Reviewerly/exHarmony huggingface dataset:
Learning from the history: Identifying configuration change in software development

Configuration design and implementation is a major develop activity of modern software systems. With the everyday evolution of code base, configuration changes also happen frequently. These changes contain a wealth of information and knowledge for developers to better design and manage their software configuration. However, those changes are drowning in the tremendous of commits, which are difficult and time-consuming for developers to find and study on. To help developers and users better understand configuration design and implementation. This paper presents a method to automatically identify configuration change in software development. By understanding the code pattern of configuration code, we design a text-based configuration program analysis for code diffs in commits. The experiment result shows that our tool can achieve an average of 87.7% accuracy.
Scene change detection based on sequence statistics using structural similarity

With the development of the short video, video processing technology is becoming more and more important, among which video scene change detection is an important basic technology. However, due to the inconspicuous feature of scene gradual change, the current algorithm cannot detect it very well. Based on this, we proposed a new scene change detection algorithm. The new algorithm is designed based on the structural similarity (SSIM) algorithm, an index to measure the similarity of two images, and the changing law of structural similarity sequence during scene change. Meantime, we designed a new measurement based on sequence statistics to measure the variation law of SSIM sequences. Experiments show that the algorithm has the characteristics of anti-interference and anti-noise, and it can well solve the above difficulty.
Significance of Blockchain in Curing Digital Marketing Security Issues: A Sociometric Approach

With the overwhelming improvement of fintech, the market climate of the customary monetary industry has gone through extraordinary changes the activity and the executives are turning out to be progressively advanced and canny, and the advertising is turning out to be more situation arranged and exact, and the assistance mode is turning out to be more customized and tweaked. It is critical to lay out an inventive administration and administration component that addresses the issues of The Times. This study centers around the sociometric approach in examining the contribution of blockchain in restoring computerized promoting security issues and guaranteed that blockchain innovation significantly affected buyer trust and could turn into a significant variable for creating trust in wood items during the worldwide exchange. The end was made given 212 examinations published in different Scopus-listed journals.
Light Deep Face Recognition based on Knowledge Distillation and Adversarial Training

Aiming at the difficulty of deploying deep learning model caused by limited processing capacity and small memory of terminal video acquisition equipment, a deep neural network lightweight algorithm based on knowledge distillation and adversarial training is proposed to transfer knowledge with classification probability and middle-layer feature. The principle of the algorithm is to improve the traditional knowledge distillation loss and add the indicator function, so that the student network only learns the correct classification results given by the teacher network. The discriminator of adversarial learning strategy is introduced to identify the difference between student network and teacher network in middle layer. In the latter part of the training, the teacher network and the student network learn from each other and update each other alternately, so that the student network can explore its own optimal solution space, and further improve the generalization ability of the student network, so that it can be applied to other detection or recognition tasks. The experimental results show that the small size student network obtained by knowledge distillation has less parameter, and the recognition accuracy decreases only about 1.5%.
An error neighborhood-based detection mechanism to improve the performance of anomaly detection in industrial control systems

Anomaly detection for devices (e.g, sensors and actuators) plays a crucial role in Industrial Control Systems (ICS) for security protection. The typical framework of deep learning-based anomaly detection includes a model to predict or reconstruct the state of devices and a detection mechanism to determine anomalies. The majority of anomaly detection methods use a fixed threshold detection mechanism to detect anomalous points. However, the anomalies caused by cyberattacks in ICSs are usually continuous anomaly segments. In this paper, we propose a novel detection mechanism to detect continuous anomaly segments. Its core idea is to determine the start and end times of anomalies based on the continuity characteristics of anomalies and the dynamics of error. We conducted experiments on the two real-world datasets for performance evaluation using five baselines. The F1 score increased by 3.8% on average in the SWAT dataset and increased by 15.6% in the WADI dataset. The results show a significant improvement in the performance of baselines using an error neighborhood-based continuity detection mechanism in a real-time manner.
Evaluation of Contact Lens Data Acquisition Approaches using Enhancement Techniques

