Reviewerly/exHarmony huggingface dataset:
Accelerating SM2 Digital Signature Algorithm Using Modern Processor Features

The public key cryptographic algorithm SM2 is now widely used in electronic authentication systems, key management systems, and e-commercial applications systems. As an asymmetric cryptographic algorithm is based on elliptic curves cryptographic (ECC), the SM2 algorithm involves many complex calculations and is expected to be sufficiently optimized. However, we found existing SM2 implementations are less efficient due to the lack of proper optimization. In this paper, we propose Yog-SM2, an optimized implementation of SM2 digital signature algorithm, that uses features of modern desktop processors such as extended arithmetic instructions and the large cache. Yog-SM2 utilizes new features provided by modern processors to re-implement functions of big number arithmetic, prime field modular, elliptic curve point calculation, and random number generation. The use of these new hardware features significantly improves the performance of both SM2 signing and verifying. Our experiments demonstrated that the execution speed of Yog-SM2 exceeds four mainstream SM2 implementations in state-of-the-art cryptographic libraries such as OpenSSL and Intel ippcp. In addition, Yog-SM2 also achieves a better performance (97,475 sign/s and 18,870 verify/s) against the OpenSSL’s optimized implementation of ECDSA-256 (46,753 sign/s and 16,032 verify/s, OpenSSL-1.1.1b x64) on a mainstream desktop processor (Intel i7 6700, 3.4 GHz). It indicates that SM2 digital signature is promising in a widespread application scenarios.
Secure Deterministic Automata Evaluation: Completeness and Efficient 2-party Protocols

Secure computation (i.e., performing computation while keeping privacy of the inputs) is a fundamental research area in cryptography and a fundamental capability in the theory of computing. Deterministic automata evaluation is a fundamental computation problem, with numerous application areas, including regular expressions, string matching, constant-space computations. In this paper, we investigate the complexity of achieving secure 2-party deterministic automata evaluation protocols. We show black-box reductions between this problem and the problem of constructing secure 2-party information retrieval protocols, and viceversa. Using previous results, this implies various interesting consequences: completeness of secure deterministic automata evaluation in the class of problems having 2-party and multi-party secure function evaluation protocols (previously, only 2 less natural problems were showed to be complete, or non-constructive characterizations of complete problems were given), and, under standard cryptographic assumptions, a communication-efficient secure protocol for automata evaluation (no such problem was given in the literature) and a time-efficient secure protocol faster than applying Yao’s benchmark general solution.
Blockchain as Middleware for Exchange of Resources

Loyalty programs that allow customers to collect loyalty points are popular means for customer relationship building. At the moment, such points can be collected and spent only within the same shop (or shops from the same consortium), which makes it only interesting for customers visiting that particular store frequently. However, especially with the increase of e-shops, customers easily switch between stores. With this new shopping behaviour, such programs are less attractive and as such become less effective, requiring a more novel approach. One possibility could be to allow customers to collect and spent loyalty points across stores. In fact, this is just one use case of a more general issue. There are multiple cases in which people receive (digital) resources from organizations or other parties and want to use or exchange them across other organizations or parties. In this paper, we propose a generic framework that allows customers to exchange resources across different parties. We propose to implement this framework with a blockchain-based solution, making the customers in full control and awareness of their available resources as well as creating a decentralized, trustable and safe environment. We present the major research questions, the general concepts of our generic framework, the proposed solution, and a proof of concept of this solution.
Heuristic Clustering Algorithms

This chapter is devoted to the most popular heuristic partitional clustering algorithms such as k-means, k-medians, and k-medoids. In addition, we give an overview of some clustering algorithms based on mixture models, self-organizing map, and fuzzy clustering. The description of these algorithms as well as their flowcharts is presented. The convergence results for the k-means and the k-medians algorithms using nonsmooth optimization techniques are discussed.
Using Very Deep Convolutional Neural Networks to Automatically Detect Plagiarized Spoken Responses

This study focuses on the automatic plagiarism detection in the context of high-stakes spoken language proficiency assessment, in which some test takers may attempt to game the test by memorizing prepared source materials before the test and then adapting them on-the-fly during the test to produce their spoken responses. When trying to identify such instances of plagiarism, experienced human raters attempt to find salient matching expressions that appear both in potential source materials and the test responses. This motivates an approach that visualizes a grid of lexical matches between a test response and a source and then applies state-of-the-art image recognition techniques to detect patterns of matching sequences. This study employs Inception networks-very deep convolutional neural networks-to build automatic detection models. The system achieves an F1-score of 79.6% on the class of plagiarized responses outperforming a baseline system based on word sequence matching (F1-score of 74.1%).
Sentiment Analysis Using Gini Index Feature Selection, N-Gram and Ensemble Learners

