Reviewerly/exHarmony huggingface dataset:
A multi-level study for trust management models assessment in VANETs

Nowadays, trust management is one of the key elements to ensure a high security level in ad hoc networks. Trust assessment can be perceived at three levels. First, the data perception trust need to be assessed in order to ensure a high quality of raw sensed data. Second, the trust relationship assessment is required to detect the selfish and malicious entities and to maintain the data integrity. Finally, the data fusion trust is essential to preserve the performance of the fusion process. In this paper, we intend to point out the need to integrate the data perception trust, the communication trust and the data fusion trust in order to preserve the information trustworthiness in VANETs. We further browse the literature to identify recent advancements with regard to each type of trust.
Robot that Expresses Human Pains by Deformations

It is not always easy for humans to understand the pain of others. Therefore, having an effective method to express and explain their pain to others is valuable. In this study, we attempt to create a robot that is capable of expressing human pains by its deformations. The user of the robot is able to edit those deformations manually by his/her hand, and the robot is capable of recording the deformation processes for replays. In this late-breaking report, we describe the basic requirements of this robot and its early prototype.
QuicklyCook: A User-Friendly Recipe Recommender

We present ‘QuickyCook’, an instance-based recipe recommender system. It uses a recently developed database called ‘Recipe 1M’ which contains 1 million recipes for a variety of food items. The proposed system is developed by taking advantage of the fast execution of available python data types and libraries which greatly enhance the proposed model’s performance. The system is built off the idea of a very popular algorithm i.e. K-nearest neighbor (KNN) where a new instance is classified based on the classes of already-seen instances. However, we have proposed a slightly different and unique way of calculating the similarity score of the new to-be-classified instance with the previous ones. The proposed system allows the user to input a unrestricted list of ingredients and finds the closest ‘k’ recipes containing preferably all of the input ingredients. Unlike many recipe recommender systems to date, our proposed system does not restrict the user to choose ingredients from an existing ingredient list thus providing more flexibility to the user. Performance-wise, its execution time is considerably faster as it does not require large feature vectors to be formed and compared for classification. Hence, QuicklyCook can be considered as a simple but powerful application.
Blockchain-based Model for Trustworthy Shared Internet of Things Device Management

In a sharing economy, the management of shared Internet of Things (IoT) devices is of primary concern. For trustworthy management, accountability regarding the shared devices must be guaranteed. Blockchain, which stores data history securely, could be one solution. This study proposes a blockchain-based model for the management of shared IoT devices. In this model, when a change event occurs in the device, the description is written on the blockchain. With the blockchain, a user or manager can verify the current status of the shared device.
Centroid-Based Hierarchy Preserving Clustering Algorithm Using Lighthouse Scanning

Clustering is an important topic in machine learning and data mining. But determining the number of clusters in a data set, a quantity that is often labelled as ‘k’ as in a k-means algorithm, is a distinctive problem from the process of actually solving the clustering problem. Another challenge faced by the existing clustering algorithms is the final result and the converge time largely depends on the predicted position of the centroids. It is an interesting problem because if the number of clusters and the centroid positions could be roughly estimated, then the convergence time of any clustering algorithm could be decreased and the resulting redundancy could be preserved. In this paper, we implemented our novel mathematical idea of two-way lighthouse scan, on existing k-means algorithm, to predict the quantity and position of the clusters. The recursive implementation of the algorithm decreases the number of iterations of any centroid-based clustering algorithm while preserving the rank of the clusters.
Artificial Intelligence as Answer to Cognitive Revolution Challenges

Networks and informational technologies caused mammoth growth of knowledge volume and sophistication. Serious cognitive and educational problems of scientific big data understanding have arisen. The part and parcel of coming system-informational culture is inter-disciplinary activity based on labor with meanings ensuring knowledge integrity and obviousness. Presented by the utmost mathematical abstractions they are formed by genus in natural science knowledge. Cogno-ontological knowledge base (CogOnt) as core of these scientific presentations consists of them. Achieving personal transcendentality by means of self-reflection secures cognogenesis success. Man’s life-long partnership technology with deep-learned artificial intelligence (DL IA) is contributed to do it. In order to carry out subject’s auto-poiesis (rational self-building) it is impossible to do without continuous assistance of DL IA using CogOnt and applying axiomatic method. Elaboration of CogOnt for DL IA implantation is displayed on example of some universalities study.
Determinants of Accounting Information System in PT. Indomarco Adi Prima, Jember Branch

