Reviewerly/exHarmony huggingface dataset:
Surveillance Based Spotting and Categorization of Automobiles

In the current era of automation, vehicle automation plays a vital role in the current technological evolution. Vehicle automation and detection mechanisms are attracting so many research enthusiasts in this period. This research work has utilized this opportunity to play our role by creating our project for Surveillance-Based Spotting and Categorization of Automobiles. The main focus of our project lies in the detection and categorization of vehicles that are in motion and in still positions. Though there are numerous methods of vehicle detection using sensors, microchips, and many other technologies, all of these tend to be vulnerable to environmental factors that affect their longevity and increase the cost of their implementation as well as their maintenance. To avoid these additional costs, a computer vision technology that uses a vision-based detection mechanism is used to detect the vehicles from the real-time video input with the help of image fragmentation and pattern recognition. The goal of this project is to provide a feasible and cost-effective system to spot and categorize various kinds of automobiles using OpenCV and YOLOv3.
Recognition of Sign Language with CNN

Sign language is one method of communicating with deaf people. Because of the inclusion of elements and differences in regional dialects, this project's ISL research has been limited. It is necessary to learn sign language in order to communicate with them. Teaching methods most commonly used are those that involve students working together in groups. Sign language study materials are few, and they are usually used when there is no comparable sign or the signer is unsure of it. The vast majority of currently available options for teaching sign language rely on high-priced third-party sensors. A dataset is gathered and several feature extraction methodologies are used to retrieve important data, which is then input into supervised learning methods in this study to take a step forward in this field.
PM-Net: A Multi-Level Keypoints Detector and Patch Feature Learning Network for Optical and SAR Image Matching

Due to the differences in radiation and geometric characteristics of optical and synthetic aperture radar (SAR) images, there is still a huge challenge for accurate matching. In this paper, we propose a patch-matching network (PM-Net) to improve the matching performance of optical and SAR images. First, a multi-level keypoints detector (MKD) with fused high-level and low-level features is presented to extract more robust keypoints from optical and SAR images. Second, we use a two-channel network structure to improve the image patch matching performance. Benefiting from this design, the proposed method can directly learn the similarity between optical and SAR image patches without manually designing features and descriptors. Finally, the MKD and two-channel net-work are trained separately on GL3D and QXS-SAROPT data sets, and the PM-Net is tested on multiple pairs of optical and SAR images. The experimental results demonstrate that the proposed method outperforms four advanced image matching networks on qualitative and quantitative assessments. The quantitative experiment results show that using our method correct matching points numbers are increased by more than 1.15 times, the value of F1-measure is raised by an average of 7.4% and the root mean squared error (RMSE) is reduced by more than 15.3%. The advantages of MKD and the two-channel network are also verified through ablation experiments.
Sweeper Arm Mechanism Design on the Line Follower Robot with Path Sensing Algorithm and Curvature-Driven Kinematic

This paper presents the development of a sweeperarm mechanism implemented on the line follower robot. The development is intended to improve the curvature-driven kinematic method currently use on a line follower robot by eliminating the fixed sensor array and replaced with a sweeper arm. The sweeper arm mechanism enables the robot to move freely while following the path without interfering with the robot’s movement. The potentiometer inside the sweeper arm enables the robot to read the path more accurately with higher resolution than a conventional fixed IR sensor array. The path sensing algorithm allows the robot to track various path characteristics. The curvature-driven kinematic lets the robot optimize speed based on curve radius. To measure the result, the test track and test bench were created. The test bench was used to test the functionality of the sweeper arm. The test track ensures that everything on the robot can work together in a realworld track. The results show that the robot can follow the curve smoothly and is reliable, demonstrating that the approach is more practical for high-speed applications. Although the prototype did not test at full speed due to physical limitations, it proves to be functional as intended.
Analysis of Deep Steganography Robustness Using Various Loss Functions

In modern data hiding systems, the information is being hidden in images using CNN’s (Convolutional Neural Networks) especially the methods like Deep Steganography have gained a lot of popularity in the field. Though, the Deep Steganography is very good on it’s own, there no support for robustness in it’s architecture. This paper focuses on the Deep Steganography, a three network encoder-decoder based architecture and it’s robustness. This research work has been carried out by experimenting with several loss functions and applying noise or cropping, or rotation on the images before sending the stego image to the Reveal Network. The results indicate that the loss functions chosen yield better Error per pixel and PSNR values when cropping, rotating, or adding noise to the stego image. Finally, we conclude that Loss 4 is a better overall loss function for robustness of Deep Steganography.
Comparison of Online Writer Identification Systems for Limited Data

