Reviewerly/exHarmony huggingface dataset:
Performance Analysis of a Hybrid Approach to Enhance Load Balancing in a Heterogeneous Cloud Environment

Cloud computing is rapidly becoming more advanced. It has become more attractive due to its cloud potentials of being easy to use and anywhere accessibility in comparison to other technologies. Load balancing is a very important component of efficient operations in the cloud computing environment. Several algorithms have been designed but yet unable to rectify the holes found in the performance area like minimizing response time, processing time and cost. In this work, it is proposed a hybrid approach that is devoted to enhancing performance for the cloud system user putting the concepts of enhanced Throttled and Equally spread current execution algorithms(ESCE) into use. The proposed hybrid algorithm was implemented in cloud analyst environment and the results obtained were analyzed. The performance of the proposed hybrid algorithm was evaluated with the existing algorithms ESCE and the improved throttled using the response time, data processing time, cost,&nbsp; physical memory and storage capacity. The results of the evaluation analysis show that with network delay, physical memory and the storage capacity, the Performance differences in various scenario vary, where scenario 1 is 10.5 (ms) average response time and processing time, $ 2.10 total cost. Scenario 2 ,5.6 (ms) response time and 8.5(ms)processing time $2.03 cost but the ESCE recoded best minimum response time of 40.30(ms). Scenario3,8.3 (ms) response time, 3.5 (ms)processing time,$2.00 cost. scenario 4 of 0.31(ms) response time, the processing time of 2.41(ms) and cost of $2.14 but the improved throttled is best in terms of minimum processing time with 2.00(ms).scenario 5,2.3 (ms) response time ,processing time 1.1 (ms)and cost of $2.00. Considering the physical memory, the fixed physical memory provided a better average response time of 0.50(ms) than the varied physical memory, and the varied physical memory provides a&nbsp; better maximum processing time of 5.03(ms). In terms of storage capacity, high storage capacity provides a better response time of&nbsp; 320.02 (ms). This work recommends the adaptation of hybridized improved Throttled and ESCE&nbsp; algorithms by cloud service providers so as to get better performances.
Advanced profiling for probabilistic Prime+Probe attacks and covert channels in ScatterCache.

Timing channels in cache hierarchies are an important enabler in many microarchitectural attacks. ScatterCache (USENIX 2019) is a protected cache architecture that randomizes the address-to-index mapping with a keyed cryptographic function, aiming to thwart the usage of cache-based timing channels in microarchitectural attacks. In this note, we advance the understanding of the security of ScatterCache by outlining two attacks in the noise-free case, i.e. matching the assumptions in the original analysis. As a first contribution, we present more efficient eviction set profiling, reducing the required number of observable victim accesses (and hence profiling runtime) by several orders of magnitude. For instance, to construct a reliable eviction set in an 8-way set associative cache with 11 index bits, we relax victim access requirements from approximately $2^{25}$ to less than $2^{10}$ . As a second contribution, we demonstrate covert channel profiling and transmission in probabilistic caches like ScatterCache. By exploiting arbitrary collisions instead of targeted ones, our approach significantly outperforms known covert channels (e.g. full-cache eviction).
Locally Linear Image Structural Embedding for Image Structure Manifold Learning

Most of existing manifold learning methods rely on Mean Squared Error (MSE) or $$\ell _2$$ norm. However, for the problem of image quality assessment, these are not promising measure. In this paper, we introduce the concept of an image structure manifold which captures image structure features and discriminates image distortions. We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold. The LLISE is inspired by Locally Linear Embedding (LLE) but uses SSIM rather than MSE. This paper builds a bridge between manifold learning and image fidelity assessment and it can open a new area for future investigations.
Hybrid Projection For Encoding 360 VR Videos

During the past five years, tons of economic 360 VR cameras (e.g., Ricoh Theta, Samsumg Gear360, LG 360, Insta 360) are sold in the market. While 360 VR videos become ubiquitous very soon, 360 VR video standardization is still under discussion in the digital industry, and more concrete efforts are desired to accelerate its standardization and applications. Though ERP has been widely used for projection and packing layout while encoding 360 VR videos, it has severe projection distortion near poles. In this paper, we introduce a new format for encoding and storing 360 VR videos using hybrid cylindrical projection after thoroughly analyzing the problems with ERP. We show that our new hybrid format can minimize stretching distortion and generate well balanced pixel distribution in the resulting projection.
Cyberbullying and Criminal Law

