Reviewerly/exHarmony huggingface dataset:
Restricting Data Sharing and Collection of Facial Recognition Data by the Consent of the User: A Systems Analysis

Mass Data Collection through Facial Recognition Technology poses a threat to the personal privacy of millions, with big data companies selling and analyzing user data points without appropriate consent and having unrestricted access to said data. Personal privacy should be a right that requires protection in our interconnected society that is becoming ever more reliant on technology. We believe that as this technology progresses without proper restriction, its implementation will effectively eliminate personal privacy, effectively placing a constraint on user autonomy. This is due in part to the vagueness of current policies centered around privacy. If this does not change, personal privacy could be eliminated. FRT has many features, some of which are controversial and impede its effectiveness. Through a systems analysis approach that examines the integration of the social and technical dimensions of these privacy problems, our preliminary research examines several approaches that will be most effective in addressing this complex problem while supporting FRT development. By proposing the following solutions, we hope to prevent personal privacy infringement via FRT: Propose and pass policy similar to the Commercial Facial Recognition Privacy Act, improve data encryption for databases with sensitive personal information (such as Facial Recognition data), and change the way privacy policy is delivered to the consumer by setting maximum length standards on the policy that is written and using less legal jargon to promote the reading and understanding of these privacy policies and what they require of the user. Based on analysis of these proposed solutions, we believe that there will be an increase in security and the feeling of personal privacy across the board.
Image Super Resolution with Sparse Data Using ANFIS Interpolation

Image super resolution is one of the most popular topics in the field of image processing. However, most of the existing super resolution algorithms are designed for the situation where sufficient training data is available. This paper proposes a new image super resolution approach that is able to handle the situation with sparse training data, using the recently developed ANFIS (Adaptive Network based Fuzzy Inference System) interpolation technique. In particular, the training image data set is divided into different subsets. For subsets with sufficient training data, the ANFIS models are trained using standard ANFIS learning procedure, while for those with insufficient data, the models are obtained through ANFIS interpolation. In the literature, little work exists for image super resolution on sparse data. Therefore, in the experimental evaluations of this paper, the proposed approach is compared with existing super resolution methods with full data, demonstrating that this work is able to produce highly promising results.
Pruning <scp>SMAC</scp> search space based on key hyperparameters

Summary Machine learning (ML) has been widely applied in many areas in recent decades. However, because of its inherent complexity and characteristics, the efficiency and effectiveness of ML algorithm often to be heavily relies on the technical experts' experience and expertise which play a crucial role to optimize hyperparameters of algorithms. Generally, the procedure tuning the exposed hyperparameters of ML algorithm to achieve better performance is called Hyperparameters Optimization. Traditional hyperparameters optimization methods are manually exhaustive search, which is unavailable for high dimensional search spaces and large datasets. Recent automated sequential model‐based optimization led to substantial improvements for this problem, whose core idea is fitting a regression model to describe the importance and dependence of algorithm's performance on certain given hyperparameter setting. Sequential model‐based algorithm configuration (SMAC) is a the‐state‐of‐art approach, which is specified by four components, Initialize, FitModel, SelectConfigurations, and Intensify. In this article, we propose to add a pruning procedure into SMAC approach, it quantifies the importance of hyperparameters by analyzing the performance of a list of promising configurations and reduces search space by discarding noncritical and bad key hyperparameters. To investigate the impact of pruning for model's performance, we conducted experiments on the configuration space constructed by Auto‐Sklearn and compared the effect of run time and pruning ratio with our algorithm. The experiments results verified that, our method made the configuration selected by SMAC more stable and achieved better performance.
Quality Metrics in Software Design: A Systematic Review

This paper presents the results of a systematic literature review, which aimed to identify metrics for the quality of software that are applied in the design stage. Fifteen papers from different electronic databases were selected to answer three questions. The analysis allowed us to provide an overview of the metrics used to design artifacts. These metrics will serve as the basis for generating models based on artificial intelligence techniques (Neuronal Networks, Regression, or some other), that help to estimate quality in the early stages of the software development process. It is concluded that most of the design metrics are object oriented. In addition, the design metrics are applied to class diagrams, package diagrams and sequence diagrams.
Breast Lesion Segmentation in Ultrasound Images Using Deep Convolutional Neural Networks

