Reviewerly/exHarmony huggingface dataset:
Corpus Summarization and Exploration using Multi-Mosaics

In fields such as translation studies and computational linguistics, various tools are used to analyze the content of text corpora, and extract keywords and other entities for analysis. Concordancing – arranging passages of text corpus in alphabetical order of user-defined keywords – is one of most widely used forms of text analysis. This paper describes Multi-Mosaics, a tool for text analysis using multiple implicitly linked Concordance Mosaic visualisations. Multi-Mosaics supports examining linguistic relationships within the context windows surrounding multiple extracted keywords.
Automatic Inspection of Power System Operations Based on Lightweight Neural Network

In the automatic inspection task of the power system industry, it is of great significance to employ artificial intelligence algorithms to automatically detect the illegal operation action of workers. Although many existing object detection algorithms can achieve good detection rate, due to the large scale and large amounts of parameters of the model, it can hardly be directly used in edge devices for real-time detection. Based on two frequency employed baseline lightweight models, this paper presents a novel deep learning algorithm for illegal operation action detection which uses the 5×5 deep detachable convolution kernel. The proposed lightweight network could not only extracts discriminative features by the two special convolutional layers, but also better detect small targets in the scene. The proposed network can be implemented on edge devices such as Huawei Atlas 200 DK. Experimental results on our collected dataset of the surveillance videos shot during the project of Ningxia Electric Power Co., LTD. Show that the proposed method could achieve comparable accuracy with only very few number of parameters, thus can be applied for real-time automatic inspection.
Monitoring Social Distancing based on Regression Object detector for reducing Covid-19

To deal with the worldwide coronavirus epidemic situation, the procedure of pulling down the Covid-19 cases will be tough to handle, if people do not take steps to thwart the virus from spreading. One of the most significant strategies in this pandemic is to keep a safe distance among the persons in public areas. This paper aims to detect persons with social distance monitoring as a preventive technique in minimizing physical connection between persons. The workflow of this paper is to detect the people in the area of interest using the YOLOv5 model. The model is trained on the Open Images V6 dataset and takes categories of people and human faces. Further, a social distancing algorithm is applied to check the distance between two persons and mark the red boundary box if any person violates the rules. The network provides inference speed capable of offering real-time results with maximum accuracy. The accuracy is achieved 99% in real-time scenarios using the proposed work. In various contexts, the suggested social distancing strategy produces promising outcomes.
Nature-Inspired Algorithms Analysis on various Benchmark Functions using Python and Golang

Recently, the Nature-Inspired Algorithm for problem-solving is now the field of interest for many researchers. The aim of this research work is to analyze the computational time and other properties from the aspect of using Python and Golang languages for naturally inspired computing algorithms. Nature-Inspired algorithms usually require immense computational capability and ease of implementation, hence in this paper, we will identify which freely available platform among Python and Golang is better for reliable performance and tremendous productivity of Nature-Inspired algorithms. By implementation of four Nature-Inspired algorithms on both python and Golang we will compare the computational time of each. Finally, we will show by drawing the graph that Golang is way better than Python for the computation when dealing with nature-inspired algorithms.
Towards Convolutional Neural Network Acceleration and Compression Based on Simonk-Means

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simonk-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27× compression and reduce 74.3% of the multiply-accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset.
Reconstruction of Bilateral Filtering Based on NLM

In order to improve the noise reduction effect of bilateral filtering, a new bilateral filtering algorithm incorporating the NLM algorithm is proposed. Considering that NLM focuses on the similarity between the location of noise reduction point and the location of adjacent points, NLM is used instead of gauss filtering to carry out a preliminary noise reduction, so that the filtering framework for noise reduction is determined. Then, based on the magnitude of the weighting coefficients of NLM and pixel greyscale obtained in the previous step, a matrix of weighting coefficients is obtained and analyzed to determine the size of the parameters in order to obtain better noise reduction. The experimental results show that the PSNR value is 2 ~ 3dB higher than that of the bilateral filter or NLM algorithm in the image with large gray difference, which shows that the reconstructed bilateral filter has obvious improvement compared with the traditional bilateral filter in the image with large gray difference.
Design Pattern Detection by Using Correlation Feature Selection Technique

