Reviewerly/exHarmony huggingface dataset:
Learning from Software defect datasets

Development of high-quality software is very much essential now-a-days. Prediction of good quality software in the early phase during the development reduces the cost of the testing resources. Various data mining and machine learning algorithms are developed to predict the quality of the software. In the real-life scenarios while dealing with software modules many of the time the underline datasets are imbalanced. In order to increase the efficiency of the prediction or classification algorithms balancing algorithms are implemented as a preprocessing stage before the prediction phase. In this paper we conduct extensive experiments to explore the effect of imbalance learning including undersampling, oversampling and hybrid methods and its interaction with different types of classifiers on various projects. We evaluate six imbalance learning methods with six classifiers on 12 data sets. The imbalance learning methods used are Random Oversampling (ROS), Random under-sampling, SMOTE, TOMEK, SMOTE+TOMEK, and SMOTE+ENN. This study reveals that the appropriate combination of imbalanced method and classifier can improve the accuracy of software fault prediction models.
Coordination in Adversarial Multi-Agent with Deep Reinforcement Learning Under Partial Observability

We propose a method using several variants of deep Q-network for learning strategic formations in large-scale adversarial multi-agent systems. The goal is to learn how to maximize the probability of acting jointly as coordinated as possible. Our method is called the centralized training and decentralized testing (CTDT) framework that is based on the POMDP during training and dec-POMDP during testing. During the training phase, the centralized neural network's inputs are the collections of local observations of agents of the same team. Although agents only know their action, the centralized network decides the joint action and subsequently distributes these actions to the individual agents. During the test, however, each agent uses a copy of the centralized network and independently decides its action based on its policy and local view. We show that deep reinforcement learning techniques using the CTDT framework can converge and generate several strategic group formations in large-scale multi-agent systems. We also compare the results using the CTDT with those derived from a centralized shared DQN and then we investigate the characteristics of the learned behaviors.
Collaborative Path Planning for Multiple Unmanned Aerial Vehicles to Avoid Sudden Threats

This paper concerns collaborative path planning for multiple unmanned aerial vehicles to avoid sudden threats. Firstly, using V-diagram and mission assignment model to obtain multi-UAV path planning in a pre-established environment with obstacles. Then, after a sudden threat, the principle of cubic spline second-order continuity and boundary conditions are used for local planning. After that, setting the total cost function to get the most suitable candidate path. Finally, using the principle of crowding mechanism and setting the priority probability, the secondary security screening is performed to get the optimal candidate path. Simulation results show the effectiveness of the proposed method.
Sentiment Analysis Of Hotel Review Using N-Gram And Naive Bayes Methods

Hotel booking service providers in the form of websites or online-based applications have provided features where consumers can provide a review regarding their assessment of the hotel. But the number of reviews available makes users unable to filter out all the reviews. Sentiment analysis can be used as a solution to overcome this by classifying reviews into positive or negative sentiments. This study aims to determine the application of n-gram and naive bayes methods in sentiment analysis classification. The research phase includes: (1) the hotel review data collection was obtained from the TripAdvisor.com website, (2) the data preprocessing process is data cleaning and case folding, (3) the process of tokenization using the n-gram method consists unigram, bigram, and trigram, (4) the process of word weighting using Term Frequence Inverse-Document Frequency (TF-IDF) method, (5) the process of classification using the Naive Bayes method to classify hotel reviews to be positive or negative, (6) the evaluation process to determine the results of the performance of the algorithm using a confusion matrix that will produce the value of precision, recall, accuracy and error rate. Based on these results, classification using the Naive Bayes and unigram methods obtained precision results of 94%, recall 100%, accuracy 97% and error rate 3%. The bigram methods obtained precision results of 89%, recall 94%, accuracy 92% and error rate 8%. The trigram methods obtained precision results of 52%, recall 80%, accuracy 58% and error rate 42%. Based on accuracy results, It can be concluded that tokenization unigram method better than other tokenization methods.
Variational Iterative Filter for Orbit Estimation

A nonlinear filter termed variational iterative filter (VIF) is presented in the paper. The measurement likelihood is modeled subtly as a Gaussian regression process by introducing the compensation parameters. Afterwards, assume the conjugate priori information of compensation parameters with Gaussian-Wishart distribution. Such assumption aims to make the posterior estimates conjugated with the prior for facilitating the computation. Then in variational Bayesian (VB) framework, the state estimation and compensation parameters identification are found alternately and iteratively by minimizing the Kullback-Leibler (KL) divergence. Finally, to evaluate the effectiveness of VIF, it is compared with Kalman filters by estimating a low-Earth spacecraft’s state with the ground radar.
Mongolian Information Retrieval Method Based on Word2vec and Topic Model