Writer identification systems attempt to determine the identity of the author, given a piece of handwritten text by comparing it against samples of known authorship. They are categorised into Online and Offline on the basis of how data has been captured. Another classification of writer identification systems is text independent and text dependent systems based on the textual content provided. Existing methods in literature for online text independent writer identification make use of a training set that consists of numerous paragraphs of handwritten data. For a practical scenario, this may not be always available. This paper aims to compare the performances of existing text-independent online writer identification systems for the scenario of limited training data and investigate the shortcomings of these systems.
Dleep Learning based Regularization as defense against Clean Label Targeted Data Poisoning

Adversarial examples are doctored inputs to predictive models intentionally designed with a malicious purpose – to manipulate the inference time outputs of the machine learning system. One such recent threat to Deep Neural Networks is the targeted clean label data poisoning attack, wherein the adversary modifies training samples in the dataset without modifying the original labels. A model trained on these seemingly benign examples is likely to misclassify specific queries to targeted labels. While ample research is ongoing on the ways to generate and attack deep models, there is little work in mitigating such attacks. This research work employs a simpler strategy of making the model robust to poison samples during training and study the effect of regularization techniques on such attacks. Specifically, this research work proposes a strong pruning and autoencoder-based strategies to defend against data poisoning attacks and show its effectiveness on various deep models. The proposed methods reduce attacker efficacy by 23% and can serve as strong baselines for future work on defense against such adversarial attacks.
EFA: A Viable Alternative to RDMA over InfiniBand for DBMSs?

RDMA over InfiniBand offers high bandwidth and low latency which provides many benefits for distributed DBMSs. However, in the cloud RDMA is still not widely available. Instead, cloud providers often invest in their own high-speed networking technology and start to expose their own native networking interfaces. For example, the largest cloud provider, Amazon Web Services (AWS), introduced instances with Elastic Fabric Adapter (EFA) in 2018. In this paper, we aim to analyze EFA as an alternative to RDMA in the cloud by performing an in-depth and systematic evaluation.
Spam Review Detection using Linguistic Methods for Specified user in Twitter

Web based rating organizations plan key measures to draw in various spammers to direct phony overviews to screen client execution changes, learning, and following. Creating viable tasks and further developing the client experience permit a few clients to interface and fortify their communications. At the present time, the objective of making a valuable and astonishing framework for recognizing spammers is to accommodate individuals' connections in light of two things at individuals believe are solid, and spammers are less inclined to be trusted. solid relationship. with normal clients. The obligations of this article are twofold: (1) how to incorporate social foundations into the need to assess is disclosed and a dependability model utilizing a weighted way to deal with trust is met, and (2) arbitrary testing destinations are chosen. Investigation on a bunch of information arranged on Yelp.com shows that trust-based proportions gain a better quality than customary CF strategies and show a solid connection among affiliation and complete score.
Developing Virtual Police Station to Receive FIR through Digital Signature

The most recent innovations throughout every different sectors such as education, commerce, agribusiness, communication, banking and other such real-time applications. Currently, the Indian Police Department is using the traditional "pen and paper" method for all document works performed at the initial stages of the cases. This method was convenient in the earlier days but as the population is increasing and lawbreakers and dishonest events in the general public are on the rising trend, many cases are being enrolled. This has made it difficult to physically deal with the cases and all of their associated paperwork. The traditional method of visiting the police station and lodging a grievance and updating individually is a lengthy and time-consuming process. This might be replaced by an internet-based process. As a result, an E-Police station is being established, where the basic data regarding the incident and complaint is recorded or can be fed by the citizens through a web/mobile application, which can then be tracked from the department side directly in online mode. As the complete process happens in online mode, this method will deliver a more efficient service from police department to the general public. The proposed article describes the process of creating a Virtual Police Station to receive complaints by using a digital signature.
Summary of Model Checking C++ Programs

This is an extended abstract of the article “Model Checking C++ Programs” by Felipe R. Monteiro, Mikhail R. Gadelha, and Lucas C. Cordeiro published at the journal of Software Testing, Verification and Reliability. We describe and evaluate a novel verification approach based on bounded model checking (BMC) and satisfiability modulo theories (SMT) to verify C++ programs. Our verification approach analyses bounded C++ programs by encoding into SMT various sophisticated features that the C++ programming language offers, such as templates, inheritance, polymorphism, exception handling, and the Standard Template Libraries. We implemented our verification approach on top of ESBMC. We compare ESBMC to LLBMC and DIVINE, which are state-of-the-art verifiers to check C++ programs directly from the LLVM bitcode. Experimental results show that ESBMC can handle a wide range of C++ programs, presenting a higher number of correct verification results. Additionally, ESBMC has been applied to a commercial C++ application in the telecommunication domain and successfully detected arithmetic-overflow errors, which could lead to security vulnerabilities.
Data augmented Approach to Optimizing Asynchronous Actor-Critic Methods

