Reviewerly/exHarmony huggingface dataset:
A Smart Scheme for Variable Selection in Partial Opposition-based Differential Evolution

The concept of opposition-based learning (OBL) has been applied to a growing number of research works, as such we proposed a new scheme which improves effectiveness of a population-based optimization algorithm, called Differential Evolution. In this paper, we propose a novel partial opposition-based DE scheme. Given that the partial OBL reduces the likelihood of converting good variables to their opposites, the main challenge of this technique is still the selection of variables which should be replaced with their opposites. The proposed scheme addresses this issue by creating a reference solution using averaging on the best candidate solutions. This reference solution plays a crucial role in intelligent selection of variables to compute their corresponding the opposite value based on their distance to the set of best solutions. The proposed partial opposition-based scheme is embedded in Differential Evolution (DE) algorithm and compared with opposition-based DE (ODE) and DE using the set of CEC-2014 benchmark functions on dimensions 30, 50, and 100. Experimental results indicate a promising improvement over DE and ODE.
XSSPro: XSS Attack Detection Proxy to Defend Social Networking Platforms

Social Platforms transpired as the fascinating attack surface to explode multitude of cyber-attacks as it facilitates sharing of personal and professional information. XSS vulnerability exists approximately in 80% of the social platforms. Hence, this paper presents an approach, XSSPro, to defend social networking platforms against XSS attacks. XSSPro operates through isolating the JavaScript code in the external file and performs decoding operation. The context of each injected JS code is identified and then similar scripts are grouped together to optimize the performance of XSSPro. Finally, extracted scripts are matched against the XSS attack vector repository to detect XSS attack. If matched then it is refined by using XSS APIs, otherwise, the response is XSS free and sent to the user. Experimental results revealed that XSSPro achieved an accuracy of 0.99 and is effective against thwarting XSS attack triggered using new features of the built-in code language with low false alarm rate.
Live blog summarization

Abstract Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give their readers a minute by minute update on an event. Good summaries enhance the value of the live blogs for a reader, but are often not available. In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. We show that live blog summarization poses new challenges in the field of news summarization, since frequency and positional signals cannot be used. We make our tools publicly available to reconstruct the corpus and to conduct our empirical experiments. This encourages the research community to build upon and replicate our results.
A Secure Video Steganography Using Framelet Transform and Singular Value Decomposition

This paper proposes a novel system based on framelet transform and singular value decomposition to hide the secret data. Framelet transform is considered as a favorable decomposition technique as it is more stable and gives perfect reconstruction. The scene change detection method is used to discover the keyframes among the frames in the input video. Once keyframe is detected, it is converted to HSV color space. The framelet transform is applied to the keyframes to get the transformed coefficients. Then, singular value decomposition is applied to these transformed coefficients. The secret data is entrenched in these coefficients. Prior to embedding, to enhance security the secret data is pre-processed using encryption. The experimental outcome epitomizes greater security, good quality of video, and robustness.
RETRACTED: Resident consumption expenditure forecast based on embedded system and machine learning

The relationship between income and expenditure is significant for understanding the shape of the residents' economic dynamics. Consumer spending and household disposable income by the relationship between Machine Learning (ML) programming nonparametric locally weighted scatterplot smoothing regression analyzes. This study aims to determine the relationship between variables directly rather than the traditional parameters of regression. According to the survey, the usual assumptions, income and number of residents increase the impact of a sharp increase in spending at first, then slowed down. This growth can be relatively high mandatory spending little residents to explain. It increases according to income levels in middle-income and high-income groups. With the changes in population size, expenditure changes are limited in middle-income levels, restricted in most high-income levels. The latest growth in the smart meter housing sector is a large data set. Near real-time access to each household's power consumption by both supply and demand can extract valuable information for effective energy management. Predicted consumption will help improve power generation companies and demand-side management programs, but it is not. Consumption of individual households is very irregular, is a trivial task. In the business field of energy load forecasting, machine learning involves a lot of work. Machine learning methods are the so-called black-box model. The internal dynamic is almost unknown. However, without manual intervention, learning the ability to complex internal representation is a significant advantage.
Algerian Dialect Sentiment Analysis: Sate of Art

