Reviewerly/exHarmony huggingface dataset:
A Cooperative Drawing Tool to Improve Children’s Creativity

Classroom meetings and cooperative homework typically require participants to be physically present in the room. In this setting, the whiteboard is a tool widely used to express ideas. However, nowadays it is more difficult for people to meet together in one place, particularly for young people, due to inherent risks in our current society. Therefore, virtual cooperative applications or tools are welcome. Such tools could reduce the money and time spent on cooperative classwork. Our application allows users to participate virtually through interactive drawings. The features are simple to use and allow for drawings to be made quickly, but this does not mean that drawings cannot be professional or complex. The application was implemented using open source frameworks and libraries, such as NodeJs, SocketIO, PaperJs, jQuery, and others. The results of the experiment demonstrate the high scalability and versatility of our system, the delay is low and acceptable considering that is capable of managing hundreds of objects in real time.
The Identity of Sweet Molly Malone: Dicent Indexical Legisigns—a New Element in the Periodic Table of Semiotics?

Peirce’s sign type of “Dicent Indexical Legisigns” has received relatively little scholarly attention, and it is sometimes confused with the “Dicent Symbol” category. This stems from an inconsistency between Peirce’s general explanation and his exemplifications of the category. Taking the lead from the latter, this paper argues that while the role of “Dicent Symbols” is descriptive, the task of “Dicent Indexical Legisigns” is not to describe but rather to establish identity of reference by connecting some proper name to an object or to another identifying sign of that object. Thus, it appears as an overlooked sign type playing an important role both in everyday agreement upon reference and in scientific negotiations over identity, terminology, existence, etc.
Do Mammographic Systems Affect the Performance of Computerized Parenchymal Analysis?

Early identification of women at high risk of developing breast cancer is fundamental for timely diagnosis and treatment. Recently, researchers have demonstrated that the computerized analysis of parenchymal (breast tissue) patterns in mammograms can be utilized to assess the risk level of patients. However, parenchymal analysis being an image-based biomarker, its performance may be affected by the acquisition parameters of the mammogram. Unfortunately, research on the effect of the mammographic system on the performance of parenchymal analysis is very scarce. In this paper, we implement a parenchymal analysis algorithm and study the effect of different mammographic systems on its performance. We show in a setting of 286 women that the use of different mammographic systems can yield differences of up to 24% in the area under the ROC curve. Results suggest the the construction of models for risk assessment based on parenchymal analysis should incorporate the imaging technologies into the analysis.
Library Usage Detection in Ethereum Smart Contracts

We analyze the usage of the SafeMath library on the blockchain with a data set of 6.9 million bytecodes of Ethereum smart contracts. This library provides safe arithmetic operations for contracts. In order to detect smart contracts that make use of the library from the bytecode alone, we perform the following five steps: download all available library versions, write test contracts that use the library, compile all test contracts with all compatible compiler versions, extract the internal library functions from the compiled bytecode, and search for contracts on the blockchain that use these library function bytecodes. In total, we detect usage of the SafeMath library in 1.34% of all smart contracts on the blockchain and in 27.52% of all distinct contract codes. To evaluate our approach, we use more than 50,000 verified contracts from Etherscan for which both the Solidity source code and the bytecode is available. Our algorithm correctly detects library usage for 86.34% of the smart contracts in the evaluation set.
Collaborative Feedback and Its Effects on Software Teams

During feedback sessions, management discusses positive attributes and areas where team members can improve. We present a strategy for providing this feedback in a collaborative manner as well as the effects it has on team engagement, agreements, and potential evolution.
Deinterleaving method of complex staggered PRI radar signals based on EDW fusion

Most of the existing pulse deinterleaving algorithms cannot deal well with complex staggered pulse repetition interval (PRI) radar signals which have >10 stagger levels. To solve this problem, a deinterleaving method based on emitter description word fusion is proposed here. The sub-PRIs of a complex staggered PRI signal are detected by applying histogram statistics with overlapped PRI bins, then a data fusion is carried out to distinguish between complex staggered PRI signals and jittered PRI signals according to their characteristics in the set of PRI values. Furthermore, a general deinterleaving framework for PRI agility complex signals including jittered PRI signals, complex staggered PRI signals, and sliding PRI signals is also presented. Finally, experiments with collected real data demonstrate that the authors’ proposed method is very effective in deinterleaving of complex staggered PRI signals.
Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Bayesian conditional models based on observed partial clinical indicators, features or shape based on Gaussian processes capturing the dependency structure. We apply the proposed method on a stroke dataset to jointly model the shape of the lateral ventricles, the spatial distribution of the white matter hyperintensity associated with periventricular white matter disease, and clinical indicators. The proposed method yields interpretable joint models for data exploration and patient-specific statistical shape models for medical image analysis.
Construction of an English-Uyghur WordNet Dataset

