Reviewerly/exHarmony huggingface dataset:
Simplification and Hardware Parallel Design of Frobenius Mapping Algorithm Based on SM9

With the development of network technology, the leakage of information has become a severe security risk, which has led to the increasing requirements for the security and processing speed of information encryption and decryption. This paper mainly optimizes the algorithm of R-ate bilinear pairing with the parameters of the SM9 and reduces its hardware cost by simplifying the Frobenius mapping algorithm. The validity of the optimized algorithm was verified based C++ from the view of software, and hardware parallelization design also was realized based FPGA respectively. The synthesis results on Xilinx Virtex-7 of the Twelfth Domain Mode Multiplication module showed that it consumes 28% of the Slice registers and 57% of the LUT. This research proposed an effective approach for accelerating the R-ate algorithm and reducing chip area.
A Calculus of Space, Time, and Causality: Its Algebra, Geometry, Logic

The calculus formalises human intuition and common sense about space, time, and causality in the natural world. Its intention is to assist in the design and implementation of programs, of programming languages, and of interworking by tool chains that support rational program development. The theses of this paper are that Concurrent Kleene Algebra (CKA) is the algebra of programming, that the diagrams of the Unified Modeling Language provide its geometry, and that Unifying Theories of Programming (UTP) provides its logic. These theses are illustrated by a formalisation of features of the first concurrent object-oriented language, Simula 67. Each level of the calculus is a conservative extension of its predecessor. We conclude the paper with an extended section on future research directions for developing and applying UTP, CKA, and our calculus, and on how we propose to implement our algebra, geometry, and logic.
Runtime Monitoring of Behavioral Properties in Dynamically Adaptive Systems

A Dynamically adaptive System (DAS) enable adaptations at runtime based on context information. DAS can be developed following the same approach used in Dynamic Software Product Lines (DSPL). Then, software engineers design the behavioral adaptations of DAS modeling context-aware features, which can be activated/deactivated at runtime. In our previous work, we proposed a model checking technique to verify behavioral properties in the specification of a DAS adaptation at design time. However, once this kind of system deals with reconfiguration at runtime, the inherent dynamism of context information and defects in the adaptation mechanism may cause unexpected behaviors, such as incorrect activation of adaptation rules. So, runtime monitoring activities are necessary to ensure the satisfaction of properties during the system execution. In this paper, we address this issue by proposing an approach to verify behavioral properties with a monitor framework during the system operation. To evaluate the approach, we perform a proof of concept with two mobile DAS by previously injecting faults in the source code. The injected faults were successfully detected when they turn into failures at system runtime.
Cross-Batch Reference Learning for Deep Retrieval

Learning effective representations that exhibit semantic content is crucial to image retrieval applications. Recent advances in deep learning have made significant improvements in performance on a number of visual recognition tasks. Studies have also revealed that visual features extracted from a deep network learned on a large-scale image data set (e.g., ImageNet) for classification are generic and perform well on new recognition tasks in different domains. Nevertheless, when applied to image retrieval, such deep representations do not attain performance as impressive as used for classification. This is mainly because the deep features are optimized for classification rather than for the desired retrieval task. We introduce the cross-batch reference (CBR), a novel training mechanism that enables the optimization of deep networks with a retrieval criterion. With the CBR, the networks leverage both the samples in a single minibatch and the samples in the others for weight updates, enhancing the stochastic gradient descent (SGD) training by enabling interbatch information passing. This interbatch communication is implemented as a cross-batch retrieval process in which the networks are trained to maximize the mean average precision (mAP) that is a popular performance measure in retrieval. Maximizing the cross-batch mAP is equivalent to centralizing the samples relevant to each other in the feature space and separating the samples irrelevant to each other. The learned features can discriminate between relevant and irrelevant samples and thus are suitable for retrieval. To circumvent the discrete, nondifferentiable mAP maximization, we derive an approximate, differentiable lower bound that can be easily optimized in deep networks. Furthermore, the mAP loss can be used alone or with a classification loss. Experiments on several data sets demonstrate that our CBR learning provides favorable performance, validating its effectiveness.
EmbSE