Design pattern detection is a popular re-engineering job in software development. It provides essential information to the developers to recognize, sustain, and recycle the software. This article proposes a graph matching algorithm using a correlation-based feature selection technique to discover the design pattern (DP) instances from the software or system design. Usually, the feature selection method is used to choose the best instances from the dataset, which mostly matches the target feature. However, this paper has applied a correlation feature selection method to match the system design to the design pattern. We have taken three DPs comparing examples for this intention:•the first example for an entire match•the second example for a partial match•the third example of a not matchIn this experiment, we took the Abstract factory DP as a model graph, and the remaining twenty-two DPs are system or DPs. Software instances groups lots of classes and objects we converted into a graph. We observed that three of the 22 DPs are completely matched to the model DP, one pattern is not matched, and the remaining 18 patterns are only partially matched.
WAWA: Mobile educational tool for learning the Kichwa language for the communities of the Napo River in Loreto, Peru

This paper presents an experience related to the design, development and validation of the Wawa mobile application to be used as an educational tool for learning the Kichwa language for the communities of the Napo River in Loreto, Peru. For the validation we have resorted to the comparison of two learning methodologies, the traditional one and the experimental one in which the use of the mobile application is given. The results of the tests with school-age students have shown a superior effectiveness with the use of the application, compared to the traditional teaching method.
Human Values Violations in Stack Overflow: An Exploratory Study

A growing number of software-intensive systems are being accused of violating or ignoring human values (e.g., privacy, inclusion, and social responsibility), and this poses great difficulties to individuals and society. Such violations often occur due to the solutions employed and decisions made by developers of such systems that are misaligned with user values. Stack Overflow is the most popular Q&A website among developers to share their issues, solutions (e.g., code snippets), and decisions during software development. We conducted an exploratory study to investigate the occurrence of human values violations in Stack Overflow posts. As comments under posts are often used to point out the possible issues and weaknesses of the posts, we analyzed 2,000 Stack Overflow comments and their corresponding posts (1,980 unique questions or answers) to identify the types of human values violations and the reactions of Stack Overflow users to such violations. Our study finds that 315 out of 2,000 comments contain concerns indicating their associated posts (313 unique posts) violate human values. Leveraging Schwartz’s theory of basic human values as the most widely used values model, we show that hedonism and benevolence are the most violated value categories. We also find the reaction of Stack Overflow commenters to perceived human values violations is very quick, yet the majority of posts (76.35%) accused of human values violation do not get downvoted at all. Finally, we find that the original posters rarely react to the concerns of potential human values violations by editing their posts. At the same time, they usually are receptive when responding to these comments in follow-up comments of their own.
Building Structured Patient Follow-up Records from Chinese Medical Records via Deep Learning

Employing deep learning (DL) method to process and analyze Chinese medical records to build patient follow-up records (PFRs) has been a very valuable task. In recent years, the identification and classification of clinical terms in electronic medical records has received increased attention. However, electronic medical records are difficult to access because of their exceedingly high privacy, so it has become more feasible to extract information from paper medical records. This study proposed a DL approach that extract text information from the pre-processed images of Chinese medical records by optical character recognition (OCR) model base on CRNN first, and then identify the clinical entities using named entity recognition (NER) model based on BERT-CRF. The experimental results of this study demonstrate that the proposed method achieves precision over 75%, which is more than 90% for some specific entities. In addition, the proposed method can be extended as a universal approach to other diseases that require the establishment of the structured patient follow-up records (PFRs).
How Experimentation in Software Engineering has been taught? Survey and Research Agenda

[Background:] Experimentation in Software Engineering (ESE) has increased in the last several years as a way to provide evidence by using statistical techniques. These techniques contribute to an auditable and reliable body of knowledge towards evolving a given topic. Therefore, teaching ESE becomes an essential task while disseminating and establishing an experimental culture to both academia and industry. [Aims:] In this paper we seek to understand how ESE has been taught including contents, materials used, strategies employed, and evaluation method applied. We also provide a research agenda on the subject. [Method:] We conducted a web-based questionnaire survey with 31 instructors who teach ESE. [Results:] We see several aspects, such as: ESE is mainly taught in an exclusive course; most of the materials used to teach ESE concepts are from third parties and some of them themselves; most people do not use any type of license; the core materials used to define the content of the courses are papers and books; learning management systems are most often used to share materials with students; key learning practices used are active learning, project-based learning, and problem-based learning; and most instructors assess their students with experimental projects and seminars. Based on such results, we provide and discuss a research agenda to improve teaching of ESE. [Conclusions:] This survey provides results towards planning a research agenda to improve teaching of ESE, thus benefiting instructors, researchers, and practitioners.
Accuracy Improvement Techniques for network traffic classification using neural networks algorithms on Amazon SageMaker

