Reviewerly/exHarmony huggingface dataset:
The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study

The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the running time of the SVM fault prediction model is not consistent with dataset size. Therefore, having fewer metrics does not guarantee a shorter execution time. From the experiments, we found that dataset size has a direct influence on the SVM fault prediction model. However, reduced datasets performed the same or slightly lower than the original datasets.
An Efficient Approach towards Face Recognition using Deep Reinforcement Learning, Viola Jones and K-nearest neighbor

Authentication of a user's identity is becoming a tough task for a system in today's era in which digital authentication becoming mandatory to satisfy the security of a system. Recognition failure of user's identity is one of the big concerns. This paper introduces an efficient mechanism to carry out the recognition of facial features in order to satisfy the authentication of a system. Earlier researches in this field have common constraints such as false acceptance and false rejection rate. The proposed method implements over video data on which deep reinforcement learning and K-nearest neighbors (KNN) have been applied to perform detection and recognize facial data accurately. The challenging task of this work is to correctly recognize the facial data under various disturbance and unprecedented noisy circumstances including bad illumination, blurring, inappropriate poses, angle, etc. The main objective of the model is to achieve a high recognition rate of facial data under different unwanted noise and attacks. Reinforcement learning is used to count the number of people in the proposed system. This concept of the KNN algorithm is used for classification based on Euclidean distance to achieve better recognition results. The average rate of accuracy for recognition is found to be 96.40%. The proposed model can be applied to an investigation into digital forensics.
A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain

Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user and the terms presently stored in the knowledge bases. Many studies have achieved encouraging results in lexical semantic resolution on the topic of word sense disambiguation (WSD), and several other works consider these challenges in the context of QA applications. Additionally, few scholars have examined the role of WSD in returning potential answers corresponding to particular questions. However, natural language processing (NLP) is still facing several challenges to determine the precise meaning of various ambiguities. Therefore, the motivation of this work is to propose a novel knowledge-based sense disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with questions posed in QA systems. The major contribution is the proposed innovative method, which incorporates multiple knowledge sources. This includes the question’s metadata (date/GPS), context knowledge, and domain ontology into a shallow NLP. The proposed KSD method is developed into a unique tool for a mobile QA application that aims to determine the intended meaning of questions expressed by pilgrims. The experimental results reveal that our method obtained comparable and better accuracy performance than the baselines in the context of the pilgrimage domain.
A Biomorphic Model of Cortical Column for Content—Based Image Retrieval

How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of "triggering" a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.
Implementing System for Rating Generation by Analysis of Social Media

the increasing utilization of online content, provides a large amount of data resources. People share their experiences, opinions, as well as day to day activities. This results in a large amount of online data which attracts developers to carry out data mining and data analysis. Thus, there is a necessity of social media screening so as to obtain results which can be used for analysis. This paper presents a system, that facilitates the generation of single rating which is obtained by analysis of social media platforms. This rating is generated by carrying out various process like Data extraction, pre-processing, Data analysis, Rating Generation. "ROCIAL" means "Rating Socially", where each enrolled user will be rated upon his social conduct. The idea behind this initiative came up because of the present requirement for online screening. So, basically what our system would do is connect the user’s different social media accounts and analyze them using the criteria which are usually required during the screening process. The main application of our system is during visa allotment process where social media screening is used. So, rather than screening each and every platform individually, our system rocial can be used to generate a rating which will act as a criterion for visa allotment. Another significant application is during the recruitment process. Various companies check the candidate’s social media behavior as a secondary criterion for judging the person. Thus, using ROCIAL this can be obtained quickly and efficiently.
Optimal Randomized Partial Checking for Decryption Mix Nets

One of the most important verifiability techniques for mix nets is randomized partial checking (RPC). This method is employed in a number of prominent secure e-voting systems, including Prêt à Voter, Civitas, and Scantegrity II, some of which have also been used for real political elections including in Australia.Unfortunately, it turned out that there exists a significant gap between the intended and the actual verifiability tolerance of the original RPC protocol. This mismatch affects exactly the “Achilles heel” of RPC, namely those application scenarios where manipulating a few messages can swap the final result (e.g., in close runoff elections).In this work, we propose the first RPC protocol which closes the aforementioned gap for decryption mix nets. We prove that our new RPC protocol achieves an optimal verifiability level, without introducing any disadvantages. Current implementations of RPC for decryption mix nets, in particular for real-world secure e-voting, should adopt our changes to improve their security.
Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.
Hiding in Plain Sight: Memory-Tight Proofs via Randomness Programming