The representation of contexts is essential in tasks involving Natural Language Processing (NLP). In the field of software engineering, classifying similar texts within a specific context has been a complex task, considering the informality and the complexity inherent of the texts produced through many software development processes (e.g. agile methods). Word embeddings capture semantic and syntactic information about unique words, allowing them to be represented in a dense and low-dimensional format. This property makes the embeddings vectors an important input feature for machine learning algorithms that aim to classify texts. Although there has been much research around the application of word embeddings in several areas, up to this moment, there is no knowledge about studies that have explored its application in the creation of a specific model for the domain of the area of software engineering. Thus, this article presents the proposal to generate an embedding model, called embeddings model for software engineering (EmbSE), which can recognize specific and relevant terms in the software engineering context. This model can be used as the main entry in the classification of several textual artifacts generated during the software development project process. The results are promising, presenting a 48% improvement in the mAP values for the EmbSE concerning the model trained on the generic corpus. This reinforces the hypothesis that a model of this nature can bring significant improvements in the classification of texts of the area.
Online Handwriting and Signature Normalization and Fusion in a Biometric Security Application

In this paper, we analyze the combined application of signatures and capital handwriting in a biometric recognition application. We combine a signature recognition system based in a multi-section vector quantization with a handwriting text recognition system based in self-organizing maps and DTW. Due to the need to normalize the scores before the combination, we study the effect of different normalization methods and we propose the application of a logarithmic transformation for signature scores previous normalize them. Experimental results show that the identification rate raises from 86.11% using capital letter words and 96.95% using signatures up to 99.72% with a fusion of both traits. Minimum detection cost function (DCF) also improves, from 3.56 and 3.51%, respectively, up to 1.0% using the fusion of both traits.
Social Data Sentiment Analysis of a Multilingual Dataset: A Case Study with Malayalam and English

Opinion Analysis is an articulate methodology that is indivisibly crypt to the sector of Emotional Sciences which concern the individual supposition, emotions or thoughts and thereby identifies the personal deeds. Natural language processing techniques presume that in common most of the user annotations are written in English dialect, but as focus shifts onto processing comments from internet sources such as microblogging services, this becomes progressively harder to guarantee. Conveying the empathy in their own language can be well thought-out as the homey means for expressing the exact opinion and it leads to the generation of multilinguistic societal data. So the challenge is to analyze the formal textual content along with the informal and mixed linguistic nature of social data. This prompts the need of Sentiment Analysis in multilingual dialects. This article surveys the methodologies used for analyzing the sentiment of multilingual data and proposed a model built using Long Short Term Memory approach.
Cryptography in WSNs

Cryptography is the field for mathematically transforming messages to protect their confidentiality from eavesdropping. It is an essential foundation for all secure protocols. This chapter is a concise review of cryptography used in wireless sensor networks (WSNs) beginning with background on symmetric key encryption and the Diffie–Hellman key agreement protocol for sharing secret keys. Asymmetric key encryption and its usefulness for digital signatures and digital certificates are described. Finally, the unique constraints on the use of cryptography in wireless sensor networks are reviewed. A short survey of the literature is presented, which mainly deal with evaluating ciphers (particularly elliptic curve cryptography), proposing new approaches for key management, and implementing identity-based cryptography.
DistProv-Data Provenance in Distributed Cloud for Secure Transfer of Digital Assets with Ethereum Blockchain using ZKP

The importance and usage of the distributed cloud is increasing rapidly over a traditionally centralized cloud for the storing and exchanging of digital assets between untrusted parties in many business sectors. Storing the digital assets in the distributed cloud is considered superior to traditional cloud computing in terms of environmentally friendly, cost, security and other technical dimensions. In this article, a contemporary architecture DistProv is proposed where an open source distributed cloud IPFS is used to store and transfer the digital assets between the consignor and consignee. These two are untrusted parties exchanging sensitive documents secured by cryptographic algorithms with permission-based access verified by ethereum smart contracts using zero-knowledge proof (ZKP) and simultaneously publishing the provenance data about the digital asset as a transaction on the blockchain. This article also discusses on verifying the integrity of the digital assets and authentication of the consignor and thus preserving a strong CIA triad.
Spelling Checker Algorithm Methods for Many Languages

Spell checking plays an important role in improving document quality by identifying misspelled words in the document. The spelling check method aims to verify and correct misspelled words through a series of suggested words that are closer to the wrong word. Currently, Spell checkers for English language are well established. This article analyses several studies conducted in various types of languages other than English. Like Africa, Arabia, China, Indonesia, India, Japan, Malaysia and Thailand. We use the systematic literature Review approach to paper published 2008 - 2018. there are 23 papers to represent each language and view methods used. The result show that each language has a different Spelling Check Method. The Damerau-Levenshtein algorithm method is most often used for spelling checkers.
The Application of Artificial Neural Network for Flood Systems Mitigation at Jakarta City

