Reviewerly/exHarmony huggingface dataset:
Electronic Wheelchair With Eye Motion Control For Physically Hectic People

A new process is executed for the eye control based independent and Cost-efficient system. The plan of eye movement controlled electronic wheelchair is to get rid of necessity of the help needed for the disabled person. In order to help the differently abled people, numerous types of interfaces have been advanced for electric wheelchair control such as joysticks controlled, head movement controlled, voice controlled etc., many people with disabilities may find it difficult to operate the chair using the above-mentioned interfaces. It is an idea to make lives of the disabled people easier i.e., independent, which will thereby increase their confidence to move from one place to another. The web camera mounted in front of the user will capture the image of the eye movement. The eye movements as prepared using python open CV and the command are sent to the Node MCUs which is programmed using Arduino IDE, which controls the direction of the wheelchair. Therefore, we have achieved more than 94 % of accuracy in determining the eye direction.
Performance Face Image Quality Assessment under the Difference of Illumination Directions in Face Recognition System using FaceQnet, SDD-FIQA, and SER-FIQ

The Face Recognition system has a challenge when the conditions of the input images have differences in quality from the images that have been enrolled in the database. One of the causes is the variation in lighting that causes illumination in image. We used images with normal lighting, as well as four images that have variations in the lighting/illumination directions. Face Image Quality Assessment (FIQA) helps the face recognition system to ensure the optimum captured image quality for enrollment and verification process. We use both supervised (FaceQnet) and unsupervised (SDD-FIQA, SER-FIQ) FIQA method against the Asian Face Image dataset. The result shows that filtering images using FIQA method can reduce FNMR by 58.89% in matching images whose light direction is from below. Images with type 2 illumination, where an image whose light comes from below matched with normal image, gave the lowest result in FRR compared to other types of illumination when tested with 3 FIQA methods.
An Empirical Analysis of Knowledge Overlapping from Big Vocabulary in Biodiversity Domain

In advancing scientific discovery, the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles have been widely adopted for managing digital assets such as data, algorithms, tools, or workflows. In addition, the principles stimulate the adoption of domain-related community standards for data sharing. Unfortunately, the stimulation also gives rise to Big Vocabulary, where multiple standardized vocabularies (i.e., ontologies) have been developed across domains. For example, more than a thousand ontologies are available for biological and biomedical sciences. Moreover, since the ontology developments were performed distributively, there is a high possibility for overlapped knowledge among those ontologies. This work analyzed the overlapped knowledge represented by multiple ontologies related to the biodiversity domain. The analysis was conducted by aligning fields from a biodiversity database to the available terms across multiple ontologies such that the scores for mapped, overlap, and coverage can be computed. Based on the findings, the score of overlapping knowledge is up to 27%, where a single ontology can represent at most 53% of fields. As an implication, when sharing data of a specific case, it is required to integrate multiple ontologies and extend it.
Application of Bayes’ Theorem in an Expert System for Diagnosing Mangosteen Diseases and Pests

Mangosteen is one of the typical fruits of Southeast Asia and Indonesia's flagship fruit with promising export prospects. However, the poor quality of the mangosteen fruit, which is caused by diseases and pest attacks, is an obstacle in increasing export targets. Disease and pest attacks can be immediately overcome if farmers can identify the types of pests and diseases quickly based on the symptoms that appear on the mangosteen plant. The lack of knowledge of farmers in identifying the symptoms that appear and the limited number of experts lead to the late process of handling pests and diseases of the mangosteen plant. Therefore, this study aimed to build an expert system for diagnosing mangosteen diseases and pests using Bayes' theorem. The knowledge base contained data of 5 diseases, 5 pests, and 42 symptoms. The inference engine utilized the Bayes' theorem to produce a probability value of a disease or pest attacking the mangosteen plant based on the symptoms that arise. This study yielded an accuracy value of 94% based on 50 test data. This expert system can help farmers in identifying the diseases and pests that attack mangosteen plants quickly as well as finding solutions to the problem.
A Knowledge Graph Exploration Method with No Prior Knowledge