Traffic classification is very important for network events management, such as data transverse to security monitoring, QOS, and accounting, to providing Internet service providers with beneficial insights for services provisioning. Yet, traffic classification accuracy is a strenuous topic because information known to the network admin, i.e., Port number, packet-headers, which does not contain sufficient forecast to consent for an accurate and efficient methodology. This leads to conventional techniques for flow classification that are often no further accurate than 35-65%. Machine learning algorithms are presented to solve complex, high-level abstract and random data, the nearest neighbor (NN)-based technique has exhibited excellent classification performance. It also has numerous major advantages, such as no necessities of training practice, no risk of overfitting of attributes, and logically being able to handle a vast number of classes. Deep learning architectures algorithms are convolutional Neural Network (CNN), K-nearest neighbors (KNN), Recurrent Neural Networks (RNNs), Support vector machines (SVMs), deep neural network (DNN), Decision tree C4.5, Naive bayes, Long Short-Term Memory Networks (LSTMs), Linear Discriminant Analysis (LDA) Nevertheless, In this study, we propose some techniques of improving traffic classification accuracy either by combining two or more algorithms through a multi-level classifier or by implementing computer aided techniques or using ensemble methods to obtain better traffic classification accuracy.
MUDIS: MUD Inspection System

The Manufacturer Usage Description (MUD) is an IETF white-list protection scheme that formalizes the authorized network behavior in a MUD file; this MUD file can then be used as a type of firewall mechanism.This demo introduces MUDIS, a MUD Inspection System that inspects the network behavior of devices, based on their formal description in the MUD file. We present several use-cases in which MUDIS is useful, including examining the impact of device location, the impact of a firmware update, the correlation of network behavior between different devices of the same manufacture, and more.MUDIS inspects two MUD files, clusters together and graph-ically visualizes identical, similar, and dissimilar rules. It then calculates a similarity score that measures the similarity between them both. It also generalizes the two MUD files where possible, such that the resulting generalized MUD covers all the permitted (white-list) network behavior for both MUDs.Our open-source MUDIS tool and proof-of-concept dataset are available for researchers and IoT manufacturers, allowing anyone to gain meaningful insights over the network behavior of IoT devices.
An Improved Linguistic Haar Fuzzy Decision Maps

Fuzzy Decision Map (FDM) was introduced as an extension of DEMATEL in 2006. It integrates the salient features of eigenvalue method, fuzzy cognitive maps, and weighting equation. Usually, researchers facilitated triangular and trapezoidal fuzzy numbers to describe weights of causal relation between factors. But it is not always plausible to use triangular and trapezoidal number. Therefore, the objective of this research is to enhance Fuzzy Decision Map by using the linguistic terms to define causality among the factors. And, heptagonal fuzzy numbers are employed to represent the linguistic terms. Furthermore, the Haar ranking method has been proposed to reduce uncertainties. The proposed Improved Linguistic Haar Fuzzy Decision Maps (ILHFDM) has been applied to analyze the environmental benefits of organic farming. After applying the proposed model, it is identified that ecological services ranked as the most influencing factor among the rest of the factors in enhancing the environment by practicing organic farming.
VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies

Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through two usage scenarios related to the creation and the review of a bibliography. Full details about the VisualBib(va) design, implementation and evaluation are available in [3]. A fully interactive environment is available at http://visualbib.uniud.it/ (video demo: http://bit.ly/3fKuZNg).
Ecorbis: A Data Sculpture of Environmental Behavior in the Home Context

