Reviewerly/exHarmony huggingface dataset:
A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets

In this article, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify DA, to characterize the spreading of DA tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that the cascades of DA tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected nonfringe nodes that work together toward such spread, thus potentially complicating the process of arresting such cascades. Furthermore, we discovered that the engagement of the users with respect to certain drugs, such as Vicodin, Percocet, and OxyContin, that were observed to be most mentioned in Twitter is instantaneous. On the other hand, for drugs, such as Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.
Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile

Santiago de Chile frequently suffers from atmospheric pollution that contributes to the decrease of solar irradiance on the surface, leading to losses in the energy output of photovoltaic systems. In this study, a simple model is used to estimate the effect of aerosols on the solar irradiance over the city throughout the year, using as input AERONET sunphotometer data and other in-situ measurements. The results show reductions of 3.5% and 14.1% for global horizontal and direct normal irradiance respectively and an increase of 35.4% for diffuse horizontal irradiance between the actual condition in Santiago and a hypothetical atmosphere free of aerosols. These effects translate approximately to an annual difference in the energy output of −7.2% and −8.7% for monocrystalline and amorphous silicon PV technologies respectively, and an annual difference of −16.4% for a CPV technology, showing that aerosols can have a significant effect on the photovoltaic energy production.
Achieving Lossless Accuracy with Lossy Programming for Efficient Neural-Network Training on NVM-Based Systems

Neural networks over conventional computing platforms are heavily restricted by the data volume and performance concerns. While non-volatile memory offers potential solutions to data volume issues, challenges must be faced over performance issues, especially with asymmetric read and write performance. Beside that, critical concerns over endurance must also be resolved before non-volatile memory could be used in reality for neural networks. This work addresses the performance and endurance concerns altogether by proposing a data-aware programming scheme. We propose to consider neural network training jointly with respect to the data-flow and data-content points of view. In particular, methodologies with approximate results over Dual-SET operations were presented. Encouraging results were observed through a series of experiments, where great efficiency and lifetime enhancement is seen without sacrificing the result accuracy.
A Phishing Detection System based on Machine Learning

As the Internet has become an essential part of human beings' lives, a growing number of people are enjoying the convenience brought by the Internet, while more are attacks coming from on the dark side of the Internet. Based on some weaknesses of human nature, hackers have designed confusing phishing pages to entice web viewers to proactively expose their privacy, sensitive information.In this article, we propose a URL-based detection system - combining the URL of the web page URL and the URL of the web page source code as features, import Levenshtein Distance as the algorithm for calculating the similarity of strings, and supplemented by the machine learning architecture. Due to the great accuracy in small sample numbers and binary classification, we implement Support-vector machine to be the machine learning algorithm model in our system. The system is designed to provide high accuracy and low false positive rate detection results for unknown phishing pages.
Secured voting through Blockchain technology

Voting is the primary factor to change the country's future. The manual voting got replaced with the electronic machines called Electronic Voting Machines(EVM). Even after replacement, the issues continue to trouble voters. Issues like missing names in voter list, misplaced votes and so on. That is why we suggest a decentralised system to be integrated with the voting system to make it error-free. One of those decentralised systems are blockchain technology. Our project is developed on Ethereum platform using solidity language. Estonia, the blockchain country uses blockchain for almost all services. The voting procedure is presented in the paper as a case study. We summarise the tools used for the project along with its features. We also appraise the working of the our project in the further sections. Finally, we include the source smart contract code in the appendix. This project open up many possibilities to secure the voting system and help for the welfare of the nations.
LEAFAGE: Example-based and Feature importance-based Explanations for Black-box ML models

Explainable Artificial Intelligence (XAI) is an emergent research field which tries to cope with the lack of transparency of AI systems, by providing human understandable explanations for the underlying Machine Learning models. This work presents a new explanation extraction method called LEAFAGE. Explanations are provided both in terms of feature importance and of similar classification examples. The latter is a well known strategy for problem solving and justification in social science. LEAFAGE leverages on the fact that the reasoning behind a single decision/prediction for a single data point is generally simpler to understand than the complete model; it produces explanations by generating simpler yet locally accurate approximations of the original model. LEAFAGE performs overall better than the current state of the art in terms of fidelity of the model approximation, in particular when Machine Learning models with non-linear decision boundaries are analysed. LEAFAGE was also tested in terms of usefulness for the user, an aspect still largely overlooked in the scientific literature. Results show interesting and partly counter-intuitive findings, such as the fact that providing no explanation is sometimes better than providing certain kinds of explanation.
Efficient nearest neighbor search in high dimensional hamming space