Learning from visual observations of an environment is a core and fundamental problem in Reinforcement Learning (RL). Although there have been several advances in the algorithms, especially with the involvement of convolutional neural networks, they are primarily lacking in two aspects: (i) learning efficiency based on observations and (ii) learning generalization. Data augmentation has been shown to be a suitable strategy for enhancing the accuracy of classifier in Deep Learning solutions. With these in mind, this paper describes an implementation of Asynchronous Advantage Actor Critic (A3C) that integrates an optimized approach to observation augmentation policy on each learning batch. This approach is known as Data Augmented Reinforcement Learning (DARL). The proposed approach uses data augmentation to create environment variations to improve the learning policy of A3C with a key idea of data variety and demonstrates a significant improvement over the base implementation, with up to 70% increase in the rewards on several OpenAI Atari benchmarks.
Procedure Enhancement for PDF Output Workflow Conformity

Applications and parameters of Raster Image Processor enable a significant influence on the final print output in a sophisticated digital prepress system. Concerns regarding the PDF workflow systems’ compatibility with PDF/X continue to exist. The capability and reliability of RIPs in conforming to PDF/X-4 specifications in digital prepress workflow systems are questionable. The majority of output workflow systems were installed with outdated settings of PDF/X-4 standard (ISO 15930-9:2010). The goal of this research is to provide a test procedure for assessing the conformity of PDF output workflow by proposing the application of the Ghent Workgroup’s Output Suite 5.0. (GOS5).
Data Access Control in Cloud Computing

Currently, news has revealed powerful attackers who can breach the confidentiality of data by means of secretly obtaining cryptographic keys. The keys are obtained through threats within software cryptography. When the encryption key gets exposed, the lone reasonable act is used for maintaining the confidentiality of data to confine the attacker access to the ciphertext. The Confining attacker’s access is accomplished using widening blocks for ciphertext over servers within several administrative domains. Thus, presuming the adversary will not pose danger. However, once encryption of data is done using prevalent designs then the process of encryption key adversary will be compromised with a single server as it will decrypt to perform encryption over the blocks of ciphertext. An adversary perceives the encryption key and will possess it for accessing a huge portion of the ciphertext blocks. The present research paper will review the existing encryption-decryption techniques for achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing.
Dynamic Coefficient Quantization Digital FIR Filter

The rationale for using the quantization of the coefficients of a digital FIR filter in the concept of dynamic programming is given as a process of stepwise quantization of the coefficients with their discrete optimization at each step according to the objective function common to the entire quantization process. An algorithm for step-by-step dynamic quantization is considered by methods of integer nonlinear programming, taking into account the given signal scaling. The effectiveness of this approach is illustrated by the example of dynamic quantization of the coefficients of a cascaded high-pass FIR filter with a minimum bit depth of representation of integer coefficients.
A Dynamic Analysis Tool for Memory Safety Based on Smart Status and Source-Level Instrumentation

Memory errors may lead to program crashes and security vulnerabilities. In this paper, we present Movec, a dynamic analysis tool that can automatically detect memory errors at runtime. To address the three major challenges faced by existing tools in detecting memory errors, low effectiveness, optimization sensitivity and platform dependence, Movec leverages a smart-status-based monitoring algorithm and performs its intrumentation at the source-level. Our extensive evaluation shows that Movec is capable of finding a wide range of memory errors with moderate and competitive overheads.Demo video link: https://youtu.be/V8H2MroNxSM, also available at https://www.bilibili.com/video/BV1H34y117tAMovec website: https://drzchen.github.io/projects/movecMovec download link: https://github.com/drzchen/movec
Deep Learning-based Production and Test Bug Report Classification using Source Files

Classifying production and test bug reports can significantly improve not only the accuracy of performance evaluation but also the performance of information retrieval-based bug localization (IRBL). However, it is time-consuming for developers to classify these bug reports manually. This study proposes a production and test bug report classification method based on deep learning. Our method uses a set of source files and model tuning to solve the problem of insufficient and sparse bug reports when applying deep learning. Our experimental results reveal that the macro f1-score of our method is 0.84 and can improve the IRBL performance by 20%.
An Efficient Deep Learning Based Chatbot for GRIET

