Reviewerly/exHarmony huggingface dataset:
Technical Debt is not Only about Code and We Need to be Aware about It

Context: It is common for a software project to incur technical debt (TD) during its development. It can impact several artifacts produced throughout the software development process. Therefore, it is necessary to carry out management actions to find a balance between the benefits of incurring it and the effects of its presence. However, so far, much of the attention has been given only to discussions relating TD to coding issues. This is a worrying scenario because other types of debt can also have impactful, or even worse, consequences on projects. Aims: This study elaborates on the need to consider other issues of the development process and not just the source-code when investigating the TD phenomenon. Method: We analyze responses from 653 practitioners concerning TD causes, effects, prevention, reasons for non-prevention, repayment, and reasons for non-repayment and investigate whether these TD management elements are related to coding or to other software development issues. Results: Coding issues are commonly related to the investigated elements but, indeed, they are only part of the big picture we draw. Issues related to the project planning and management, human factors, knowledge, quality, process, requirements, verification, validation, and test, design, architecture, TD management, and the organization are also common. Lastly, we present a hump diagram that, in combination with the detailed results, provides guidance on what to expect from the presence of TD and how to react to it considering several issues of software development. Conclusion: The results shed light on other concerns beyond code that the research community and practitioners need to be aware of.
Crowd Sourcing and Blockchain-Based Incentive Mechanism to Combat Fake News

With advancing technology, social media sites have become narratives for news surfacing the digital world. Social media users generally have no qualm for forwarding whatever comes their way. The flexibility of social media platforms offers no way to prove the credibility of a shared piece of information. The authenticity of the news and maintaining netiquette over social media sites have become precedence areas. This has challenged the technology in maintaining the ethics and objectivity of journalism. This chapter presents a shared, decentralized framework that implements an alternate vision to the customary way people share information. In particular, we provide an innovative blockchain and crowd sourcing-based framework for demonstrating the provenance of news surfacing through digital media. Under this platform, the truth is sourced from the crowd, stored, and consequently transformed into a real-time interface. Moreover, using the immutable feature of blockchain, all users are made accountable and valued based on their reputation. Further, nodes are provided with incentives for their rightful behavior and are penalized for their malicious actions. Thus, we aim to offer a much-needed layer for secure, reliable information exchange in our present-day social media infrastructure that does not rely on a single source of truth.
A CPU-FPGA Based Heterogeneous Accelerator for RepVGG

RepVGG is a Convolutional Neural Network (CNN) that has a VGG-like inference-time body with a good performance. Compared with other devices, the FPGA accelerator has the advantages of flexibility and efficiency. In this paper, we proposed a heterogeneous CPU-FPGA accelerator for RepVGG. Specific computing kernels are designed for convolution layers. Weight and bias parameters are quantized to 8 bits without a large loss of accuracy. The memory access performance and computing performance of each layer of the network are also optimized. The accelerator achieves 758 Gop/s. Compared with the CPU, the accelerator is more than three times faster.
A Survey and Taxonomy of Blockchain-based Payment Channel Networks

Blockchain technology has the potential to turn into the mainstream in many enterprises including finance, personal identity security and real-time operating systems. The usage of public or permissionless blockchains, in which any participant has the option of joining and leaving at any moment, is growing abruptly. However, the decentralization of permissionless blockchains comes with some main costs which one of them is the limitation of scalability: the transaction loads managed by blockchains are remarkably less than those managed by traditional financial systems. Among many proposals to address the scaling issue, one of the most promising solutions is the Payment Channel Network (PCN), which deploys off-chain settlement of transactions with minimal involvement of expensive on-chain blockchain operations. This work explores the existing PCN solutions, their challenges and the suggested improvements on PCNs, especially routing mechanisms.
Research on English Online Test Platform Based on Real-Time Web Communication Technology

