Reviewerly/exHarmony huggingface dataset:
Intra-round Pipelining of Keccak Permutation Function in FPGA Implementations

The Keccak permutation function constitutes the essential part of the computations in the SHA-3 (Secure Hash Algorithm-3) standard. Its fast implementation in hardware is crucial for efficient operation of many contemporary ICT systems which commonly apply hashing e.g. in data storage and transmission or in security protection. This paper analyzes potential improvements in computation speed of the function if its hardware implementation uses a pipelined organization where the single Keccak round is divided into two or three pipeline stages. The discussion starts with examination of various options for such pipelining and then the proposed architectures are implemented in a Spartan-7 FPGA device from Xilinx. Estimations of their maximum frequencies of operation illustrate speed gains (in terms of total throughput) which can be accomplished by the increased parallelization achieved through fine-grained pipelining. The results indicate that after careful tuning of the overall control framework the complete module calculating the 1600-bit permutation can operate at frequencies exceeding 500 MHz even in a device from this economy-grade FPGA family.
Deriving Spatial Occupancy Evidence from Radar Detection Data

Central low-level sensor data fusion approaches are getting more popular in advanced driver assistant systems. They allow for the resolution of ambiguities in the retrieval of environmental information on the basis of a large, raw data pool. Hereby, one emerging challenge is the unification of sensor data of different formats and sensor types. A popular intermediate layer of data is given by spatial occupancy grids. The conversion of a discrete list of radar detections, which is a commonly utilized measurement format, is problematic due to the sparse spatial resolution. This work addresses this conversion by interpolating the data spatially using generic sensor model knowledge. Traditional approaches derive occupancy evidence in the vicinity of a detection. In addition, we analyze spatial and kinematic properties derived from Doppler measurements, compute likelihoods that multiple detections are caused by the same object and deduce the space between them accordingly. The incorporation of sensor parameters allows full-and short-range radars to be used generically. In addition, we outline the deduction of free space evidence. The elaborated models and algorithms are evaluated on realworld datasets and discussed w.r.t. their applicability in a subsequent Dempster-Shafer-based sensor data fusion approach.
Human Pose Estimation Using Articulated ICP

This paper proposes a method of human pose reconstruction using the depth data inputs in multi depth sensors. Utilizing the articulated ICP technique for pose estimation, the proposed method firstly maps a model from parent nodes, and then searches the points adjacent to joints. The proposed method addresses the challenges of the existing method of using a single sensor such as occlusion, and enables real-time 3D pose reconstruction involving movements and 360°omni-directional rotations while tracking 1~5 user IDs. Precluding the necessity of pre-training data or motion capture data, the method uses a simple model to compute the ICP and applies the point search, which allows the fast and accurate pose reconstruction.
A User Behavior Clustering Algorithm Combines Association Rules and Multi-valued Discrete Features

The purpose of user behavior clustering analysis is to analyze the features of core user groups. However, existing user behavior clustering algorithms cannot directly handle multi-dimensional and variable-length sequence, called multi-valued discrete features (MDF), which exists in user behavior data. This leads to lower utilization of data and a decrease in the accuracy of user behavior similarity calculations. Aim at the above problems, a clustering method that combines association rule mining and MDF is proposed. Firstly, association rules (AR) are introduced into the calculation process of the Jaccard distance (ARJD). Then, the center update method is modified based on the K-mode clustering algorithm. Finally, a user behavior clustering algorithm ARJDKM combining ARJD and the center updating method is proposed. Experimental results on real data show that the ARJDKM algorithm outperformed the other comparison algorithms in purity, entropy, silhouette coefficient (SC). Finally, the proposed algorithm improves the quality of user behavior clustering.
A novel approach to minimize classifier computational overheads in Big Data using neural networks

Generally, Big Data denotes that datasets may not be apparent, accomplished, deal with or even acquired by the traditional tools in IT and the software or hardware tools in an acceptable period. This paper presents a new method for the minimization of the For the purpose of feature extraction, the Term Frequency–Inverse Document Frequency (TF–IDF) has been employed. A Feature Selection process takes place using correlation based feature selection (CFS) technique to advance the performance of prediction and further provide some faster predictors that are cost-effective to give a good insight of the fundamental process. Finally, classification process takes place by Particle Swarm optimization (PSO) based support vector machine (SVM) model. As neural networks can create some complex error surfaces using numerous local minima, a Back Propagation Neural Network (BPNN) may also fall into the local minima as opposed to a global minimum. SVMs will further minimize the upper bound of a true error. The PSO algorithm is simple in its implementation, easy to understand and is capable of solving several problems in optimization. The proposed model achieves a minimal misclassification rate of 0.133, precision of 0.846, recall of 0.87 and F-measure of 0.855.
Evaluating Visualizations of Sets and Networks that Use Euler Diagrams and Graphs

