Reviewerly/exHarmony huggingface dataset:
Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke

Data mining is often called knowledge Discovery in Database (KDD). Data mining is usually used to improve future decision making based on information obtained from the past. For example for prediction, estimation, association, clustering, and description. Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Currently, the development of the Industrial Revolution Era 4.0 is collaborating in the fieldsof technology and health science so that it becomes something useful by using Machine Learning. There are so many benefits that are used in predicting several diseases that can be anticipated. In this study the dataset is dividedinto 2 parts, namely training data and testing data using split validation. Based on the results of the test that have been carried out in this study, the algorithm that has the highest accuracyvalue on balanced data is Logistic Regression with an accuracy rate of 75.65%, while for unbalanced data, the algorithm that has the highest accuracy results is Logistic Regression, Random Forest, SVM, and KNN with an accuracy rate of 98.63%. This testing process is carried out to identify stroke with data mining algorithms
Positioning of Tower Crane Trolley Based on D-S Evidence Theory and Kalman Filter

Due to the complex structure of the tower crane and the signal noise, it is difficult to estimate and predict the real position signal, which may lead to inaccurate positioning of the tower crane trolley. A tower crane positioning algorithm based on D-S evidence theory and unscented Kalman filter (DS-UKF) is established and developed. First, the nonlinear dynamic model, state equation, and detection equation of the tower crane are constructed based on the complex structure of the tower crane. Second, the arm angle is estimated, and the distance between the trolley and the root of the arm is positioned. Finally, in order to calculate the weight of a single sensor, the D-S evidence theory is used to process the measurement information of the sensor and the estimated value of the filter. The Kalman filter, which has good signal tracking and estimation ability, is used to predict and estimate the position of the trolley. The accurate positioning of the tower crane trolley is realized through the organic fusion of information. The simulation results show the effectiveness of the DS-UKF positioning algorithm.
Using Cinematic Virtual Reality to Get Acquainted with the Metaverse

Abstract This paper discusses the opportunities for using cinematic virtual reality (CVR) to enhance our understanding of the imagined metaverse – the recently emerged new digital age phenomenon. The paper approaches the concept of the metaverse from an interaction point of view, which is one of the more common characteristics used to conceptualise the metaverse, approaching it as a phenomenon involving the convergence of real and virtual worlds, but also humans and machines. The article investigates the possibilities of using the relatively unexplored phenomenon of CVR, which facilitates the desired interaction while requiring the least prior knowledge and skills from the user, and thus might ease our acquaintance with the imagined metaverse. The difference between virtual reality (VR) and CVR lies in the generation of the VR world: traditional VR is typically generated through 3D graphics processing and audio triggers in real time, whereas CVR exclusively uses pre-rendered pictures and sound elements. Thus, the main difference between the two lies in the textual content and intended way of consuming it without dissimilarity in technology. With reference to three drama genre cases, the aim of this paper is to explore one of the crucial components of CVR—namely, through the established convention of the viewer perspective in traditional cinema (first-person, second-person and third-person perspective).
A shape mapping for generalized beta exponential distribution of the first kind in the entropy-parametric space

Abstract Modern methods of tracking and controlling complex systems are associated with the analysis of the shape of statistical models of sample arrays. Often the values of the monitored parameters are maintained only on a semi-infinite range of values. In these cases, it is possible to use generalized beta exponential distribution models of the first kind to smooth the statistics, that it is including such models as exponential and generalized Gompertz-Verhulst distributions as special cases. The distributions with a large set of subfamilies rarely are use due to lack of methods of preliminary estimation of the distribution shape parameters. The paper illustrates that the displayed of distributions in the parametric space of skewness and kurtosis does not allow distinguishing the features of the position of the main subfamilies of distributions. Mapping the subfamilies of the Generalized Beta Exponential Distribution of the first kind in the Entropy-Parametric space makes it possible to distinguish between subfamilies and to comparative analysis of many their properties. It is convenient to use the space of the entropy coefficient and skewness when you are comparing the skew properties of distributions. The space of the entropy coefficient and antikurtosis is more suitable for comparing the weights of distribution tails and for analysing monotonicity. In particular, it is shown that the Generalized Beta Exponential Distribution of the first kind contains as monotonic and as non-monotonic leptokurtic distributions. The property of monotonicity is well discernible when compared with the antikurtosis of the exponential distribution.
Gaussian‐like measurement likelihood based particle filter for extended target tracking