This study aims to analyze the impact of Education and Training Programs, Involvement of System Users, and Human Resources (HR) Competence on the Quality of Accounting Information Systems at PT. Indomarco Adi Prima Jember Branch, since there were ineffective process in selling application, by the late of manager approval and the network problem. The population in this study are all employees who use Information Systems at PT. Indomarco Adi Prima Jember Branch. The analytical method in this study uses the validity and reliability test, the Classic Assumption Test is a normality test, a multicollinearity test, and the heteroscedasticity test. Multiple Linear Regression Analysis, hypothesis test, using t test and coefficient of determination (R2). The results showed that Education and Training Programs, System Users Involvement, and HR Competencies significantly impact the Quality of Accounting Information Systems, and the coefficient of determination (R2) of all independent variables strongly explained the dependent variable.
Guessing What's Plausible But Remembering What's True: Accurate Neural Reasoning for Question-Answering

Neural approaches to natural language processing (NLP) often fail at thelogical reasoning needed for deeper language understanding. In particular,neural approaches to reasoning that rely on embedded \emph{generalizations} ofa knowledge base (KB) implicitly model which facts that are \emph{plausible},but may not model which facts are \emph{true}, according to the KB. Whilegeneralizing the facts in a KB is useful for KB completion, the inability todistinguish between plausible inferences and logically entailed conclusions canbe problematic in settings like as KB question answering (KBQA). We proposehere a novel KB embedding scheme that supports generalization, but also allowsaccurate logical reasoning with a KB. Our approach introduces two newmechanisms for KB reasoning: neural retrieval over a set of embedded triples,and memorization of highly specific information with a compact sketchstructure. Experimentally, this leads to substantial improvements over thestate-of-the-art on two KBQA benchmarks.
Communities of Related Terms in a Karst Terminology Co-occurrence Network

Karst science is an attractive field of interdisciplinary research with rich terminology. This study was performed as part of a project aiming at developing novel approaches to terminology extraction and visualization, in line with the understanding of knowledge, as represented in texts, as conceptually dynamic and linguistically varied. The aim of this paper is to investigate how powerful graph-based methods can be used for visualizing and analysing domain terminology. In order to detect communities in karst terminology, we analyse the frequently co- occurring karst terms in a scientific corpus of karstologic literature. The most frequent co- occurrence pairs, which included ten or more co-occurrences within the whole corpus, are delivered as input to the Louvain community detection algorithm and visualized as a domain graph. The resulting data was evaluated by domain experts who found that the detected term groups are meaningful and correspond to different types of karst phenomena. The results are further discussed in relation to more standard topic modelling approaches, using Latent Dirichlet Allocation and Non-negative Matrix Factorization algorithms.
Smart Tree Learning Media - We Can Be Based on Android For Arabic Subjects/ Media Pembelajaran Pohon Pintar-Kita Bisa Berbasis Android Untuk Matakuliah Bahasa Arab

This study aims to: (1) determine the design process of learning media Smart Tree-We Can (Rule, Arabic Vocabulary of Islamic Economics) based on android applications on vocabulary material (mufrodat) and Arabic rules, (2). Knowing the effectiveness of learning Arabic by using learning media, Smart Tree-We Can be based on Android applications. The trial subjects in this study were 1110 3rd semester students taking Arabic courses at the Trunojoyo University Madura Islamic Studies Program. The results showed: (1) the learning process of the design of learning media Smart Tree-We Can be based on android applications consisting of four phases, namely the investigation phase, the design phase, the realization phase and the fifth is the evaluation and revision phase (2). Smart Tree-We Can learn media based on android applications is considered useful in increasing students' motivation and understanding of the rules and vocabulary of Arabic Sharia Economics.
Examining Exploitability Risk of Vulnerabilities: A Hazard Model

With the increasing number and severity of security incidents and exploits, information technology (IT) vendors, security managers, and consumers have begun to place more emphasis on security. Yet, fixing the sheer volume of vulnerabilities remains a challenge as IT vendors race against attackers to evaluate system vulnerabilities, prioritize them, and issue security patches before cybercriminals can exploit them. In this study, we posit that IT vendors can prioritize which vulnerabilities they should patch first by assessing their exploitability risk. Accordingly, we identified the vulnerabilities that cybercriminals will most likely exploit using vulnerability-related attributes and vulnerability types. To do so, we employed survival analysis and tested our models using historical data of vulnerabilities and exploits between 2007 and 2016. Our results indicate that IT vendors benefit the most from fixing remotely exploitable vulnerabilities; non-complex vulnerabilities; vulnerabilities that require no authentication; and vulnerabilities that affect confidentiality, integrity, and availability components. Furthermore, our findings suggest that IT vendors can mitigate the risk of exploit-related attacks by remedying code-injection vulnerabilities, buffer-overflow vulnerabilities, and numeric-error vulnerabilities.
Konsepsi Implementasi Sistem Informasi Managemen dalam Mendukung Pelayanan Tenaga Pendidik