Due to the multiattribute and multi-related problems of the target English test platform, the efficiency of the platform is low. In order to solve the problem of insufficient control of the difficulty of the test questions in the process of English test paper generation, this paper designs an English test platform based on real-time network communication technology. The English test platform designed in this paper includes three modules: user management module, question bank management module, and test paper management module. Among them, in the test paper management module, we extract English test questions according to the test paper generation strategy, then use real-time web communication technology according to the test paper generation target, and, through effective update of pheromone, solve the problem of high test question repetition rate in automatic test paper generation and design the network. The self-learning platform and English test platform modules have completed the design of the English test platform based on real-time network communication technology. The experimental results show that the designed English test platform has a high success rate, quick test paper generation time, and good application value.
Anti-sweep-jamming Method Based on Instantaneous Correlation and Harmonic Time Sequence Test for Pseudo Code and Linear Frequency Hybrid Modulation Detector

Sweep-jamming, combining the jamming effect of “suppress” jamming and “aim” jamming, has been widely used in electronic warfare systems. In order to improve the anti-sweep-jamming performance of the pseudo code and linear frequency hybrid modulation detector (hereinafter referred to as the hybrid modulation detector), this paper analyses the detector's response under sweep jamming the proposes an anti-jamming method based on frequency instantaneous correlation and harmonic timing sequence test approach, which combines time-domain instantaneous correlation with frequency-domain timing sequence test to obtain the range information of the target and utilizes the multiple harmonic time information to improve the “anti-sweep-jamming” performance. For proving the feasibility of the proposed method, this paper designs the simulation and the measurement and verifies that the “frequency instantaneous correlation and harmonic timing sequence test” method can effectively improve the anti-interference performance of the hybrid modulation detector.
A Collaborative Modeling Method for Multi -behavior Models of Electromechanical CPS Systems

Aiming at the characteristics of large heterogeneity and strong time constraint of airborne electromechanical system, an electromechanical event model and collaborative modeling method based on CPS is proposed. According to the complex characteristics of electrical, gas and hydraulic components of electromechanical system, the model describes the time and space information, proposes the system modeling and processing based on event response, adopts a unified model to capture all kinds of events in electromechanical CPS application, and provides upper level services across applications and data sources, The problem of semantic diversity of spatiotemporal data and consistency of interaction semantics of various models in electromechanical CPS system simulation is solved.
Self-Attention Based Virtual Staining for Bright-field Images of Label-free Human Carotid Atherosclerotic Plaque Tissue Section

Histological analysis of carotid atherosclerotic plaque tissue specimens is a widely used method for studying the diagnosis of ischemic heart disease and stroke. Understanding the physiological and pathological mechanisms of carotid atherosclerotic plaque is of great significance for the effective prevention and treatment of plaque formation and rupture. In this work, we adapted a self-attention generative adversarial model to virtually stain label-free human carotid atherosclerotic plaque tissue sections into corresponding H&E stained sections. The self-attention mechanism and multi-layer structure are introduced into the residual steps of the generator and in the discriminator. Our method achieved the best performance (SSIM, PSNR, and LPIPS of 0.53, 20.29, and 0.30, respectively) in comparison with other state-of-the-art methods.Clinical Relevance - The proposed approach allows for the virtual staining of unlabeled human carotid plaque tissue images. It identifies the histopathological features of atherosclerotic plaques in the same tissue sample which could facilitate the development of personalized prevention and other interventional treatments for carotid atherosclerosis.
The DiGreC Treebank

Abstract The DiGreC (DIachrony of GREek Case) treebank is a corpus of selected sentences from Greek texts, ranging from Homer to Modern Greek, which have been annotated morphosyntactically and semantically. The corpus comprises excerpts from 655 texts, for a total of 3385 sentences and 56,440 word tokens; automated tagging and lemmatisation has been supplemented with manual review to ensure accuracy. The data exist in xml and csv formats, which can be manipulated and converted automatically to other schemata. A web site has also been created to allow users to interact with the data more easily, and to provide specialised functionality for searching and visualisation. This corpus was created to inform theoretical debates regarding the role of case in grammar, and may be of use to researchers searching for specific attestations of a range of different constructions in Greek.
Prioritized Indoor Exploration with a Dynamic Deadline