Contact lenses can be helpful to improve the quality of human life. The inspection process plays a big role to produce good quality contact lens products. However, there is a challenge to detecting the defects in contact lenses during the production line. The transparent type of silicone hydrogel contact lens is one of the most difficult to detect the defects inside it. The primary purpose of this paper is to examine the differences in quality images between four different data acquisition approaches based on two image enhancement techniques, Gaussian blurring and Contrast Limited Adaptive Histogram Equalization (CLAHE). Acquiring a clear and good-quality image, required a specific experimental setup which consists of a high-resolution camera lens and also the right position of the camera stand and camera angle. Based on performance metrics for both enhancement techniques, Approach 2 showed better performance compared to other approaches when the result from Gaussian blurring showed the highest value of PSNR (29.02321), lowest values of MSE (81.42533), and lowest value AMBE (-0.55510). While for the CLAHE method, the result showed the highest value of PSNR (28.50377), the lowest value of MSE (91.77044), and the lowest value of AMBE (-0.05532). This proves that approach 2 provides a better quality image due to less noise.
Rebooting Quantum Computing

Hardware R&D projects in quantum computing are mostly divided into the sub areas of qubits, cryo-CMOS control, and control based on Josephson junction electronics. This paper makes the case that hybrid solutions are possible and should give better results. Specifically, there is recent work demonstrating how to trade the performance of qubits for the energy dissipation of the control electronics. In addition, reversible logic can have properties that lie in between those of cryo-CMOS and Josephson junction electronics, forming an “impedance matcher” that allows all three technologies to be used together. The idea of hybridization also applies to Rebooting Computing's goal of picking the successor to Moore's law from a series of options. Instead, this paper suggests a second reboot that considers hybrids of several of the originally proposed approaches.
AMAS: A DNN-Based Automatic Manhours Approval System

Manhours estimation and automatic approval plays an important role in project scheduling and reducing the development cost for factory. Traditional approaches usually have some limitations, such as technical text formatting limitations and industry-specific limitations. This paper presents a software system that estimates the manhours and approves the technology texts that have been submitted. Firstly, we collect a technology texts dataset and complement the dictionaries. Secondly, the word2vec is used to extract the features implicated in the technology text, and a DNN (deep neural network) model is proposed to estimate the manhours. Finally, an Odoo-based software system is developed, which provides the estimation manhours and approves automatically of the submitted technology texts. The advantages of approach in which is proposed in this paper is that, it has less limitations on the format of submitted technology texts. And, it can be used for various production technology.
Knowledge Enhancement using Question Generation for Images and Chart Data Input

In the current era, pictorial representation of data in the form of patterns, charts, graphs and trends are gaining a lot of attention in the fields of finance and science. Deep and correct study of the trends going on is necessary to better one's financial and general knowledge and helps reading and comparing the patterns over a period of time. Correct understanding of the pictorial representation of data in the form of line charts, bar charts or any textual representation is a major task. Hence, the proposed method aims at reading these charts and images, converting them into text and generating questions and then using sentiment analysis to determine positive or negative trends. This is followed by generating commonly asked questions based on the extracted text and providing more context and information on this text using web crawling, which helps provide greater dimensionality and understanding of the problem at hand.
Quancorde: Boosting fidelity with Quantum Canary Ordered Diverse Ensembles

On today's noisy imperfect quantum devices, execution fidelity tends to collapse dramatically for most applications beyond a handful of qubits. It is therefore imperative to employ novel techniques that can boost quantum fidelity in new ways. This paper aims to boost quantum fidelity with Clifford canary circuits by proposing Quancorde: Quantum Canary Ordered Diverse Ensembles, a fundamentally newapproach to identifying the correct outcomes of extremely low-fidelity quantum applications. It is based on the key idea of diversity in quantum devices - variations in noise sources, make each (portion of a) device unique, and therefore, their impact on an application's fidelity, also unique. Quancorde utilizes Clifford canary circuits (which are classically simulable, but also resemble the target application structure and thus suffer similar structural noise impact) to order a diverse ensemble of devices or qubits/mappings approximately along the direction of increasing fidelity of the target application. Quancorde then estimates the correlation of the ensemble-wide probabilities of each output string of the application, with the canary ensemble ordering, and uses this correlation to weight the application's noisy probability distribution. The correct application outcomes are expected to have higher correlation with the canary ensemble order, and thus their probabilities are boosted in this process. Doing so, Quancorde improves the fidelity of evaluated quan-tum applications by a mean of 8.9x/4.2x (wrt. different baselines) and up to a maximum of 34x.
Improving Adversarial Detection Methods for SAR Image via Joint Contrastive Cross-entropy Training

