objective is to identify the students’ emotions from High school with respect to their learning in a virtual environment. They experience a series of emotions that according to Lin, Su, Chao, Hsieh and Tsa, (2016) can generate the ideal environment to develop more successful learning processes through positive emotions. To achieve this objective, a quantitative investigation was developed, and a questionnaire with a Likert scale was designed and applied to a sample from the Enrique Cabrera Barroso Regional High School. This instrument revealed the emotions they experience in a virtual learning environment during their learning process and their interaction among the actors. Considering the results, we find that students experience a series of internal emotions that are decisive for adapting to a virtual environment.
Face-Assisted Authentication System Based on Mobile Traffic Distribution Platform for English Cloud Classroom Mixed Guidance

This article relies on the online and offline interactive teaching of the cloud classroom intelligent platform established by Internet technology. By combining online and offline, it gives full play to the role of online teaching resources and information technology, and starts from the cloud classroom intelligent platform activity teaching based on mobile traffic distribution. Use face-assisted verification to analyze the authentication method of this online course, discuss its construction method and application strategy, and then analyze the advantages and application of online and offline hybrid teaching mode in college English teaching, and finally put forward online and offline the optimization of blended teaching mode in college English teaching.
Increasing naturalness of human–machine dialogue: The users’ choices inference of options in machine-raised questions

In many practical applications, the machine needs to actively ask humans to obtain their intents. The process that the machine raises questions and users return answers is called reverse QA, which is an important part of a human–machine dialogue. However, in many dialogue systems, the machine restricts users from answering questions by clicking on option items, which is unnatural and restricted. In addition, this method may lose important information expressed by users. Users should be allowed to answer questions in natural language in a more natural and intelligent dialogue system. To obtain users’ intents, users’ choices of questions’ options must be inferred from their answers. In this paper, we propose an advanced answer understanding network (UCINet) which infers users’ choices of options in machine-raised questions accurately and efficiently according to the users’ answer. Furthermore, metric learning is introduced for the model to learn better text representations. Based on the assumption that texts are determined by both semantics and styles, we propose a style-based answer generation network (SAGNet) which can generate various answers with different styles for a question. The generated answers are used to achieve data augmentation for UCINet’s training. Experimental results on two reverse QA data sets demonstrate that UCINet achieves impressive results compared to other strong competitors. Using SAGNet for answer generation, we obtain answers with various styles and good quality. Our work can be widely used in intelligent customer service, mobile phone assistants, and other human–machine dialogue systems.
Cooperation and distrust in extra-legal networks: a research note on the experimental study of marketplace disruption

Cybercriminal markets serve as hubs for offenders and enable the sale of illegal goods and services. Thus far, the primary tactics that have been employed against these sites are arrests of cybercriminals and takedowns of marketplace infrastructure. This research note examines a different genus of disruptive strategy: attacks on user reputation. In this area, there has been some scholarly discussion of slander and Sybil operations as a means of fostering distrust. But carrying out empirical work on the effectiveness of these tactics is challenging. This research note presents a possible method for investigating this topic: social laboratory experiments. It reports on a feasibility pilot study inspired by cybercrime disruption, but which speaks to a broader range of extra-legal markets.
ExaAEC: A New Multi-label Emotion Classification Corpus in Arabic Tweets

Nowadays the usage of social media tools by individuals to share their emotions and opinions in the form of short text messages is increasing. Emotion has a significant role in people’s interactions. Detecting Emotion specially from short texts in social media, has become a challenging problem in natural language processing. Despite recent deep learning successes in a variety of fields of NLP, previous studies on low resource languages mostly suffer from scarcity of data. In this research, we are going to introduce a new dataset of emotion recognition on Arabic tweets called ExaAEC which contains about 20000 tweets annotated by six native Arab taggers. The aforementioned data is labeled in ten classes inspired from the Plutchik emotion model. To classify the emotions, Embedding from Language Models (ELMO) and Long Short Term Memory (LSTM) neural networks are applied. According to the results, the F1-score in the experiments was 0.65 which indicates, our data and model can perform adequately in Emotion Detection. The corpus is publicly available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Lightweight domain adaptation: A filtering pipeline to improve accuracy of an Automatic Speech Recognition (ASR) engine