Abstract Extended target tracking based on the star‐convex model is a multi‐dimensional nonlinear estimation problem. To approximate the shape of an extended target, the star‐convex model requires a higher‐order Fourier series expansion, which will increase the dimensionality of the extended state, so that the nonlinear filters under Gaussian assumptions cannot converge to the optimal state estimation. In this paper, a novel particle filter algorithm based on Gaussian‐like measurement likelihood (GL) is proposed. As latent variables with high uncertainty in the measurement model, the scattering centres are modelled as a Gaussian‐like probability distribution characterising the target's shape. Then the measurement noise is integrated into the Gaussian‐like probability distribution via Gaussian quadrature to obtain the accurate measurement likelihood. Moreover, particle swarm optimization is employed to reduce computational complexity. It enables particles to move into the vicinity of measurements, thereby increasing the diversity of the particles belonging to the target's shape. The simulation results show that the GL can improve the accuracy of shape estimation under high measurement noise and low measurement rates. Furthermore, the proposed particle filter can track the extended target effectively with an aeroplane‐like shape.
Cryptopace and problems of decentralization of the financial system: view from Russia

The article examines the problems of the formation of cryptospace in modern Russia, determines the objective prerequisites for the formation of a parallel fi nancial system, which is based on blockchain technology and the idea of cryptocurrency circulation. The author presents the characteristic features of centralized and decentralized financial systems, considering differences in the way to achieve goals, the functioning of regulatory norms of legislation, the degree of fi nancial independence and manufacturability. It is indicated that the very ideology of a free market economy and free circulation of information in the context of digitalization led to the emergence of these phenomena. The objectivity of these phenomena raises the question of the admissibility of their introduction into the fi nancial system of the Russian Federation. The author critically comprehends the provisions regarding the advantages and disadvantages of cryptocurrencies in the context of the development of the fi nancial system. It is indicated that this form of circulation of fi nance is new and promising, although it carries signifi cant risks for both the fi nancial system and users. At the same time, arguments are given in favor of the legalization of cryptocurrencies in Russia, a model for the circulation of cryptocurrencies is given that considers the interests of all participants in virtual monetary relations, and a promising direction for further research on cryptocurrencies and blockchain technology is identified.
Water Surface Target Detection Algorithm for Unmanned Cleaning Ship Based on Improved YOLO V5

The appearance of the unmanned cleaning ship has improved the efficiency and safety of water surface cleaning. Accurate water surface target recognition is the premise for the unmanned cleaning ship to complete the cleaning inspection task. However, the current water surface target detection algorithm is not accurate enough in small target detection because of its network structure is lack of optimization in small target detection, which affects the completion of the water surface cleaning task. In this study, the improved YOLO V5 algorithm is used for water surface target detection. The C3 structure in YOLO network is replaced by the encoder structure in transformer network, and the small target detection layer and the Convolutional Block Attention Module (CBAM) are integrated. Finally, this study constructs water surface target detection datasets, and test the processed datasets using the improved YOLO V5 algorithm. The experimental results show that the improved surface target detection algorithm has better performance and higher average detection accuracy in small target detection, which proves that the method has certain practicability and progressiveness in the application of clean unmanned ship.
A Secure Design Pattern Approach Toward Tackling Lateral-Injection Attacks

Software weaknesses that create attack surfaces for adversarial exploits, such as lateral SQL injection (LSQLi) attacks, are usually introduced during the design phase of software development. Security design patterns are sometimes applied to tackle these weaknesses. However, due to the stealthy nature of lateral-based attacks, employing traditional security patterns to address these threats is insufficient. Hence, we present SEAL, a secure design that extrapolates architectural, design, and implementation abstraction levels to delegate security strategies toward tackling LSQLi attacks. We evaluated SEAL using case study software, where we assumed the role of an adversary and injected several attack vectors tasked with compromising the confidentiality and integrity of its database. Our evaluation of SEAL demonstrated its capacity to address LSQLi attacks.
Affective Embedding Framework with Semantic Representations from Tweets for Zero-Shot Visual Sentiment Prediction

