Reviewerly/exHarmony huggingface dataset:
Colour Component Analysis Approach for Malaria Parasites Detection Based on Thick Blood Smear Images

Abstract Malaria is a plasmodium parasite disease that affects millions of people in the world every year. Hence, early detection tests are needed to prevent the malaria parasites spread throughout the body. For centuries, manual microscopic blood test remains as the most common method that still being used for malaria detection. However, this procedure contains the probability of miscalculation of parasites due to human error. Computerized system is recognized as a quick and easy ways to analyze a lot of blood samples images by providing direct visualization on the computer screen without the need to examine under the microscope. Therefore, this paper aims to analyze different colour components for improving the parasites counting performance based on thick blood smear images. In this study, five different colour spaces namely YCbCr, RGB, CMY, HSV and HSL have been analyzed and eight colour components which are Y, Cb, R, G, C, M, S and L have been extracted in order to determine which colour component is the best for malaria parasites counting. Overall, experimental results indicate that segmentation using Y component of YCbCr proved to be the best with average counting accuracy of 98.48% for 100 images of malaria thick blood smear.
Simulation Extractability in Groth’s zk-SNARK

A Simulation Extractable (SE) zk-SNARK enables a prover to prove that she knows a witness for an instance in a way that the proof: (1) is succinct and can be verified very efficiently; (2) does not leak information about the witness; (3) is simulation-extractable -an adversary cannot come out with a new valid proof unless it knows a witness, even if it has already seen arbitrary number of simulated proofs. Non-malleable succinct proofs and very efficient verification make SE zk-SNARKs an elegant tool in various privacy-preserving applications such as cryptocurrencies, smart contracts and etc. In Eurocrypt 2016, Groth proposed the most efficient pairing-based zk-SNARK in the CRS model, but its proof is vulnerable to the malleability attacks. In this paper, we show that one can efficiently achieve simulation extractability in Groth's zk-SNARK by some changes in the underlying language using an OR construction. Analysis and implementations show that in practical cases overload has minimal effects on the efficiency of original scheme which currently is the most efficient zk-SNARK. In new construction, proof size is extended with one element from $${\mathbb {G}}_1$$ , one element from $${\mathbb {G}}_2$$ , plus a bit string that totally is less than 256 bytes for 128-bit security. Its verification is dominated with 4 pairings which is the most efficient verification among current SE zk-SNARKs .
An Ontology Based Approach for Data Model Construction Supporting the Management and Planning of the Integrated Water Service

The Italian Ministry of Infrastructures and Transport has started the implementation of SINFI, the National Federated Infrastructure Information Service, whose goal is both to share information about infrastructures and underground utilities, and offer a single dashboard that efficiently manages and monitors all interventions. Although the data model underlying SINFI has been designed with a general-purpose approach, it doesn’t result in line with the actual needs of the operators of the Integrated Water Service that instead has to be compliant with the updating of the plans of intervention according to the macro-indicators of technical quality referred to in the ARERA resolution. The aim of this paper is to propose a data model that allows measuring such macro-indicators and generating interoperable datasets. An ontological approach has been used that has produced a data storage model as implemented in the Semantic Web.
Channel Attention Networks for Image Translation

Existing image-to-image translation methods usually adopt an encoder-decoder structure to generate images. The encoder extracts the features of input images using a sequence of convolution layers until a bottleneck, and then, the intermediate features are decoded to the target image. However, the existence of bottleneck layer in such structure may lead to blurry and bad quality of the translated images, since different domain translations may be related to the global or local region in the input image or even in an abstract level. To prevent these problems, we propose the channel attention networks for image translation in this paper. It is a novel model that supports the multi-domain image-to-image translation using one single model. Conditioning on the target domain label, an auto-encoder-like network with multiple attention connections is trained to translate the input image into the target domain. The attention connections better shuttle the low-level information in the encoder to the decoder, which helps to preserve the structure. A multi-level attention mechanism is also designed in the proposed model to further improve the performance of our model. More specially, the feature maps in the encoder are first squeezed by average pooling and used to output a channel-wise attention mask. The attention mask softly determines which channels of the feature maps are translated and which channels are kept. By enforcing the model to learn a cyclic domain transformation during training, our model does not require paired training data, which greatly improves the versatility to different kinds of data. We experimentally demonstrated the effectiveness of our proposed model on the facial and clothing image translation tasks. The extensive ablations are also conducted to further validate the contribution of the proposed attention module used in our model.
Elasticoin: Low-Volatility Cryptocurrency with Proofs of Sequential Work