Automatically building semantic resources is essential to low resource-languages like Uyghur. However, Uyghur suffers from a lack of publicly available evaluation dataset for automatically building semantic resources like WordNet. To cope with this problem, first, we build the largest Uyghur-English and English-Uyghur dictionaries by exploiting many possible online and offline resources. Then by using Princeton WordNet (PWN) 3.0 and Contemporary Uyghur Detailed Dictionary (CUDD), we construct an English-Uyghur WordNet evaluation dataset which is publicly available ( https://github.com/kaharjan/uywordnet ). In this dataset, more than 73,000 English synsets are mapped Uyghur automatically, in which over 20,000 are annotated manually. And the corresponding Uyghur words include definition and examples in Uyghur language context. We also propose a Synset Mapping based on Word Embeddings (SMWE) method. The experimental results on the dataset are promising.
Robot Path Planning for Multiple Target Regions

Optimal path planning to point goals is a well-researched problem. However, in the context of mobile robotics, it is often desirable to generate plans which visit a sequence of regions, rather than point goals. In this paper, we investigate methods for planning paths which visit multiple regions in a specified order, whilst minimising total path cost. We propose Multi-Region A*, an extension to the A* algorithm with an admissible heuristic for traversing multiple target regions. The heuristic is used to trim sub-optimal paths from the search, thereby reducing the computation time required to find the optimal solution. Additionally, we extend this method to create the Windowed Multi-Region A* which plans through overlapping sequences of regions. This provides a mechanism to trade-off optimality against computation time. We evaluate the performance of the proposed methods against point-to-point A* planning methods using a simulation of a wheeled office robot. The evaluation shows that Multi-Region A* with search pruning produces an optimal path, and the Windowed Multi-Region A* with a small window size gives a good approximate solution without compromising the total navigation time, in addition to providing robustness to dynamic obstacles.
A lightweight privacy-preserving data aggregation scheme with provable security for internet-of-things

In this work, an autonomous lightweight certificateless aggregate signature-based data aggregation scheme for internet-of-things (IoT) is proposed. In the scheme, IoT terminals only need to contact the key generation center (KGC) once. Subsequently, they can generate their pseudonyms on their own without any interaction with the KGC. An efficient revocation mechanism is provided to punish malicious terminals. The proposed scheme is anonymous, can provide conditional traceability, resist various attacks, and solves private key compromise problem. The formal security proof demonstrates that the scheme is secure against both Type I and Type II adversaries in the random oracle model based on the intractability of the Discrete Logarithm Problem. The performance evaluation shows that the proposed scheme is more efficient than the state-of-the-art schemes and fits the resource-constraint IoT environment.
Trusted Data Integration in Service Environments: A Systematic Mapping

This paper identifies and discusses trends and open issues regarding the use of trust in data-provisioning service environments, especially cloud environments. Applying a systematic review method [2], we propose a classification scheme used to provide a quantitative view of current trust solutions insisting in open issues. Finally, using analysis results, we give the general lines of an approach for improving data provisioning in multi-cloud using Service Level Agreement (SLA) and proposing the notion of multi-level trust.
Automatic Robot Processing Using Speech Recognition System

Nowadays, speech recognition is becoming a more useful technology in computer applications. Many interactive speech-aware applications exist in the field. In order to use this kind of easy way of communication technique into the computer field, speech recognition technique has to be evolved. The computer has to be programmed to accept the voice input and then process it to provide the required output, using various speech recognition software. Speech recognition is the process of converting speech signal to a sequence of words using appropriate algorithm. This provides an alternative and efficient way for the people who are not well educated or not having sufficient computer knowledge to access the systems and where typing becomes difficult. This speech recognition technique also reduces the manpower to accept and process the commands. In our research work, we have to implement this speech recognition technique in customer care center, where many queries have to be processed every day. Some of the queries are repeated often and the responses also seem to be the same. In such cases, we have to propose a methodology to automate the query-processing activities using this speech recognition technique. The ways of how to automate the system and how to process the queries automatically are explained in our methodology with suitable algorithm.
Study of Socio-Linguistics Online Review System Using Sentiment Scoring Method