Various sectors now widely adopt knowledge graphs to describe and share their organizational knowledge bases. Unfortunately, the majority of knowledge-sharing systems are designed for domain experts. Making it extremely difficult for a non-expert to understand the content and explore the graph. A solution to this issue is using a machine-assisted knowledge graph exploration approach. This research introduces a knowledge exploration method to systematically and efficiently navigate a knowledge graph. First, we modeled the knowledge graphs based on the existing common schema. Second, we created a search tree technique to navigate the knowledge graph efficiently. The algorithm solves the problem by determining the path of knowledge graph exploration. We evaluated the method using a knowledge base of morphological characteristics of Capsicum. The goal of graph exploration was to identify a Capsicum species correctly. As a result, the proposed mechanism can achieve high precision, even when the search’s starting point is unknown beforehand.
Sentiment Analysis of Indonesian New Capitol (IKN) Tweets by Stacked Generalization of Deep Learning

The increasing use of Twitter for conveying the general public's sentiment toward a specific public policy generates pros and cons and has led to much research in sentiment analysis. Instead of exploring the most suitable classifier for a sentiment analysis model individually, there is a trend of employing an ensemble of classifiers to improve the accuracy and performance of the model. We proposed a model, initially by training word embedding using word2vec from 12.5K Indonesian Twitter on the relocation issue of the new capitol city of Indonesia (IKN) and by utilizing CNN, Bidirectional LSTM, and MLP as the base classifiers. Finally, we performed a stack generalization ensemble technique using MLP and LR as the meta-classifiers and compared the performance of the ensemble techniques with individual base classifiers. The base classifiers take advantage of the weights the word embedding provides to do the learning process. The results show that the stacking ensemble using MLP performs slightly better than LR as the meta-classifier, with the F-1 score of 74.65% vs. 73.78%, respectively. MLP meta-classifiers also perform somewhat better than the hard and soft majority voting ensemble with difference F-1 scores of 3.75% and 2.56%, respectively. The results show that the proposed stacked generalization technique model has improved the performance of the sentiment analysis model.
A 13 GHz PA with Amplitude Modulation for Entanglement Generation in Multi-Qubit <sup>171</sup>Yb<sup>+</sup> Gates of an Ion-Trapped Quantum Computer

This work presents a power amplifier (PA) for microwave-driven entanglement operation in ion-trap-based Ytterbium qubit gates. The design is optimized for a center frequency of 13 GHz with a 3 dB bandwidth of 6 GHz thereby fulfilling the requirements to drive the 12.6 GHz hyperfine transisiton in the electronic ground state of a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">171</sup> Yb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ion. The PA achieves a saturated output power of 14 dBm and a gain of 20 dB at the center frequency. Due to a tunable operating point it is possible to realize a power-efficient amplitude modulation allowing for a qubit-gate fidelity improvement. The presented circuit achieves a measured power-added-efficiency (PAE) of 28 %. Due to maintaining an electrically stable operation during operation-point changes, this circuit is highly suitable for analyzing the effect of various pulse envelopes on the gate fidelity. The proposed circuit is silicon-proven and fabricated in a 0.13 µm SiGe BiCMOS process covering a core area of only 0.14 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile; this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that; the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.
Shot Boundary Detection from Lecture Video Sequences Using Histogram of Oriented Gradients and Radiometric Correlation

In this chapter, we put forward a new technique for lecture video segmentation and key frame extraction. In this chapter, the advantages of Histogram of Oriented Gradients (HOG) features and radiometric correlation with entropic measures are explored to detect the shot boundaries and the key frames of the lecture video sequences. In the initial stage of the algorithm, HOG feature is used to project all frames into an n-dimensional feature space. The similarities between the n-dimensional extracted HOG features for two consecutive frames are obtained using radiometric correlation measure. The radiometric correlation between the successive frames of the video is found to have a significant amount of uncertainty, due to variation in color, illumination, or object motion. We have used entropic measure to find the shot boundaries. The key frames are obtained after detection of the shot boundaries by analyzing the peaks and valleys of the radiometric correlation measures. The proposed scheme is tested on several lecture video sequences and compared against six existing state-of-the-art techniques by considering two evaluation measures: computational time and shot transitions.
A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques

Video summarization or highlights from long-hour sport video have been appreciated as one of the interesting and challenging techniques. Generally, the viewers of sports would be interested to have short summary of video. There are some interesting methods published in the literature addressing the issues on automatic sports video summarization. In this chapter, a systematic review on existing video summarization techniques is discussed by focusing on various algorithms and methods categorized under common ideas such as boundary shot detection, players/crowd/umpire shot classification and identification, key events detection, replay, strokes, commercials and play breaks-based detection, event, text, and excitement-based summarizations. The intention of the chapter is to recapitulate decades of development in sports video summarization for the benefit of the prospective researchers and exhibit future avenues to strengthen the outcome of video summarization techniques.
Evaluation of Autonomous Navigational Accuracy for Different Reference Points in PurePursuit Algorithm for Center-Steered Articulated Vehicles

Accurate autonomous navigation for off-terrain utility vehicles with no human intervention is an essential requirement to achieve full automation. Within several applications, though higher autonomous navigational accuracy (almost ±2.5 cm) has been achieved in some commercially available vehicles yet requirements for human intervention is still very much required in several situations. This study investigates autonomous navigational accuracy of PurePursuit algorithm for different reference points for a non-steerable-wheels center-steered articulated vehicle. PurePursuit algorithm is preferable choice for path tracking for its simplicity and for vehicles where high speed is not a requirement. Evaluation of PurePursuit algorithm for utility vehicles of type studied in this paper is somewhat less explored area. We have compared the autonomous navigational accuracy of PurePursuit algorithm for different reference points for a set of different path complexities that also includes vehicles kinematic constraints in simulation environment. Average lateral and heading deviations were calculated for a set of different path complexities, and it was found that in general proposed reference point for PurePursuit algorithm for center-steer articulated vehicle shows better lateral and heading autonomous navigational accuracy than the traditional PurePursuit algorithm.
Comprehensive analysis of tropical rooftop PV project: A case study in nanning

Solar photovoltaic (PV) is favored by the market because of its clean and renewable characteristics. There are abundant solar resources in the tropical regions of China. It is important and necessary to carry out comprehensive analysis of rooftop PV projects for tropical regions for scientific policy-makings. Here, we select Nanning as a case study to analyze the optimal options for PV installation on different roof types and estimate the electricity generation potential of rooftop PVs and its additional returns. Our analysis shows that: 1) the annual optimal azimuth and tilt angle in Nanning are 245° and 32.5°, respectively; 2) the tilt angle in southwest orientation has more space for adjustment, while that in the opposite orientation should remain horizontal; 3) for flat roofs, being fixed at the annual optimal angles is practical, while for gable roofs, the east-west direction is favorable; 4) the total potential of rooftop PV projects in Nanning can reach 19.99 TWh/year, resolving 76.1% of the social electricity demand. This research is valuable for rooftop PV installation and optimization in the tropical regions of China, which also could provide reference for other regions.
A design of movie script generation based on natural language processing by optimized ensemble deep learning with heuristic algorithm

Movies offer users a huge range of visual information like attractive stories. The traditional approaches have demonstrated that knowing about movie stories via only visual information is complicated. The natural data of graphical texts play a significant task in portraying information in various domains like entertainment, education, communication, etc. Similarly, script generation by considering the earlier dialogue is more complex owing to the inherent nature of the text. Text identification in natural data considers script identification that needs localization of text. Especially for natural scene data, script generation is a complicated task due to different background/foreground components. The texts comprise varied textures, variations in size, orientation, colors, and fonts. It is essential to have a system that produces scripts or stories from a storyline automatically. However, this research is not emerging nowadays. While considering the dialogue systems would also help drive dialogues through a dialogue plan. Hence, a new movie script generation model is suggested through processing the movie text data. Initially, the text data is collected related to a different movie that consists of characters, scenes, genre, location, etc. Secondly, the data pre-processing is carried out to enhance data quality. Further, the significant features are extracted from the pre-processed data through Term Frequency-Inverse Document Frequency (TFIDF) and word2vector. The deep features are extracted to get the noteworthy features using a Deep Belief Network (DBN) from RBN layers. Finally, the deep features are given to the Ensemble-based Movie Scrip Generation (EMCG), where the Optimized hybrid script generation process using ensemble learning is performed by Bidirectional Long Short-Term Memory (Bi-LSTM), Generative Pre-Trained Transformer version 3 (GPT3), and GPT Neo X models, where the parameters of deep learning algorithms are optimized using the Adaptively Improved Cat and Mouse-based Optimizer (AI-CMO) algorithm. Here, the outcomes are attained through taking averaging among the classified outcomes. The standard performance measures are used for evaluating the effectiveness of the proposed method.
Multi-targeted audio adversarial example for use against speech recognition systems