The problem of flooding in the Special Capital Region of Jakarta has not been fully resolved. Floods still occur in various regions. Puddles still occur when it rains, which results in not only inhibiting community activities, but also stifling the economy in the capital city, Jakarta. Floods in Jakarta are not only caused by natural factors such as extreme weather, but also because human behavior is part of the cause, so that flooding has not been fully avoided for Jakarta residents. Although the regional government has designated the South Jakarta area as a water catchment area, these efforts have not resolved the existing problem. This study tries to look at data patterns related to flooding problems in Jakarta by utilizing time series data from records of flood events several years ago and processed using artificial intelligence programs with artificial neural network methods that are expected to explain the relationship between flooding, rainfall, population, and community behavior in handling waste. The results obtained are models of flood data patterns in Jakarta, which are expected to be used to predict and anticipate flood behavior in the capital city of Jakarta in the future.
Real-time multiple object tracking based on optical flow

Deep matching and Kalman filter-based multiple object tracking (DK-tracking) has been demonstrated to be promising. Traditional DK-tracking, however, relies heavily on high-performance detectors and assumes that the tracking target always moves at a uniform speed over short distances. But in complex environments, it is difficult to satisfy some special targets. So we propose a novel model called DK-Compensation-Flow-tracking (DKCF-tracking) which introduces a new inference pipeline to build data association for the state information of the missing targets across frames so that we can predict some simple results satisfying the conditions. Besides, we introduce optical flow information to achieve the target motion information and to guide Kalman filter prediction more accurately. Moreover, the accurate target positions predicted by Kalman filter will help to compensate for the missing objects. Experiments are performed on public datasets: MOT2016 and a competition datasets from The Chines Intelligent Vehicle Future Challenge. The proposed method achieves better performances compared to the DK-tracking with the assumption of a constant velocity movement.
Examining Structure of Word Embeddings with PCA

In this paper we compare structure of Czech word embeddings for English-Czech neural machine translation (NMT), word2vec and sentiment analysis. We show that although it is possible to successfully predict part of speech (POS) tags from word embeddings of word2vec and various translation models, not all of the embedding spaces show the same structure. The information about POS is present in word2vec embeddings, but the high degree of organization by POS in the NMT decoder suggests that this information is more important for machine translation and therefore the NMT model represents it in more direct way. Our method is based on correlation of principal component analysis (PCA) dimensions with categorical linguistic data. We also show that further examining histograms of classes along the principal component is important to understand the structure of representation of information in embeddings.
Recommending External Developers to Software Projects based on Historical Analysis of Previous Contributions

The software development industry has evolved in recent years and new challenges have emerged. One of them is the difficulty in recruiting developers who are familiar with technologies that may be important to global software development projects. Currently, this task is aided by tools that delegate the mission of actively seeking developers to human recruiters. Unfortunately, when delegating such a task to humans, these tools may miss opportunities, as they may not be able to find all the information about the candidates. Following the methodology of Design Science Research (DSR), this work presents an architecture that helps to find collaborators for projects, along with an ontology. By mining social-coding repositories, the architecture can provide information, through inference (e.g. dependencies in projects in which developers worked) to be used in the recommendation tasks. An initial feasibility study was conducted to assess the feasibility to recommend developers to projects on the Node.js ecosystem. The recommendation system analyzes the previous contributions of the developers in other projects (n = 7000) and extracts technologies in which they explicitly worked and those they may be familiar as they are present in projects they worked with. After, the system compares them to a set of technologies that are part of the project that is the target of the recommendation. The feasibility study evidenced that the architecture can aid in the task of identifying candidates and technologies known to them.
A new aggregated search method

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals (images, videos, news, etc.), into the Web search results. Major search engines perform what is known as Aggregated Search. Aggregated search is relatively new and its advantage s need to be evaluated. With the increasing size of the data set on the network, different results retrieved from different dimensions are sometimes entirely different for a given search problem. In this paper, a new aggregated search algorithm was proposed. Firstly, the retrieval data in the vertical domain was textualized, and Doc2Vec makes the vector representation of the data. Then the results are aggregated and output by dimension reduction and density clustering. The experimental results show that the model achieves good accuracy in 20 given queries and significantly improves the aggregated search results. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches.
Fundamental Models for Missions of Engineered Systems