This paper continues the study of memory-tight reductions (Auerbach et al., CRYPTO ’17). These are reductions that only incur minimal memory costs over those of the original adversary, allowing precise security statements for memory-bounded adversaries (under appropriate assumptions expressed in terms of adversary time and memory usage). Despite its importance, only a few techniques to achieve memory-tightness are known and impossibility results in prior works show that even basic, textbook reductions cannot be made memory-tight. This paper introduces a new class of memory-tight reductions which leverage random strings in the interaction with the adversary to hide state information, thus shifting the memory costs to the adversary. We exhibit this technique with several examples. We give memory-tight proofs for digital signatures allowing many forgery attempts when considering randomized message distributions or probabilistic RSA-FDH signatures specifically. We prove security of the authenticated encryption scheme Encrypt-then-PRF with a memory-tight reduction to the underlying encryption scheme. By considering specific schemes or restricted definitions we avoid generic impossibility results of Auerbach et al. (CRYPTO ’17) and Ghoshal et al. (CRYPTO ’20). As a further case study, we consider the textbook equivalence of CCA-security for public-key encryption for one or multiple encryption queries. We show two qualitatively different memory-tight versions of this result, depending on the considered notion of CCA security.
A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios

Prediction of the manoeuvres of other vehicles can significantly improve the safety of automated driving systems.A manoeuvre prediction algorithm estimates the likelihood of a vehicle's next manoeuvre using the motion history of the vehicle and its surrounding traffic.Several existing studies assume full observability of the surrounding traffic by utilising trajectory datasets collected by top-down view infrastructure cameras.However, in practice, automated vehicles observe the driving environment using egocentric perception sensors (i.e., onboard lidar or camera) which have limited sensing range and are subject to occlusions.This study firstly analyses the impact of these limitations on the performance of lane change prediction.To overcome these limitations, automated vehicles can cooperate in observing the environment by sharing their perception data through V2V communication.While it is intuitively expected that cooperation among vehicles can improve environment perception by individual vehicles, the other contribution of this work is to quantify the potential impacts of cooperation.To this end, we propose two perception models used to generate egocentric and cooperative perception dataset variants from a set of uniform scenarios in a benchmark dataset.This study can help system designers weigh the costs and benefits of alternative perception solutions for lane change prediction.noeuvre of the TV.Automated vehicles observe their surrounding using egocentric perception sensors (e.g., camera and LiDAR) which have limited range and are subject to a
Domain Adaptive Learning with Multi-Granularity Features for Unsupervised Person Re-identification

Unsupervised person re-identification (Re-ID) aims to improve the model’s scalability and obtain better Re-ID results in the unlabeled data domain. In this paper, we propose an unsupervised person Re-ID method based on multi-granularity feature representation and domain adaptive learning, which can effectively improve the performance of unsupervised person re-identification. The multi-granularity feature extraction module integrates global and local information of different granularity to obtain the multi-granularity person feature representation with rich discriminative information. The source domain classification module learns the labeled source dataset classification and obtains the person’s discriminative knowledge in the source domain. On this basis, the domain adaptive module further considers the difference between the target domain and the source domain to learn adaptively for the model. Experiments on multiple public datasets show that the proposed method can achieve a competitive performance among other state-of-the-art unsupervised Re-ID methods.
Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak

Health informatics is an interdisciplinary field in the computer and health sciences. Health informatics, which enables the effective use of medical information, has the potential to reduce both the cost and the burden of healthcare workers during the pandemic process. Using the machine learning algorithms support vector machines, naive bayes, k-nearest neighbor, and C4.5 algorithms, a model performance evaluation was performed to identify the algorithm that will show the highest performance for the prediction of the disease. Three separate training and test datasets were created 70% - 30%, 75% - 25%, and 80% - 20%, respectively. The implementation phase of the study was carried out by following the CRISP-DM steps, and the analyses were made using the R language. By examining the model performance evaluation criteria, the findings show that the C4.5 algorithm showed the best performance with 70% training dataset.
Applying Geo-Encryption and Attribute Based Encryption to Implement Secure Access Control in the Cloud

Cloud computing is utility-based computing provides many benefits to its clients but security is one aspect which is delaying its adoptions. Security challenges include data security, network security and infrastructure security. Data security can be achieved using Cryptography. If we include location information in the encryption and decryption process then we can bind access to data with the location so that data can be accessed only from the specified locations. In this paper, we propose a method based on the symmetric cryptography, location-based cryptography and ciphertext policy – Attribute-based encryption (CP-ABE) to implements secure access control to the outsourced data. The Symmetric key is used to encrypt that data whereas CP-ABE is used to encrypt the secret key and the location lock value before uploading on the server. User will download encrypted data and the symmetric secret key XORed with the Location Lock value, using his attributes based secret key he can obtain first XORed value of Symmetric secret key and location lock value. Using anti-spoof GPS Location lock value can be obtained which can be used to retrieve the symmetric secret key. We have adopted Massage Authentication Code (MAC) to ensure Integrity and Availability of the data. This protocol can be used in the Bank, government organization, military services or any other industry those are having their offices/work location at a fixed place, so data access can be bounded to that location.
Kvasir-Instruments and Polyp Segmentation Using UNet

