Reviewerly/exHarmony huggingface dataset:
Prototype for Peruvian Sign Language translation based on an artificial neural network approach

This document aims to develop and demonstrate the operation of a prototype capable of recognizing the alphabet of the Peruvian Sign Language and translating it into the Spanish language alphabet, based on an optimized multilayer artificial neural network approach, with the purpose of improving the communication, fluency and reciprocity of a daily conversion with listeners who do not know sign language. Bend sensors were used to record the movement and variation of each letter. Then, through an Arduino – Excel interface, a database of a test user was compiled for training and validation, likewise, the neural network was developed in Matlab software for the recognition of signals (letters). Subsequently, the real-time data acquisition of the test user was performed through a Matlab-Arduino interface that, in conjunction with the online prediction algorithm and the neural network, recognized the desired letter. Finally, an artificial voice algorithm developed in the Matlab software was used to reproduce the recognized lyrics, and another, which allows the corresponding signal to be viewed online. The prototype developed has an accuracy of 94.60% for the training set and 94.32% for the validation set, in turn, the cost was 120 USD. It is concluded that this prototype is capable of translating Peruvian Sign Language with a high percentage of accuracy, also is viable and economical, so it is recommended to test the effectiveness of this prototype in multiple people with speech disabilities.
The Design of Outdoor Technologies for Children

Children have different needs, motivations, interaction styles, and perspectives than adults as they utilize technology and explore the world around them. We present an overview of technologies for children outdoors and lessons learned from developing some of those technologies as well as a survey of adults’ experience with technology while hiking. The literature and our experience inform our recommended design considerations for technology for children outdoors, namely that it should: support social interactions; accommodate groups that are generally led by an adult; motivate via a narrative that piques children’s interest; promote exploration in open, generative and creative ways; and keep children safe and allow communication with parents.
A Smart Ontology for Project Risk Management Based on PMI’s Framework

Whenever Project Risk (PR) exists, there is complexity. The difficulty to make concerned decisions related to Project Risk Management (PRM) increases project complexity and even its failure. In order to assist practitioners and professionals to better study the potential impacts of their decisions and assess the PR as precisely as possible, this chapter put forward an ontological approach based on OWL ontology with SWRl rules, that provides the project team clear guidelines to effectively manage PR, and then make the appropriate decisions based on the right recommendations. This approach takes advantages of ontology semantic strengths as it represents a unified PRM knowledge relying on PMI’s frameworks. As well, through SWRL reasoning rules, the proposed ontology generates recommendations by which a team member ask for risk-related request more targeted. The proposed ontological approach was evaluated, in term of content and structure, achieving promising results based on the F-measure metric.
Local Forward Model Learning for GVGAI Games

In this paper, we are going to explain the design process for our GVGAI game-learning agent, which is going to be submitted to the GVGAI competition's learning track 2020. The agent relies on a local forward modeling approach, which uses predictions of future game-states to allow the application of simulation-based search algorithms. We first explain our process in identifying repeating tiles throughout a pixel-based state observation. Using the tile information, a local forward model is trained to predict the future state of each tile based on its current state and its surrounding tiles. We accompany this approach with a simple reward model, which determines the expected reward of a predicted state transition. The proposed approach has been tested using multiple games of the GVGAI framework. Results show that the approach seems to be especially feasible for learning how to play deterministic games. Except for one non-deterministic game, the agent performance is very similar to agents using the true forward model. Nevertheless, the prediction accuracy needs to be further improved to facilitate a better game-playing performance.
Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood

Nonparametric regression using Gaussian Process (GP) models is a powerful but computationally demanding method. While various approximation methods have been developed to mitigate its computation complexity, few works have addressed the quality of the resulting approximations of the target posterior. In this paper we start from a general belief updating framework that can generate various approximations. We show that applying using composite likelihoods yields computationally scalable approximations for both GP learning and prediction. We then analyze the quality of the approximation in terms of averaged prediction errors as well as Kullback-Leibler (KL) divergences.
Analysis on the Trends and Characteristics of Automobile Recalls in China

