Reviewerly/exHarmony huggingface dataset:
An Implementation of Support Vector Machine Classification for Developer Academy Acceptance Prediction Model

In order to prepare graduates with work readiness in the IT industry, specifically in mobile apps development, one of its ways is to create a Developer Academy where final year students are prepared in an intensive program for two consecutive semesters to learn the stages of mobile apps development. To ensure the quality of participants in the Developer Academy, a set of selection procedures needs to be prepared, consisting of Aptitude Test, Portfolio Showcase, and Individual Interview. The problem arises when applicants are far more than the class capacity. Hence selection procedures take a longer time. The Developer Academy registration team record showed a ratio of 1: 12, which overburdens the team when it comes to selecting the applicants. More effective procedures are needed with the help of machine learning tools to help with decision making. This study aims to produce a prediction model for developer academy applicants. Several classification algorithms such as k-nearest neighbors, support vector machine, decision tree, and random forest were analyzed. Data was collected from 527 valid applicant's data which submit complete documents based on due date, other applicants who did not submit complete documents were not included in the analysis. Preliminary findings from the study show that the Support Vector Machine algorithm performs best with an accuracy of 86% and this score was then increased by applying oversampling and kernel tricks to get an accuracy rate of 98%. Hence it can be concluded that the prediction model has excellent performance. Keywords-developer academy, artificial intelligence, machine learning, support vector machine, data science, classification
Vulnerabilities in Discovery Tech

Recent technological advances are changing the litigation landscape, especially in the context of discovery. For nearly two decades, technologies have reinvented document searches in complex litigation, normalizing the use of machine learning algorithms under the umbrella of “Technology Assisted Review” (TAR). But the latest technological developments are placing discovery beyond the reach of attorney understanding and firmly in the realm of computer science and engineering. As lawyers struggle to keep up, a creeping sense of anxiety is spreading in the legal profession about a lack of transparency and the potential for discovery abuse. Judges, attorneys, bar associations, and scholars warn that lawyers need to closely supervise the technical aspects of TAR and avoid the dangers of sabotage, intentional hacking, or abuse. But these commentators have not fully defined with precision what the risks entail, furnished a clear outline of potential dangers, or defined the appropriate boundaries of debate.This Article provides a systematic assessment of the potential for abuse in technology-assisted discovery. The Article offers three contributions. First, our most basic aim is to provide a technical but accessible assessment of vulnerabilities in the typical TAR process. To do so, we use the latest computer science research to identify and catalogue the different ways that TAR can go awry, either due to intentional abuse or mistakes. Second, with a better understanding of how discovery can be subverted, we then map potential remedies and reassess current debates in a more helpful light. The upshot is that abuse of technology-assisted discovery is possible but can be preventable if the right review processes are in place. Finally, we propose reforms to improve the system in the short and long term, with an emphasis on improved metrics that can more fully measure the quality of TAR. By exploring the technical background of discovery abuse, the Article demystifies the engineering substrate of modern discovery. Undertaking this study shows that with the right technical knowledge and assistance, lawyers can safeguard technology- assisted discovery without surrendering professional jurisdiction.
Research on CarMaker and Cloud Computing Platform Co-Simulation Based on APO

Abstract An cloud computing platform based on B/S architecture and docker container technology for autonomous driving simulation has been established in this paper. The map editor module of the cloud platform lets users design 3D scenes for simulating and testing automated driving systems. When the customized roadway scene for simulation created, it would be saved as OpenDrive format both for the server of cloud platform and CarMaker’s TestRun which all parameters of the virtual environment (vehicle, road, tires, etc.) are sufficiently defined. Then, based on the application online (APO) communication protocol of CarMaker, the local APO agent service was created. When the 27 parameters of vehicle dynamics received from CarMaker server, they were sent to the cloud platform in real time through UPD protocol. The process of data communication is completed by APO agent. Through the work above, a co-simulation between cloud platform and CarMaker could be successfully established for autonomous driving with seventeen-degree-of-freedom. Through the co-simulation experiment, it is found that the real-time data sampling frequency of the co-simulation is 70Hz, which completes the synchronous simulation of carmaker and cloud platform.
Resource Allocation for Improved User Experience with Live Video Streaming in 5G