Blockchain-based cryptocurrencies have gained increasing adoption in recent years, and many hope that they may usher in a new era of decentralized electronic money. Unfortunately, they perform the core functions of money quite poorly due to their extremely volatile market value. On the other hand, blockchain “stablecoins” aiming to reduce this volatility, usually through a peg to an external currency like the US dollar, tend to greatly sacrifice decentralization of the money supply that make cryptocurrencies so attractive in the first place. Elasticoin is a novel currency issuance algorithm which greatly reduces price volatility by using the cryptographic puzzle of proofs of sequential work to fix the cost of minting a coin to sequential computation time. This causes coin supply to be highly elastic, quickly responding to demand for new coins and greatly dampening price swings. We argue that Elasticoin's fixed-minting-cost approach to low volatility has significant advantages over using pegs or explicit measurement of demand to adjust supply.
Hiding Patient Injury Information in Medical Images with QR Code

Watermarking has been applied in the medical field that is used to enhance the safety of medical information. QR Code is used in this research to store medical image data and insert a watermark into the image using the Least Significant Bit - LSB method that can insert data into the bit sensitive area. Watermark insertion using the LSB method does not affect the image size and cannot be seen by the eye. This method insert a watermark that is distributed throughout the image. The experimental have rotated the image in 90 degrees in a clockwise direction, rotated 90 degrees in a counterclockwise direction and rotated in the opposite direction. The results of the experiment showed that the rotation of the image in the above direction did not affect the reading of the patient's injury data from the QR Code.
Survey of Textbased Chatbot in Perspective of Recent Technologies

Chatbots are computer programs capable to carry a conversation with human. They can be seen as an artificial agent designed to serve the purpose of conversation with the end user. Chatbots are gaining popularity especially in business and health sector as they have the potential to automate service and reduce human efforts. Widespread use of Apps, maturation of Artificial Intelligence (AI) technologies and integration of Natural Language Processing (NLP) fuels up the growth of chatbot. In this paper, we present different models of chatbots along with an architectural overview of computationally intelligent chatbot in context of recent technologies. In the three layer architecture, we have given insights of how the NLP, Natural Language Understanding (NLU) and Decision engine work together with Knowledge Base to achieve AI using Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). In addition, we also discuss different chatbot platforms and development frameworks of recent times. Our core emphasis is on analysis of recent development approaches of textbased conversational systems. We identify few challenges in intelligent chatbot development that may be helpful for future research works.
Can people detect errors in shadows and reflections?

The increasing sophistication of photo-editing software means that even amateurs can create compelling doctored images. Yet recent research suggests that people’s ability to detect image manipulations is limited. Given the prevalence of manipulated images in the media, on social networking sites, and in other domains, the implications of mistaking a fake image as real, or vice versa, can be serious. In seven experiments, we tested whether people can make use of errors in shadows and reflections to determine whether or not an image has been manipulated. Our results revealed that people’s ability to identify authentic and manipulated scenes based on shadow and reflection information increased with the size of the manipulation, but overall, detection rates remained poor. Consistent with theories of incomplete visual representation, one possible reason for these findings could be that people rarely encode the details of scenes that provide useful cues as to the authenticity of images. Overall, our findings indicate that people do not readily make use of shadow and reflection cues to help determine the authenticity of images—yet it remains possible that people could make use of these cues, but they are simply unaware of how to do so.
SootDiff: bytecode comparison across different Java compilers

Different Java compilers and compiler versions, e.g., javac or ecj, produce different bytecode from the same source code. This makes it hard to trace if the bytecode of an open-source library really matches the provided source code. Moreover, it prevents one from detecting which open-source libraries have been re-compiled and rebundled into a single jar, which is a common way to distribute an application. Such rebundling is problematic because it prevents one to check if the jar file contains open-source libraries with known vulnerabilities. To cope with these problems, we propose the tool SootDiff that uses Soot's intermediate representation Jimple, in combination with code clone detection techniques, to reduce dissimilarities introduced by different compilers, and to identify clones. Our results show that SootDiff successfully identifies clones in 102 of 144 cases, whereas bytecode comparison succeeds in 58 cases only.
The Automated Grading of R Code Snippets: Preliminary Results in a Course of Health Informatics