Transformer models have accelerated the field of speech recognition; deriving a low word error rate (WER) is demonstrably achievable under varying conditions. However, most ASR engines are trained on acoustic and language models constructed from corpora that include news feeds, books, and blogs in order to demonstrate generalization, leading to errors when the model is applied to a specific domain. While the increase in WER is acute for very specific domains (health and medicine), our work shows that it is sizable even when the domain is general (hospitality). For such domains, a lightweight adaptation approach can help; lightweight because the adaptation does not require extensive post-hoc training of additional domain-specific acoustic- or language-models that act as adjutants to the base ASR engine. We present our work on such lightweight filtering pipeline that seamlessly integrates lightweight models (n − gram, decision trees) with powerful, pre-trained, bi-directional transformer models, all working in conjunction to derive a 1-best hypothesis word selection algorithm. Our pipeline reduces the WER between 1.6% to 2.5% absolute while treating the ASR engine as a black box, and without requiring additional complex discriminative training.
Recognizing Chinese Texts with Multi-width Feature Extractor and Attention-based Fusion

Recognizing texts plays an important role in optical characters recognition (OCR). Although the previous text recognition methods have made great progress, most of them focus on Latin characters, while few are about Chinese characters. There are two main differences between Chinese characters and Latin characters. First, the amount of Chinese characters is much larger than that of Latin characters. Second, the width of Chinese characters varies from character, while that of Latin characters is stable. This paper proposes a multi-width text recognition method to solve the two challenges in Chinese text recognition. A multiple feature extractor module is introduced to obtain multiple features of characters in a Chinese task. Besides, an attention-based fusion module is employed to dynamically fuse the multiple features. We conduct experiments on a popular Chinese dataset, and the results demonstrate that our model is superior to other state-of-the-art (SOTA) baselines.
Web specific information detection based on features and deep neural network

Internet information is spreading rapidly, and the task of detecting web specific information is arduous. To solve this problem, this paper proposes a web specific information detection method that combines features and deep neural network (DNN). It combines text features and uses a DNN model to analyze and calculate topic relevance, so as to improve the quality of web page information detection for specific topics. By a feature-based web specific information extraction algorithm which focuses on the two dimensions of web page characteristics and structure web behavior characteristics, it reduces the omission of web page information, improves the effect of text classification and analyzes the result comprehensively. The DNN model of layered architecture is described to explain the main principles of the process, which reduces the complexity of system operation. Practical application result shows that this method extracts web page information on specific topics more accurately, improves the accuracy and recall rate of web specific information extraction, and improves the efficiency of web specific information extraction quality.
A Study of Split Learning Model

Split learning is considered a state-of-the-art solution for machine learning privacy that takes place between clients and servers. In this way, the model is split and trained, so that the original data does not move to the client from the server, and the model is properly split between the client and the server, reducing the burden of training. This paper introduces the concept of split learning, reviews traditional, novel, and state-of-the-art split learning methods, and discusses current challenges and trends.
Cloud Storage Performance Improvement Using Deduplication and Compression Techniques

Cloud Computing, an internet based computing is composed of various applications and hardware deployed to the end-user. Existing system deals with block level de-duplication. In this deduplication process, unique chunks of data or byte patterns are identified and stored in the cloud file system. Then the other chunks are compared to the stored copy and whenever a match occurs, the redundant chunk is replaced with a small reference that points to the stored chunk. But in case of block-level deduplication maintenance of large number of blocks is highly difficult. It also requires high processing power when compared to other deduplication techniques. This is the reason why File-level Deduplication comes into picture. Replication is the presence of same file in all servers. This technique increases the efficiency by making the data available at all time. Replicating data avoids single point failure. At the same time it leads to wastage of space. For increasing the cloud storage efficiency, deduplication and compression techniques are adapted. Our approach to deduplication and compression aims at enhancing the efficiency of cloud storage. Deduplication, specialized data compression technique stores only single copy of the redundant data. Detecting the presence of duplicate files and storing it in compressed format in all other servers other than the original server improves the efficiency of cloud storage. In this approach, Deduplication is done at file level. Files present in the compressed format can be uncompressed (unzipped) and downloaded by the user, hence maximizing the storage efficiency and increasing the availability.
Non-Functional Requirements Analysis Based on Application Reviews in the Android App Market

There are more than 3 million mobile apps in the Android market. The development process of every mobile application is rigorous, and many types of research on application quality requirements are derived, which are highly related to the development of applications. Research shows that user reviews of mobile applications are an unused large database that can provide feedback on user needs. In this article, user comments are automatically classified into non-functional requirements (NFRs) and other types. This paper proposes a loop matching classification technique (Loop Matching Classification). The three classification techniques of LMC, BOW, and TF-IDF were used to classify user comments, and the accuracy, recall rate, and F-measure of the results of the three classification techniques were compared. It was found that the Precision value of the LMC classification technique was 74.2%, the Recall was 82.5% and the F-measure was 78.1%.