Presently, social media are interactive and more user friendly in nature. The web users enable to provide a medium of exchange in analysing the opinionated comments of different reviewers. The review expressed in different commercial websites ranges from simple in form of sentence or paragraphs which is converted into graphical representation in form of star ratings. Commercial websites is interested to express the opinions in a broader sense driving as a revolution to e-commerce. Reviews expressed by the reviewers are also called as raters. On contrary, a huge people who are trustworthy provide fake reviews or bogus reviews to get their products. It is necessary to permit trusted people to review the products and post them on the web. Other approaches like trusted network, fixed machine address could be used for ranking the products. Since there are large number of people prefers to sell or buy products through e-commerce. The potential applications of opinion mining rate the product by decision making, product analysis and improving business. In this paper a framework is presented for mining online reviews extracted from by different reviewers & commercial websites.
Deep Set-to-Set Matching and Learning

Matching two sets of items, called set-to-set matching problem, is being recently raised. The difficulties of set-to-set matching over ordinary data matching lie in the exchangeability in 1) set-feature extraction and 2) set-matching score; the pair of sets and the items in each set should be exchangeable. In this paper, we propose a deep learning architecture for the set-to-set matching that overcomes the above difficulties, including two novel modules: 1) a cross-set transformation and 2) cross-similarity function. The former provides the exchangeable set-feature through interactions between two sets in intermediate layers, and the latter provides the exchangeable set matching through calculating the cross-feature similarity of items between two sets. We evaluate the methods through experiments with two industrial applications, fashion set recommendation, and group re-identification. Through these experiments, we show that the proposed methods perform better than a baseline given by an extension of the Set Transformer, the state-of-the-art set-input function.
Detection of Fake Twitter Accounts with Multiple Classifier and Data Augmentation Technique

Due to the continuous growth of data size on platforms with large data such as social media, the detection of fraudulent accounts on these platforms is becoming more difficult. Although social media is preferred for communication purposes, it is becoming an increasingly attractive target for spammers and fraudsters. A suggestion system can be developed in order to provide better products to customers by analyzing the shares and interactions of people on social media. But if the messages are not sent by a real people, the analysis is wrong. In this study, malicious accounts have been identified in order to prevent dirty and false information circulating on the internet by using the messages sent by social media users. For this purpose, a system has been developed to classify automatic or normal accounts using intelligent techniques. The nearest neighbor, logistic regression and random forest algorithms were used for the identification of counterfeit accounts. The classification performances of these methods were compared and smote and majority voting techniques were applied to related algorithms to improve performance.
Investigation of Performance of HHO Algorithm in Solving Global Optimization Problems

In recent years, the use of meta-heuristic optimization algorithms has been increasing in most areas of science and engineering. These algorithms have advantages and disadvantages to each other. In this study, the performance of the Harris hawks optimization (HHO) algorithm has been verified by performing comparative statistical analysis of the optimal solutions of some well-known benchmark functions. Sphere, Rosenbrock, Schwefel, Ackley, Egg Crate and Easom are the chosen benchmark functions that are commonly used. From the analysis results, it is seen that the HHO algorithm were superior to artificial bee colony (ABC), wind driven optimization (WDO) and atom search optimization (ASO) algorithms. In addition, the results of the statistical boxplot prove the unique performance and efficiency of the HHO algorithm.
Inhomogeneity Inforced Piecewise Smooth Chan-Vese Model for Image Segmetation

Approach to efficient piecewise smooth Chan-Vese model for images segmentation is developed. Computationally effective, robust to initialization and illumination inhomogeneity solution to Chan-Vese model was developed. Proposed approach was tested on synthetic and real images. Experimental results are presented.
Deception/Honesty Detection and (Mis)trust Building in Manipulable Multi-Agent Argumentation: An Insight

In manipulable multi-agent argumentation, each agent may transmit deceptive information to others for tactical motives. We contemplate epistemic states and their roles in deception/honesty detection and (mis)trust-building. We propose the use of intra-agent preferences for handling deception/honesty detection and inter-agent preferences for determining which agent(s) to believe in more. We illustrate how deception/honesty in an argumentation of an agent, if detected, may alter the agent’s perceived trustworthiness, and how that may affect agents’ judgement as to which arguments they should accept. A detailed comparison to an earlier study on deception detection highlights wider applicability of our approach.
Distinct coordinate solutions of linear equations over finite fields