Sentiment analysis is becoming increasingly popular due to its wide variety of applications. The use of machine learning is paving a way to sophisticated algorithms so as to have better performance. In this paper, the sentiment analysis is performed on a data set containing movie reviews. Firstly, the n-gram technique is used to analyse more than one word in a text document. Second, we introduce the Gini index method for feature selection. Then we use the ensemble learning technique for classification. Finally, the results are procured from the confusion matrix.
Real-Time Detection and Tracking Using Hybrid DNNs and Space-Aware Color Feature: From Algorithm to System

Object detection and tracking are vital for video analysis. As the development of Deep Neural Network (DNN), multiple object tracking is recently performed on the detection results from DNN. However, DNN-based detection is computation-intensive. In order to accelerate multiple object detection and tracking for real-time application, we present a framework to import the tracking knowledge into detection to allow a less accurate but faster DNN for detection and recover the accuracy loss. By combining different DNNs with accuracy-speed trade-offs using space-aware color information, our framework achieves significant speedup (6.8$$\times $$) and maintains high accuracy. Targeting NVIDIA Xavier, we further optimize the implementation from system and platform level.
Transforming Maple into an Intelligent Model-Tracing Math Tutor

This article describes an intelligent, model-tracing system for tutoring expansion and factoring of algebraic expressions. The system is implemented as a set of procedures in a Maple document that tutor a breadth of 18 top-level mathematical skills (algebraic operations). Twelve (12) skills for expansion (monomial multiplication, monomial division and power of monomial, monomial-polynomial and polynomial-polynomial multiplication, parentheses elimination, collection of like terms, identities square of sum and difference, product of sum by difference, cube of sum and difference) and six (6) skills for factoring (common factor, identities square of sum and difference, product of sum by difference, quadratic form by sum and product, quadratic form by roots). These skills are further decomposed in simpler ones giving a deep domain expertise model of 68 primitive skills. The tutor has two novel features: (a) it exhibits intelligent task recognition by identifying all skills present in any expression through intelligent parsing, and (b) for each identified skill, the tutor traces all the sub-skills, a feature called deep model tracing. Furthermore, based on these features, the tutor achieves broad knowledge monitoring by recording student performance for all skills present in any expression.
Improved algorithm of Context Graph based on feature selection

In order to solve the problem of low efficiency of traditional theme crawlers in searching theme pages, the crawling algorithm based on Context Graph was discussed. After analyzing the working principle and process of the algorithm, we introduced a new algorithm idea named feature selection algorithm. This new algorithm improved the original TF-IDF formula accordingly and solved the algorithm problems.
WIZARD

When an Android mobile device starts for the first time, the Android Setup Wizard (ASW) is loaded so the user can do his initial configurations, such as Wi-Fi network, date, user account, data restore, terms and condition, and others. However, in the context of manual or automated tests, every time a device starts for the first time, this ASW should be consistently configured before the tests begin. This configuration should not affect the test measurements to make them reproducible. It is impracticable to manually pass through all the steps of the ASW before every new test since this limits scalability. Moreover, a manual process can be error-prone, since two testers might use different navigation paths in the ASW, and this can affect the output. An automated script to do this task is not trivial as steps of the ASW can change depending on the device model, Android version (N, O, P, Q, etc.) and vendor (Verizon, Global, etc.). This means that the number of steps and the actions that should be executed in each of them can vary. Therefore, we propose WIZARD, an intelligent system that navigates automatically and consistently through the steps of the ASW, allowing the specialist to customize this navigation. To do that, WIZARD uses information retrieval (IR), vector space model (VSM), and re-rank techniques. We validate our approach on a set of real-world and commercially-available Android builds of a large company that comprises 1505 unique records. In the task of predicting the action to execute, our approach achieved an average [email protected] of 98,82% and an [email protected] of 99,87%. To the best of our knowledge, WIZARD is the first system that automatically navigates in the ASW to prepare devices for reproducible tests.
Optimization and Improvement of Lucene Document Sorting Algorithm

This paper analyzes the implementation principle of lucene document sorting algorithm and index structure, and proposes the weight optimization scheme of title field and summary field in text. In order to improve the efficiency of data compression, the principle of PForDelta algorithm is analyzed in detail, and the Big-Better strategy is proposed and verified. Effectively optimizes the efficiency of the Lucene architecture search engine.
Perceptual level of intonation in whispered voice