On account of the painful impact of mammography, ultrasound imaging has gained wide acceptance in screening and diagnosis of breast lesions. But the existence of speckle noise and low resolution of ultrasound images limits the accuracy in some of the significant tasks like automated localization and segmentation of any diseased prone region. Therefore, to aid in computer-aided diagnoses, and to address the complex structure of breast lesions in ultrasound images, an efficient segmentation method can clinically assist in the treatment of patients by locating potential areas of interest and quantitative measurements of clinical information. Till date, most of the current methods for breast lesion segmentation from ultrasound imaging depends on manual delineation methods that are acutely tedious and impressionistic and may lead to human errors. As a consequence, an automated method for breast lesion segmentation from ultrasound images is of high demand from the radiologists now a day. The overarching objective of the work is to establish a light weighted convolutional neural network architecture encuraged by two popular networks (U-Net and ResNet), on 163 ultrasound images with size 760 x 570 pixels from patient with different types of diseases like benign cyst, malignant invasive ducal carcinoma (IDC), to automatically segment the breast lesion. The proposed architecture used a feature extraction unit at the beginning from a stack of convolutional layers to extract features from the input dataset. The final prediction of the architecture has been made after feeding the features from the U-Net and modified ResNet. This approach results in significant improvement in terms of accuracy, dice coefficient, mean-IoU, recall and precision with respect to some of the state-of-the-art architectures. Our comprehensive evaluation, model analysis, and antique insights can be extended to the development of the Computer-Aided Diagnostic (CAD) tool to assist the Ultrasound image reading process for the detection of breast lesion in the near future.
Eye Movement Features in response to Comprehension Performance during the Reading of Programs

Some features of eye movement during the reading of program code were analysed in order to develop a procedure to assess viewer comprehension ability. A set of eye movement data which was created by the measurement of eye movement during the viewing of a code programming project was used. While backward eye movement is natural under normal reading circumstances, this paper focuses on intentional eye movement in the opposite direction of the usual pattern of reading while viewing blocks of code, and the impact of the frequency of this on the comprehension of a code, which was confirmed. In examining the frequency of this reading behaviour, there were significant differences in both overall fixation times and mean saccade lengths for the two levels of comprehension of the code.
Impact of IoT-Enabled Supply Chain—A Systematic Literature Review

In this era of high competition, success lies in implementing an efficient supply chain. The problem faced by modern-day supply chain is that it is not proactive. Proactive supply chain helps in attacking the problem well in time, along with reducing all potential threats and risks. Internet of things (IoT) is the solution to this problem as it helps in making supply chain proactive. IoT-enabled supply chain is useful in tracking and tracing products in the entire supply chain along with delivering the right product at the right place at the right time. All this has huge impact on the business because IoT-enabled supply chain ensures a proactive supply chain. Thus, in nut shell it helps in revenue increase, enhancing customer service and providing real-time visibility to the entire supply chain. This paper reviews the existing literature to study and consolidate the impact of IoT-enabled supply chain on organizations. This study will provide a framework to academicians and practitioners working in the industry to focus on IoT-enabled supply chain along with its impact on organization.
CBOL: Cross-Bank Over-Loan Prevention, Revisited

With the development of credit businesses, privacy data leakage and data accuracy in loan transactions among different banks tend to be worrisome issues hindering the prosperity of the industry. To address the problem, we propose a blockchain-based cross-bank over-loan prevention (CBOL-ring) mechanism, which ensures that, on the one hand, the plaintext of loan transactions cannot be access to neither participants on the nodes except the bank that handles loan/repayment requests, so as to prevent the borrower from loaning without revealing their privacy data; on the other hand, the other participants are able to prove the effectiveness of the plaintexts through checking the ciphertexts on the blockchain. In addition, we propose a blockchain-based cross-bank over-loan prevention mechanism with low communication volume (CBOL-bullet), which reduces the size of the range proof generated by the BBCBOLP mechanism, thereby reducing the size of the communication volume and saving resources during the data transmission process. Finally, we analyze the security and performance of the two mechanisms, and compare the communication volume of the two mechanisms.
FastReID: A Pytorch Toolbox for Real-world Person Re-identification