We are in the middle of a climate crisis. Never before has the impact of climate change been this visible, problematic, and timely. While most people have a basic awareness of the enormous potential impact of climate change, the reality is that only a few people have a detailed understanding of how their own (combined) personal activities – including food, transport, energy – impact the climate. In this pictorial, we explore the design process and principles of a data sculpture – Ecorbis – that is designed to help people reflect on how their day-to-day activities translate to climate impact. Ecorbis provides abstract and numerical weekly feedback on the overall environmental behavior of households and allows for in situ reflection and comparison. We conducted an initial 8-day field study with two families that highlighted that Ecorbis raised environmental awareness on climate impact and that the layered design of Ecorbis facilitated reflection.
Object Tracking Algorithm for Siamese Network Combined with Channel Attention Mechanism

As an important branch in the field of computer vision, object tracking has been widely used in many fields such as intelligent video surveillance, human-computer interaction and autonomous driving. Although object tracking has imposing development in recent years, tracking in the complex environment is still a challenge. Due to problems such as occlusion, object deformation, and illumination change, tracking performance will be inaccurate and unstable. In this paper, an object tracking algorithm for Siamese network combined with channel attention mechanism is proposed. Firstly, the Siamese network is used to improve the ability to discriminate features; secondly, the channel attention mechanism is introduced to design a cross correlation module DCAM (Depth-wise Cross-correlation with Attention Mechanism, DCAM), which pays more attention to the features that are beneficial to the tracking results; finally, the stochastic weight averaging method is used to train the network to further improve the overall performance of the tracker. Experimental results on public data sets show that the proposed algorithm has higher accuracy and more stable tracking performance in complex tracking environment
Inter-Architecture Portability of Artificial Neural Networks and Side Channel Attacks

Side-channel attacks (SCA) have been studied for several decades, which resulted in many techniques that use statistical models to extract system information from side channels. More recently, machine learning has shown significant promise to advance the ability for SCAs to expose vulnerabilities. Artificial neural networks (ANN) can effectively learn nonlinear relationships between features within a side channel. In this paper, we propose a multi-architecture data aggregation technique to profile power traces for a system with an embedded processor that is based on three types of deep NNs, namely, multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). This is one of the first works to explore the inter-architecture portability of NNs and SCAs. We demonstrate the robustness of the ANNs performing power-based SCAs on multiple architecture configurations with different architectural features, such as L1/L2 caches' size and associativity, and system memory size. We provide a comprehensive set of benchmarks to demonstrate that architecturally identical devices are not essential for profile-based SCAs
Highly-Multiplexed Superconducting Detector Readout: Approachable High-Speed FPGA Design

Superconducting devices represent a class of breakthrough technologies enabling detection and manipulation of quantum signals [1] – [3] . Many thousands of interconnected superconducting devices are needed to support quantum error correction for quantum computing and mega-pixel-scale scientific cameras for single-photon-sensing applications [4] – [6] . With each device requiring up to 1 MHz of bandwidth, full readout systems must perform analysis on multi-gigahertz, ultra-wideband signals in real time [7] .
Automatic Text Summarization for Telugu Language

Telugu is a common south Indian language communicated by more than 74 million people in India. Today with the advent of the Internet huge volume of text in the form of news articles, editorials, blogs etc. are available for access to the users. Summarization of these contents becomes an essential requirement to enable faster and efficient search of such documents. Manual text summarization consumes a plenty of time, effort, and money, and it's even impractical when there's a lot of text. Since the 1950s, researchers are working to develop various ATS techniques. There are three types of ATS approaches: extractive summarization, abstractive summarization and hybrid. The extractive method picks out the most important sentences from the input documents and combined them to produce the summary. The abstractive method translates the input documents into an intermediate representation before generating a description of sentences that differ from the originals. The extractive and abstractive methods are combined in the hybrid approach. A review and study of text summarization techniques developed for Telugu language documents are presented in this paper.
A PCA-Based Active Appearance Model for Characterising Modes of Spatiotemporal Variation in Dynamic Facial Behaviours

Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on animating graphical head models. Here, we present a principal component analysis (PCA)-based active appearance model for capturing patterns of spatiotemporal variation in videos of natural dynamic facial behaviours. We demonstrate how this technique can be applied to generate dynamic anti-caricatures of biological motion patterns in facial behaviours. This technique could be extended to caricaturing other facial dimensions, or to more general analyses of spatiotemporal variations in dynamic faces.