Abstract Based on the statistical data provided by the State Administration for Market Regulation, China Automobile Industry Association and China Consumers’ Association, this study analyzes the trends and characteristics of automobile recalls in China from 2008 to 2017. Studies have shown that automobile recalls in China have become a normal trend, there is a recall almost every two days and most recalls are initiated by a few automobile manufacturers. The recall of auto brands such as Volkswagen and Honda usually occur in the first 5 years of the model year. This shows that consumers should pay more attention to the after-sales and maintenance services in this period when buying a car.
JumpEstimate: a Novel Black-box Countermeasure to Website Fingerprint Attack Based on Decision-boundary Confusion

Recent research shows that website fingerprinting (WF) is a growing threat to privacy-sensitive web users, especially when using machine learning techniques such as deep learning or machine learning (DL / ML) to attack website fingerprint, reducing the effectiveness of the previous defense strategies. The reason is that the features targeted by the previous defense countermeasures are manually extracted, the range of it can’t be large enough to cover the range of features automatically extracted by DL / ML-based attacks. This paper proposes a black box defense countermeasure based on decision boundary confusion. Instead of manually extracting features, it uses the classification results of the classifier to determine the decision boundary of the classifier then automatically find the adversarial traffic that may cause the classifier to be confused. At the same time, to solve the retraining problem caused by adversarial traffic, we also utilize Monte Carlo estimation to modify adversarial traffic, to confuse decision boundary, improve the retraining resistance of adversarial traffic. Therefore, it is difficult for the classifier to form a stable and effective decision boundary after training the adversarial traffic. Results shows that our method gets a average defense success rate of 78.2% when facing the baseline WF Attacks, outperforming existing SOTA method Walkie-Talkie’s 63.6% average defense success rate. At the same time, our method improves the ability of the adversarial traffic to resist retrain, increased the retrain defense success rate from 12% to 78.2% under 31% overhead.
FLASH: FPGA Locality-Aware Sensitive Hash for Nearest Neighbor Search and Clustering Application

A locality sensitive hash (LSH) is a function to identify similar items in data sets. However, traditional LSH based algorithms are rarely implemented on hardware due to the high demand of computation, which limits its usage. In this paper, we propose a novel LSH design and hardware implementation called FPGA locality-aware sensitive hash (FLASH). With the unique hardware delay generated during the fabrication process, a FLASH can reduce the dimensionality of coordinate distance calculation and improve the efficiency of nearest neighbor search (NNS). It is also applied to 2-D image clustering. The experimental results show the practical value of our FLASH applications.
Do Political Regimes Matter for Technology Diffusion?

Productivity growth is important for long-term economic growth and development, and technology adoption is one of its key drivers. This study empirically assesses whether political regimes are a significant determinant of technology diffusion. Specifically, we examine the effects of political regimes on diffusion of technology using data based on a sample of 104 technologies from 137 countries for the period 1901 to 2000. We consider detailed categories of technologies and investigate the differences in the impacts of political regimes on each technology. Our estimation results show that democracy does not have a significant impact on the overall diffusion of technology but it is positively associated with the diffusion of health- and agriculture-related technologies. Furthermore, the diffusion of infrastructure, general, and other sector-specific technologies is not influenced by political regimes. Considering different types of democracies and dictatorships, we find that parliamentary democracy has a positive impact on health- and agriculture-related technology diffusion. On the contrary, all types of dictatorships, namely civilian, military, and royal, have negative impacts on diffusion of technology.
Grammatikalisering af type-konstruktioner og pseudo-partitiver i dansk

The aim of this paper is to argue that Danish type-noun binominals (en slags cykel ‘a kind of bike’) and pseudo-partitive binominals (et stykke ost ‘a piece of cheese’) with the formal structure [N1 + N2] have followed analogical patterns of grammaticalization both structurally and functionally. On the basis of a study of 2946 tokens of slags ‘kind’ in the construction EN/ET-slags-S(noun), retrieved from KorpusDK, we discuss agreement features and polysemy in relation to the fact that slags occurs more frequently as a modifier marking ‘association’ than with its original taxonomic meaning. Against this background, we suggest that ultimately N2 is winning the competition for discourse prominence over slags, which, thus, is in the process of converting from a lexical to a grammatical element.
Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm

