Reviewerly/exHarmony huggingface dataset:
Incremental Map-Reduce on Repository History

Work on Mining Software Repositories typically involves processing abstractions of resources on individual revisions. A corresponding processing of abstractions of resource changes often depends on working with all revisions of the repository history to guarantee a high resolution of the measured changes. Abstractions of resources and abstractions of resource changes are often very related up to the point that they can be used interchangeably in the processing. In practice, approaches working with abstractions processed over high revision counts face a scalability challenge. In this work, we contribute to the challenge by incrementalizing the processing of repository resources and the corresponding abstractions. Our work is inspired by incrementalization theory including insights on Abelian groups, group homomorphisms and indexing. We provide a map-reduce interface that enables calls to foreign functionality and convenient operations for processing abstractions, such as mapping, filtering, group-wise aggregation and joining. Apache Spark is used for distribution. We compare the scalability of our approach with available MSR approaches, i.e., with LISA that reduces redundancy and with DJ-Rex that migrates an analysis to a distributed map-reduce framework.
Expert System for Diagnosis of Motor Failures in Electronic Injection Vehicles

Today, cars are an indispensable element in the society, as well as the vehicle diagnosis of minor and serious mechanical failures. This process is carried out through two methods: (i) manually, inspecting possible common causes; and (ii) automatically, using a failure identification scanner. In both cases the assistance of a car expert is required. However, how could a common user briefly diagnose vehicle failures? The objective of this project has been to build an expert system module for vehicular diagnosis to help the common user, identifying automotive failures and the severity of the vehicle damage. It also helps to prevent major damages and possible accidents, as well as to achieve a technical and effective communication when the situation is being explained to the mechanical assistance which can be even by telephone. The module design was composed by four phases: (i) do background research about failure diagnosis, (ii) production rules; (iii) inference engine; and (iv) user interface. The results show that the expert system module is 71,43% effective, so that it helps the common user to identify electronic engine failures without the assistance of a professional in the area.
Accurate and fast single shot multibox detector

With the development of deep learning, the performance of object detection has made great progress. However, there are still some challenging problems, such as the detection accuracy of small objects and the efficiency of the detector. This study proposes an accurate and fast single shot multibox detector, which includes context comprehensive enhancement (CCE) module and feature enhancement module (FEM). To integrate more efficient information when aggregating context information, the conv4_3 and fc_7 feature maps are merged to design the CCE module. To obtain more fine‐grained feature information, this study presents a FEM and special feature enhancement module (FEM‐s) module that can fuse different receptive field sizes to better adapt to the scale change of the object. Compared to existing methods based on deep learning, the proposed method helps to gradually produce more detailed feature maps with better performance. Under the premise of ensuring real‐time speed, the authors network can achieve 81.2 mean average precision on the PASCAL VOC 2007 test with an input size of 320 × 320 on a single Nvidia 2080Ti GPU.
Isometry-dual flags of AG codes

Consider a complete flag $$\{0\} = C_0< C_1< \cdots < C_n = \mathbb {F}^n$$ of one-point AG codes of length n over the finite field $$\mathbb {F}$$ . The codes are defined by evaluating functions with poles at a given point Q in points $$P_1,\dots ,P_n$$ distinct from Q. A flag has the isometry-dual property if the given flag and the corresponding dual flag are the same up to isometry. For several curves, including the projective line, Hermitian curves, Suzuki curves, Ree curves, and the Klein curve over the field of eight elements, the maximal flag, obtained by evaluation in all rational points different from the point Q, is self-dual. More generally, we ask whether a flag obtained by evaluation in a proper subset of rational points is isometry-dual. In Geil et al. (2011) it is shown, for a curve of genus g, that a flag of one-point AG codes defined with a subset of $$n > 2g+2$$ rational points is isometry-dual if and only if the last code $$C_n$$ in the flag is defined with functions of pole order at most $$n+2g-1$$ . Using a different approach, we extend this characterization to all subsets of size $$n \ge 2g+2$$ . Moreover we show that this is best possible by giving examples of isometry-dual flags with $$n=2g+1$$ such that $$C_n$$ is generated by functions of pole order at most $$n+2g-2$$ . We also prove a necessary condition, formulated in terms of maximum sparse ideals of the Weierstrass semigroup of Q, under which a flag of punctured one-point AG codes inherits the isometry-dual property from the original unpunctured flag.
A Modified Dragonfly Algorithm for Real Parameter Function Optimization