This work aims to develop a chatbot that can provide required information about the educational institute, GRIET. At present, there is no chatbot system available for the institute’s website. The entire website needs to be scrolled in order to get the required data. The proposed chatbot, GrietBot, helps in easy access of information for the user’s query. The chatbot model exploits algorithms in order to process user queries and retrieve appropriate information efficiently. Various text preprocessing techniques and deep learning techniques are used to provide the state of art model in developing the first chatbot for GRIET. This chatbot helps access the information faster without the user’s physical presence in this pandemic.
Finding Appropriate User Feedback Analysis Techniques for Multiple Data Domains

Software products now have more users than ever. This means more people to please, more use-cases to consider, and more requirements to fulfill. These users can then write feedback on software in any number of public or private online repositories. Many tools have been proposed for classifying, embedding, clustering, and characterizing this feedback in aid of generating requirements from it. I am investigating which techniques and machine learning models are most appropriate for enabling these analyses across multiple feedback platforms and data domains.
The Gibbs-Rand Model

Due to its many applications, the clustering ensemble problem has been subject of intense algorithmic study over the last two decades. The input to this problem is a set of clusterings; its goal is to output a clustering that minimizes the average distance to the input clusterings. In this paper, we propose, to the best of our knowledge, the first generative model for this problem. Our Gibbs-like model is parameterized by a center clustering, and by a scale ; the probability of a particular clustering decays exponentially with its scaled Rand distance to the center clustering.
Investigating User Perceptions of Conversational Agents for Software-related ExploratoryWeb Search

Conversational agents that respond to user information requests through a natural conversation have the potential to revolutionize how we acquire new information on the Web (i.e., perform exploratory Web searches). Recent advances to conversational search agents use popular Web search engines as a back-end and sophisticated AI algorithms to maintain context, automatically generate search queries, and summarize results into utterances. While showing impressive results on general topics, the potential of this technology for software engineering is unclear. In this paper, we study the potential of conversational search agents to aid software developers as they acquire new knowledge. We also obtain user perceptions of how far the most recent generation of such systems (e.g., Facebook’s BlenderBot2) has come in its ability to serve software developers. Our study indicates that users find conversational agents helpful in gaining useful information for software-related exploratory search; however, their perceptions also indicate a large gap between expectations and current state of the art tools, especially in providing high-quality information. Participant responses provide directions for future work. CCS CONCEPTS• General and reference $\rightarrow$Empirical studies.
FORMATION OF PROFESSIONAL AND PEDAGOGICAL FUTURE TEACHER ORIENTATIONS AS FUNDAMENTALS HIS PEDAGOGICAL CULTURE

The author in the article determines the result of research on the problem of professional and pedagogically-oriented forming as basic part of teacher’s culture.
Global Software Engineering in a Global Classroom

Due to globalization, many software projects have become large-scale and distributed tasks that require software engineers to learn and apply techniques for distributed requirements analysis, modeling, development, and deployment. Globally-distributed projects require special skills in communication across different locations and time zones in all stages of the project. There has been advancement in teaching these concepts at universities, but adapting global software engineering in a curriculum is still in infancy.The main reasons are the effort and coordination required by teachers to set up the project, manage distributed development and enable distributed delivery. It becomes even more difficult when teaching distributed software engineering involving Internet of Things (IoT) applications. The situation has changed with recent advances in continuous deployment and cloud platform services that make globally-distributed projects more feasible, teachable, and learnable, even for short-term projects. However, no experience report in education research describes a truly distributed global setup in continuous software engineering for IoT applications.This paper describes a ten-day project involving three universities in different countries with 21 students located across the world to substantiate this claim. It provides teachers with recommendations for conducting a global software engineering course in a global setting. Recommendations include access for all students to (remote) hardware, stable network infrastructure in all locations, the use of a central development platform for continuous integration and deployment, and the application of distributed pair deployment.
CIDER: Concept-based Interactive Design Recovery

In this paper, we introduce CIDER, a Concept-based Interactive DE-sign Recovery tool that recovers a software design in the form of hierarchically organized concepts. In addition to facilitating design comprehension, it also enables designers to assess design quality and identify design problems. It integrates multiple clustering algorithms to reduce the complexity of the recovered design structure, leverages information retrieval techniques to name each cluster using the most relevant topic terms to ease design comprehension, and identifies and labels highly-coupled file clusters to reveal possible design problems. It enables interactive selection of concepts of interest and recovers partial design structures accordingly. The user can also interactively change the levels of recovered hierarchical structure to visualize the design at different granularities.