Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Textual Tweets using Ensemble Classifier (LSTM-GRU)

Sentiment analysis has become increasingly difficult due to the rise of social media. Product reviews, trends, and political commentary can all be found on Twitter. Twitter may be used to assess public sentiment toward current events, legislation, social movements and public figures, as well as the attitudes, feelings and opinions of various individuals. Opinion mining can be accomplished without manually reading tweets using Machine Learning and Deep Learning models. This data can be used to launch new policies, products, and events by governments and corporations alike. When it comes to categorising tweets according to their emotional content, there are a number of models that have been developed for classifying tweets as love, joy, sad, anger, fear, anxiety. The proposed Ensemble Classifier (LSTM-GRU) was determined to be the most effective after a thorough evaluation of its performance.
Estimation of Remaining Useful Life(RUL) of BLDC Motor using Machine Learning Approaches

The remaining useful life (RUL) is the capability of the machine to function before a replacement or repair is required. Estimation of RUL is considered to be important in predictive maintenance because it helps in optimizing the operating efficiency and to avoid unplanned downtime. The proposed project is based on the detection and calculation of RUL life of BLDC motor. For calculating the remaining useful life of BLDC motor parameters like temperature, current and voltage are measured from the sensors connected to the ARM7LPC2148 board and the values are given as data sets to the machine learning algorithm. The obtained data sets are trained using Machine learning algorithms like K-Nearest Neighbor(KNN), Gaussian Naïve Bayes,Random forest and Support vector machine(SVM).The main idea of the proposed project is that it can be used in the estimation of RUL in real time and they are also independent of external factors like temperature etc., Experimental results indicate that Random Forest algorithm is found to be efficient in terms of metrices like accuracy, precision, F1 score and recall. And hence RUL is estimated from random forest algorithm and the accuracy of the algorithm is 88%.
Resource-Efficient Pareto-Optimal Green Scheduler Architecture

Rapidly developing cloud technology with enormous number of clients creates need of reducing power consumption of data centers. VM live migration is the most promising tool to achieve resource consolidation but it creates overheads in terms of additional CPU, disk I/O and network bandwidth utilization. This paper proposes a power-aware VM live migration based dynamic VM consolidation mechanism that focuses on reduction in datacenter’s resource utilization. Proposed mechanism is Pareto Optimal because during live migration it not only optimize the migration overheads but also select the VM and destination server by considering all the performance overheads to be generated during and after live migration. The proposed algorithm reduces nearly 60% of the VMs migration overheads. In terms of energy saving the proposed mechanism is 43% more efficient than the greedy scheduling approach and about 47% more energy efficient than the round-robin approach and thus achieves green computing goal.
Rapid Prototyping of Business Rule-Based Systems with Controlled Natural Language and Semantic Web Software

We present methods for applying controlled natural language (CNL) and Semantic Web user interfaces to make rapid prototypes of business rule-based systems. This helps system developers quickly demonstrate design choices to stakeholders. CNLs provide a balance between understandability for laypersons and unambiguous specificity for programmers. Executable CNLs generate rudimentary systems automatically and thus quickly, which suits rapid prototyping. The Semantic Web provides a wide range of free and open tools for standardized forms of data representation and logic, whose potential application includes business rules. We identify several general categories of business rules that each map to a core form of Semantic Web logic that can implement it. This work then presents prototyping methods that apply these mappings. Different Semantic Web software interface components demonstrate different aspects of each form of logic. The Semantic Web ontology editor Protégé illustrates our methods, but any equivalent Semantic Web software can apply. Example rapid prototypes in Protégé from the frequently used fictional business rules case EU-Rent serve here as illustrative scenarios that evaluate our methods.
Alternating Fixpoint Operator for Hybrid MKNF Knowledge Bases as an Approximator of AFT

Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various types of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. [8] for the study of well-founded semantics for hybrid MKNF knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the power of abstraction of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.’s construction. This leads to an improved computation for the well-founded semantics.
RFID analysis of the complexity of cellular pathology workflow—An opportunity for digital pathology

Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2-3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.
Cyber threats: taxonomy, impact, policies, and way forward

The continuous evolution and proliferation of computer technology and our increasing dependence on computer technology have created a new class of threats: "cyber threats."These threats can be defined as activities that can undermine a society's ability to maintain internal or external order while using information technology.Cyber threats can be mainly divided into two categories, namely cyber-terrorism and cyber-warfare.A variety of malware programs are often used as a primary weapon in these cyber threats.A significant amount of research work has been published covering different aspects of cyber threats, their countermeasures, and the policy-making for cyber laws.This article aims to review the research conducted in various important aspects of cyber threats and provides synthesized information regarding the fundamentals of cyber threats; discusses the countermeasures for such threats; provides relevant details of high-profile cyber-attacks; discusses the developments in global policymaking for cyber laws, and lastly presents promising future directions in this area.
Keyword Error Detection on Product Title Data Using Approximate Retrieval and Word2Vec