Fast approximate nearest neighbor search has been well studied for real-valued vectors, however, the methods for binary descriptors are less developed. The paper addresses this problem by resorting to the well established techniques in Euclidean space. To this end, the binary descriptors are firstly mapped into low dimensional float vectors under the condition that the neighborhood information in the original Hamming space could be preserved in the mapped Euclidean space as much as possible. Then, KD-Tree is used to partitioning the mapped Euclidean space in order to quickly find approximate nearest neighbors for a given query point. This is identical to filter out a subset of nearest neighbor candidates in the original Hamming space due to the property of neighborhood preserving. Finally, Hamming ranking is applied to the small number of candidates to find out the approximate nearest neighbor in the original Hamming space, with only a fraction of running time compared to the bruteforce linear scan. Our experiments demonstrate that the proposed method significantly outperforms the state of the arts, obtaining improved search accuracy at various speed up factors, e.g., at least 16% improvement of search accuracy over previous methods (from 67.7% to 83.7%) when the search speed is 200 times faster than the linear scan for a one million database.
Variants of phishing attacks and their detection techniques

Phishing is a treacherous effort to steal private data from users like address, aadhar number, PAN card details, credit/debit card details, bank account details, password for online shopping sites, etc. Pinching or phishing of private information on the web has caused havoc on a majority of users due to the lack of internet security. Phishing attacks make use of fake emails or websites, intended to fool users into revealing personal or financial information by posing as the trusted bank/shopping site. The various types of phishing attacks and the recent approaches to prevent the attacks are discussed. A framework to detect and prevent phishing attacks is also proposed. A combination of supervised and unsupervised machine learning techniques is used to detect known and unknown attacks.
The Usability of the Microsoft HoloLens for an Augmented Reality Game to Teach Elementary School Children

Our objective in this research is to compare the usability of three distinct head gaze-based selection methods in an Augmented Reality (AR) hidden object game for children: voice recognition, gesture, and physical button (clicker). Prior work on AR applications in STEM education has focused on how it compares with non-AR methods rather than how children respond to different interaction modalities. We investigated the differences between voice, gesture, and clicker based interaction methods based on the metrics of input errors produced and elapsed time to complete the tutorial and game. We found significant differences in input errors between the voice and gesture conditions, and in elapsed tutorial time between the voice and clicker conditions. We hope to apply the results of our study to improve the interface for AR educational games aimed at children, which could pave the way for greater adoption of AR games in schools.
Semi-Unknown Environments Exploration Inspired by Swarm Robotics using Fuzzy Cognitive Maps

This paper aims to present a fuzzy cognitive map (FCM) controller on multiple autonomous mobile robots (multirobot system) in order to complete a foraging task in semiunknown environments, and to compare the FCM results with a fuzzy logic controller (FLC). In this work, the limits of the searching area are known. However, everything within it (goals, obstacles and position of other robots during navigation) is unknown. The foraging task simulates a real-life application of robots on rescue missions, where the main objective is rescuing the victims of a tragedy, or lost in a forest, or even victims of a major accident in an industry. Although it is a simulated environment, we tested the autonomy and cooperation among all robots for this kind of operation in different simulated scenarios. We used a reactive (real-time) architecture to enhance the robots' global robustness on dealing with unpredictable situations. The results imply that the FCM approach presented, in general, less simulation processing time and distance traveled, without significant prejudice in the explored area. In this way, we expect to collaborate and serve as inspiration for future works on this research area.
Harmonized temporal feature modeling to uniformly perform, track, analyze, and replay software product line evolution