As a result of the rapid growth and availability of information and communication technology (ICT) traditional bullying and harassment activities have started to appear on the internet. Current forms of cyberbullying and cyberharassment are a cause of great concern with respect to children’s safety and well-being. Cyberbullying lacks a universally accepted definition, namely there is no consensus on a single definition of cyberbullying - which is, after all, generally understood as bullying occurring on the internet - determined at an international or European level. However, in this study we have identified the distinctive features of cyberbullying, that is, the high degree of anonymity, power imbalance between the offencer and the victim, potentially public nature of cyberbullying and defencelessness of the victim, and the high probablity of repetition with anonymity being one of the unique features of cyberbullying. We shall analyse all these elements of cyberbullying, respectively. After this conceptual analysis, we shall assess the approaches and solutions that have been adopted towards cyberbullying in a comparative manner. Finally, we will address the question of whether acts of cyberbulllying should be criminalized and we will discuss the employability of options other than criminal law.
Making (Implicit) Security Requirements Explicit for Cyber-Physical Systems: A Maritime Use Case Security Analysis

The increased connectivity of critical maritime infrastructure (CMI) systems to digital networks have raised concerns of their vulnerability to cyber attacks. As less emphasis has been placed, to-date, on ensuring security of cyber-physical maritime systems, mitigating these cyber attacks will require the design and engineering of secure maritime infrastructure systems. Systems theory has been shown to provide the foundation for a disciplined approach to engineering secure cyber-physical systems. In this paper, we use systems theory, and concepts adapted from safety analysis, to develop a systematic mechanism for analysing the security functionalities of assets' interactions in the maritime domain. We use the theory to guide us to discern the system's requirement, likely system losses, potential threats, and to construct system constraints needed to inhibit or mitigate these threats. Our analyses can be used as springboards to a set of principles to help enunciate the assumptions and system-level security requirements useful as the bases for systems' security validation and verification.
Fully Convolutional Search Heuristic Learning for Rapid Path Planners

Path-planning algorithms are an important part of a wide variety of robotic applications, such as mobile robot navigation and robot arm manipulation. However, in large search spaces in which local traps may exist, it remains challenging to reliably find a path while satisfying real-time constraints. Efforts to speed up the path search have led to the development of many practical path-planning algorithms. These algorithms often define a search heuristic to guide the search towards the goal. The heuristics should be carefully designed for each specific problem to ensure reliability in the various situations encountered in the problem. However, it is often difficult for humans to craft such robust heuristics, and the search performance often degrades under conditions that violate the heuristic assumption. Rather than manually designing the heuristics, in this work, we propose a learning approach to acquire these search heuristics. Our method represents the environment containing the obstacles as an image, and this image is fed into fully convolutional neural networks to produce a search heuristic image where every pixel represents a heuristic value (cost-to-go value to a goal) in the form of a vertex of a search graph. Training the heuristic is performed using previously collected planning results. Our preliminary experiments (2D grid world navigation experiments) demonstrate significant reduction in the search costs relative to a hand-designed heuristic.
Relational Network of People Constructed on the Basis of Similarity of Brain Activities

The relational network of people (RNP) model has been attracting the interest of not only researchers but also industrial engineers. RNP can be constructed from friend lists in online social networking services (SNSs) and from inter-contact logs between individuals. One of the killer applications of RNP is the prediction of user demands, which is key to maximizing user satisfaction in content delivery services such as video streaming and video advertising. It is well known that an RNP representing social closeness between individuals (a so-called social network) can estimate user preferences simply, as we expect that people close to each other will have similar preferences. However, although there are many metrics that enable the social closeness between individuals to be measured, it is unclear which metric is best suited for individual services. Therefore, this paper introduces a new approach based on brain imaging. Brain imaging using functional Magnetic Resonance Imaging (fMRI) is powerful because it enables us to directly observe how a video content stimulates the brains of individual people. We propose a brain imaging-based RNP that represents the similarity of video-evoked brain activities between people as a network graph. We show an application scenario featuring predictive content delivery using the proposed RNP in which, when a user shows interest in a video content in some way, other users close to him or her can be expected to also be interested in it because their brain activities are correlated. Through numerical evaluation using multiple real datasets obtained by fMRI, we demonstrate that the proposed RNP is generalizable across brain imaging results for different sets of video content, thus suggesting that brain imaging data can be used to robustly generate RNP for utilization as a powerful tool for estimating user preferences.
Language Independent POS-tagging Using Automatically Generated Markov Chains (S)