In this paper, I propose to analyze the prosodic realization of two modalities (declarative and interrogative) through perception study of whispered voice in French.I present results of two modality identification tasks.Results of identification test based on natural stimuli show a good identification rate of the modality.The values of the duration of final syllable seem to be the main acoustic cue that is relevant in the identification of modality.I present also the results of modality identification task based on re-synthesized stimuli allowing to control the role played by duration parameter.Results of this second experiment show modality identification depends on perceptual level of the duration.
Research on Virtual Machine Load Balancing Based on Improved Particle Swarm Optimization

In order to solve a series of data concurrency problems caused by massive terminal data access platform in power management system, the virtualization technology of cloud computing is analyzed, and a better load balancing method is proposed to reasonably place virtual resources on the physical host cluster. Firstly, the K-nearest neighbor classification algorithm is used to classify high-concurrence request tasks into groups. Then the particle swarm optimization algorithm based on discussion mechanism is used to solve the optimal server. Compared with the common cloud center load balancing strategy, the improved algorithm can effectively optimize the load balancing in the power management cloud computing system.
Automatic Composite Action Discovery for Hierarchical Reinforcement Learning

Even with recent advances in standard reinforcement learning, hierarchical reinforcement learning has been discussed as a promising approach to solve complex problems. From human-designed abstraction, planning or learning with composite actions are well-understood, but without human intervention, producing abstract (or composite) actions automatically is one of the remaining challenges. We separate this action discovery from reinforcement learning problem and investigate on searching impactful composite actions that can make meaningful changes in state space. We discuss the efficiency and flexibility of the suggested model by interpreting and analyzing the discovered composite actions with different deep reinforcement learning algorithms in different environments.
Bunkers: Jail Application Level Firewall for the Mitigation and Identification of Service Takeover Attacks on HardenedBSD

Jails are a lightweight operating-system based virtualization framework that allow safe delegation of subsets of a FreeBSD operating system to guest root users. HardenedBSD is a security-enhanced fork of FreeBSD, with Jail capabilities. In this paper we introduce Bunkers for Bank IT infrastructure security. Bunkers are security-enhanced HardenedBSD jails having only UNIX domain sockets enabled, and refusing all other types of socket creation including networking sockets. Bunkers also disable the execve() system call inside and only allow bit exact validated binaries from a global whitelist to be loaded and executed.The main objectives are to prevent elevation of privilege attacks and to isolate remote payloads and exploits from their source of origin. Bunkers detect, log, monitor and prevent all attempts to use network communications or unwanted binaries by isolating all the internal processes to UNIX domain sockets and filtering the execve() system call. Two use-cases are presented for isolating the ClamAV antivirus engine and all the necessary compressed file unpackers into HardenedBSD Bunkers: for e-mail security in a store and forward system and a real mail server and for web browsing security through the Squid proxy. Extensive benchmarks show that in both cases, for store and forward systems and for timely content delivery web systems the impact of the Bunker kernel module is comparable to rival approach Integriforce or with Regular Jails. More importantly, enforcing UNIX domain sockets for internal communication provides faster and safer inter-process communication mechanisms, between service processes and between Jails. The bit-exact execve() firewall has a consistent 13%–19% additional computation regardless of the type of service protected (web application firewall, SQL database). For the utmost security of mission-critical services we consider the results to be adequate.
The Past, Present, and Future of Virtual Reality in Higher Education

This chapter focuses on how Virtual Reality (VR) can be used to educate students in a variety of disciplines. Authors include a comprehensive synthesis of previous virtual reality in the educational setting literature. They also provide specific examples of virtual reality usage in the education setting utilizing specific VR class assignments from multiple universities. Based on their comprehensive review of previous research, the authors provide suggestions for future research and application of VR both inside and outside of the classroom. Two specific VR class assignments/activities are incorporated and can be utilized by professors to provide undergraduate and graduate students with an introduction to the application of VR.
Performance Considerations for Edge Blockchain Systems in Emerging 5G Data Networks

As edge computing applications become increasingly relevant to society, data processing and storage requirements are expected to increase at the 5G networked edge. At the same time, with the emergence of blockchain and distributed ledger technologies, one can provide support for trust, immutability, and transparency to share information among networked entities at the edge and in the cloud in 5G networks. A performance model for a permissioned private blockchain platform is developed. Depending on the latency sensitivity and throughput requirements of a 5G edge application, different possibilities are explored to provide support for blockchain technology at the edge. In particular, applications to 5G use-cases are considered, and lazy ledger decoupling between the 5G edge and the 5G cloud is proposed to meet edge latency constraints.
Processing of Big DNA Sequence Alignment on Hadoop Cluster