Pramanik, Sabari Setua, S. K.In this article, our aim is to suggest an algorithm which optimizes the real parameter function. We improve the performance of normal dragonfly algorithm, which is a swarm intelligence algorithm, by incorporating opposition-based learning with some alternation. We have applied our proposed algorithm over a set of 22 functions for real parameter function optimization. From the output, it can be found that the proposed algorithm gives a better result than the normal dragonfly algorithm for these function sets. The Wilcoxon rank-sum test, which is a statistical test, is also performed to confirm the superiority of our proposed algorithm.
Swarm Intelligence based Virtual Machine Migration Techniques in Cloud Computing

Cloud computing is a utility where compute resources are accessed on pay-per-use ground. It is enacted through virtualizationwhich is a central technology in cloud computing. Virtualization provides a secure and commendable environment for the functioning of cloud applications. Virtual machine (VM) plays a crucial role in the cloud data center when a host machine is exhausted due to tremendous network traffic, then the load is stabilized by shifting highly loaded VM to the under-utilized host machine. This review article recapitulates energy-efficient VM migration, VM migration techniques, objectives and issues related to it. The existing Swarm intelligence based VMmigration techniques are conferred and compared.
Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.
Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process

Web phishing is one of the many crimes that occur in cyberspace and often threatens internet users around the world. Web phishing works by tricking the victim into a website page that has been designed to resemble the original page and then directing the target to submit the important information they have. Web phishing detection system needs to be developed to minimize attacks and theft of information using the website. Research related to web phishing detection system has been carried out by many researchers, one of them using data mining techniques, but still uses a single classification algorithm. Therefore, the addition of meta-algorithm is proposed to support the improvement of classification performance for the development of various web phishing detection systems. From the testing phase that conducted using Web Phishing dataset from UCI Machine Learning Repository, an increase in accuracy value of 97.1% is obtained by the addition of the bagging process, 97.3% by using the boosting process, and 97.5% by the addition of the stacking process. With the resulting improved performance, it is hoped that the model can be used as a reference in perfecting the development of various phishing web detection systems.
Context-Aware Conversational Agent for a Closed Domain Task

With the ever-growing complexity in automated business processes and systems, manual navigation through the large volumes of data and hyperlinks on applications has become impractical and inefficient. Thus, conversational agents for closed domain tasks have received considerable interest in recent years. One of the fundamental characteristics of a conversational agent is its ability to interpret a statement based on the context of the entire conversation or in the context of a specific task. Keeping this view as the central focus, we study the scope of the Rasa chatbot development framework for identifying user intent and predicting the agent's future actions. We have presented a processing pipeline that allows optimized and accelerated data generation, action path definition and evaluation of the different components to demonstrate the generalization ability of the chatbot as a whole. The proposed application demonstrates fairly good performance in identifying user intent and completing the user's intended action.
Envelope Curves Unify Sinusoidal Graphing

Transferring fundamental concepts across contexts is difficult, even when deep similarities exist. This article leverages Desmos-enhanced visualizations to unify conceptual understanding of the behavior of sinusoidal function graphs through envelope curve analogies across Cartesian and polar coordinate systems.
Face Swapping with Limited Input

We propose an end-to-end face swapping pipeline. To replace the faces in a video, we take in a single photo to produce the result, while previous methods either need sophisticated equipment or large amount of data. Our pipeline consists of three parts, expression transfer, details refining and merging back. By combing 3D model and Generative Adversarial Network, we show that our pipeline is easy to implement, and is able to produce decent results even with limited input.
RS Code and Compressive Sampling on Video Watermarking-based DWT-SVD

Watermarking is a method to protect the copyright of a work. Watermark can be either text, image, audio, or video. Data insertion is done in such a way so that the data will not damage the secured digital information. The data that is inserted cannot be removed from the digital information and must be able to be re-extracted. This research will analyze the compressive sampling based on DCT-DWT for watermark compression on video watermarking using DWT-SVD and OMP reconstruction with RS Code to check on the error bit. Our system produces BER 0.25, PSNR 54.577 dB of PSNR, and MSE 0.215. This system is resistant to rescaling attack.
The equivalence of two definitions of compatible homography matrices