This paper presents a zero-shot visual sentiment prediction method using semantic representation features of texts from tweets as the non-visual auxiliary data. Previous studies show that visual sentiment prediction methods can only predict the sentiment labels that are the same as the labels of the sentiment theory used in the training dataset, which means that they cannot predict the new sentiment label used in different sentiment theories. To solve the problem of predicting new labels, zero-shot learning has been proposed. The previous zero-shot visual sentiment prediction method uses Word2vec features and the adjective-noun pair features to obtain the semantical relationship between images and sentiment words to predict unseen sentiments. However, many adjective-noun pairs are not related to sentiments, which makes it difficult to compensate for an affective gap between low-level visual features and high-level sentiment semantics. Thus, to better compensate for the affective gap, it is considered to introduce the new non-visual auxiliary data. As people tend to share their feelings with both images and texts on social networking services, the texts from tweets are effective as the side information of the images in visual sentiment prediction. Thus, we introduce the semantic representations from tweets as the new non-visual auxiliary data to construct an affective embedding space, which makes a more effective zero-shot visual sentiment prediction model. Moreover, we propose a cross-dataset zero-shot task for visual sentiment prediction, which is more consistent with the real situation that the testing and training images may be in different domains. The contributions in this paper are to combine several semantic representation features for zero-shot visual sentiment prediction and the proposal of the cross-dataset zero-shot task for visual sentiment prediction. The experiments on several open datasets show the effectiveness of the proposed method.
Interval-Valued Fuzzy Normal Subnearrings of a Near Rings

The theory of interval-valued set is to concentrate the single membership functions. The manuscript aims to develop the concept of interval-valued Fuzzy normal subnearrings of a near ring (i-v FNSN). Based on these concepts, we analyzed some properties and results for the development of the theorem. We also initiate the study of homomorphism, union, and some algebraic properties. The concept of intersection is also proved. The results are beneficial to researchers.
Soundness and completeness results for LEA and probability semantics

Abstract In Artemov (2020, J. Logic Comput., 30, 61–76), a logical system called the logic of evidence aggregation (LEA) was introduced, along with an intended semantics for it called probability semantics. The goal was to describe probabilistic evidence aggregation in the setting of formal logic. However, as noted in that paper, LEA is not complete with respect to probability semantics. This leaves the tasks open to find sound and complete semantics for LEA and a proper axiomatization for probability semantics. In this paper, we do both. We define a class of basic models called deductive basic models. We show LEA is sound and complete with respect to the class of deductive basic models. We also define an axiomatic system LEA$_+$ extending LEA and show it is sound and complete with respect to probability semantics. Along the way, we develop an intermediate system LEA$_-$ which, it turns out, is equivalent to propositional lax logic with a classical logic base. Sequent systems and decidability results for LEA$_-$ and LEA$_+$ are also presented.
Predicting the future of the foodservice industry: A robot-based economy perspective

This paper ascertains the perceptions of Turkish foodservice industry experts (i.e., executive chefs, business owners, and academicians) to predict the future of the foodservice industry in the robot-based economy. A content analysis was performed by drawing on the qualitative data that occurred through 18 semi-structured interviews. The findings illustrate the present and anticipated status of the usage of robots and AI and the possible impact of robotization and AI on employment in the foodservice industry based on its’ experts’ perspective. It reveals a futurity glimb for the foodservice industry in the robot-based economy era. Several theoretical and managerial implications are discussed.
Transfer-Learning-Based Opinion Mining for New-Product Portfolio Configuration over the Case-Based Reasoning Cycle

Due to the ever-changing business environment, enterprises are facing unprecedented challenges in their new-product development (NPD) processes, while the success and survival of NPD projects have become increasingly challenging in recent years. Thus, most enterprises are eager to revamp existing NPD processes so as to enhance the likelihood of new products succeeding in the market. In addition to the determination of sustainable new-product ideas and designs, new-product portfolio management (NPPM) is an active research area for allocating adequate resources to boost project development, while projects that perform poorly can be terminated. Since the existing new-product portfolio configuration is manually decided, this study explores the possibility of standardising NPPM, particularly the configuration mechanism, in a systematic manner. Subsequently, case-based reasoning can be applied to structure the entire NPPM process, in which past knowledge and successful cases can be used to configure new projects. Furthermore, customer feedback was analyzed using the transfer-learning-based text classification model in the case-retrieval process to balance the values of enterprises and customers. A new-product portfolio was therefore configured to facilitate NPPM under an agile–stage-gate model. To verify the effectiveness of the proposed system, a case study in a printer manufacturing company was conducted, where positive feedback and performances were found.
A Method for Local Contrast Enhancement of Endoscopic Images Based on Color Tensor Transformation into a Matrix of Color Vectors’ Modules Using a Sliding Window