A feature model (FM) describes commonalities and variability within a software product line (SPL) and represents the configuration options at one point in time. A temporal feature model (TFM) additionally represents FM evolution, e.g., the change history or the planning of future releases. The increasing number of different TFM notations hampers research collaborations due to a lack of interoperability regarding notations, editors, and analyses. We present a common API for TFMs, which provides the core of a TFM ecosystem, to harmonize notations. We identified the requirements for the API based on systematically classifying and comparing the capabilities of existing TFM approaches. Our approach allows to work seamlessly with different TFM notations to perform, track, analyze and replay evolution. Our evaluation investigates two research questions on the expressiveness (RQ1) and utility (RQ2) of our approach by presenting implementations for several existing FM and TFM notations and replaying evolution histories from two case study systems.
Deeply Associative Two-Stage Representations Learning Based on Labels Interval Extension Loss and Group Loss for Person Re-Identification

Person Re-identification (ReID) aims to match people across non-overlapping camera views in a public space, which is usually regarded as an image retrieval problem to match query images with pedestrian images in the gallery. It is challenging since many difficulties exist such as pose misalignments, occlusions, similar appearance when detecting people. Existing researches on ReID mainly focus on two major problems: representation learning and metric learning. In this paper, we target at learning discriminative representations and make two contributions in total. (i) We propose a novel architecture named Deeply Associative Two-stage Representations Learning (DATRL). It contains the global re-initialization stage and fully-perceptual classification stage employing two identical CNNs associatively at the same time. On the global stage, we take on the backbone of one deep CNN e.g., dozens of layers in the front of Resnet-50 as a normal re-initialization subnetwork. Meanwhile, we apply our own proposed 3D-transpose technique into the backbone of the other CNN to form the 3D-transpose re-initialization subnetwork. The fully-perceptual stage is actually made up of the leftover layers of the original CNNs. On this stage, we take both the global representations learned at multiple hierarchies and the local representations uniformly-partitioned on the highest conv-layer into consideration, and then optimizing them separately for classification. (ii) We introduce a new joint loss function in which our proposed Labels Interval Extension loss (LIEL) and Group loss (GL) are combined to enhance the performance of gradient decent as well as increasing the distances between image features with different identities. We apply the above DATRL, LIEL and GL to ReID thus obtaining DATRL-ReID. Experimental results on four datasets CUHK03, Market-1501, DukeMTMC-reID and MSMT17-V2 demonstrate that DATRL-ReID shows excellent performance in improving recognition accuracy and is superior to state-of-the-art methods.
BADAM

The application of handwritten text recognition to historical works is highly dependant on accurate text line retrieval. A number of systems utilizing a robust baseline detection paradigm have emerged recently but the advancement of layout analysis methods for challenging scripts is held back by the lack of well-established datasets including works in non-Latin scripts. We present a dataset of 400 annotated document images from different domains and time periods. A short elaboration on the particular challenges posed by handwriting in Arabic script for layout analysis and subsequent processing steps is given. Lastly, we propose a method based on a fully convolutional encoder-decoder network to extract arbitrarily shaped text line images from manuscripts.
Dual-archive-based particle swarm optimization for dynamic optimization

In dynamic optimization problems, although the problem environments keep changing, a new environment is usually related to its previous environments. Based on the relevance, the search experience in the previous environments can be reused to accelerate the optimization of the current new environment. Thus, in this paper, we propose a dual-archive-based particle swarm optimization to utilize the useful information accumulated in past environments as well as to explore the emerging information of each new environment. Specifically, in the proposed algorithm, the good solutions found in past environments are stored in two different archives, i.e., a fine-grained archive and a coarse-grained archive, so as to preserve both detailed information and systemic information, respectively. Once the environment is changed, the solutions in the two archives will be used for guidance to quickly find high-quality solutions in the new environment. The proposed algorithm is evaluated on the famous moving peaks benchmark in terms of two performance measures. The experimental results show that the proposed algorithm is competitive with state-of-the-art algorithms for dynamic optimization problems.
Uncovering the Semantics of Wikipedia Categories

The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguation or semantic similarity estimation. Wikipedia’s categories are a rich source of taxonomic as well as non-taxonomic information. The category German science fiction writers, for example, encodes the type of its resources (Writer), as well as their nationality (German) and genre (Science Fiction). Several approaches in the literature make use of fractions of this encoded information without exploiting its full potential. In this paper, we introduce an approach for the discovery of category axioms that uses information from the category network, category instances, and their lexicalisations. With DBpedia as background knowledge, we discover 703k axioms covering 502k of Wikipedia’s categories and populate the DBpedia knowledge graph with additional 4.4M relation assertions and 3.3M type assertions at more than 87% and 90% precision, respectively.
A new class of regression model for a bounded response with application in the study of the incidence rate of colorectal cancer