Automated grading tools either execute the submitted code against sample data or statically analyse it. The paper presents a tool for the automated grading of R code snippets, submitted by students learning Health Informatics in the degree course of Medicine and Surgery of the University of L’Aquila (Italy). The tool performs a static analysis of the R commands, with the respective output, as well as of the sentences written in natural language. The paper details the problem in general and through examples. Then, it describes the proposed solution and reports on the comparison between the automated grading and the human one. Finally, the paper ends by describing the foreseen use of the tool and the needed improvements.
Gaps in the System of Higher Education in Russia in Terms of Digitalization

The authors study the main aspects of economy digitalization as a global megatrend, its trends and directions, as well as the impact of economy digitalization on gaps in the system of higher education in Russia. The digital economy affected processes in the main social and socio-economic systems (SES). The purpose of this contribution is to study the impact of the digital economy on the processes in the system of higher education in Russia, namely the impact on gaps. The main range of research tasks can be outlined by analyzing such a phenomenon as gaps in socio-economic systems, the nature of their occurrence, as well as the role and influence of digitalization on them.
Use of OWL and Semantic Web Technologies at Pinterest

Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, and much more. In the last year, the company adopted Semantic Web technologies to create a knowledge graph that aims to represent the vast amount of content and users on Pinterest, to help both content recommendation and ads targeting. In this paper, we present the engineering of an OWL ontology—the Pinterest Taxonomy—that forms the core of Pinterest’s knowledge graph, the Pinterest Taste Graph. We describe modeling choices and enhancements to WebProtégé that we used for the creation of the ontology. In two months, eight Pinterest engineers, without prior experience of OWL and WebProtégé, revamped an existing taxonomy of noisy terms into an OWL ontology. We share our experience and present the key aspects of our work that we believe will be useful for others working in this area.
NEMSI

We present NEMSI, a cloud-based multimodal dialog system designed to have naturalistic interactions with individuals for the purpose of screening neurological or mental conditions. The system has been used by thousands of people capturing audio and video responses to open-ended questions and structured health surveys.
Novel Modulation Recognition for WFRFT-Based System Using 4th-Order Cumulants

We present a novel modulation recognition for weighted-type fractional Fourier transform (WFRFT)-based systems using the fourth-order cumulants. First, the constellation characteristics of the basic digital modulations ASK, PSK, and QAM are analyzed, and the corresponding relationships between the neighboring constellation points' distance and the constellation size are deduced. Second, the closed-form expressions of the fourth-order cumulants (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">42</sub> ) for the WFRFT-based systems with ASK, PSK, and QAM are derived. Finally, through the first- and second-order derivatives of the C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">42</sub> , we prove that the optimal WFRFT order α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> can be obtained through the minimization of the C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">42</sub> . The simulation results show that the novel recognition for the WFRFT-based system is feasible and can reach an accuracy of almost 90% when energy per bit-to-noise power spectrum density ratio (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) is greater than 6.
User-Oriented Design of Active Monitoring Bedside Agent for Older Adults to Prevent Falls

A small bedside agent for preventing falls has been developed. It talks to a person on a bed to prevent them from getting out of bed abruptly, until a care worker arrives. This paper describes the user-oriented design process of the agent system. The development process involving users, such as nurses and caregivers, as well as older adults is described. First, hardware design, such as the outer shape, size, and function of the agent was reviewed by nurses and caregivers, mainly from a safety viewpoint. The prototype agent incorporating improvements based on their opinions was used experimentally by older adults after several review processes. Second, the software design of the agent, such as the content of voice call, was studied through multiple experiments to improve its acceptability. Lastly, the integrated model was introduced into care facilities and hospitals to investigate the practical serviceability of the system.
Mining Rule Violations in JavaScript Code Snippets

Programming code snippets readily available on platforms such as StackOverflow are undoubtedly useful for software engineers. Unfortunately, these code snippets might contain issues such as deprecated, misused, or even buggy code. These issues could pass unattended, if developers do not have adequate knowledge, time, or tool support to catch them. In this work we expand the understanding of such issues (or the so called "violations") hidden in code snippets written in JavaScript, the programming language with the highest number of questions on StackOverflow. To characterize the violations, we extracted 336k code snippets from answers to JavaScript questions on StackOverflow and statically analyzed them using ESLinter, a JavaScript linter. We discovered that there is no single JavaScript code snippet without a rule violation. On average, our studied code snippets have 11 violations, but we found instances of more than 200 violations. In particular, rules related to stylistic issues are by far the most violated ones (82.9% of the violations pertain to this category). Possible errors, which developers might be more interested in, represent only 0.1% of the violations. Finally, we found a small fraction of code snippets flagged with possible errors being reused on actual GitHub software projects. Indeed, one single code snippet with possible errors was reused 1,261 times.
Automated Fine-Grained Requirements-to-Code Traceability Link Recovery