Sentiment Analysis tries to predict the polarity of comments in social media. In this paper we present a state of art about sentiment analysis for the Algerian dialect, one of the Arabic languages used on social media and which has few resources. First, a brief description is given regarding the characteristics and challenges of the Algerian dialect. Then the study of papers dealing with sentiment analysis for the Algerian dialect is done with their classification by approach considering tools used and the best results obtained. We also try to enumerate resources that are used in Algerian dialect sentiment analysis works.
Non-Communicable Diseases Classification using Multi-Label Learning Techniques

Non-communicable diseases: NCDs are one of leading causes of death in the world. Multi-NCDs patients tend to undergo and suffer from multiple coexistent diseases. This research aims at classifying NCDs patients who are diagnosed with other NCDs. Multi-label classification was used in this research. There are four diseases types used in this study, i.e. diabetes, hyper-tension, cardiovascular and stroke. Binary relevance (BR), Classifier Chains (CC), The random k-Iabelsets (RAkEL) and Multi-Label k-Nearest Neighbor (ML-KNN) are adopted to transform Multi-NCDs to disease label. The experiments are conducted on the physical examination datasets collected from electronic health records. In the experiments, the comparative results of the techniques are demonstrated. The result showed that the RAkEL method outperformed other methods and achieved the best accuracy of 91.07%.
A conceptual framework for AI system development and sustainable social equality

Artificial intelligence (AI) technology has been used for some years and is growing rapidly. We are living in a world where AI has been involved in many different ways; from helping us to perform online search, shopping on the internet, customer service over internet, medical research, advices over banking activities, advices for legal matters, or to determine different stages of our life, even since when a baby is born. AI has also been significantly having a strong impact on the way we conduct business; for example, customer analysis, product research, trend analysis, making price policy, and recruiting process. However, awareness levels among end-users is still low. Authorities and industries are still looking for possibilities to regulate, optimise and harmonise negative issues that have been raised. In addition to just following general policies, developers and companies also need to take ethical issues into consideration in order to build trustworthy AI powered systems. This paper is aiming to design a conceptual framework by seeking possibilities in/among known debates, issues, theories, policies, dilemmas, and with personal view, instead of finding general solutions. The opinion of this paper does not necessarily reflect the views of the organisation.
Sarcasm Detection Using Role Pairs: Bootstrapping Role Pairs and Weighting

Sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Sarcasm detection is a significant challenge for sentiment analysis because sarcasm involves a positive expression with negative meaning. Previous work focused on relation information between pairs of role expressions, such as “boss and staff,” and proposed a sarcasm detection method based on surface and relation information. Although they showed the effectiveness of the relation information, due to the small scale of the list of role pairs, the effectiveness was limited to a small scale. In this paper, we attempt to improve the performance of the sarcasm detection method. We propose a role pair extraction method using a bootstrap method to obtain a larger role pair list than the previous work. In addition, we propose weighting methods for each role pair. Then, we evaluate the method using our role pair list and weighting methods by comparing the method using the list of the previous work. We find that we can obtain approximately threefold increase for role pairs. Although the effectiveness is limited, the experimental result shows a potential benefit because the method with topic similarity can reduce the influence of such noise pairs.
3D real-time path planning of UAVs in dynamic environments

Unmanned Aerial Vehicles (UAVs) are taking active roles in personal, commercial, industrial and military applications due to their efficiency, availability and low-cost.UAVs must operate safely and in real-time in both static and dynamic environments.An extensive literature review, defines the dynamic environment term, the need for dynamic path planning and reviews different solutions.This paper presents a 3D real-time path planning platform to assess the performance of the A* and RRT algorithms.Four test scenarios with varying difficulty are constructed consisting of V-obstacles, cubes and 2D planes moving at time-varying speed, direction and orientation.Two rationales to either wait or move further in the direction of the goal when an intermediate goal point is not available are considered.Results show that for both A* and RRT the moving variant case performs better especially in complex scenarios.RRT performs better in simple scenarios and complex scenarios at low speed while A* performs better at high speeds in complex scenarios.A success rate of over 95% is recorded for three scenarios for all considered speeds and for both algorithm.
Statistical Tests Ensemble Drift Detector