The algorithms for sequence alignment are among the main tools of bioinformatics. They are used for sequence searches and alignment of similar sequences. In this paper, we present research work related to processing of Big DNA (Deoxyribonucleic acid) sequence alignment using Apache Hadoop framework. We present different algorithms for pattern matching and their usability in DNA sequence alignment. We propose the Hadoop implementation of these algorithms and test them on a cluster of commodity computers for various Big data size. The experimental evaluation are performed and results demonstrate the feasibility of our approach.
Dynamic Cloud Resources Allocation

Many software companies have clients that use Microsoft Azure services. Clients may have varying needs for resources, so Microsoft Azure has a very dynamic feature called elastic pool that allows resources to expand and shrink automatically on demand. However, this dynamic feature is very costly both for the clients and the software companies. Thus, there is a growing need to be able to predict the usage ahead of time on daily basis. In this paper we propose and develop an intelligent usage prediction model using the user's resource usage history. According to our research, the work done till date is limited to other specific cloud providers or private servers but none related to Microsoft Azure. The classification algorithm that we use is LSTM. However, we have also report and document results obtained by ARIMA, SVM and Bayesian Networks. The best performance is given by LSTM.
Pengaruh Partisipasi Orang Tua dan Motivasi Belajar Terhadap Hasil Belajar IPA Siswa SMPN 5 Bittuang

Abstract: Motivation to learn is a mental impulse that moves and directs human behavior to learn. Learning motivation can be influenced by oneself (intrinsic), which is based on the need to learn, and from outside themselves (extrinsic), namely motivation that comes from the family (especially parents). Parental attention is an interaction developed by parents characterized by care, warmth, approval, and various positive feelings of parents towards children. The purpose of this study was to determine the relationship between the attention of parents with learning motivation towards learning outcomes of Smp Negeri 5  Bittuang. This research is descriptive correlative with a cross sectional design. The sampling technique used is proportional random sampling. Data collection used is through questionnaires, interviews and documentation. The research data was processed using simple regression statistical techniques, multiple regression, and second level partial correlations. As a prerequisite for analysis, a normality test and heredity test were carried out. Keywords: Parental Attention, Learning Motivation, and Learning Outcomes.
An Analysis of Influence of Emoticons on Affective Impressions Feeling from Tweets

In this paper, we investigated how Twitter users perceived affective impressions from tweets (with and without emoticons), and formulated the influence of emoticons on affective impressions. Initially, we conducted questionnaires, and quantified the impressions associated with three types of text: tweets with emoticons, tweets without emoticons, and emoticons. Multiple regression analysis was then applied to the three types of impression data, and consequently, multiple regression equations representing the relationships among them were obtained, where impression data on the tweets with emoticons were used as the objective variable, and impression data on the tweets without emoticons and the emoticons were used as the explanatory variables. Finally, the accuracy of the equations was estimated for learned and unlearned data, and their effectiveness was shown. Note that our target impressions are limited to the following eight types: "Offensive and/or Unpleasant," "Negative," "Good feeling," "Happy and/or Pleasant," "Positive," "Warm feel," "Gloomy," and "Scary."
Reliability Design against Catastrophic Failure Caused by Rare Event

When a system involves storage, control, usage and transformation of large amounts of energy, failures can have catastrophic consequences. However, ultimate failure is almost always preceded by a chain of events involving multiple factors, including system configuration, coupling behavior, interactions, unexpected feedback loops, weak links, operator errors, random events, common mode failures, and the like. In this paper, the position is taken that no matter how well a system has been designed, failure is inevitable. By thinking in terms of energy and process physics, a new tool to assist in this analysis, the Energy Expansion Tree, is employed to look for opportunities to short circuit the chain of events in such a way as to achieve relatively fail-safe behavior.
Analysis of Cryptographic Communications Using Bit-Plane Measures and Fuzzy Computing

Saha, Suprativ Bhattacharya, TanmayIn cryptographic communications, messages may be transmitted in plain form or in encrypted form with same encryption keys due to customer’s mistakes and system’s weaknesses. Such communications should never happen in any situation as it is very dangerous and can be misused by adversaries. Identification of plain messages and crypts with same encryption keys is a very important problem to analyze such communications. In the paper, we consider the problem of identification to segregate traffic of encrypted visual messages as such form of messages are being used widely over communication networks in current era of information technology. We use bit-plane specific image measures and fuzzy computing scheme in our methodology of identification of plain images and encrypted images with same keys from encrypted images. Bit-plane specific measures are the row-wise (column-wise) frequency of ones, maximum run length and correlation between adjacent rows (columns) which exhibit characteristics of images and help to analyze images at bit-plane level. The results indicate that the methodology presented is able to identify plain images and encrypted images with same keys successfully.