Problem: Existing approaches for requirements-to-code traceability link recovery rely on text retrieval to trace requirements to coarse-grained code documents (e.g., methods, files, classes, etc.), while suffering from low accuracy problems. Hypotheses: The salient information in most requirements is expressed as functional constraints, which can be automatically identified and categorized. Moreover, people use recognizable discourse patterns when describing them and developers use well-defined patterns for implementing them. Contributions: Recasting the requirements-to-code traceability link problem as an accurate matching between functional constraints and their implementation.
Confidence in Programming Skills: Gender Insights From StackOverflow Developers Survey

Diversity is being intensively discussed by different knowledge areas of society and discussions in Software Engineering, are increasing as well. There are unconscious bias and lack of representativeness and when we talk about characteristics as ethnicity and gender, to mention a few. Studies say that diversity builds better teams and delivers better results, among other benefits. StackOverflow is a popular community question & answer forum, with a high engagement of software developers. Yearly, they apply a survey, present straightforward results, and made the anonymized results available for download, so it is possible to perform additional analysis beyond the original ones. Using data visualization techniques, we analyzed 2018 data to extract some insights about how genders see their confidence in programming skills. Results show that respondents from under-represented groups tend to believe they are not as good as their peers. From that, we propose a discussion about the unconscious bias, stereotypes, and impostor syndrome and how to provide support on that.
Metamorphic Exploration of an Unsupervised Clustering Program

Machine learning has been becoming increasingly popular and widely-used in various industry domains.The presence of the oracle problem, however, makes it difficult to ensure the quality of this kind of software.Furthermore, the popularity of machine learning and its application has attracted many users who are not experts in this field.In this paper, we report on using a recently introduced method called metamorphic exploration where we proposed a set of hypothesized metamorphic relations for an unsupervised clustering program, Weka, to enhance understanding of the system and its better use.
Analysis of Autonomous Underwater Vehicle (AUV) navigational states based on complex networks

To determine the navigational states of an autonomous underwater vehicle (AUV), a data analysis approach of AUV navigation based on complex networks is proposed in this study. First, the noise in AUV navigation data is eliminated by the projection-density peaks clustering algorithm (Pro-DPCA), and the weighted complex networks of the de-noising data are constructed. The nodes of networks characterize AUV navigation states. Subsequently, we compute the topological statistics of the complex networks to obtain the fluctuation patterns of the AUV navigational data. This is used to analyse AUV navigational states. For verifying the approach, the heading data at different depths is analysed in our experiments. The experimental results indicate that the topological statistics of the complex networks accurately describe the navigational states of AUV at different depths.
Neural Compatibility Ranking for Text-based Fashion Matching

When shopping for fashion, customers often look for products which can complement their current outfit. For example, customers want to buy a jacket which can go well with their jeans and sneakers. To address the task of fashion matching, we propose a neural compatibility model for ranking fashion products based on the compatibility matching with the input outfit. The contribution of our work is twofold. First, we demonstrate that product descriptions contain rich information about product comparability which has not been fully utilized in the prior work. Secondly, we exploit such useful information from text data by taking advantages of semantic matching and lexical matching both of which are important for fashion matching. The proposed model is evaluated on a real-world fashion outfit dataset and achieves the state-of-the-art results by comparing to the competitive baselines. In the future work, we plan to extend the model by incorporating product images which are the major data source in the prior work on fashion matching.
Distilling Knowledge from User Information for Document Level Sentiment Classification

Combining global user and product characteristics with local review information provides a powerful mechanism for predicting users' sentiment in a review document about a product on online review sites such as Amazon, Yelp and IMDB. However, the user information is not always available in the real scenario, for example, some new-registered users, or some sites allowing users' comments without logging in. To address this issue, we introduce a novel knowledge distillation (KD) learning paradigm, to transfer the user characteristics into the weights of student neural networks that just utilize product and review information. The teacher model transfers its predictive distributions of training data to the student model. Thus, the user profiles are only required during the training stage. Experimental results on several sentiment classification datasets show that the proposed learning framework enables student models to achieve improved performance.