Adversarial examples (AEs) have become a critical security concern for intelligent synthetic aperture radar (SAR) target recognition system. Current defense methods design certain distance metric to characterize the difference between AEs and natural samples in feature space, but expose severe performance degradation against SAR AEs with small perturbation scale. By exploiting Siamesed sample augmentation and feature wised contrastive regularization, we propose a joint contrastive cross-entropy training method to endow natural SAR test samples with more consistent distribution to training samples and higher separability from AEs. Experiments demonstrate that our method significantly improves the performance of current adversarial detections against SAR AEs especially when the perturbation scale is small.
Blockchain-based Trusted Software Distribution Mechanism

In the traditional software distribution mechanism, the software developer will add a hash check code to the current version of the software to ensure that the software is forgery-proof. However, in the process of software release and upgrade, there will always be hackers and some unscrupulous elements attacking some genuine software, destroying the internal functions of normal software, or adding some backdoors to steal user information and data. Blockchain is a distributed p2p system with the advantages of trusted traceability, anti-tampering and consensus mechanism, which can effectively prevent hackers from modifying the data on its chain. In order to solve the loss caused by using the attacked software, this paper proposes blockchain-based trusted software distribution mechanism, which brings a new idea for the research of trusted software release mechanism.
Chinese text summary generation based on Seq2Seq framework

The sequence-to-sequence model based on recurrent neural network shows good results in the practical application of text summary generation. However, most of these models have two problems: generating summary repetition and exposure bias. To solve the text repetition problem, a combined self-attention mechanism and a pointer network are used to overcome the problem by storing attention for each generated time step, using a combined reinforcement learning and maximum likelihood estimation to alleviate exposure bias, correcting the loss function through secondary training. When the scheme is evaluated in the LCSTS Chinese dataset, the ROUGE value is calculated to prove that the model can be alleviated by text repetition and reinforcement learning by training the pointer network, and the scheme is very effective on the test set.
Pixhawk-based scalable platform for multi-purpose surface unmanned vehicle

Unmanned vehicle has entered a high-speed development stage with the rise of intelligent vehicle. The multi-purpose unmanned vehicle platform based on PX4-Autopilot firmware we proposed, which is running on Pixhawk controller. The vehicle can realize remote control and autonomous navigation. In the end, we conducted remote control experiments and mission autonomous navigation experiments to verify the feasibility of the scheme and the navigation error can be controlled below 3.5m. In real application field, it can apply in water quality detection, topographic mapping and surface garbage collection.
Lightweight Face Pose Estimation Multi-task Model Based On Rotation Matrix

A lightweight multi-task sequence model is proposed to accommodate real-time computation on embedded devices. The model uses face pose estimation as the main task and face alignment as the secondary task and uses a 3×2 rotation matrix instead of the Euler angles to improve the model accuracy. The model uses a Sequence-sharing Multi-task Learning Model based on the MobileNet-V2 backbone network for structural de-branching, significantly reducing the number of model parameters. Experimental results show that, without loss of accuracy, the optimized model parametric is reduced to 0.5MB, it can run on mobile devices at 144 frames per sec, and its face alignment task has a 27.63% accuracy improvement compared with similar lightweight models.
Sleep Stage Classification via Kernel Extreme Learning Machine with Particle Swarm Optimization Using Heart Rate Variability as Features

The sleep stage classification is the most important step in the sleep research. It does not only provide a reference for evaluating the sleep quality clinically, but it also uses to diagnose the sleep disorders. Various methods have been developed for performing the sleep stage classification based on the electrocardiograms (ECGs). However, the computational complexity of these existing methods is very large. Also, the accuracy is still very low. To address this issue, this paper employs a kernel extreme learning machine (KELM) with the particle swarm optimization (PSO) for performing the sleep stage classification using the heart rate variability (HRV) as the features. The results show that the classification accuracies yielded by our proposed method for the three sleep stage classification and the four sleep stage classification are 80.18% and 76.02%, respectively. Obviously, our proposed method is superior to the existing methods.
3D virtual environment of the sugar refining process and automatic control using the hardware in the Loop technique