In the past two decades, the word error detection problem in documents has attracted the attention of many researchers and is an important problem in the field of data science and natural language processing, with many practical applications. This paper presents how to solve a problem of its class. Given a set of product titles, for each product title, we need to identify related keywords to the product brands and determine if this keyword is faulty or not. We will propose a method containing two main stages, the first fast approximation retrieval method is used to identify raw error keyword candidates, and then the most potential candidates are selected by a ranking algorithm and are verified as keyword errors or not by the word2vec model. We conduct experiments on dataset consists of 10,000 product titles, the obtained results have shown the effectiveness of the proposed method.
Text image secret sharing with hiding based on color feature

The Secret Sharing is a scheme for sharing data into n pieces using (k, n) threshold method.Secret Sharing becomes an efficient method to ensure secure data transmission.Some visual cryptography techniques don't guarantee security transmission because the secret information can be retrieved if the hackers obtain the number of shares.This study present a secret sharing method with hiding based on YCbCr color space.The proposed method is based on hiding the secret text file or image into a number of the cover image.The proposed method passes through three main steps: the first is to convert the secret text file or image and all cover images from RGB to YCbCr, the second step is to convert each color band to binary vector, then divide this band in the secret image into four-part, each part is appended with a binary vector of each cover image in variable locations, the third step is converting the color space from YCbCr to RGB color space and the generated shares, hidden with covers, are ready for transmission over the network.Even if the hackers get a piece of data or even all, they cannot retrieve the whole picture because they do not know where to hide the information.The results of the proposed scheme guarantee sending and receiving data of any length.The proposed method provides more security and reliability when compared with others.It hides an image of size (234x192) pixels with four covers.The MSE result is 3.12 and PSNR is 43.74.The proposed method shows good results, where the correlation between secret and retrieved images is strong ranging from (0.96 to 0.99).In the proposed method the reconstructed image quality is good, where original and reconstructed images Entropy are 7.224, 7.374 respectively.
Optimal population size of particle swarm optimization for photovoltaic systems under partial shading condition

&lt;span&gt;Particle swarm optimization (PSO) is the most widely used soft computing algorithm in photovoltaic systems to address partial shading conditions (PSC). The success of the PSO run heavily depends on the initial population size (NP). A higher NP increases the probability of a global peak (GP) solution, but at the expense of a longer convergence time. To find the optimal value of NP, a trade-off is typically made between a high success rate and a reasonable convergence time. The most used trade-off method is a trial-and-error approach that lacks explicit guidelines and empirical evidence from detailed analysis, which can affect data reproducibility when different systems are used. Hence, this study proposes an empirical trade-off method based on the performance index (PI) indicator, which takes into account the weighted importance of success rate and convergence time. Furthermore, the impact of NP on achieving a successful PSO was empirically investigated, with the PSO tested with 16 different NPs ranging from 3 to 50, and 10,000 independent runs on various PSC problems. Overall, this study found that the best NP to use was 25, which had the best average PI value of 0.9373 for solving all PSC problems under consideration.&lt;/span&gt;
The Phi Angle: A Theoretical Essay on Sense of Presence, Human Factors, and Performance in Virtual Reality

Abstract The question of the relationship between the sense of presence and performance in virtual reality is fundamental for anyone wishing to use the tool methodologically. Indeed, if the sense of presence can modify performance per se, then individual factors affecting the human–computer interaction might have repercussions on performance, despite being unrelated to it. After a discussion on the sense of presence and the particularities it provokes, this work studies the psychophysiology of virtual reality. This in virtuo experience is understood according to a constitutive and reciprocal relationship with the subject's cognitive profile, made up of all the human, contextual, and motivational factors impacting the processing of immersion. The role and importance of performance in virtual reality is described in this framework in such a way as to be studied methodologically. The presence–performance relationship is discussed based on previous works and analyzed in terms of attentional resources. Finally, the degree of ecological validity of the performance is described as the factor modulating the relationship between the sense of presence and performance (the Phi Angle). Limitations, applications, and test hypotheses of the model are presented. This work not only aims to help explain the conceptualization of virtual reality, but also to improve its methodological framework.
Study on collision avoidance strategy of multiple UAVs based on genetic algorithm

To address the problem that the number of UAVs is increasing dramatically and the collision risk is increasing dramatically, autonomous collision avoidance and collision avoidance path optimization for conflict risk among multiple UAVs by using genetic algorithms. Under the performance constraints of UAVs, genetic algorithm can realize autonomous collision avoidance of multiple UAVs by using adaptive crossover probability and adaptive mutation probability, and an adaptation function based on the shortest collision avoidance path length and the lowest collision risk. The results of the three-UAVs collision avoidance simulation show that the genetic algorithm can prevent the occurrence of chain collisions while solving the current flight conflict problem and guarantee the safety of UAV flight.
Performance Analysis of the Hybrid Voting Method on the Classification of the Number of Cases of Dengue Fever