In many computer vision applications, one acquires images of planar surfaces from two different vantage points. One can use a projective transformation to map pixel coordinates associated with a particular planar surface from one image to another. The transformation, called a homography, can be represented by a unique, to within a scale factor, 3 × 3 matrix. One requires a different homography matrix, scale differences apart, for each planar surface whose two images one wants to relate. However, a collection of homography matrices forms a valid set only if the matrices satisfy consistency constraints implied by the rigidity of the motion and the scene. We explore what it means for a set of homography matrices to be compatible and show that two seemingly disparate definitions are in fact equivalent. Our insight lays the theoretical foundations upon which the derivation of various sets of homography consistency constraints can proceed.
Research on Detection and Warning of Air Threat Situation for UAV Collision Avoidance

In order to provide the early warning for UAV flight collision avoidance according to air threat situation, a detection and warning method of air threat situation is proposed for UAV flight collision avoidance. In the method, firstly, the track of the intruder is dynamically predicted by a prediction method. Then, using the predicted track and the flight information of UAV and intruder, the dynamic collision avoidance zone of UAV is established to the intruder. Finally, the detection and warning for flight collision trend between UAV and intruder is carried out in a specific flight scenario. Simulation show that the proposed method is effective and the feasible.
Revisiting the Challenges and Opportunities in Software Plagiarism Detection

Software plagiarism seriously impedes the healthy development of open source software. To fight against code obfuscation and inherent non-determinism of thread scheduling applied against software plagiarism detection, we proposed a new dynamic birthmark called DYnamic Key Instruction Sequence (DYKIS) and a framework called Thread-oblivious dynamic Birthmark (TOB) for the purpose of reviving the existing birthmarks and a thread-aware dynamic birthmark called Thread-related System call Birthmark (TreSB). Though many approaches have been proposed for software plagiarism detection, they are still limited to satisfy the following highly desired requirements: the applicability to handle binary, the capability to detect partial plagiarism, the resiliency to code obfuscation, the interpretability on detection results, and the scalability to process large-scale software. In this position paper, we discuss and outline the research opportunities and challenges in the field of software plagiarism detection in order to stimulate brilliant innovations and direct our future research efforts.
A process to support the creation of iStar extensions

iStar is a popular goal-based modelling language that has been used in different domains. The language has been extended to incorporate new constructs related to an application domain or to adjust it to practical situations during requirements modelling. However, often iStar extensions are proposed in an ad-hoc way resulting in several problems such as incompleteness, inconsistency and conflicts. This work aims to propose a process to be used by those interested in proposing systematic iStar extensions. A systematic literature review and a qualitative study were conducted previously to identify not only the current iStar extensions, but also to understand what the researchers feel that are important when proposing them. In this paper, we propose a process to support the proposal of new iStar extensions. It is driven by model-based development concepts, reuse of existing iStar extensions and the experts' best practices. It has already been used to propose several iStar extensions. Furthermore, the feedback on the proposal provided by several iStar experts was quite positive. Our process can avoid several problems in current iStar extensions, such as the absence of the constructs meaning, lack of metamodel definition, inconsistencies between abstract and concrete syntaxes and conflicts in the concrete syntax.
Regulatory changes for redesigned securities markets with distributed ledger technology

Abstract Distributed ledger technology (DLT) is regarded as a revolutionary solution that offers immutability, transparency, trust, and efficiency while ‘transcending law and regulation’. One of the potential applications of DLT is in the securities market. Share registration, settlement, regulatory compliance, information disclosure, payment systems, and market service requirements can be redesigned with the use of DLT. This paper will examine the impact that such changes will have on legal theories and governance, while also discussing the effects on enforcement techniques. In addition, general blockchain-legal issues will be critically analyzed in the context of securities markets.
Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation

Deep learning has achieved enormous breakthroughs in the field of image recognition. However, due to the time-consuming and error-prone process in discovering novel neural architecture, it remains a challenge for designing a specific network in handling a particular task. Hence, many automated neural architecture search methods are proposed to find suitable deep neural network architecture for a specific task without human experts. Nevertheless, these methods are still computationally/economically expensive, since they require a vast amount of computing resource and/or computational time. In this paper, we propose several network morphism mutation operators with extra noise, and further redesign the macro-architecture based on the classical network. The proposed methods are embedded in an evolutionary algorithm and tested on CIFAR-10 classification task. Experimental results indicate the capability of our proposed method in discovering powerful neural architecture which has achieved a classification error 2.55% with only 4.7M parameters on CIFAR-10 within 12 GPU-hours.
Toward FAIRness and a User-Friendly Repository for Supporting NMR Data

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTToward FAIRness and a User-Friendly Repository for Supporting NMR DataBarbara C. Sorkin*Barbara C. SorkinU.S. National Institutes of Health, Bethesda, Maryland 20892, United States*Email: [email protected]More by Barbara C. Sorkin, Joseph M. BetzJoseph M. BetzU.S. National Institutes of Health, Bethesda, Maryland 20892, United StatesMore by Joseph M. Betz, and D. Craig HoppD. Craig HoppU.S. National Institutes of Health, Bethesda, Maryland 20892, United StatesMore by D. Craig HoppCite this: Org. Lett. 2020, 22, 8, 2867Publication Date (Web):April 3, 2020Publication History Received30 March 2020Published online3 April 2020Published inissue 17 April 2020https://doi.org/10.1021/acs.orglett.0c01143Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views2391Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (427 KB) Get e-AlertsSUBJECTS:Dietary supplements,Nuclear magnetic resonance spectroscopy,Organic chemistry Get e-Alerts
Dual Encryption Method for File Security

Lots of important, confidential and personal information are stored on the computer and sent on the internet need to be protected. Cryptography is one of technique to protect the file. This research proposes a cryptography method by combining the RSA algorithm and the RC4 algorithm. The power of the RSA method dependent on the key quality, which is obtained from the computation of two different prime numbers of p and q. The encryption result can encrypt the file byte but beyond the limit of the byte. RSA algorithm needs to be modified, so the key is more various and works on file byte. Encrypt file from RSA algorithm, combined with RC4 algorithm to get the higher security of file. To test the performance of the proposed algorithm, entropy measuring, avalanche effect counting, and bit error ratio (BER) measuring, and time required for the computation process were performed. Based on the proposed method, proved that file security has improved because the testing method produces a satisfying result.
The Impacts of Library Services on Interlibrary Loan Requests Canceled due to Local Availability

Interlibrary loan (ILL) services receive requests for resources that are available locally and form a major portion of canceled requests. The purpose of this study is to examine the relationship between requests canceled due to local availability and three aspects to identify impacts on ILL. The authors explored library service aspects such as collection format, the link resolver knowledge base, and discovery system functionality for potential influence on requests for locally available resources. Quantitative methods were used to identify evidence of different aspects’ impact through analysis of ILL borrowing request data. The most important finding was that the discovery system had impact on ILL services by improving retrievability of locally available resources published recently. The results suggest that a modern discovery system can efficiently provide more relevant results sourced from a carefully curated set of authoritative resources, save users’ time to access available materials, and reduce library staff members’ time and effort. This study will be helpful to libraries through its analysis of library services that impact the cancelation of ILL requests due to local availability and how those services may influence service improvement.
Skills, Efficiency, and Timing in a Simple Attack and Defense Model

In this paper, we consider a simple two-player attack and defense model, focusing on the role of players’ abilities and choice timing. Abilities are divided into skills and efficiency, where the former is an absolute notion and the latter a relative notion of ability. Timing is investigated by comparing players’ investments in a simultaneous and a Stackelberg game. In the simultaneous game, the Nash Equilibrium investment level in attack and defense resources is symmetric, increasing in the skills but non-monotonic in the relative efficiency. In the Stackelberg game, the equilibrium investment levels are asymmetric, increasing in the skills, but with their ranking affected only by the relative efficiency. Therefore, interestingly, players’ choice is mostly characterized by players’ relative efficiency rather than by their skills, in regards to timing.