This paper presents an empirical evaluation of state-of-the-art visualization techniques that combine Euler diagrams and graphs to visualize sets and networks. Focusing on SetNet, Bubble Sets and WebCola – techniques for which there is freely available software – our evaluation reveals that they can inaccurately and ineffectively visualize the data. Inaccuracies include placing vertices in incorrect zones, thus incorrectly conveying the sets in which the represented data items lie. Ineffective properties, which are known to hinder cognition, include drawing Euler diagrams with extra zones or graphs with large numbers of edge crossings. The results demonstrate the need for improved techniques that are more accurate and more effective for end users.
Vehicle Detection Using Faster Recurrent Convolution Neural Network

Extension of fast R-CNN (Recurrent Convolution Neural Network) [1] and Recurrent-CNN [2] is Faster Recurrent CNN detection techniques. All these three strategies use convolutional neural networks (CNN). The distinction between them is the way to select the regions to work and the way those regions are categorised. Recurrent CNN and fast R-CNN used algorithm proposed region as a pre-processing step before running CNN. Algorithms are usually technical proposals such as Borders [3] or Selective Search [4] boxes, which are independent of CNN. Fast Recurrent CNN, the use of these systems becomes bottleneck process with relevance to CNN’s operation. Fast R-CNN solves this downside by applying the projected mechanism to use the region and then CNN. An RPN is a fully convex network that predicts object boundaries and object scores at each location simultaneously.
Towards Non-Intrusive Software Introspection and Beyond

Continuous verification and security analysis of software systems are of paramount importance to many organizations. The state-of-the-art for such operations implements agent-based approaches to inspect the provisioned software stack for security and compliance issues. However, this approach, which runs agents on the systems being analyzed, is vulnerable to some attacks, can incur substantial performance impact, and can introduce significant complexity. In this paper, we present the design and prototype implementation of a general-purpose approach for Non-intrusive Software Introspection (NSI). By adhering to NSI, organizations hosting in the cloud can as well control the software introspection workflow with reduced trust in the provider. Experimental analysis of real-world applications demonstrates that NSI presents a lightweight and scalable approach, and has a negligible impact on the performance of applications running on the instance being introspected.
Hybrid Self-Interactive Attentive Siamese Network for Medical Textual Semantic Similarity

The rapid development of medicine produces a large number of medical texts, but it is difficult to process these texts due to many similar sentences. Therefore, estimating the similarity of medical texts has become a key technology, filtering out medical texts quickly. Nowadays, many methods for estimation similarity between medical sentences extract semantic features mainly via Siamese network. However; these methods don't achieve the best results due to the large amount of noise in the texts. To improve the performance of the Siamese network, a hybrid self-interactive attention model is proposed in this paper. The aim is to reduce the noise of the text and strengthen the token with high correlation between the two texts. In addition, this proposed model also uses BERT as the embedding layer to carry out a preliminary pre-training of text. Then, two datasets are employed to verify the effectiveness of our method and our method achieves better results on Pearson correlation coefficient, compared with the other existing methods. The experimental results still indicate that the results of pre-trained BERT are better than that of Word2Vec, and the hybrid self-interactive attention model obtains better results due to the effect of interactive attention.
Design and Implementation of Toolbox Based on Component Technology

With the country's implementation of smart mines, Internet technology has been well integrated into the mine disaster prevention system. In order to improve the accuracy and timeliness of mine monitoring data, each mine has established a mine data monitoring system suitable for its situation. As the most important part of the monitoring software, the data monitoring module needs to be able to dynamically meet the various needs of users. This article mainly introduces the tools that use the component technology to package the data monitoring module into a toolbox. The user calls the dynamic link library through reflection technology to implement various functions in the toolbox, and implements the toolbox controls and data monitoring software developed by third-party platforms Organic combination. Solved the poor scalability of monitoring software, Problems requiring frequent software replacement for customers after system update. This method has proved its good flexibility and timeliness in a hydrological monitoring system.
An International Case Study on Student Achievement Assessment in the High School Credit System