Indoor exploration using mobile robots has typically focused on exploring the entire environment without considering deadlines. This paper introduces a priority-based exploration algorithm for situations with an initially unknown and dynamically assigned deadline. The goal of our exploration strategy is to determine the geometric structure of an unknown environment as rapidly as possible and return to the home location. This is necessary for dangerous environments where an initial rapid robot exploration provides critical information about the layout for subsequent operations. For example, firefighters, for whom time is of the essence, can utilize the map generated by this robotic exploration to navigate a building on fire. We present a three-part strategy to solve this problem. First, we represent the physical environment as an exploration graph, whose vertices represent the local environment with its geometric and semantic information. Second, we assign priority values to these vertices based on their environment regions. Third, we present a graph exploration algorithm that employs the vertex priorities. Simulation experiments on a set of graph environments and Gazebo environments demonstrate that, in contrast to prior approaches that ignore the semantic information, our priority-based exploration algorithm enables the robot to efficiently explore more of the environment while satisfying its deadline constraints.
An Enhancement Method for Chinese Environment Semantic Slot Filling Based on POS Tagging

To solve the problem of Chinese semantic understanding and extraction, this paper presents an enhancement method for Chinese environment semantic Slot Filling based on part-of-speech(POS) tagging. Firstly, construct a special dictionary with a given task domain where each word has its own part of speech and word frequency tagging; Secondly, based on the Chinese word segmentation technique called Jieba, Chinese sentences are partitioned to obtain a finite sequence with words as the basic elements. And then, a new finite sequence is obtained by replacing the words with the same POS tagging in the finite sequence with the same symbol. Finally, the Bidirectional Long Short-Term Memory with Conditional Random Fields(BLSTM-CRF) network training model is used with a new limited sequence set and its tag sequence set as input, and the slot filling criteria based on POS tagging are appended. Applying popular data set ATIS training model, it shows that the method has high accuracy and reliability through simulation comparison experiment.
An image destriping method combining feature extraction and nonlinear fitting

With the development of semiconductor technology, the performance of many new image sensors, such as infrared focal planes and CMOS image sensors, have been improved. Also, it is an alternative choice of CCD, vacuum tube or other equipment that promotes this kind of sensors to become the mainstream of the market. However, this kind of image sensors has a weakness that is the stripe noise, due to the inconsistent readout circuit response. This noise influences the quality of image directly, as a result, the poor image quality in-benefits for recognition and classification. Thus, how to remove the stripe noise arises the interests for many researchers. Most of them only focus on one certain type of stripe features, instead of combining different features together, leading to the poor performance of denoising. This paper proposes a method of removing stripes that combines feature extraction and function fitting. Firstly, extract the statistics and spatial position of the stripes, and then perform two steps of rough processing and fine processing on the image. The PSNR, SSM value show that the algorithm meets the design expectations and has a certain degree of robustness.
Recognize, Annotate, and Visualize Parallel Content Structures in XML Documents

We present a four-phase parallel approach for capturing, annotating, and visualizing parallel structures in XML documents. We designed a highlighting strategy that first decomposes XML documents in various data streams, including plain text, formulae, and images. Second, those streams are processed with external algorithms and tools optimized for specific tasks, such as analyzing similarities or differences or differences in the respective formats. Third, we compute comparison metadata such as annotations and highlighting marks. Fourth, the position information is concatenated based on the original XML's computed positions document. Eventually, the resulting comparison can then be visualized or processed further while keeping the reference to the source documents intact. While our algorithm has been developed for visualizing similarities as part of plagiarism detection tasks, we expect that many applications will benefit from a well-designed and integrative method that separates between addressing the match locations and inserting highlight marks. For example, our algorithm can also add comments in XML-unaware plaintext editors. We also treat the edge cases, overlaps as well as multi-match with our approach.
Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome

Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.
mobile YOLACT: Toward Lightweight Instance Segmentation for Mobile Devices

In this paper, we present a lightweight instance segmentation model, mobileYOLACT which is designed for mobile environments where the computational resources are limited. We propose several modifications to YOLACT to improve computational efficiency. First, we use a quantized lightweight backbone for feature extraction. Second, we reduce the computational burden with marginal degradation in accuracy by employing the depthwise separable convolution on the entire model. Third, we simplified the structure of prototype mask generation branch. Last, we used TorchScript and NCNN to further optimize the model and deploy it on mobile device. We validate the effectiveness of the proposed method from the experiments on COCO dataset. The proposed model can run at the speed of 21 FPS on Samsung Galaxy S20 with 23 APmask at 0.5 IoU threshold.
A Quantum Ring Signature Scheme Based on the Quantum Finite Automata Signature Scheme

In quantum cryptography research area, quantum digital signature is an important research field. To provide a better privacy for users in constructing quantum digital signature, the stronger anonymity of quantum digital signatures is required. Quantum ring signature scheme focuses on anonymity in certain scenarios. Using quantum ring signature scheme, the quantum message signer hides his identity into a group. At the same time, there is no need for any centralized organization when the user uses the quantum ring signature scheme. The group used to hide the signer identity can be immediately selected by the signer himself, and no collaboration between users.Since the quantum finite automaton signature scheme is very efficient quantum digital signature scheme, based on it, we propose a new quantum ring signature scheme. We also showed that the new scheme we proposed is of feasibility, correctness, anonymity, and unforgeability. And furthermore, the new scheme can be implemented only by logical operations, so it is easy to implement.
Panoramic Projection of 3D CT Scan Teeth Images

Dental panoramic image is widely used by both doctor and researcher. Panoramic image shows the condition of the teeth clearly. Some pathological condition such as teeth caries or cyst can be detected using panoramic image. However, taking panoramic image acquisition means that the patient needs to be exposed to radioactive exposures. Patient that has CT scan image of their head might not need to take panoramic image acquisition since previous research has shown that CT scan image can be used to create the panoramic proj ection of the patient teeth. However, the result of the panoramic projection shows that some of the teeth are missing. In this research we propose a method using morphological operations and curve fitting with a certain threshold on the fitted curve on images produced by CT scan can create a better panoramic image of patient dental condition. Our results show that using both of the methods can produce a more complete panoramic projection of patient teeth.
An Improved Firefly Algorithm Based on An Attraction Switch

Firefly algorithm(FA) is a new swarm intelligence optimization algorithm. For any two firefly individuals that are far apart.the attraction module of the FA at this moment loses its attraction during each iteration. This is a poor convergence of the F A,and it is easy to fall into a local optimum. In order to overcome the shortcoming, based on the standard firefly algorithm and LF-FA, the paper proposes a improved firefly algorithm(AS-IFA) that switches attractive modules. The AS-IF A has a strong attraction for any firefly that is far away. At the same time.it also has obvious attraction to the closer fireflies. The experimental results show that compared with the standard firefly algorithm and its variant algorithm, the AS-IFA has the best convergence behavior, and its global exploration efficiency is the best.
Design of diversified teaching platform of college aerobics course based on artificial intelligence

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.
An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era

Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The experiments are conducted on five different datasets including medical gloves, hand sanitizer, medical oxygen, face masks, and a combination of all these datasets. The results show that the harmony search algorithm successfully reduced the number of features by 94.25%, 89.5%, 89.25%, 92.5%, and 84.25% for the medical glove, hand sanitizer, medical oxygen, face masks, and whole datasets, respectively, while keeping a competitive performance in terms of accuracy and root mean square error (RMSE) for the classification techniques and decreasing the computational time required for classification.