Response variables in medical sciences are often bounded, e.g. proportions, rates or fractions of incidence of some disease. In this work, we are interested to study if some characteristics of the population, e.g. sex and race which can explain the incidence rate of colorectal cancer cases. To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution. The proposal is to obtain compound power normal distribution as a base distribution with a quantile transformation of another family of distributions with the same support and then is to study some properties of the new family. In addition, the new family is extended to regression models as an alternative to the regression model with a unit interval response. We also present inferential procedures based on the Bayesian methodology, specifically a Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of parameters. An application to real data to illustrate the use of the new family is considered.
M-Hair: Creating Novel Tactile Feedback by Augmenting the Body Hair to Respond to Magnetic Field

In this paper, we present M-Hair, a novel method for providing tactile feedback by stimulating only the body hair without touching the skin. It works by applying passive magnetic materials to the body hair, which is actuated by external magnetic fields. Our user study suggested that the value of the M-hair mechanism is in inducing affective sensations such as pleasantness, rather than effectively discriminating features such as shape, size, and direction. This work invites future research to use this method in applications that induce emotional responses or affective states, and as a research tool for investigations of this novel sensation.
Impacts of Bitcoin on USA, Japan, China and Turkey stock market indexes: Causality analysis with value at risk method (VAR)

In this study, the impacts of Bitcoin on Japan, China, Turkey and USA stock indexes were investigated. Nikkei225, SSE380, BIST100 and S&P500 were selected as the stock market indexes. The weekly data including dates between January 03, 2016 and December 16, 2018 were analyzed using EViews program and firstly the time-dependent, non-stationary data set was stabilized. Then, the stabilized data was analyzed with VAR(3) model according to Akaike information criteria. According to the Johansen cointegration test, 2nd model was selected as the most appropriate model for the study. The variables were rearranged with error correction model and then Granger causality analysis was performed. As a result of these analysis, it was determined that Bitcoin only affected BIST100 and that there were two-way causality relation between them. In addition, a one-way causality from Nikkei225 to SSE380, from SSE380 to Bitcoin, from S&P500 to Nikkei225 and from Nikkei225 to Bitcoin were observed.
Proxino

We propose blending the virtual and physical worlds for prototyping circuits using physical proxies. With physical proxies, real-world components (e.g. a motor, or light sensor) can be used with a virtual counterpart for a circuit designed in software. We demonstrate this concept in Proxino, and elucidate the new scenarios it enables for makers, such as remote collaboration with physically distributed electronics components. We compared our hybrid system and its output with designs of real circuits to determine the difference through a system evaluation and observed minimal differences. We then present the results of an informal study with 9 users, where we gathered feedback on the effectiveness of our system in different working conditions (with a desktop, using a mobile, and with a remote collaborator). We conclude by sharing our lessons learned from our system and discuss directions for future research that blend physical and virtual prototyping for electronic circuits.
A T1OWA fuzzy linguistic aggregation methodology for searching feature-based opinions

Online services such as Amazon, Tripadvisor, Ebay, etc., allow users to express sentiments about different products or services. Not only that, in some cases it is also possible to express sentiments about the different features characterizing those products or services. Most users express sentiments about individual features by using numerical values, which sometimes do not allow users to reflect properly what they are meaning and therefore they are misleading. To overcome this key issue and make users’ opinions in online services more comprehensive, a new methodology for representing sentiments using linguistic term sets instead of numerical values is presented. In addition, this methodology will allow to implement importance degrees on the different features characterizing users’ opinions. From both sentiments and importance of the features, the most important opinions for each user is derived via an aggregation step based on the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate the corresponding fuzzy set representations of linguistic terms. Furthermore, the final output of the T1OWA based-search process can easily be interpreted by users because it is always of the same type (fuzzy) and defined in the same domain of the original fuzzy linguistic labels. A case study is presented where the T1OWA operator methodology is used to assess different opinions according to different user profiles.