Designing a system requires clear evaluations of the system's abilities in supporting missions/requirements, which can be subsequently used for evaluating a system's -ilities such as adaptability. In reality, the missions and requirements can be arbitrary, ambiguous, and contain overlapping contents, causing difficulties in clear understanding and quantifications. This paper studies these problems, provides fundamental insights to models on missions/requirements, using Dynamical Systems or Transition Systems. It generalizes the Missions/Requirements Evaluation Space, presents the fundamental and practical semantics to effectiveness functions of a mission and a set of missions. Based on our prior work of hierarchical task breakdown, using weights and probabilities, a top-down mission analysis process is mathematically shown. These allow them to be Measures of Effectiveness (MOE) in practice, and also generalize the adaptability theory. An HVAC example is used to demonstrate the generalized models that are backward compatible with existing adaptability theory. Copyright © 2019 United Technologies Corporation.
Combining Word Embeddings with Taxonomy Information for Multi-Label Document Classification

In business contexts, documents often need to be classified using company-specific taxonomies. Text-classification approaches based on word embeddings have become increasingly popular as they enable words, documents, and tags to be represented in a semantically robust way (as distributed representations of their contexts) and make documents and tags processable in an algebraic vector space. However, these distributed representations of contexts have their shortcomings when used for multi-label classification tasks: the more similar the contexts of two tags, the more difficult they are to separate in classification. Intensified by poor training data, poor training, or inherent limitations of the word-embedding approach, in practice, we find areas of indistinguishability, leading to false positive predictions (typically in leaf tags of a taxonomy tree). We contribute an approach to tackle the problem of indistinguishable areas for multi-label classification tasks based on word embeddings by including taxonomy information during prediction.
User Identity Linking Across Social Networks by Jointly Modeling Heterogeneous Data with Deep Learning

People have presence across different information networks on the social web. The problem of user identity linking, is the task of establishing a connection between accounts of the same user across different networks. Solving this problem is useful for: personalized recommendations, cross platform data enrichment and verifying online information among others. In this paper, we propose a deep learning based approach that jointly models heterogeneous data: text content, network structure as well as profile names and images, in order to solve the user identity linking problem. We perform experiments on a real world problem of connecting the social profile (Twitter) and academic profile (DBLP) of researchers. Our experimental results show that our joint model outperforms state-of-the-art results that consider profile, content or network features only.
An exploratory study of pay satisfaction among public sector employees in Jordan

Pay satisfaction is regarded as an important factor in promoting organizational productivity based on its motivational factor. Accordingly, this research sought to explore pay satisfaction in the Jordanian public sector with an objective of establishing the level of satisfaction among employees. The inquiry is based on the Pay Satisfaction Questionnaire developed by a previous study. Using weighted averages for the analysis of data, this research establishes that the pay satisfaction of the Jordanian public sector employees is relatively low and that employees are less than satisfied with the various pay aspects queried including pay level, benefits, pay raise, and pay structure and administration. This has the implication that the low productivity in the public sector could be as a result of lack of staff motivation due to low pay. The research determines that in average, the 210 participated employees were least satisfied with pay structure and administration, whereas the other factors, namely, benefits, pay raise, and pay structure/administration, achieved an average score.
A Low-Complexity RS Decoder for Triple-Error-Correcting RS Codes

Reed-Solomon (RS) codes have been widely used in digital communication and storage systems. The commonly used decoding algorithms include Berlekamp-Massey (BM) algorithm and its variants such as the inversionless BM (iBM) and the Reformulated inversionless BM (RiBM). All these algorithms require the computation-intensive procedures including key equation solver (KES), and Chien Search & Forney algorithm (CS&F). For RS codes with the error correction ability t ≤ 2, it is known that error locations and magnitudes can be found through direct equation solver. However, for RS codes with t = 3, no such work has been reported yet. In this paper, a low-complexity algorithm for triple-error-correcting RS codes is proposed. Moreover, an optimized architecture for the proposed algorithm is developed. For a (255, 239) RS code over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ), the synthesis results show that the area-efficiency of the proposed decoder is 217% higher than that of the conventional RiBM-based RS decoder in 4-parallel. As the degree of parallelism increases, the area-efficiency is increased to 364% in the 16-parallel architecture. The synthesis results show that the proposed decoder for the given example RS code can achieve a throughput as large as 124 Gb/s.