The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness value. Self-organizing migrating algorithm, a swarm intelligence algorithm, is used to predict the next positions that the drone will move to. These positions both satisfy the requirement to avoid obstacles and shorten the distance to the target. A map of two drones, two corresponding targets and four static obstacles was modeled on Matlab. The simulation results verify the correctness and effectiveness of the proposed method.
Using Generative Adversarial Networks for Relevance Evaluation of Search Engine Results

In the article a new approach to the problem of relevance evaluation of the search engine results, based on generative adversarial networks (GAN), is proposed. To improve the quality of search, the generative adversarial networks are used to distinguish between relevant and irrelevant search results.We used a simplistic model based on fully automated reference results selection and multi-layered generator and discriminator networks with dense layers. The queries needed to generate the reference results were themselves generated by a GPT-2 like network using the same text corpus as a source, to make them potentially relevant to the search space.The results clearly demonstrate the principal possibility and feasibility of using the described approach, despite the fact of used models being simplistic.
Weakly supervised monocular depth estimation method based on stereo matching labels

Current self-supervised monocular methods only learn effectively by imposing consistency constraints without relying on any geometric constraints or ground truth depth constraints, which makes the accuracy of the estimation result suboptimal. Compared with the monocular algorithm, the stereo matching network usually follows the geometric process of the traditional stereo algorithm, which makes the estimation result more accurate. Inspired by these findings, we proposed a weakly supervised monocular learning approach that makes use of the disparity maps generated by the self-supervised stereo matching model as the “ground truth” labels to train a self-supervised monocular depth estimation model. To obtain more accurate ground truth labels, we improve the layer of geometry and context in self-supervised deep stereo regression by replacing the 3D convolutional layer with a guided aggregation layer. The design can also reduce computational costs and memory consumption. Then, we build our weakly supervised monocular model by improving the U-Net model and designing a loss function composed of a weakly supervised cost and a self-supervised cost. The estimation results obtained using our model outperform those of the existing self-supervised depth estimation methods under the same training conditions on the challenging KITTI dataset, and the results can easily be generalized to the Cityscapes dataset.
DADS: Decentralized (Mobile) Applications Deployment System Using Blockchain : Secured Decentralized Applications Store

This paper proposes a comprehensive framework using blockchain architecture to create a decentralized applications store, in order to reduce the approval time for apps that are to be deployed on deployment systems i.e. applications stores, and specially for mobile platforms e.g. Apple and Google play stores. This work potentially can solve some of the existing challenges with the current centralized application approval process by Apple and Google. These companies currently charge a flat commission rate of 30% [1] of the total applications revenues from the applications owners. This commission is considered to be very high, especially for applications with revenues in the order of few million dollars. In addition, the length of the review period prior to deployment for any new or revised apps delays the release to the marketplace hence costing economic losses to the app owners. Currently, Apple and Google stores takes around two days and two hours respectively to review and approve the apps on their stores. However, by using decentralized technologies such as the blockchain, the time to review and publish a new or a revised app can be significantly reduced from few days to few minutesIn this paper a novel framework is proposed based on the blockchain architecture incorporating the Ethereum network, Interplanetary Files System (IPFS), VueJS, and a backend database. The framework is experimentally validated, and the results show the reduction in terms of the number of minutes to approve apps by the app stores.
Avoiding Plagiarism in Systematic Literature Reviews

Background: The number of Systematic Literature Reviews published in Software Engineering has been increasing in recent years. Due to this fact, one point not addressed is the possibility of plagiarism when dealing with SLRs, especially when dealing with SLR maintenance (update) activities.
Task‐based <scp>human‐structured</scp> research data interaction: A discipline independent examination

Abstract With the development of open data movement, an increasing number of structured research datasets (SRD) are available online due to the successful data infrastructure and the strong demand for sharing data. Yet there still lacks a thorough, systematic investigation of researcher‐SRD interactions, which is important to understand users' needs, challenges, and expectations. Our work extends from the current scholarship and proposes a task‐based approach to examining how researchers interact with SRD, without considering individual disciplines. This study identifies two types of research tasks, the data‐driven tasks and model‐driven tasks, and also proposes a framework for researcher‐SRD interaction. Our findings will contribute to the research field of human data behaviors.
WalnutDSA™: a group theoretic digital signature algorithm