A new method aimed at endoscopic color images’ local contrast enhancement is proposed, based on local sliding histogram equalization with adaptive threshold limitation, color distortions correction, and image brightness preservation. For this, the original RGB image, represented as a tensor of size M × N × 3, is transformed into a matrix of size M × N, composed by the color vectors’ modules. As a result of local contrast enhancement, the obtained color vectors are symmetrical in respect of the input ones, because they satisfy the requirement for invariance after rotation. To enhance the local contrast, recursive local histogram equalization with adaptive thresholding is applied to each matrix element. This threshold divides the histogram into two regions of equal areas. A new metric for local contrast enhancement evaluation based on the mean square difference entropy is proposed. The presented new method is characterized by low computational complexity, due to the lack of direct and inverse color conversion and the possibility for adaptive local contrast enhancement, which is essential for accurate medical diagnosis based on endoscopic images analysis. In addition, the presented method performs both the correction of color distortions and the brightness preservation of each pixel.
Hybrid Model with Multi-Level Code Representation for Multi-Label Code Smell Detection (077)

Code smell is an indicator of potential problems in a software design that have a negative impact on readability and maintainability. Hence, detecting code smells in a timely and effective manner can provide guides for developers in refactoring. Fortunately, many approaches like metric-based, heuristic-based, machine-learning-based and deep-learning-based have been proposed to detect code smells. However, existing methods, using the simple code representation to describe different code smells unilaterally, cannot efficiently extract enough rich information from source code. In addition, one code snippet often has several code smells at the same time and there is a lack of multi-label code smell detection based on deep learning. In this paper, we present a large-scale dataset for the multi-label code smell detection task since there is still no publicly sufficient dataset for this task. The release of this dataset would push forward the research in this field. Based on it, we propose a hybrid model with multi-level code representation to further optimize the code smell detection. First, we parse the code into the abstract syntax tree (AST) with control and data flow edges and the graph convolution network is applied to get the prediction at the syntactic and semantic level. Then we use the bidirectional long-short term memory network with attention mechanism to analyze the code tokens at the token-level in the meanwhile. Finally, we get the fusion prediction result of the models. Experimental results illustrate that our proposed model outperforms the state-of-the-art methods not only in single code smell detection but also in multi-label code smell detection.
Postural instability-induced compensative movements in virtual reality

Abstract During virtual reality usage, two egocentric mental representations are constructed simultaneously. The first representation is rooted in the physical reality in which VR is set up, and the second originates from the mental construction of a computer-generated virtual environment. In both cases, participants configure their posture based on multimodal stimuli while responding to environmental cues. In most cases, the postural cues provided by the digital and real environment may be conflicting. In this study, 50 right-handed volunteers were enrolled. In a pre-test session, attentional focus-related personality bias (perspective-taking) was assessed, and afterward, postural movements and presence experiences were measured while the participants performed a spatial orientation task in VR. Participants were placed in an upright position with their right hands positioned in front of a physically real point on the laboratory wall. Afterward, participants were exposed to a VR environment in which they performed a room-tilting task. Participants with higher hand-related presence scores showed decreased compensatory hand drift in the VR environment. The rate of contralateral hand drift showed a reversed association with the intensity of the perspective-taking trait. VR-induced postural instability can be attenuated by the compensative hand drift that alleviates the conflicts between the two rival inner VR and outer VR environments that compete for attention and provide different reference cues.
Investigation of the Impact of Damaged Smartphone Sensors’ Readings on the Quality of Behavioral Biometric Models

Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact on UX (User Experience) is making its popularity rapidly increase among branches in the area of confidential data handling, such as banking, insurance companies, the government, or the military. Although behavioral biometric methods show a high degree of protection against fraudsters, they are susceptible to the quality of input data. The selected behavioral biometrics are strongly dependent on mobile phone IMU sensors. This paper investigates the harmful effects of gaps in data on the behavioral biometry model's accuracy in order to propose suitable countermeasures for this issue.