The introduction of a high school credit system in 2022 follows in the steps of a plethora of curriculum studies which have focused on topics such as expanding the range of subjects offered and the minimum number of credits required for graduation. Moreover, sufficient discussion and policies regarding achievement assessment and the utilization of credits for college admissions are needed to implement an evaluation system in high schools. Against this backdrop, the present study investigated student achievement assessment in the USA, Germany, and Australia which currently implement a high school credit system which encompass graduation requirements and minimum credits, grade calculations, student evaluation methods, and the use of high school grades in college admissions. The results of the international case studies indicate that these countries - based on a high degree of teacher autonomy and trust strongly built into teachers’ evaluation - maintained a criterion-referenced evaluation system graded by achievement percentile according to national or state evaluation standards. In addition, teacher-specific evaluation was implemented and efforts were made to derive reliable evaluation results by clearly describing evaluation standards and ensuring that evaluation is objective and fair through teachers’ councils. Lastly, it was found that minimum achievement standards were set, what constitutes a failed credit stated, and credit recovery was allowed. In the case of failed credits, which have a significant impact on the overall achievement of credits and graduation, remedial measures were provided to support students. Some important implications derived from this international comparative study are as follows: (1) to implement a high school credit system in Korea successfully, criterion-referenced evaluation and teacher-specific evaluation should be introduced. However, measures should be taken to prevent grade inflation and secure reliability and fairness in teacher evaluation; and (2) graduation requirements and minimum achievement standards should be defined to include the criterion of credit failure and credit recovery should be offered.
Physical Visualization Design

We demonstrate PVD, a system that visualization designers can use to co-design the interface and system architecture of scalable and expressive visualization.
MazeRunVR: An Open Benchmark for VR Locomotion Performance, Preference and Sickness in the Wild

Locomotion in virtual reality (VR) is one of the biggest problems for large scale adoption of VR applications. Yet, to our knowledge, there are few studies conducted in-the-wild to understand performance metrics and general user preference for different mechanics. In this paper, we present the first steps towards an open framework to create a VR locomotion benchmark. As a viability study, we investigate how well the users move in VR when using three different locomotion mechanics. It was played in over 124 sessions across 10 countries in a period of three weeks. The included prototype locomotion mechanics are arm swing,walk-in-place and trackpad movement. We found that over-all, users performed significantly faster using arm swing and trackpad when compared to walk-in-place. For subjective preference, arm swing was significantly more preferred over the other two methods. Finally for induced sickness, walk-in-place was the overall most sickness-inducing locomotion method.
Decision support system for determining thesis supervisor using analytical hierarchy process (AHP) method

Abstract The determination of lecturers is important to facilitate the selection of lecturers in accordance with lecturers’ expertise and titles proposed by students. Currently, the assignment of determining the thesis supervisor is usually determined by the Head of Study Program, in the process of determining still using a conventional way by relying on personal knowledge. Often occurs less than an optimal decision, where the appointed lecturer, the field of expertise is less precise with the title of the proposed student thesis. Based on the above problems, it takes a solution that can consider the field of lecturer expertise in accordance with the criteria for determining lecturers, lectured subjects, conformity of thesis title topics, and duration of guidance. Data is processed from Google Scholar extraction, Letter of Decision of teaching each semester, student thesis title submissions, long guiding students, followed by Analytical Hierarchy Process (AHP) method, highly effective and structured method. The result obtained by the largest eigenvector value is 32% means that the proposed title of the proposed thesis is the variable that has influence on the determination of the lecturer, followed by the lecturer’s research interest with 30%, the lecture taught by the lecturer with 28% and the last criterion determine the supervisor is the old lecturer has guided by 8%.
Information Transmission across Cryptocurrency Markets and the Role of the Blockchain

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The Seven Year Glitch: Unpacking Beauty and Despair in Malfunction

I(am)MEI: 013709002488246. I was born in many countries - my accelerometer came from Germany, my battery from China, the lithium in my battery was mined in Chile, my gyroscope from Switzerland, my camera... from Japan. I was assembled carefully from these component parts, and had two less than careful owners before R picked me up from a reseller, and brought me back to his house in London, UK. We had a good time together - at first: he revelled in my speed and ability to find things, we viewed the world via a lens with infinite options. But I was not built to last. This is my story.
A Lightweight Association Rules Based Prediction Algorithm (LWRCCAR) for Context-Aware Systems in IoT Ubiquitous, Fog, and Edge Computing Environment