Several classifiers use supervised inductive learning and, to improve accuracy, they are often combined with concept drift detectors. The ideal learning algorithm associates robustness to noise with sensitivity to concept drifts. Motivated by these statements, this paper proposes a concept drift detector aiming to validate empirically the idea of implementing a drift detection method based on the combination of statistical tests as a viable option to improve the classification. The Statistical Tests Ensemble Detector (STED) uses the results of Brown-Forsythe, O'Brien, and ANOVA statistical tests combined by two voting strategies. To signal warnings, the majority vote is used with the results of the three tests, and, to detect concept drifts, the “Earlyfind-early-report” rule is adopted with the results of the Brown-Forsythe and O'Brien tests only. The experiments' results using Hoeffding Tree (HT) as base learner in 24 artificial and seven real-world datasets corroborates the efficiency of this proposal: STED achieved the best accuracies and was balanced in the detections of drifts, based on its second position in the evaluation using the Matthews Correlation Coefficient (MCC).
A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor networks.Quantum computing, theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or communication.In this paper, we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model, which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with iterations.The proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional complexity.Moreover, a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic feature.Sufficient simulations present the correctness, validity and efficiency of the proposed estimation method.
Blockchain Technology as a Tool to Manage Digital Identity: A Conceptual Study

In recent times, one of the most noticeable developments is of digital identity. Despite its origin in finance, several applications have found digital identification helpful in different ways. Digital identity has been growing quickly. In the near future, individuals around the world shall be concerned with managing their digital identities. This study shows how blockchain technology plays a significant role in digital identity management systems. The shortcomings and weaknesses of different literature are reviewed, and the benefits that blockchain provides for achieving an effective identity management system. This paper will discuss the importance of data privacy in various applications and identify the scope of blockchain technology in digital identity management. Blockchains eliminate the role of middlemen along with reducing the wait lines required for authentication, attestation, and authorization. People are allowed to choose whether to reveal or not to reveal.
Naïve bayes and maximum entropy comparison for translated novel’s genre classification

Abstract In the last two decades, novel translation had become one of the popular products among the literature community. People had favorited some genre based on their ages. The reader needs to finish reading until the end first before they could determine what genre a novel should have. There were some cases where the genre written in the description differs from the real novel’s content, which made readers felt upset and had not pleasant reading experience. This research is going to do classification for the novel’s genre automatically. Naïve Bayes is the method chosen for classification, later the result of Naïve Bayes classification is going to be compared with another algorithm, which is Maximum Entropy algorithm. Each method would apply algorithms to label the data based on an existing class. The data origin was taken from 12 translated novel that has 3746 lines. Data was portioned into three genre classes, “Action-Fantasy” for about 1293 lines, “Modern-Slice-of-Life” for 1203 lines, and 1250 for “Other”. Evaluation of the two models, Naïve Bayes and MaxEnt, would be using confusion matrix that generated the highest number precision, recall, and F-score which values were 77,52%; 75,59%; and 77,55% for the Naïve Bayes method, and 78,11%; 83,82%; and 75,81% for MaxEnt method. The value of accuracy were 72,72% for Naïve Bayes, and 71,86% for MaxEnt. Both methods showed that the genre “Action-Fantasy” was the correct genre for almost every novel among 12 novels listed.
MERLIN: Multi-Language Web Vulnerability Detection

Although there is continuous research to improve web security, web applications are constantly being attacked due to vulnerable source code. A common way used to find vulnerabilities in code is with source code static analysis tools. However, these tools have two problems: they must be coded manually to deal with all types of vulnerabilities and they only work with a specific programming language. This paper presents an approach that aims to improve security of web applications by identifying vulnerabilities in code written in different languages. Moreover, we do not hard-code the rules of detection, but instead use machine learning to configure them. The approach was implemented in a tool called MERLIN. This tool was tested with samples from the SRD database and real-world web applications written in Java and PHP.
State-of-the-art in Privacy Preservation for Enterprise Data