Deep neural networks are widely used in fields such as image recognition, speech recognition, text recognition, and pattern recognition. However, such networks exhibit weakness against adversarial examples. An adversarial example is a sample that is correctly recognized by humans but is misclassified by the target model; it is typically created by adding a minimal amount of noise to an original sample. In this paper, we propose a method for creating a multi-targeted audio adversarial example that is designed to be misinterpreted differently by each of several models. The proposed method configures the loss function so that the probability of misclassification into the desired class for each model is highest, in order to insert the optimal amount of adversarial noise into the original sample. In the experimental evaluation, the Mozilla Voice dataset was used as the test data source, and TensorFlow was used as the machine learning library. The results show that the proposed method creates a multi-targeted adversarial example that has a 98.02% attack success rate with different classes for the three models while minimizing the distortion from the original sample to an average value of 137.28.
A Non-Revisiting Equilibrium Optimizer Algorithm

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.
An Ultra Lightweight Cipher Algorithm For IoT Devices and Unmanned Aerial Vehicles

Drones and the Internet of Things (IoT) have become of paramount importance to individuals, entities, organizations, and states due to the benefits they provide in daily life. Most of the consumer devices around us have become connected to the Internet, no matter how small they are, including smart home applications, automatic sensors, portable medical devices, implants inside the human body, etc. The connection of these devices to the Internet has opened the way for many intrusions, either to steal data or cause harm to humans. This research suggests a method to protect this data from being exposed to security breaches by proposing a new lightweight encryption algorithm. The proposed algorithm presents new ways to encrypt data by exploiting the different faces of the characters in ASCII code, where the text is converted into four different forms, including binary, hexadecimal, characters, and digits. Each of these forms undergoes a certain process, either summation or subtracting, or passing it through the logic gates to obtain high encoding confidentiality to give more complexities to increase data security. Experimental results of the proposed algorithm show a significant improvement in coding rate with high encryption and decryption speeds, in addition to the correlation coefficient and entropy reaching -0.4735 and 7.9983, respectively.
Proliferation of social media And Cybercrime Against Women and Girls in Nigeria

Social media offers numerous opportunities and benefits, particularly in terms of information access. However, it can be misused by criminals to cause harm to people, especially women and girls. This research seeks to assess how social media usage influences cyberviolence against women and girls (Cyber-VAWG). The study’s findings, based on descriptive and inferential data, showed that most of the population’s females had suffered cyber-based violence at least once. Unwanted sexual photos, impersonation online, and privacy invasion via hacking social media accounts were also shown to be significant forms of cyber abuse faced by victims. When it comes to the motivations behind cyber-VAWG, it’s all about the money and the sex. The findings further imply that the amount of security knowledge and education exposure, as well as the influence of age, are more important in exposing victims to cybercrime than their exposure and the frequency with which they use social media. Cyber-VAWG. In terms of the impact of cyber-VAWG on victims, findings from the study further revealed that most of the respondents were reportedly angered and irritated; worried and disturbed.
Integrated Photonic Computing beyond the von Neumann Architecture

In the context of a doomed end of the Moore’s law, various new types of computing architectures have been emerging, aiming to meet the demands of intractable computation and artificial intelligence. Photonic computing is a competitive candidate, in light of the inherent properties of photons, including high propagation speed, strong robustness, and multiple degrees of freedom to encode information. Also, the progress of integrated photonics continues to provide novel possibilities, apart from boosting the scalability and stability of photonic computing architectures. Moreover, an introduction of quantum technology might open a new chapter for photonic computing, from the view of single photons. In this Perspective, we highlight the unique features and advances of integrated photonic platforms, whose roles in constructing a non-von Neumann computing architecture are also outlined. We show their potential in solving problems beyond the reach of traditional computers and in machine learning and further discuss the conceivable challenges and opportunities.