Dengue hemorrhagic fever (DHF) is a health problem in Indonesia. The region in Indonesia that has the highest number of cases in West Java with the highest ranking with 10,772 cases. The city of Bandung is recorded to have the highest number of cases at this time, namely 4,424 cases. Dengue fever can be caused by high rainfall. Judging from the high number of cases and fluctuations that occur, it is necessary to predict the spread of the disease so that in the future it can be anticipated by the government. Prediction of the spread of dengue fever in the city of Bandung using various classification algorithms has been done. Therefore, the author wants to make a new breakthrough by using hybrid ensemble learning using a hard voting method from three classification methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Using the Bandung City DHF disease dataset from 2012 to 2018. The results obtained using the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) were 84%, 87%, 79%. to improve the classification accuracy of the three methods using a hybrid classification with the hard voting method to get 91% results.
Exploring the Acceptance of Augmented Reality-Based Educational Games

Recent studies have integrated educational games with augmented reality (AR) to develop learner motivation. However, few reviews focused on the acceptance of AR-based educational games. This review aims to provide comprehensive insight into the acceptance of AR-based educational games. This review followed the selection process of the preferred reporting items for systematic review and meta-analysis (PRISMA), including nine peer-reviewed articles published from January 2019 to March 2022. It is concluded that studies preferred to recruit students to play AR-based educational games via handhelds and used questionnaires to understand student acceptance. Technology acceptance model was the most frequently used model. Perceived usefulness and perceived ease of use were two dominant factors influencing student acceptance of AR-based educational games. In the future, game designers should integrate target learning knowledge in designing AR. Future studies should focus on the effect of socio-cultural constructs on different participants' acceptance of AR-based educational games.
The effects of principal instructional leadership, collective teacher efficacy and teacher role on teacher self-efficacy: A moderated mediation examination

This study investigated the moderating effect of teacher role between principal instructional leadership and teacher self-efficacy relationships.A random sample was selected from 120 teachers in a private school in Nakhonsawan province, Thailand.The data were analyzed based on Regression Path Model using PROCESS macro version 3.5.The results showed that the principal instructional leadership affected teacher self-efficacy via collective teacher efficacy moderated by teacher role (1 = managerial role, 0 = non-managerial role).For teachers with a managerial role, the indirect effect was 0.481.For teachers with a non-managerial role, the indirect effect was 0.310.The moderating effect in the model was 0.376.All independent variables explained the variance in teacher self-efficacy, accounting for 63.2 percent.The results enhance the theoretical knowledge and empirical evidence regarding the relationship between principal instructional leadership and teacher self-efficacy, emphasizing the moderated mediating effect of teacher role.Moreover, the study contributes to school strategy in terms of enhancing teacher performance in private school contexts.
The innovative way of development of pedagogical sciences: a modern look at the study of current problems

The article aims to identify the main innovative changes taking place in modern pedagogical sciences. The methods used to obtain the expected results: analysis of scientific sources, comparative analysis of the processed literature and its generalization, description of factual information, the method of synergy, and identification of the highest priority further innovative ways of transformation of the complex pedagogical sciences in modern conditions. As a result, the vectors of development of individual pedagogical sciences are outlined and the pedagogical conditions necessary for innovative approaches are indicated.
Detecting Data Exfiltration over Encrypted DNS

Data breaches linked to individual and company information are exposed on an almost daily basis. With increasing media attention and visibility of this security issue, users are becoming more aware of privacy concerns related to their activity on the Internet. Fundamental to the operation of the Internet is the Domain Name System (DNS), which translates domain names to IP addresses enabling easy web browsing. Encrypted DNS has become popular to increase user privacy by ensuring that activity transmitted over domain queries is not visible to intermediary network devices between the client and the DNS endpoint. Unfortunately, this undermines the security services designed to analyse DNS traffic for the detection of exploitation of DNS for use as a covert communication and data exfiltration channel. In this work, we propose a solution, DoHxP, to enable protection of DNS over HTTPS (DoH) traffic from data exfiltration without compromising user privacy. Our results show that DoHxP successfully prevents up to 99.88% of the malicious DoH traffic from being transmitted outside of the network.
A Smart Home Based on Multi-heterogeneous Robots and Sensor Networks for Elderly Care

To tackle behavioral assistance for elderly care in daily life, our laboratory rollout seven homecare robots: walking support robot, gait rehabilitation robot, intelligent wheelchair robot, excretory support robot, personal care robot, intelligent bed, and transport robot. By integrating the multi-heterogeneous robot systems (MHRS) and distributed sensor networks, we propose a novel smart home for elderly care that can cover most life behaviors. Furthermore, to realize that the MHRS can efficiently serve multi-user within the architecture of the smart home. A self-organizing MHRS architecture is proposed. This architecture combines specific tasks to establish a robot group communication mechanism, and multi-robot in the group can complete adaptive control according to the user’s real-time position, ensuring assist safety and adaptability. Finally, we conducted experiments in the proposed smart home to rising transfer, standing transfer, and behavior assistance. The experiments show the proposed smart home has the auxiliary capabilities for basic activities of daily living (ADLs), instrumental activities of daily living (IADLs), enhanced activities of daily living (EADLs) even continuous assistance in safety and comfortable way, which can be used in homes, hospitals, rehabilitation center and other scenes for elderly care.
The contribution of different face parts to deep face recognition

The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.