This paper presents an in depth discussion of WalnutDSA, a quantum resistant public-key digital signature method based on the one-way function E-multiplication. A key feature of WalnutDSA is that it provides very efficient means of validating digital signatures which is essential for low-powered and constrained devices. This paper presents an in-depth discussion of the construction of the digital signature algorithm, and delves deeply into the underlying mathematics that facilitates analysing the security of the scheme. When implemented using parameters that defeat all known attacks, WalnutDSA is among the fastest quantum resistant signature verification methods; it performs orders of magnitude faster than ECC, even on low-end embedded hardware. WalnutDSA delivers a 12–25× speed improvement over ECDSA on most platforms, and a 31× speed improvement on a 16-bit microcontroller, making it an ideal solution for low-resource processors found in the Internet of Things (IoT).
A Study on Diverse Methods and Performance Measures in Sentiment Analysis

: With the vast development of internet technology 2.0, millions of people are sharing their opinions on different social networking sites. To obtain the necessary information from the huge volume of user-generated data, the attention on sentiment analysis among the research community is growing. Growth and prominence of sentiment analysis is synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps to produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of user-generated content such as noisy, irrelevant information and fake reviews, there is a significant demand for an effective methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies and highlights the challenges and performance factors of various approaches of sentiment analysis including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Background: User-generated content is growing all over the globe and people more eagerly express their views on social media towards various aspects. The opinionated text is difficult to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the usergenerated data. Objective: The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis namely text pre-processing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.
Elements of XPath-like languages in problems of constructing semantic XML models of natural language texts

The subject of the research is the possibility of using XPath-like micro-languages of programming in the generation systems of programs of the PGEN ++ class for the selection and completion of XML-models describing the plan for solving the original problem, according to which the solver program is generated. It is supposed to build such models according to the description of the problem in natural language, thus, we are talking about elements of artificial intelligence technologies. XPath-like language works in the layer of regular-logical expressions (highlighting elements of the primary XML document), performing primary processing of the data obtained in the layer of grammatical parsing of the source text. In addition, XPath-like elements are used to render the final XML models. The standard natural language parsing libraries are used. Non-standard XPath query language extensions are used. For the first time, the idea of expanding the XPath query language to the level of an algorithmic language by introducing the minimum required number of syntactic elements is proposed. It is also proposed to use syntactic elements with an XPath-like structure as both generating and controlling weak constraints of the process of direct inference of final semantic XML models.
Feature Extraction in Liver Cancer Based on Abdominal CT Scan Images using Contour Analysis and GLCM Method

Liver cancer is a type of cancer that affects the largest organs of the stomach, where some are grown from the liver and some grow in other organs, then spread to the liver. One of the technologies used to analyze and diagnose liver cancer is CT Scan (Computer Tomography Scanner). The CT Scan is often preferred for diagnosing liver cancer, especially as being considered of high accurate imaging, high imaging speed and relatively lower cost. However, the results of the CT Scan are often different depending on the accuracy and experience of the doctor so that it can lead to different diagnoses. In this study, a system was created that was able to extract features from CT Scan images of liver cancer to recognize the object of cancer and distinguish it from other objects. This system will be tested on 50 data abdominal CT Scan images with a diagnosis of liver cancer, where 21 data for benign liver cancer and 29 data for malignant liver cancer. This research has three main stages, that is preprocessing to improve image quality using scaling image, histogram equalization, and median filtering. Segmentation to identify the object being observed and separate it from the background using watershed method and binary thresholding with accuracy is 90%. The last is feature extraction based on cancer area, edge irregularity, and texture to identify liver cancer.
Efficien Finite-State Decoding of Rice Codes for Large Alphabets

In this paper, we present a finite-state approach to efficiently decode a sequence of Rice codes. The proposed method is capable of decoding a byte stream for any Golomb parameter for unboundedly large alphabets with constant space complexity. Performance of the approach is evaluated in comparison to the conventional bit-level decoding algorithm on compressed inverted indexes with respect to various parameter values. It is observed that decoding performance of the method increases with the mean value of encoded integers. Speed gains up to about a factor of 2 are empirically obtained in comparison with the conventional decoding from the point where the optimal value of the divisor is computed as 128. Results show that it is particularly effective for tasks in which a stream of large integers are encoded such as compression of document identifier gaps in inverted indexes.