Privacy preservation is at the heart of Artificial Intelligence and Machine Learning practice to make it widely adopted across the Enterprise applications. Data privacy forms the primary requirement for accessing data of individuals. Laws are being formulated and in effect across different geographies to make privacy a significant element in designing further strategies of data management by different companies. Practices of privacy protection and privacy preservation are evolving to provide more reliable and easily consumable methods for managing data usage. This paper presents a consolidated view of the latest practices across the enterprise and highlights the challenges faced. A brief investigation on available technologies is also presented with critique on vital elements to meet expectations from Enterprise and the regulators.
Energy Management in Data Centers from Design to Operations and Maintenance

Data centers are the information factories of the digital evolution. Creating, storing, processing, distributing, and analyzing data all need energy. Therefore, data center industry consumes energy more than 2 percent of the global electricity consumption. Energy efficiency need to discuss at the outset of data center design. The root cause of oversizing data center design is the research question because this will affect investment or CAPEX and long-term operating costs or OPEX of data center as long as data center life cycle (DCLC). Data center measurement in power usage effectiveness (PUE) unit helps data center owners and consultants realized on relationship between oversizing data center design and total cost of ownership (TCO). The research results propose modular data center as a solution to handle uncertainty demand of IT equipment, scalability for growth as your need, flexibility in any size of infrastructure, fast deployment because of prefabricated design, and more efficiency by applying energy management platform called data center infrastructure management (DCIM).
Enhancements on the Efficacy of Firefighting Robots Through Team Allocation and Path-Planning

Firefighting robots with the ability to detect and extinguish fires are extremely helpful in saving lives and preventing damage. Various methods to enhance the efficacy of firefighting robots are discussed in this paper. An algorithm to find the minimum distance that the robot should maintain from the fire based on its size and intensity is proposed. The use of path-planning algorithms such as probabilistic roadmaps (PRMs) is explored so that the robots can operate autonomously and the shortest path to the destination can be found as quickly as possible. The path length and computational time taken to create a PRM with a different number of nodes are compared to choose a suitable number of nodes to create the PRM. The path length and computational time taken by PRM to find the shortest path for different start and end positions in a single-robot and multiple-robot situations are tabulated and presented in this paper. From the comparative analyses, the best possible option to enhance the efficiency of the fire-extinguishing process has been identified. An enhanced algorithm that can be used to allocate the total number of available robots into teams of different sizes to target different fires is also implemented and validated.
The Deep Web and Children Cyber Exploitation

The deep web has been announced by some as the last bastion of internet anonymity in an increasingly oppressive period, although others find it one of the worst sites on the internet. There are number of instances that have confirmed the misuse of deep web for conducting criminal and illegal practices in a secret way. This chapter provides a description of deep web and the different plugins used to navigate deep web pages. A summary of the different types of online child sexual exploitation that take place across the deep web is explored, as well as the complexities of investigation and response being discussed so that readers can become conscious of this form of sexual abuse.
Knowledge-Base Optimization to Reduce the Response Time of Bangla Chatbot

Chatbots have been very popular in recent years for being able to serve as a customer representative, a language learner and so forth. Long short-term memory abbreviated as LSTM is a ubiquitous artificial recurrent neural network that is frequently being used for the chatbot. Nevertheless, if a user makes the line break of sequence, then it is rare to inform the right information without the impact of the previous sequence. As a result, in case of a help desk chatbot, LSTM is not the best option for taking steps of the right information. On the other hand, mathematical and statistical procedures are prominently useful for providing the proper knowledge without having back the impact of sequence. Still, it takes more execution time to respond. The goal of this paper is to present the optimal chatbot for the lowest execution time and three mathematical and statistical strategies for Bangla Intelligence chatbot in light of information obtained from Noakhali Science and Technology University (NSTU). As the procedures, we have followed cosine similarity, Jaccard similarity, and Naive Bayes classifier. To reduce the response time, we decorated the whole path into a 3-depth tree, such as a question, topic, and answer. We have compared the performance of the selected strategies where the best accuracy was 93.22% using the cosine similarity. Contribution-This paper presents techniques to reduce the response time of statistical and mathematical Bangla Chatbot.