General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research. In FastReID, highly modular and extensible design makes it easy for the researcher to achieve new research ideas. Friendly manageable system configuration and engineering deployment functions allow practitioners to quickly deploy models into productions. We have implemented some state-of-the-art projects, including person re-id, partial re-id, cross-domain re-id and vehicle re-id, and plan to release these pre-trained models on multiple benchmark datasets. FastReID is by far the most general and high-performance toolbox that supports single and multiple GPU servers, you can reproduce our project results very easily and are very welcome to use it, the code and models are available at this https URL.
Trends and Challenges in Measuring Performance of Reviewer Paper Assignment

The remarkable increase in the number of authors and publications is observed in almost all fields in last decade. Inherently publication engages process of peer review and is a vital segment in research and development progression. The Reviewer Assignment Problem (RAP) is one of the most important and apparent process that involves the process of assigning a reviewer to paper. RAP has motivated many researchers in the recent decade as the conference organizers and journal editors are demanding for automatic assignment with fair and accurate reviews. Fair and accurate reviews mostly depend on accurate expert assignment to the paper. RAP entails two core phases: matching submitted manuscript and experts, and expert assignment to paper for review. Matching manuscript and expert involves building both paper and expert profiles correctly and then matching them accurately. Similarly assigning an expert to paper accurately involves solving assignment problem by satisfying constraints like a conflict of interest, fulfilling coverage requirements and balancing the load of the expert reviewer. Many researchers have contributed to the topic by proposing solutions for RAP. Each proposed methodology necessitates using an appropriate performance metric for proving its effectiveness. In the case of RAP, we can measure the performance of the whole process; one can measure the performance of the core phases separately as both phases engross different processes and different metric. Testing performance and reducing errors in phases will lead to overall improved results. Both subjective (also known as qualitative) and objective (also known as quantitative) measures have been practiced by the researchers. In this paper, we present trends in using performance measures, their applicability and suitability for RAP. It is observed that ‘Unanimity’ is the most appropriate measure for the overall review process. Use of performance measures- matching score, authority, coverage, and diversity are the most suitable for measuring the performance of phase one- matching paper and reviewer. It is noticed that load balance, fairness and accuracy are suitable for measuring the performance of phase two that assigns a reviewer to paper by satisfying constraints. The study derives the conclusion that hybrid approach with the proper combination of these measures is the best for measuring the performance of process of assigning reviewer to the paper.
Using DevContainers to Standardize Student Development Environments: An Experience Report

In computer science classes it can be a challenge to ensure every student has a functioning development environment. Running pre-configured servers that provide students with remote access can help mitigate most of these setup issues, however they can also introduce new limitations of their own. We propose using DevContainers to overcome the local machine setup difficulties for the students. DevContainers allow the instructional staff to provision a development environment (a Docker image) with all the correct software versions pre-configured. This development environment can be used on any major OS through Docker. Moreover, through this DevContainer configuration, Microsoft Visual Studio Code can integrate seamlessly with the Docker container to provide an experience for the user that is practically the same as working on the native OS. This work examines the value of employing a DevContainer setup in an Advanced Data Structures course and provides details for those interested in using DevContainers in their courses.
Balinese glyph recognition with gabor filters

Abstract Recognizing Balinese glyphs from the Balinese script on palm leaf manuscripts is not trivial. In Balinese script, there are more than a hundred glyphs which represent basic syllables and compound syllables, and also some punctuation marks. They naturally share a strong interclass similarity between each other related to the form of their writing curves. The degraded image of textured palm leaf manuscript also offer some challenging parts in recognizing the Balinese glyph. In this paper, we investigated the use of Gabor filter bank as the feature extraction method to recognize the Balinese glyphs. By using Gabor filter, we can detect many texture variations with different orientations and frequencies. In our experiments, the published dataset of AMADI_LontarSet for glyph recognition was used. It showed a very promising result by using a single hidden layer Neural Network as the classifier. Gabor filters with Zoning method achieved a high enough recognition rate. For future works, Gabor filters will be analyzed in combination with the Histogram of Gradient, Neighborhood Pixel Weight and Kirsch Edges features.
Reducing Response Time for Multimedia Event Processing using Domain Adaptation