This paper proposes a method to predict word grammatical classes using automatically generated discrete-time Markov chains to model typical sentences.Such method advantage relies on the availability of input resources needed to build an efficient and effective solution to virtually any language, dialect, or domain lingo.One of the main advantages of the proposed method is its simplicity when compared to other sophisticated approaches based on Hidden Markov Models or even more complex formalisms.The proposed method is instantiated to an example and we show that the achieved efficiency and effectiveness bring advantages to traditional similar solutions.
Improved Face Detection Using YCbCr and Adaboost

Accurate face detection plays a crucial role in surveillance and security. It has been a challenging issue because of the fact that human faces are dynamic object with high degree of variability in their appearance. This paper proposed a method of face detection which involves pre-processing of input images to extract skin tone in YCbCr domain followed by Haar cascade classifiers to detect a face in the extracted area. These classifiers are selected by AdaBoost training with help of particle swarm optimization. On the exhaustive study on various images, it is elucidated that the proposed approach provides superior performance compared to the existing Viola–Jones technique. The proposed algorithm is also able to detect the side or occluded faces in the input image.
The Curious Case of Connectionism

Abstract Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest.
Entanglement balance of quantum <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>e</mml:mi><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mi>e</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> scattering processes

The theory of quantum information constitutes the functional value of the quantum entanglement, i.e., quantum entanglement is essential for high fidelity of quantum protocols, while fundamental physical processes behind the formation of quantum entanglement are less relevant for practical purposes. In the present work, we explore physical mechanisms leading to the emergence of quantum entanglement in the initially disentangled system. In particular, we analyze spin entanglement of outgoing electrons in a nonrelativistic quantum $(e,2e)$ collision on a target with one active electron. Our description exploits the time-dependent scattering formalism for typical conditions of scattering experiments, and contrary to the customary stationary formalism operates with realistic scattering states. We quantify the spin entanglement in the final scattering channel through the pair concurrence and express it in terms of the experimentally measurable spin-resolved $(e,2e)$ triple differential cross sections. Besides, we consider Bell's inequality and inspect the regimes of its violation in the final channel. We address both the pure and the mixed initial spin state cases and uncover kinematical conditions of the maximal entanglement of the outgoing electron pair. The numerical results for the pair concurrence, entanglement of formation, and violation of Bell's inequality obtained for the $(e,2e)$ ionization process of atomic hydrogen show that the entangled electron pairs indeed can be formed in the $(e,2e)$ collisions even with spin-unpolarized projectile and target electrons in the initial channel. The positive entanglement balance---the difference between entanglements of the initial and final electron pairs---can be measured in the experiment.
Pengaruh Metode Tanya Jawab Terhadap Hasil Belajar Siswa

ABSTRACT Yayan Abdika,Student ID 911415115 “The Influence of the Question and Answer Method on Students” UnderGraduate Thesis. Department of Economic Education, Faculty of Economics Universitas Negeri Gorontalo., 2019. The Purpose of the Study is to find out “The Influence of the Question and Answer Method on Students. The Method Used in this Study was a quantitative method with population of 103 students from a population of 30 students. Further, data collection techniques used primary and secondary data, primary data obtained directly in the field through questionnaires and observations and secondary data obtained were from data that has been processed or throught literature. This study employed simple regression with a series of statatical tests. The Results of the analysis showed that there is a positive and significant effect of the question and answer method on students learning outcomes, thus each change in the unit of question and aswer method would improve the learning outcomes. The regression results shows that 43.2% can be explained while the remaining 56.8% is explained by other variables Keyword : Question and Answer Method, Students, Learning Outcames
Classical Leakage Resilience from Fault-Tolerant Quantum Computation