The use of management information systems in the field of education (SIMDIK) is very important in management, both in terms of managing academic administration, staffing administration, administration of facilities and infrastructure, reporting administration and so on. However, managing the administrative staff is more prioritized in educational institutions, especially teaching staff because they are the most important element in the education process. The use of management information system as a service system for teaching and education staff will be managed and empowered through SIMPATIKA (Information System for Educators and Education Personnel of the Ministry of Religion). It is helpful the management of teaching and educational staff, although some weaknesses in it. SIM is not only necessary but it is used to realize the vision, improve the educators and educational staff quality in accordance with the educators and education standards. So the use of SIM is as a mandatory step to carry out its mission. SIM maintenance should be prioritized to serve educators and education staff so it realizes the educators and educational staff quality in accordance with the educators and education standards.
Multiscale Quantum Gradual Approximation Algorithm: An Optimization Algorithm With a Step-by-Step Approximation Strategy

In quantum swarm intelligence algorithms, the tunneling effect of the particles is determined by the potential energy acting on the particles.The tunneling effect of the particles affects the global search ability and convergence speed of the algorithm.Quantum algorithms with a single potential energy are prone to premature convergence under certain complex test functions.In this paper, we propose a multiscale quantum gradual approximation algorithm (MQGAA), which simply uses different approximation strategies to obtain different potential energy functions, to solve the premature problem of the optimization algorithm.In the MQGAA, particles undergo a transition from an unconstrained state to a constrained state at each scale.To demonstrate the effectiveness of the proposed algorithm, experiments are carried out with several common and effective stochastic algorithms on N-dimensional double-well potential functions and classical benchmark functions.We also use the Wilcoxon rank test to detect the performance of MQGAA.The experimental results show that the algorithm using a step-by-step approximation strategy achieves a better optimization performance on some complex test functions.
PENGARUH MOTIVASI DALAM PEMBELAJARAN SISWA UPAYA MENGATASI PERMASLAHAN BELAJAR

PENGARUH MOTIVASI DALAM PEMBELAJARAN SISWA UPAYA MENGATASI PERMASLAHAN BELAJAR Siti Marisa Universitas Islam Sumatera Utara sitimarisa27101981@gmail.com ABSTRACTION Learning is a process of changing behavior from not knowing to knowing or from not being able to be able to. Because of that learning becomes something very important for one's life. In conducting learning activities, of course, requires a force called motivation. This paper tries to look further at how motivation influences one's learning activities. Literature approach is employed in this study to describe everything obtained to increase understanding related to motivation to learn. In general, motivation is divided into two, namely intrinsic and extrinsic. Problems that always cause difficulties for students to learn are internal factors that include cognitive, affective, and psychomotor domains, while external factors cover all situations and environmental conditions that do not support student learning activities. In addition, other factors that are more specifically causing difficulties for students in learning are dyslexia (inability to learn to read); dysgraphia (inability to learn or to write) and dyscalculia (inability to learn mathematics).
Quantum Algorithm for Poisson Equation with Inhomogeneous Media

Quantum computer has attracted much attention from academy and industry due to the marvelous acceleration compared with classical computers. A quantum computing algorithm for Poisson equations with inhomogeneous media is presented here. The original equation is discretized with square grids and transformed into linear equations using finite difference method. Then the quantum algorithm for solving sparse matrix equations is applied to the discretized Poisson equation. The quantum algorithm can achieve O(log N) complexity. Numerical simulations verify the accuracy of such algorithm.
Ex ante coherence shifts

Abstract Cushman characterizes rationalization as the inverse of rational reasoning, but this distinction is psychologically questionable. Coherence-based reasoning highlights a subtler form of bidirectionality: By distorting task attributes to make one course of action appear superior to its rivals, a patina of rationality is bestowed on the choice. This mechanism drives choice and action, rather than just following in their wake.
Multimedia quality evaluation model in streaming service environments

Recently, mobile broadcast services such as digital media broadcasting (DMB) have generated a rapid increase in multimedia services via the internet compared to traditional cable or satellite delivery. Multimedia via the internet is consumed under different network conditions and through various devices. In this study, we propose a multimedia quality measurement model to assess quality consumes perspective. First, we select the parameters that affect the quality of multimedia at both the application and network levels, and then use these parameters to produce multiple altered versions of an original video. For each video and device, participants provide a mean opinion score (MOS). After determining the effect of parameters on videos and devices. Multiple regression on the MOS dataset collected from the subjects is applied to devise a model called MOS multimedia streaming (MOSMS). Finally, dividing the collected data as a training set and a test set, we validate the new model.
Sentiment Analysis in Spanish Tweets: Some Experiments with Focus on Neutral Tweets