The Internet of Multimedia Things (IoMT) is an emerging concept due to the large amount of multimedia data produced by sensing devices. Existing event-based systems mainly focus on scalar data, and multimedia event-based solutions are domain-specific. Multiple applications may require handling of numerous known/unknown concepts which may belong to the same/different domains with an unbounded vocabulary. Although deep neural network-based techniques are effective for image recognition, the limitation of having to train classifiers for unseen concepts will lead to an increase in the overall response-time for users. Since it is not practical to have all trained classifiers available, it is necessary to address the problem of training of classifiers on demand for unbounded vocabulary. By exploiting transfer learning based techniques, evaluations showed that the proposed framework can answer within ~0.01 min to ~30 min of response-time with accuracy ranges from 95.14% to 98.53%, even when all subscriptions are new/unknown.
Knowledge Base Question Answering System Based on Knowledge Graph Representation Learning

Knowledge Base Question Answering (KBQA) refers that questions are answered by acquiring the relationship or entity from knowledge graph. The knowledge base is being high frequent used in modern question answering systems, which can find the exact answer from the knowledge base and return it to the users. However, due to the flexibility, richness and fuzziness of natural language, it is not easy to match the semantic information of questions with the text answers. How to accurately match these natural language questions with a large number of knowledge graph and improve the accuracy of questions and answers is an urgent problem to be solved. In this paper, we propose a question answering algorithm named TransE-QA based on knowledge graph representation learning to solve Simple QA problem. This algorithm is an end-to-end algorithm for question answering of simple QA problem. TransE and TextCNN are mainly used to represent the knowledge graph and the question. Based on knowledge graph representation learning, we propose a new scoring function, which the answer can be returned directly that required by the question. Besides, based on the TransE-QA algorithm proposed in this paper, we develop a KBQA system to visualize the process. The experiment shows that the algorithm TransE-QA, which proposed in the thesis, achieves 80.2% accuracy on FB5M dataset. It achieves better performance than previous state-of-the-art methods.
Model-Based Analysis for Smart University Development

The modern scientific approach to the design and development of education is featured by the intellectualization of knowledge and the organization’s ability to quickly restructure its activities according to the conditions of the external environment. This activity is typical for self-study organizations, where the self-organizing feature is justified as a basic component of the smart university characteristic. Another important factor is the ability of knowledge transfer within the performing team, allowing the learning organization to develop. In this article, the training organization is considered as a higher education institution. The state of its development is characterized by a set of evaluated indicators. The boundaries of change for these indicators allow top managers assessing the level of the training organization’s development and the possibility of its transition to a new level through the transformation of knowledge. The aim of the article is to model the processes of a self-learning organization transformation into a smart system based on the models of Markov’s processes and Kolmogorov’s theorem.
Research status of mobile robot path planning based on genetic algorithm

Abstract With its strong search ability and other characteristics, the genetic algorithm in mobile robot path optimization has been rapidly developed and applied, and has become one of the most successful algorithms to solve the mobile robot path planning problem. Firstly, this paper introduces the basic principle of genetic algorithm and gives a brief overview of its development and improvement strategies. Then, combining several representative improvement strategies, path planning of mobile robot is explained in detail. Finally, combining the theoretical and applied research results of genetic algorithm, the further development of the algorithm is summarized and prospected.
Apps for Behavior Management, Communication, and Learning: Using Sociocultural Theory to Understand Mobile Technology in PK-12 Settings

Mobile applications (apps) are currently used for classroom management, communication with parents, and student learning. Although teachers have acknowledged an unfamiliarity with tools to support their selection of apps for effective use with students, they have welcomed guidance for such evaluations. Thus, we have reviewed mobile technology (apps) from a sociocultural perspective, synthesized evidence-based research, and offered directions for future research.
A Distributed Vulnerability Scanning on Machine Learning

Traditional scanners are often isolated and single in the enterprise environment and cannot adapt to the development trend of future cypher security defense in depth, coordination, and system. With the emergence and development of ransomware and APT attacks, it is particularly important to design an automated vulnerability detection framework that identifies related threats. This paper designs a new distributed vulnerability detection framework based on machine learning technology, which optimizes scanning profiles, enhances new attack detection capabilities, and realizes linkage with other security devices to form a security management system for vulnerability detection, identification and interception.
Partial separability/entanglement violates distributive rules

We found three qubit Greenberger–Horne–Zeilinger diagonal states which tell us that the partial separability of three qubit states violates the distributive rules with respect to the two operations of convex sum and intersection. The gaps between the convex sets involving the distributive rules are of nonzero volume.