Let Fq be the finite field of q elements and a1,a2,…,ak,b∈Fq. We investigate NFq(a1,a2,…,ak;b), the number of ordered solutions (x1,x2,…,xk)∈Fqk of the linear equationa1x1+a2x2+⋯+akxk=b with all xi distinct. We obtain an explicit formula for NFq(a1,a2,…,ak;b) involving combinatorial numbers depending on ai's. In particular, we obtain closed formulas for two special cases. One is that ai,1≤i≤k take at most three distinct values and the other is that ∑i=1kai=0 and ∑i∈Iai≠0 for any I⊊{1,2,…,k}. The same technique works when Fq is replaced by Zn, the ring of integers modulo n. In particular, we give a new proof for the main result given by Bibak, Kapron and Srinivasan ([2]), which generalizes a theorem of Schönemann via a graph theoretic method.
Dependable Public Ledger for Policy Compliance, a Blockchain Based Approach

The ever increasing amount of personal data accumulated by companies offering innovative services through the cloud, Internet of Things devices and, more recently, social robots has started to alert consumers and legislative authorities. In the advent of the first modern laws trying to protect user privacy, such as the European Union General Data Protection Regulation, it is still unclear what are the tools and techniques that the industry should employ to comply with regulations in a transparent and cost effective manner. We propose an architecture for a public blockchain based ledger that can provide strong evidence of policy compliance. To address scalability concerns, we define a new type of off-chain channel that is based on general state channels and offers verification for information external to the blockchain. We also create a model of the business relationships in a smart home setup that includes a social robot and suggest a sticky policy mechanism to monitor cross-boundary policy compliance.
Joint Representative Selection and Feature Learning: A Semi-Supervised Approach

In this paper, we propose a semi-supervised approach for representative selection, which finds a small set of representatives that can well summarize a large data collection. Given labeled source data and big unlabeled target data, we aim to find representatives in the target data, which can not only represent and associate data points belonging to each labeled category, but also discover novel categories in the target data, if any. To leverage labeled source data, we guide representative selection from labeled source to unlabeled target. We propose a joint optimization framework which alternately optimizes (1) representative selection in the target data and (2) discriminative feature learning from both the source and the target for better representative selection. Experiments on image and video datasets demonstrate that our proposed approach not only finds better representatives, but also can discover novel categories in the target data that are not in the source.
Measurement and Visualization of IIIF Image Usages

In these years, a lot of libraries and museums have adopted IIIF (International Image Interoperability Framework), which promotes mutual use of images among institutions. In IIIF-compatible digital collections, images are retrieved via IIIF Image API with specifying regions of images. Thus, we can investigate the detailed image usage by examining requested regions. In this paper, we demonstrate an application that measures and visualizes which regions of images are looked by users. Specifically, we count the number of accesses to each pixel of images. Since a pixel is the smallest unit that composes an image, it enables to show the detailed image usage. Finally, we visualize the result as heat maps and display heat maps over original images on Mirador, a IIIF-compatible image viewer. Thus, users can interactively investigate which regions of images are looked by users with panning and zooming.
Author Correction: Quantum computing with graphene plasmons

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Reengineering Cyber Security Process: A New Perspective on Cyber Security Quality Management

Information systems is greatly identified as the engine that drives and supports the U.S. and global economy, giving industry a strategic competitive advantage in global markets., enabling the federal government and agencies to collaborate among themselves, and building a 21st century digital government platform to provide better services to its citizens. The 21st century is the era age of digital information when information within corporation becomes an imperative important strategically and valuable resource than ever as the development of such field like business economics intelligent underlines it. But information systems are exposed to serious internal and external threats that can have adverse effects on organizational operations. Unfortunately, the complexity of security attacks and lack of quality cyber security management implementation within organizations, and increased number of vulnerabilities, and lack of effectively protecting against the dynamically evolving attacks within organization networks have greatly increased over the past few years, even the best security mechanisms can be bypassed by professional hackers. This research article proposes, creates and develops a new perspective in cyber security management, and it introduces a new concept which is called Reengineering Cyber Security Process, and applies toward quality of cyber security management.