To capture the real intention of users' needs more accurately from the increasingly abundant Mongolian information and return the retrieval results that best meet their needs, a Mongolian information retrieval method based on Word2vec and LDA topic model is proposed in this paper. Combining Mongolian grammatical features, this method builds a model based on LDA three-tier Bayesian structure to mine the hidden topic distribution and feature word distribution of documents, expands user queries according to Word2vec model to obtain words similar to user query keywords semantically, and then uses topic model to model extended vocabulary. Finally, according to the user's query topic, the similarity between the query topic and the document topic is calculated, and the document with high relevance to the query topic is returned. The experimental results show that the effective combination of Word2vec and LDA model achieves better results than the traditional model with initial query in the representation of latent semantics.
Conditional Event Algebras: The State-of-the-Art

We review the fundamentals of Conditional Event Algebras (CEA), provide an overview of current research surrounding CEA, and offer insights into future research inspired from CEA, including quantum logic.
Query-Oriented Patent Document Summarization System (QPSS)

Girthana, K. Swamynathan, S.Innovations play a major role in the development of the country in terms of both technology and economy. They involve the creation and transfer of knowledge into a new product or process. Efficient patent analysis allows the R&D team to foster knowledge in the latest technological trends, thereby accelerating the production and impedes patent infringements. This work focuses on analyzing the patent documents through summarization. This proposed work automatically summarizes every patent document based on the search query through extraction and Restricted Boltzmann Machine (RBM). The resulting summary is evaluated in terms of precision, recall, and F-measure with search queries from different domains and it was found that the proposed system yields better results than open text summarizer.
You Shall Not Register! Detecting Privacy Leaks Across Registration Forms

Most of the modern web services offer their users the ability to be registered on them via dedicated registration pages. Most of the times, they use this method so the users can profit by accessing more content or privileged items. In these pages, users are typically requested to provide their names, email addresses, phone numbers and other personal information in order to create an account. As the purpose of the tracking ecosystem is to collect as many information and data from the user, this kind of Personally Identifiable Information (PII) might leak on the 3rd-Parties, when the users fill in the registration forms. In this work, we conduct a large-scale measurement analysis of the PII leakage via registration pages of the 200,000 most popular websites. We design and implement a scalable and easily replicable methodology, for detecting and filling registration forms in an automated way. Our analysis shows that a number of websites ($$\approx $$5%) leak PIIs to 3rd-Party trackers without any user’s consent, in a non-transparent fashion. Furthermore, we explore the techniques employed by 3rd-Parties in order to harvest user’s data, and we highlight the implications on user’s privacy.
Deep Neural Networks as Similitude Models for Sharing Big Data

The amount of data grows rapidly with time and shows no signs of stopping. Ubiquitous computing continues to collect and generate more and more data as both the number of devices grows and the capabilities of devices increase. We suggest processing the data on end devices by building a representative model of the data (“similitude” model). Sharing a smaller model instead of the entire data allows for saving computing power, network time, processing time and also, keeping the collected data private. In the past research, we suggested the use of similitude models, as compact models of data representation instead of the data itself. In this paper, we suggest the use of deep neural networks (DNN) as a data model to answer different types of queries. More specifically, we show that by building two models (generative network and auto-encoder) it is possible to answer approximately both statistical queries and membership queries without exposing the entire dataset.
Detecting Toxicity with Bidirectional Gated Recurrent Unit Networks

As large amounts of data keep being generated by users on social media platforms, some of the information can be considered as harmful. These kinds of textual information can be generated in forums, online discussions or any other communication exchange in an online medium. As such, it is sometimes difficult to filter out what information is actually meaningful. Detecting these harmful pieces of information can help in providing a means of online moderation so that a safe discussion can be maintained, which helps in preventing issues such as cyber bullying. Using the Kaggle Jigsaw dataset of comments that are classified as toxic labels, we can implement deep learning models to implicitly extract textual features from the comments and solve this supervised learning problem. This paper focuses on using the variations of recurrent neural networks, with the main focus on using bidirectional gated recurrent units, and evaluating their performances against each other.
Passwords a Lesson in Cyber Security Failure?