Providing a high-quality real-time video streaming experience to mobile users is one of the biggest challenges in cellular networks. This is due to the need of these services for high rates with low variability, which is not easy to accomplish given the competition among (usually a high number of) users for constrained network resources and the high variability of their channel characteristics. A way of improving the user experience is by exploiting their buffers and the ability to provide a constant data rate to everyone, as one of the features of 5G networks. However, the latter is not very efficient. To this end, in this paper we provide a theoretical-analysis framework for resource allocation in 5G networks that leads to an improved user experience when watching live video. We do this by solving three problems, in which the objectives are to provide the highest achievable video resolution to all one-class and two-class users, and to maximize the number of users that experience a given resolution. The analysis is validated by simulations that are run on traces. We also compare the performance of our approach against other techniques for different QoE metrics. Results show that the performance can be improved by at least 15% with our approach.
Pattern Search Algorithm in FPGA Implementation View

In many fields of research pattern search problems can be viewed as a string-matching problem for which many solutions were developed over the years with varying ease of implementation and characteristics of the pattern and the input text. In this work, the design of several string-matching algorithms for logic synthesis implementation is presented. Based on the abstraction of the algorithmic state machine (ASM), the design is converted to VHDL finite state machine and synthesized for FPGA logic. The final implementations are evaluated in performance and resource utilization metrics, and against pure software implementation for the same algorithms using higher-level language.
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning

Supervised contrastive learning optimizes a loss that pushes together embeddings of points from the same class while pulling apart embeddings of points from different classes. Class collapse&mdash;when every point from the same class has the same embedding&mdash;minimizes this loss but loses critical information that is not encoded in the class labels. For instance, the &ldquo;cat&rdquo; label does not capture unlabeled categories such as breeds, poses, or backgrounds (which we call &ldquo;strata&rdquo;). As a result, class collapse produces embeddings that are less useful for downstream applications such as transfer learning and achieves suboptimal generalization error when there are strata. We explore a simple modification to supervised contrastive loss that aims to prevent class collapse by uniformly pulling apart individual points from the same class. We seek to understand the effects of this loss by examining how it embeds strata of different sizes, finding that it clusters larger strata more tightly than smaller strata. As a result, our loss function produces embeddings that better distinguish strata in embedding space, which produces lift on three downstream applications: 4.4 points on coarse-to-fine transfer learning, 2.5 points on worst-group robustness, and 1.0 points on minimal coreset construction. Our loss also produces more accurate models, with up to 4.0 points of lift across 9 tasks.
Semi-supervised deep learning for breast anatomy decomposition in ultrasound images

Breast anatomical structures are distributed layer by layer in breast ultrasound (BUS) images. Breast anatomy decomposition (BRAND) has several clinical potentials in providing localization constraints for breast lesion detection and supporting multimodal registration. To take advantage of convolutional neural network (CNN) and reduce the requirements of manual labels, we propose a semi-supervised deep learning method for BRAND. A CNN is first trained on labeled training data. Pseudo labels for unlabeled training data are generated by smoothing the network's predictions with simple linear iterative clustering (SLIC) super-pixel algorithm. The proposed approach achieves Dice similarity coefficient (DSC) of 89.6 ± 9.3%, 88.2 ± 9.7%, 84.6 ± 17.7% and 89.5 ± 21.6% for subcutaneous fat, mammary gland, muscles and thorax layers, respectively, which outperforms the reported results. The proposed approach is demonstrated to be sufficient for BRAND in BUS images both with and without breast lesions.
Motorbike Counting in Heavily Crowded Scenes

Vehicle density estimation has an important role in intelligent traffic systems. As of now, most established studies only focused on areas where people mainly travel by four-wheeled vehicles rather than motorbikes. However, in some countries such as Vietnam where motorbikes are the majority, traffic scenarios will pose different issues. Motorbikes are intrinsically more flexible so they can cause cluttered and chaotic visual. As a result, traffic video data captured in such environment is more challenging to existing systems. In this work, we performed an empirical survey on a set of vision-based counting methods covering a wide range of models and techniques. To our knowledge, there has not been many works dedicated to tackle this problem. Based on our experimental results, some of the top performers is ready to be used in real systems due to their robustness.
Protected Online Path Planning for UAVs over Congested Areas Within Convex Regions of Obstacle-Free Space

We present an approach for protected contingency online planning in high-risk environments with strict regulatory boundary conditions. For this purpose, we integrate a deterministic online planning method into a UAM path planning framework, which operates within precomputed convex regions of obstacle-free space. As part of an offline planning phase, a database of trajectories and convex, obstacle-free regions along these trajectories are computed. During the online phase, transitions between any two preplanned trajectories inside the same region can be computed using a planning method with performance-guaranteed maneuvres. Within a given envelope of environmental disturbances, our method guarantees to contain the flight path inside its convex region, while ensuring deterministic attributes. We demonstrate the method in a scenario representing the intended application and give estimations for offline computation time on desktop-grade hardware.
An Investigation into Keystroke Dynamics and Heart Rate Variability as Indicators of Stress