Proactive is one main aspect of ubiquitous context-aware systems in IoT environment. Ubiquitous context-aware systems in IoT environment needs a light-weight intelligent prediction techniques especially within fog and edge computing environment where technologies capabilities are poor. On the other hand, the data that ubiquitous context-aware systems depends on to learn is big. This paper suggests a light-weight prediction algorithm to help such system to work effectively. The proposed algorithm is improvement of RCCAR algorithm. RCCAR utilizes association rules for prediction. The contribution of this paper is minimize the number of association rules by giving a priority for associations that produced of high order itemsets before the lowest ones. The prediction is scored and formulated mathematically using confidence association rules measure. A real dataset is used in many different scenario experiments. The proposed algorithm achieves good with reasonable prediction score. For future work, extensive experiments with many datasets is recommended.
Usage Comparison between an English-Language and a French-Language E-Monograph Collection in a Bilingual Canadian University

This article reports the results of a quantitative and systematic analysis comparing usage rates between English-language and French-language e-monograph titles at Laurentian University, Canada. Full-text viewings were collected as usage metrics. English-language e-monographs exhibited medium strength linear relationships between collection size and usage. On the other hand, French-language e-monographs exhibited weak linear relationships between collection size and usage. The French-language e-monograph collection experienced a higher rate of full-text use, relative to size. The cost-per-viewing ratio for the English-language e-monograph collection was higher than those that calculated for the French-language collection.
Holistic Model for Designing a Climate Service Application on the KaiOS Platform

Interest towards building and operating climate services is constantly growing globally, especially regarding utilization of mobile technologies in those services in the Global South, in order to reach rural farmers effectively. However, multiple issues are currently limiting the design and development of these services for reaching their full impact.
A Proposal for Dual Data Selection Using Parallel Genetic Algorithm

Although huge number of features and instances give more information about the data yet it presents irrelevant and superfluous (noisy) features and instances. Therefore, there is a need to remove such type of features and instances for efficient classification. The more appropriate term used for simultaneous reduction of features and instances, called dual selection, leads to faster classification without sacrificing much for the classifier’s performance. In the current paper, we suggest a parallel genetic algorithm (PGA) for dual selection that allows an efficient selection of features as well as instances. The paper encapsulates experimental analysis on ten medium- and large-sized datasets. The results confirm the superiority of proposed PGA in terms of higher accuracy along with considerable reduction rates.
The criterion power for identifying symmetric models

Abstract The paper contains a theoretical material of research of power of entropy-parametrical criterion of the consent that used simultaneously both informative and statistical properties the samples data. As a result of the study, it was shown that the space of the reduced signs of the competing hypothesis is anisotropic. For this reason, in the entropy coefficient and anty-kurtosis space as the position as and the shape of the decision area depends on the direction and distance between the null and competing hypotheses. For various significance levels of the criterion, the dependences of the power of the entropy-parametric criterion on the modulus of the distance between the null and alternative hypotheses are obtained. These dependences are given for various directions in the space of reduced signs of distributions of the competing hypothesis provided that zero hypotheses is Laplace distributions.
In-database connected component analysis

We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relational database. The algorithm described is a linear-space, randomised algorithm, always terminating with the correct answer but subject to a stochastic running time, such that for any ε > 0 and any input graph G = 〈V,E〉 the algorithm terminates after O(log|V |) SQL queries with probability of at least 1 - ε, which we show empirically to translate to a quasi-linear runtime in practice.
Crafting Digital Experiences for Feminine Stress-care: An Exploratory Approach

This paper presents a series of preliminary studies aimed at developing a framework for designing personal stress-care technology for women. We engaged 28 women in different research activities to investigate their relationships with daily-life stress and self-care. The paper concludes by presenting the initial findings and future plans for taking this research forward, which include the design and development of a Voice User Interface (VUI) to facilitate self care.
Study of Identity-Based Encryption for Cloud Data Security

In this computer era, a new model has transformed the process of cloud computing, which is known as cloud computing. This model, i.e., cloud computing has strong features of “Utility computing” and “On-demand” services which has raised its demand in a broad way and is still going on day by day. With the growing use of cloud computing, it has become a thrust area to study data security in the model of cloud computing. Privacy and security issues hamper the embracing of cloud computing and storage in threatened environments. Because every user who outsources their data on cloud is interested to have a guarantee of integrity, confidentiality and access control. This paper presents the security and privacy threats in cloud computing which has added very significant concerns with the client. In this paper, we have studied the ID-based encryption model for security and privacy of cloud data.