This paper proposes the design of a 3D virtual environment of the sugar re-fining process for the implementation of the Hardware in the Loop (HIL) technique and the design of PID controllers by means of a commercial programmable logic controller (PLC), developing operation and control tests in the plant. The 3D modeling of the virtual environment is developed from the P&ID diagram of the process, which indicates the arrangement of equipment and instruments with-in the virtual environment, the mathematical models of the process were developed from base equations, theorems and experimentation to determine the dynamics of operation of the sugar refining process, while the animations and realism were designed in the Unity 3D graphics engine. The interchange of information between the virtualized plant and the PLC, using the Modbus TCP/IP protocol, determined a fast and reliable communication. Finally, different control tests were developed, allowing to demonstrate that the HIL technique presents a complete control over the virtual plant as if it resembled reality.
Dark channel defogging enhancement algorithm for haze image in complex environment

Under the lighting conditions of artificial light sources, point lighting causes the image to receive uneven light, leading to the characteristics of color distortion and poor dehazing enhancement effect when the existing dark channel dehazing algorithm is used for dehazing enhancement of haze images. Aiming to solve the above problems, this paper proposes a dark channel defogging enhancement algorithm for haze image in complex environment. First, by calculating the intensity histogram of the initial image, the corresponding mean, median and maximum value are obtained, and a novel brightness factor is constructed accordingly. Then, the over-bright areas in the image are extracted through the brightness factor and processed by down-value. Finally, the down-value image is used to estimate the atmospheric light value to enhance the accuracy of the atmospheric light value, so as to solve the problems of color distortion and poor dehazing effect. The experimental results show that the proposed algorithm has good applicability to dust fog images under non-uniform illumination. Compared with other advanced algorithms, the no-reference evaluation index ranks high, and the qualitative results are more realistic and natural.
DOCBOT: A CLINICAL CHATBOT THAT PROVIDES END-TO-END SOLUTIONS FOR THE USER

With India's growing population, birth rates are rising, and death rates are declining due to advances in the medical field. It is found that the number of doctors is the main factor that makes young people help increase population growth. This situation is better understood when we go to urban public hospitals where the low availability of doctors is a significant cause of patient neglect and in some cases leads to death. Sometimes even doctors can make mistakes in providing appropriate treatment that leads to the death of the patient. To solve those problems there is a smart and intelligent chatbot that offers medical advice and specific safety precautions that patients should take
BERT-based ensemble model for Hindi summarization

A growing percentage of internet users report Hindi as their primary language of communication. Hindi, one of the world's most widely spoken languages, is India's national language. Because of its proliferation, Hindi data poses a considerable problem in organising, analysing, and summarising its vast stores of information for a variety of purposes. However, the scope of language modelling and NLP efforts with this audience in mind is extremely limited. Even the most sophisticated multilingual models struggle with the intricacies of the language. To bridge this gap, the MuRIL [37] language model was developed and trained using sizable Indian text corpora. Document summarization in Hindi is the topic of this research. Leverage the power of the MuRIL model by embedding the language model for a novel extractive summarization-based solution. The model developed in this study outperforms previous baselines on the accuracy metric by using a wide range of newspaper articles from different categories as training data.
Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper “FaceGuard: A Wearable System To Avoid Face Touching1.” It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data.
Augmented Reality in Agriculture

The World population is expected to become around 9.8 billion by 2050 feeding the rising population is a big challenge before agriculture sector worldwide. Mechanized farming, automation, use of sensing is becoming a normal practice. Emerging technologies like Augmented Reality can add value to and can help aim the sustainable and efficient ways of handling the issues at various stages of farming. This paper attempts to find various areas of applications of Augmented Reality for Precision Farming and way to automate the farming tasks in order to gain higher yields and superior quality of agricultural product to make agriculture sustainable It also aims to help people associated with agriculture (Businessmen, Scientists, Academicians) in finding a way for smart farming.
Multiple AI Based Web Mining

Artificial intelligence (AI) refers to the computational process of finding, identifying, and evaluating patterns in huge data sets using techniques that lie at the confluence of machine learning, statistics, and database strategies. Its major purpose is to extract relevant information from raw data sets and reformat them into the desired format for analysis and application. Web Mining (WM), an expanding viewpoint of data mining, encompasses all data mining and associated processes. It is employed for automatically locating and extracting data from online records and services. So, the goal of WM is to glean useful information from the web. Given its significance, this research conducts a survey of DM methods for web mining.