Abstract Passwords are dead, writes Steven Furnell FBCS of the Centre for Security, Communications and Network Research at the University of Plymouth. Numerous people have said so (just search for the phrase to see for yourself).
Modality-Specific Learning Rate Control for Multimodal Classification

Multimodal machine learning is an approach to performing tasks with inputs containing multiple expressions for a single subject. There are many recent reports on multimodal machine learning using the framework of deep neural networks. Conventionally, a common learning rate has been used for the network of all modalities. This has led, however, to a decrease in the overall accuracy due to overfitting in some modality models in cases when the convergence rate and generalization performance differ among modalities. In this paper, we propose a method that solves this problem by constructing a model within the framework of multitask learning, which simultaneously learns modality-specific classifiers as well as a multimodal classifier, to detect overfitting in each modality and carry out early stopping separately. We evaluated the accuracy of the proposed method using several datasets and demonstrated that it improves classification accuracy.
Emoji Prediction for Portuguese

Besides alternative text-based forms, emojis became highly common in social media. Given their importance in daily communication, we tackled the problem of emoji prediction in Portuguese social media text. We created a dataset with occurrences of frequent emojis, used as labels, and then compared the performance of traditional machine learning algorithms with neural networks when predicting them. Either considering five or ten of the most popular emojis, an LSTM neural network clearly outperformed Naive Bayes in the latter task, with F1-scores of 60% and 52%, respectively, against 33% and 23%.
MFCC-Based Bangla Vowel Phoneme Recognition from Micro Clips

Paul, Bachchu Mukherjee, Himadri Phadikar, Santanu Roy, KaushikSpeech recognition has developed highly and different solutions are available, most of them in English and few other non-Indian languages. People face difficulty in handling them who are not proficient in such languages. Bangla is the sixth most spoken language in world [1] and speech-based solutions are not fully available due to complex nature of the language. Thus Bangla speech recognizer is important. Every language has atomic sound called phonemes. Vowel phonemes are one of the most important aspects of a language, as most words are constituted with them. In this paper, we have categorized Bangla vowel phonemes with MFCC features and knn-based classification. The phonemes were split into micro clips of 30 ms before categorization in order to uphold real-world scenario which often consists of incomplete or extremely short duration data. We had experimented with disparate classifiers on our dataset of 92,649 short-duration clips and obtained a highest accuracy of 98.87%. We have compared the accuracy with standard MFCC features for our data set and found better result.
Agricart: An Innovative Methodology for Enhancing Farmers Livelihood

We propose Agricart (website and android application) which will help the user to identify the required Fertilizers, Pesticides, Seeds, Saplings, Manure and other farming equipment availability shops which are nearby their location. This application will reduce manual work by finding the right store where we can order the farming goods online and receive them at respective location. The payment can be done through online payment portal. The main aim of this project is to solve the problems of marketing and help the farmers to buy their products in a timely manner and to manage agricultural activities those are done on daily, weekly and monthly basis in the farm. So, we propose Agricart (website and android application) where it helps farmers to communicate with the market directly and providing a showcase platform of their products. The main objective is to maintain the agricultural activities on daily bases and user can easily interact with shops regarding the need of their agriculture products and create an event corner where all the details about latest events, seminars regarding agriculture or horticulture can be posted regularly.
A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment

A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features of an image and retrieve the best matched image. Based on our experiments, we compare those algorithms on parameters such as sum of square difference (SDD), precision, recall, number of detected, matched features and processing time. Empirically, we have found that SURF algorithm produce minimum SSD score to achieve best matching. The MSER and MinEign algorithm extracts high and low number of features respectively. In respect of processing time, BRISK takes maximum and FAST method takes minimum time when compared to the other algorithms.
Bayes-Probe: Distribution-Guided Sampling for Prediction Level Sets.

Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence failures or ambiguous classifications. To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx. Given a data distribution, Bayes-TrEx finds in-distribution examples with a specified prediction confidence. We demonstrate several use cases of Bayes-TrEx, including revealing highly confident (mis)classifications, visualizing class boundaries via ambiguous examples, understanding novel-class extrapolation behavior, and exposing neural network overconfidence. We use Bayes-TrEx to study classifiers trained on CLEVR, MNIST, and Fashion-MNIST, and we show that this framework enables more flexible holistic model analysis than just inspecting the test set. Code is available at this https URL.
FORMATION OF DIGITAL COMPETENCE IN MODERN YOUTH: PROBLEMS, EXPERIENCE, PROSPECTS

The implementation of the policy of digitalization in the Russian Federation requires the higher school to develop new ways of organizing training for the new formation, and from the state - to take the necessary organizational and incentive measures for their early widespread implementation. This is due to the fact that it is the human resources potential, which has the necessary competencies in the conditions of constantly growing digitalization of all spheres of the economy, that can become the main source of growth in labor productivity and competitiveness of economic entities of Russia. The study of research works in the field of Economics, management, pedagogy and psychology, as well as conversations with teachers and students of engineering and technological faculties of the Ural state agrarian University allowed to determine that among the problems associated with training for the digital environment, the leading are the following: the changes in the cognitive capabilities of modern young people; the imperfection of the organization and methods of teaching digital competencies; lagging behind the level of professional training of the teaching staff of educational organizations from the speed of development of digital technologies and a number of others. In order to solve these problems, the authors summarize the positive experience of domestic and foreign enterprises and educational organizations in the formation of digital competencies among young people. The characteristic of the modern trends of education for digital competences. Their correlation with cognitive-behavioral features of the younger generation is determined. The authors identify the most effective ways of forming competencies in the field of digital technologies, depending on the psychological characteristics of modern youth. Taking into account the impossibility of meeting the needs of the modern digital labor market only by specialized agencies, the proposals for the organization of the digital environment for students in other educational organizations are given.
Evaluating the User Experience of an Augmented Reality Prototype for Enterprise Architecture

Enterprise architecture (EA) visualizations like text, diagrams, and models are commonly displayed on 2D screens and are manipulated with a computer mouse and keyboard. The additional application of augmented reality (AR) promises improvements in terms of understanding complex architectural relationships and enables more natural manipulation of visualizations, ultimately leading to better decision-making. As part of a Design Science Research (DSR) project, we report on an empirical evaluation of user experience of an EA visualizing prototype deployed on an optical see-through head-mounted display (HMD) with 13 practitioners. The examined prototype displays a three-layer model that allows the analysis of randomly generated EAs. The participants performed 13 tasks which differed in complexity and context. In this study we qualitatively observe users’ behavior. Our results indicate that AR is suitable to analyze EA. In particular those stakeholders less experienced with EA can benefit from utilizing EA visualizations in AR.
Color image enhancement algorithm based on edge extraction

To solve the problem of blurred edges in some color images, a color image enhancement algorithm based on edge extraction is proposed. This algorithm is carried out in YUV space. The improved Laplace operator is used to extract image edge, which improves the clarity of edge while preserving image color. In addition, this algorithm can also improve the edge clarity of blurred and noisy images.
A Study on the Priorities of Blockchain Adoption for Port Logistics in Korea Using AHP: Focused on Busan and Incheon Ports

Purpose- This study has attempted to find appropriate factors of blockchain adoption in Korea related to maritime and port logistics industry to...
Detection of Web Service Anti-patterns Using Machine Learning Framework

Web services are being embraced by IT industry in the recent past to enable rapid development of distributed systems with optimal cost. Web services in SOA are self-adaptable to context, which makes SOA widely recognized in IT system as the technology, which has the potential of improving the receptiveness of both business and IT organizations. Web services help in building Service Based Systems (SBS) like Paytm, Amazon, Paypal, e-bay etc. which evolves frequently to fit the new user requirements which impacts the evolvability and quality of software design. Similar to software systems built using other paradigms, Service based systems also suffer from bad or poor design choices as in anti-pattern, code smells etc. Anti-patterns are explicit structures in the design that indicates violation of fundamental design principles and negatively impact the design quality. Anti-patterns have obstructive influence on the maintainability and perception of software systems. Thus there is a rising need for the early prediction of anti-patterns and refactoring them to improve the software quality in terms of execution cost, maintenance cost and memory consumption. In this work, a frame work is proposed for significant feature selection from source code metrics which includes Wilcoxon signed rank test, Univariate logistic regression analysis and Cross-correlation analysis. Then the different sets of features from various steps along with the original source code metrics are considered and are used for anti-pattern detection using 13 machine learning algorithms. Experimental results show the approximation capability of different classifiers and data balancing techniques with the features selected from the various steps of feature validation framework in addition to the original features for developing anti-pattern prediction model. The results also shows that the prediction model built with by the ensemble techniques using the features obtained from proposed feature selection framework outperforms other techniques.