This paper aims to describe the methodology used to develop, fine-tune and analyze a UNet model for creating masks for two datasets: Polyp Segmentation and Instrument Segmentation, which are part of MedAI challenge. For training and validation, we have used the same methodology on both tasks and finally on the hidden testing dataset the model resulted in an accuracy of 0.9721, dice score of 0.7980 for the instrumentation task, and the accuracy of 0.5646 and a dice score of 0.4100 was achieved for the Polyp Segmentation.
Concise Mercurial Subvector Commitments: Definitions and Constructions

Vector commitment and its variants have attracted a lot of attention recently as they have been exposed to a wide range of applications in blockchain. Two special extensions of vector commitments, namely subvector commitments and mercurial commitments, have been proposed with attractive features that are desirable in many applications. Nevertheless, to the best of our knowledge, a single construction satisfying all those attractive features is still missing. In this work, we analyze those important properties and propose a new primitive called mercurial subvector commitments, which are efficiently updatable, mercurial hiding, position binding, and aggregatable. We formalize the system model and security model for such a primitive and present a concrete construction with security proofs to show that it satisfies all of the properties. Moreover, we also illustrate some applications of mercurial subvector commitments, including zero-knowledge sets and blockchain with account-based models.
Learning High-Order Graph Convolutional Networks via Adaptive Layerwise Aggregation Combination

Graph convolutional networks have attracted wide attention for their expressiveness and empirical success on graph-structured data. However, deeper graph convolutional networks with access to more information can often perform worse because their low-order Chebyshev polynomial approximation cannot learn adaptive and structure-aware representations. To solve this problem, many high-order graph convolution schemes have been proposed. In this article, we study the reason why high-order schemes have the ability to learn structure-aware representations. We first prove that these high-order schemes are generalized Weisfeiler–Lehman (WL) algorithm and conduct spectral analysis on these schemes to show that they correspond to polynomial filters in the graph spectral domain. Based on our analysis, we point out twofold limitations of existing high-order models: 1) lack mechanisms to generate individual feature combinations for each node and 2) fail to properly model the relationship between information from different distances. To enable a node-specific combination scheme and capture this interdistance relationship for each node efficiently, we propose a new adaptive feature combination method inspired by the squeeze-and-excitation module that can recalibrate features from different distances by explicitly modeling interdependencies between them. Theoretical analysis shows that models with our new approach can effectively learn structure-aware representations, and extensive experimental results show that our new approach can achieve significant performance gain compared with other high-order schemes.
Visual Servoing and Deep Capsule Network Learning for Contactless Smart Waste Segregation

Due to the rapid urbanization and industrialization, lots of waste is generated but not properly disposed. Domiciliary wastes are also dumped either in the public places or waste land leading to various pollutions. This leads to the growth of micro-organisms which cause harmful diseases. These wastes are picked up and segregated manually by sanitation workers which may later affect their health. This paper presents a smart waste segregation model that can distinguish and separate the waste into degradable, non-degradable and also high radioactive and low radioactive waste materials. The automated waste segregator uses sensors as well as visual servoing and deep capsule network learning technologies so that human intervention is completely avoided. It ensures accurate segregation of waste into categories like paper, plastic, metal, non-metal and uncategorizable wastes. These waste products are further sent for recycling or incinerated without eliminating any hazardous gases into the atmosphere. This model is extremely beneficial during pandemic situations for contactless waste segregation as well as in safe handling of hospital waste.
Online hate speech in Belarus: Highlighting the topical issues

Summary Hate speech can be viewed as a form of socio-political conflict that might be considered an inevitable result of the shift of the historical paradigm in the former Soviet republics. In the meantime, the study of hate speech in Belarus demonstrates its relevance not only in the context of the current socio-political situation, but also because of the lack of the related research in the country. This study provides a general outline of hate speech in the Belarusian online discourse at the present time. At the beginning of the article, a brief literature review and the definition of the key terms are given. Afterwards, extralinguistic factors that influence hate speech functioning within the discourse community are described. Most attention is paid to the linguistic analysis of the legal documents of the Republic of Belarus as compared to those of other countries. Then, based on the analysis of the Internet users’ comments to online news, the most topical issues are determined and some observations on labeling as a move aimed at creating an image of a social group are provided.
Approximate Neural Architecture Search via Operation Distribution Learning

The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections. In this work, we instead propose to search for the optimal operation distribution, thus providing a stochastic and approximate solution, which can be used to sample architectures of arbitrary length. We propose and show, that given an architectural cell, its performance largely depends on the ratio of used operations, rather than any specific connection pattern in typical search spaces; that is, small changes in the ordering of the operations are often irrelevant. This intuition is orthogonal to any specific search strategy and can be applied to a diverse set of NAS algorithms. Through extensive validation on 4 data-sets and 4 NAS techniques (Bayesian optimisation, differentiable search, local search and random search), we show that the operation distribution (1) holds enough discriminating power to reliably identify a solution and (2) is significantly easier to optimise than traditional encodings, leading to large speed-ups at little to no cost in performance. Indeed, this simple intuition significantly reduces the cost of current approaches and potentially enable NAS to be used in a broader range of applications.