Lifelogging has become a prominent research topic in recent years. Wearable sensors like Fitbits and smart watches are now increasingly popular for recording one’s activities. Some researchers are also exploring keystroke dynamics for lifelogging. Keystroke dynamics refers to the process of measuring and assessing a person’s typing rhythm on digital devices. A digital footprint is created when a user interacts with devices like keyboards, mobile phones or touch screen panels and the timing of the keystrokes is unique to each individual though likely to be affected by factors such as fatigue, distraction or emotional stress. In this work we explore the relationship between keystroke dynamics as measured by the timing for the top-10 most frequently occurring bigrams in English, and the emotional state and stress of an individual as measured by heart rate variabiity (HRV). We collected keystroke data using the Loggerman application while HRV was simultaneously gathered. With this data we performed an analysis to determine the relationship between variations in keystroke dynamics and variations in HRV. Our conclusion is that we need to use a more detailed representation of keystroke timing than the top-10 bigrams, probably personalised to each user.
Executing Effective Social Engineering Penetration Tests: A Qualitative Analysis

Penetration testing is an increasingly common strategy adopted by organizations to mitigate security risks including those posed by social engineering—the deception of individuals for the purposes of circumventing information security measures. Drawing from 54 interviews with security auditors, IT professionals, and social engineers, this study explores participant descriptions of the (1) importance of social engineering penetration tests, (2) measurement of assessment outcomes, (3) use of penetration tests as part of security awareness programs, and (4) attitude social engineers should adopt in working with client organizations and their employees. Implications for security research and penetration testing are considered.
Federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks

Federated Learning (FL) is a novel technique that allows for performing the training of a global model without sharing data between entities. This research focused on the analysis of existing solutions for Federated Learning in the context of medical image classification. Selected frameworks: TensorFlow Federated, PySyft and Flower were tested and their usability was assessed. Additionally, experiments on classification of X-ray lung images with the use of the Flower framework were performed in a fully distributed setting using Google Cloud Platform.
Augmented Reality in Chinese Language Pronunciation Practice

Augmented reality (AR) has the unique ability to situate an activity within a physical context, making it valuable for training and education. The affordances of AR include multimodal visualizations and auditory support. The disciplines of phonetics and phonology in applied linguistics concern how sounds are produced and articulated. We have designed and developed a pronunciation training app for the novice second language student learning Mandarin pinyin by applying linguistic theory while adhering to usability principles. This paper describes the requirements that drove the development of the app and the planned online experiment design that compares the same training under two conditions: AR and non-AR. The design features peer assessment, drills, in-app assessment, surveys, and a posttest, and is designed for a remote study without having participants come to the lab.
Fast elastic motion estimation with improved Levenberg-Marquardt optimization

The elastic motion estimation is an efficient temporal predictive coding tool for predicting complex motions including zooming, shearing and twisting. However, its optimization solution still suffers from high computational complexity and unstable convergence yet. We thus present a novel optimization framework to improve the elastic motion estimation efficiency. By introducing a diagonal matrix into the Gauss-Newton’s Hessian matrix, we employ the Levenberg-Marquardt optimization to compute the elastic motion vectors. It obtains superior convergence to the conventional Gauss-Newton’s method, especially when the starting point is far away from the globally/locally optimum. Furthermore, we prove that the Gauss-Newton’s Hessian matrix for elastic motion estimation is positive semi-definite by discussing the convexity of the motion-compensated error function. Such a finding allows us to adopt a sign-alternating factor, instead of a positive constant used in the traditional L-M method, to update iteratively the diagonal matrix coefficient. And a bound is determined for the diagonal matrix coefficient to guarantee the non-singularity of the Hessian matrix, with whose constraints we propose an adaptive method to compute the sign-alternating factor to restrict the iterative process into a reasonable motion vector space. Finally, a novel elastic motion estimation algorithm is presented based on the modified Levenberg-Marquardt optimization. Experimental results show that the proposed algorithm gains higher motion-compensated peak signal-to-noise ratio (PSNR) than several benchmark methods. Moreover, our algorithm converges fast which needs only 1–2 iterations before it achieves higher PSNR than conventional elastic motion estimation.
A Framework for Automatic Exploit Generation for JIT Compilers