We present different methods for Sentiment analysis in Spanish tweets: SVM based on word embeddings centroid for the tweet, CNN and LSTM.We analyze the results obtained using the corpora from the TASS sentiment analysis challenge, obtaining state of the art results in the performance of the classifiers. As the neutral category is the hardest one to classify, we focus in understanding the neutral tweets classification problems and we further analyze the composition of this class in order to extract insights on how to improve the classifiers.
Singularities in the Image-Based Visual Servoing of Five Points

Image-based Visual Servoing (IBVS) hinges on the observation of geometric entities such as 3D points on a target object and their projected image on the camera to control a robot. It includes a mapping between two vector spaces: the time variation of the projected 2D points on the image plane of the camera and the instantaneous spatial velocity of the camera induced by the motion of the robot. The configuration of the camera and the points is said to be singular if the matrix that maps the two vector spaces, the so-called interaction matrix, loses rank, leading to problems in control. Utilizing the results on singularities in visual servoing of three and four 3D points, the goal of this article is to determine the configuration of five points leading to singularities. The problem boils down to examining the vanishing conditions of all the minors of the interaction matrix. It turns out that it is not necessary to consider all the minors. Thus, the simplest set of equations is considered and tools from computational algebraic geometry are used to derive a necessary condition leading to singularities in IBVS of five points.
A Literature Review on Path Planning of Polyhedrons with Rolling Contact

Path planning of polyhedrons by rolling on edges includes arbitrary relocation, obstacle avoidance and goal achievement. These tasks have various applications in robotics fields such as autonomous vehicles, navigation and robotic surgery. This paper reviewed path planning approaches applied to rolling contact with different types of polyhedral parts. The advantages and disadvantages of some mainstream algorithms were analyzed and compared. We further pointed out the directions of future research on rolling polyhedron path planning in discrete space.
Prefix-Based Multi-Pattern Matching on FPGA

Multi-pattern matching refers to the search for multiple patterns in a given text at the same time. This matching on FPGA is expected to scale with the number of patterns in hardware consumption. In this paper, we propose a matching architecture that compares the prefixes of multiple patterns with the prefix of the matching window in parallel. The comparison will continue with the body of each pattern if the corresponding prefix is matched. This architecture is called the prefix-based multi-pattern matching architecture. Our implementation on FPGA shows that the proposed matching architecture achieves much higher performance than the implementation on CPU, while the hardware cost is low.
A Case Study on the Growth Process of Grammar Learners as ’Linguistic Subjects’

This study aims to qualitatively explore the existential process and meaning of grammar learners growing into linguistic subjects through grammar lessons as a reflection on the "linguistic subjects" that had previously only been abstractly discussed. For this purpose, we selected four high school learners who were shown to have grown into linguistic subjects through a grammar class in one semester, and analyzed them by thematic analysis. As a result of in-depth analysis by case and case, focusing on the growth process of linguistic subjects, learners A, B, and C experienced a change in the transformative perspective on grammar, and learner D experienced a qualitative refinement of the perspective on grammar. These changes were interpreted to have been made through expansion of grammar knowledge, improvement of grammar inquiry ability, and strengthening attitude toward grammar. Considering the content, direction, and attitude of language awareness, A could be categorized as a knowledge collector, B as a Knowledge explorer, C as a language researcher, and D as a communicator and practitioner. These results allowed us to derive the following educational implications. First, the relationship between knowledge, inquiry ability, and attitude, which are the main contents of grammar education, could be actualized. Second, the necessity and importance of cognitive/emotional integration support program for learner growth process was discussed. Third, it can be seen that the abstract entity of linguistic subject can be embodied in various aspects in the actual classroom. Fourth, it is very important to check the learner"s initial grammar perception and observe the trend in the classroom. Fifth, it is necessary to present educational contents that learners" intuition as native speakers can be discussed together.
Improving Chaotic Image Encryption Using Maps with Small Lyapunov Exponents

Chaos-based encryption is one of the promising cryptography techniques that can be used. Although chaos-based encryption provides excellent security, the finite precision of number representation in computers affects decryption accuracy negatively. In this paper, a way to mitigate some problems regarding finite precision is analyzed. We show that the use of maps with small Lyapunov exponents can improve the performance of chaotic encryption scheme, making it suitable for image encryption.
Nonparametric Estimation for a Slope of a Replicated Linear Functional Relationship Model

In this paper, we propose a nonparametric method to estimate the slope of a replicated linear functional relationship model. The nonparametric method is a robust method in nature and does not affect when the observations have outliers. Additionally, the nonparametric method does not require the normality assumption. Using simulation studies, we compared the performance of the proposed nonparametric method with the traditional method using maximum likelihood estimation. It is found that without any outlier, the maximum likelihood estimation works well but when outliers exist in the data, our proposed nonparametric method gives a small mean square error, thus suggesting a better estimate. Keywords: linear functional relationship model; nonparametric method; outliers