Physical implementations of cryptographic algorithms leak information, which makes them vulnerable to the so-called side-channel attacks. The problem of secure computation in the presence of leakage is generally known as leakage resilience. In this work, we establish a connection between leakage resilience and fault-tolerant quantum computation. We first prove that for a general leakage model, there exists a corresponding noise model in which fault tolerance implies leakage resilience. Then we show how to use constructions for fault-tolerant quantum computation to implement classical circuits that are secure in specific leakage models.
Reducing Implementation Efforts in Continuous Auditing Certification Via an Audit API

Continuous auditing reduces the frequency in which compliance is verified. This results in more trustworthiness for the cloud service and therefore lowers the barrier of adopting cloud for customers in high-risk sectors such as banking. However, implementing continuous auditing as of today is a tedious task and not standardized, which leaves the service providers implementing the whole audit process and the technical infrastructure. We are proposing a solution for this problem by defining a standardized way of establishing the continuous auditing process for an IT infrastructure as well as providing the necessary tools as a reference implementation. In this paper we present how complexity in setting up the technical requirements for continuous auditing can be highly reduced by providing an easy to implement Audit API and continuous auditing methodology.
تطوير اللغز الرقمي لمادة التطبيق الصرفي 1 على الهاتف المحمول كالتدريبات الإضافية خارج الفصل الدراسي

Abstract: This research is generally aimed on producing a cellphone-based digital learning quiz for Tathbiq Sharfi I course in Arabic Department, Faculty of Letters, State University of Malang. Specifically, this research aimed at describing (1) the characteristic of a cellphone-based digital learning quiz for Tathbiq Sharfi I course, (2) degree of properness of a cellphone-based digital learning game for Tathbiq Sharfi I course, (3) the model of using of a cellphone-based digital learning quiz for Tathbiq Sharfi I course. The development of this media is using Sadiman &amp; Rahardjo’s development design. The data collection is done by observation and giving questionnaire. The softwares used on developing this media are adobe flash CS 3, action script 2, and flash player. Based on the scoring by the material expert, it is known that this digital learning quiz is highly valid. Meanwhile, based on the scoring done by the media expert, the result shows that this digital learning game is valid to use. On the small-group tryout, it is known that this digital learning game is highly valid, and the field test also shows that this digital learning game is highly valid to use. Therefore, this cellphone-based digital learning game for Tathbiq Sharfi I course is highly valid to be used.Keywords: Digital Quiz, Cellphone, Learning Tathbiq Sharf I
Path Planning Research Based on An Improved A* Algorithmfor Mobile Robot

Abstract With the development of technology, the path planning technology of mobile robots has received extensive attention. The principle and process of A* algorithm are outlined. Because there are many break points in the A* algorithm, the path is not smooth and there are too many problems in searching for grid points. So we need improve the A-star algorithm. Firstly, in order to reduce the number of search grid points, a threshold value N is added to the openlist. If the number of search times is greater than N and the first node inserted is not expanded, the node is set to the highest priority. Secondly, we use the path planning based on the combination ofFloyd algorithm and A* algorithm to remove redundant nodes and reduce the sharpness of the breakpoints. Finally, the geometric optimization process results in a smooth path. Simulations verify that the improved A* algorithm reduces the path length and the path is smoother, more suitable for robot navigation.
SCUBA VR: Submersible-Type Virtual Underwater Experience System

In this paper, we propose the development of an underwater virtual reality (VR) system for scuba training. The aim is to enable scuba students to experience an arbitrary underwater environment and enable training using VR technology in limited water environments such as a pool or shallow water. Using this system, scuba training, which can currently only be done by hands-on training, can be reproduced through a VR experience in a pool. This system consists of a cable-connected PC-HMD (UWHMD: Underwater Wired Head Mounted Display) that can be used underwater and a motion capture system. According to the pilot test, it was confirmed that position tracking was successful at widths of about 2.25 m, lengths of about 3.0 m, and depths of about 1.3m.
Game Equivalence and Bisimulation for Game Description Language

This paper investigates the equivalence between games represented by state transition models and its applications. We first define a notion of bisimulation equivalence between state transition models and prove that it can be logically characterized by Game Description Language (GDL). Then we introduce a concept of quotient state transition model. As the minimum equivalent of the original model, it allows us to improve the efficiency of model checking for GDL. Finally, we demonstrate with real games that bisimulation equivalence can be generalized to characterize more general game equivalence.