This paper proposes a framework for automatic exploit generation in JIT compilers, focusing in particular on heap corruption vulnerabilities triggered by dynamic code, i.e., code generated at runtime by the JIT compiler. The purpose is to help assess the severity of vulnerabilities and thereby assist with vulnerability triage. The framework consists of two components: the first extracts high-level representations of exploitation primitives from existing exploits, and the second uses the primitives so extracted to construct exploits for new bugs. We are currently building a prototype implementation of the framework focusing on JavaScript JIT compilers. To the best of our knowledge, this is the first proposal to consider automatic exploit generation for code generated dynamically by JIT compilers.
Implementing a Security Architecture for Safety-Critical Railway Infrastructure

The digitalization of safety-critical railroad infrastructure enables new types of attacks. This increases the need to integrate Information Technology (IT) security measures into railroad systems. For that purpose, we rely on a security architecture for a railway object controller which controls field elements that we developed in previous work. Our architecture enables the integration of security mechanisms into a safety-certified railway system. In this paper, we demonstrate the practical feasibility of our architecture by using a Trusted Platform Module (TPM) 2.0 and a Multiple Independent Levels of Safety and Security (MILS) Separation Kernel (SK) for our implementation. Our evaluation includes a test bed and shows how certification and homologation can be achieved.
Integrating Perceptron with Simple Linear Regression and Random Sampling

In recent years hybrid systems have gained more popularity in soft computing and machine learning applications. Neuro Fuzzy, Genetic Fuzzy are the most widely used hybrid techniques, by merging Fuzzy, Genetic Algorithm and Neural Network together. Hybrid System is a merging of two or more different soft computing techniques to give out a new soft computing technique. Most of the hybrid systems in soft computing consist of merging Fuzzy, Genetic Algorithm and Neural Network together. The main objective of this research is to integrate perceptron rule, simple linear regression and Random Sampling to make a new hybrid system. This new hybrid technique is applied on a famous advertising dataset, consisting of three features relating to advertising on Television, Radio and Newspaper and sales as label. Hybrid model accomplished 89 percent accuracy on train data and test data.
Blockchain Technology: A case study in supply chain management

As blockchain technology has already become a critical priority for enterprises, it is acknowledged that it might be as much important as to reform and reconstruct businesses in the near future. In this paper we review the strengths and weaknesses of the blockchain technology, and present an example application in supply chain management. We start by presenting the key characteristics of the blockchain technology, and then summarize its advantages and vulnerabilities. We also describe some of the existing solutions to known vulnerabilities. Finally, we review and discuss in detail the example of a decentralized application for the supply chain of a frozen food factory. This includes a scenario presenting the implementation of this solution, the steps for the development of the idea, and the constraints of the requirements.
Development of E-polling Website using MERN

The proposed web application makes the voting process easy, user-friendly, secure, and reliable. This web application provides an online website for the voters to cast their votes in a secured way. Voting is a process by which a person selects a capable candidate to rule the nation. Presently, vote casting is performed by electronic voting machines. For that the voters have to visit the voting booth to cast their votes. So, it expands the voters’ valuable time. So, in this proposed system the vote casting is performed on a website. It saves the voter's valuable time and makes the counting process easy. The voting application is developed using MERN technologies (MongoDB, Express, React and Node). It helps us to create a secure, fast, easy and error free voting application. The application consists of two modules, the admin panel and the polling website. The database maintains the entire election details about voters, parties, and votes. The voter verification happens based on secret PIN provided by the election commission. This application helps the people to take part in online voting and cast the vote remotely from any place. Thereby, it increases the percentage of voting.
Technology 4.0 in Accounting: What Future for Education?

Globalization has given rise to digital accounting with new ways of sharing information, particularly digitalization of the profession. The teaching of accounting must go along with technological changes in preparing students for the challenges of 4.0 environments. The new coronavirus pandemic in 2020 forced, among others, companies and universities to migrate their working and teaching methods to the remote format. As a result, teachers and students had to adapt to a new routine for carrying out their tasks, previously in person. Knowing how to use technology in favor of more efficient and effective teaching was one of the biggest challenges that the teachers faced during this period of suspension of face-to-face classes. Thus, the present study aims to evaluate how the teachers of the accounting course at the UTFPR in Brazil and at ISCAP in Portugal perceive the use of information technology in the process of education at distance (EaD) during the period of the new coronavirus pandemic and in the future. The study reveals the concern of teaching with digital technologies regarding the effectiveness of this model in the teaching and learning process. The methodology used was quantitative and qualitative, through the case study and the survey carried out in both higher education institutions. Thus, it is possible to state that the study highlighted the importance of technological resources for remote teaching, as well as the difficulty still existing in the management of these tools, and some limitations of interaction existing in non-presential relationships.