Reviewerly/exHarmony huggingface dataset:
Hierarchical POI Attention Model for Successive POI Recommendation

The rapid growth of location-based social networks developed a large number of point-of-interests (POIs). POI recommendation task aims to predict users’ successive POIs, which has attracted more and more research interests recently. POI recommendation is achieved based on POI context, which contains a variety of information, including check-in sequence pattern, POIs’ textual contents, and temporal characteristics. Existing efforts only model part of them and lose valuable information of other aspects.In this paper, we propose a hierarchical POI attention model (HPAM), which jointly takes advantage of both POIs’ text contents, temporal characteristics, and sequential patterns. Specifically, HPAM proposes a lower-level POI representation layer to explore textual content with word attention mechanism, and a higher-level contextual sequence layer to depict the temporal characteristics with a temporal-level attention mechanism. Experimental results on a public dataset show that HPAM consistently outperforms the state-of-the-art methods. Experiment results on HPAM variants evaluate the effectiveness of the proposed multiple attention mechanisms.
Mind the Scraps: Attacking Blockchain Based on Selfdestruct

A smart contract is a program that resides its binary code and states in blockchain to provide contract-like functionality. The binary code is unchangeable once it is deployed into the chain. This can ensure the underlying blockchain to be an append-only decentralized and secure database, but it may also bring potential threats. For example, Selfdestruct is a typical command used to disable a smart contract and clean all relevant information. It is believed that if an address of a deactivated contract is inaccessible, it is impossible for an attacker to explore. However, in this work, we identify that instead of erasing or overwriting the previously recorded data, the smart contract’s running environment may store its latest data and states in the latest block even after using Selfdestruct. Motivated by this observation, we show how these remained traces can reveal private information, i.e., how a privatized function can be externally accessed, and discuss how to attack the contract without knowing its application binary interface (ABI). In the end, we also discuss some potential solutions to protect information leakage in such scenario.
Research on Chinese-Korean Entity Alignment Method Combining TransH and GAT

Cross-language entity alignment is one of the important techniques for carrying out linguistic research and constructing a multilingual knowledge graph. At present, there is a lack of research content for Chinese and Korean bilingualism in the field of knowledge graphs. At the same time, mainstream entity alignment methods are susceptible to the impact of data set size and graph structure heterogeneity. This paper proposes a cross-language entity alignment model that combines GAT and TransH, which can alleviate the negative impact of the model on the number of data sets and the heterogeneous graph structure. Firstly, this article uses crawlers to collect and sort out a high-quality Chinese-Korean aligned bilingual data set for training the alignment model; secondly, using the model proposed in this article, through learning the structure and relationship characteristics of the bilingual knowledge graph, automatically discover the existence of cross-language entities with the same semantics. Experiments show that this method has a high accuracy rate. When tested on the Chinese-Korean alignment data set, Hits@1, Hits@5 and Hits@10 reached 49.62%, 80.89% and 91.76%, respectively.
Consistency Regularization for Ensemble Model Based Reinforcement Learning

It’s generally believed that model-based reinforcement learning (RL) is more sample efficient than model-free RL. However, model-based RL methods typically suffer from model bias, which severely limits the asymptotic performance of the algorithm. Although previous model-based RL approaches use ensemble models to reduce the model error, we find that vanilla ensemble learning does not consider the model discrepancy. The discrepancy between different models is huge, which is not conducive to policy optimization. To alleviate the problem, this paper proposes an Ensemble Model Consistency Actor-Critic (EMC-AC) method to decrease the discrepancy between models while maintaining the model diversity. Specifically, we design ablation experiments to analyze the effects of the trade-off between diversity and consistency on the EMC-AC algorithm performance. Finally, extensive experiments on the continuous control benchmarks demonstrate that our approach achieves the significant performance to exceed the sample efficiency of prior model-based RL methods and to match the asymptotic performance of the state-of-the-art model-free RL algorithm.
A brief review of dashboard visualizations employed to support management or business decisions

This work aims to review the academic literature on information visualization techniques used in dashboards applied to strategic business or management decision-making in different application areas. The review used the snowballing method to obtain academic works and applied a filter to focus on papers published in journals or conference proceedings, reaching 44 papers. We propose four research questions and one taxonomy to classify the works, carry out the analyses, and later a research agenda to address the identified gaps. For instance, this review revealed a lack of academic papers that discuss the subject involving state-of-the-art information visualization and machine learning techniques. This paper represents our initial effort to examine academic works that use information visualization techniques and theory applied to dashboards that support decision-making in business or management areas.
Noncatastrophic convolutional codes over a finite ring

Noncatastrophic encoders are an important class of polynomial generator matrices of convolutional codes. When these polynomials have coefficients in a finite field, these encoders have been characterized as polynomial left prime matrices. In this paper, we study the notion of noncatastrophicity in the context of convolutional codes when the polynomial matrices have entries in the finite ring [Formula: see text]. In particular, we study the notion of zero left prime in order to fully characterize noncatastrophic encoders over the finite ring [Formula: see text]. The second part of the paper is devoted to investigate free and column distance of convolutional codes that are free finitely generated [Formula: see text]-modules. We introduce the notion of [Formula: see text]-degree and provide new bounds on the free distances and column distance. We show that this class of convolutional codes is optimal with respect to the column distance and to the free distance if and only if its projection on [Formula: see text] is.
The Use of Literary Works for Stimulating Students’ Creativity

One of the main tasks of the modern higher education system is to develop students’ ability to find ways for solving different tasks creatively and independently. Creativity is considered in the article as a training ability inherent in any individual, however, requiring an appropriate social environment for its development, including the original material for further creative processing, and the audience for perceiving and evaluating the product of creativity. The purpose of the study is to explore the possible ways of stimulating students’ creativity based on the case study project method. Conducting projects include several stages: reflection and creative processing of foreign language literary works, as well as subjective evaluation of the projects’ effectiveness by students. The authors suggest the project work for developing creative products through cooperative learning. The case study concludes the implementation of such projects is consistent with the opportunities and interests of students of higher education and allows activating and improving a whole complex of communicative skills for production and comprehension activities, as well as the so-called soft skills. Creative projects based on the use of literary works can be applied at the early stage of foreign language learning in order to develop creative abilities reflecting students’ individual interests and skills.
Design of Adaptive Weighted Whale Optimization Algorithm

This paper focuses on developing an adaptive weighted whale optimization algorithm that helps avoid the getting trap at local minima during the convergence. The adaptive weight during each iteration of the algorithm also helps achieve optimal solutions during exploration and exploitation. Further, various adaptive weights, including the chaotic consequences and traditional algorithm, are executed on multiple benchmark functions. The statistical results determined in terms of mean, best and standard deviation depict that the proposed adaptive weighted algorithm performs a higher level than conventional and chaotic weighted algorithms. The results also highlighted that the developed technique showed preferable performance for most minor functions than the traditional technique on achieving the worst global minima.
Feature Selection using Pre-clustering via Affinity Propagation for Speech Classification in Low-resource Languages

Speech analysis is an active research field where different feature extraction techniques are studied for solving various issues. Such studies help to improve the time complexity of solutions by understanding necessary clues to select the features. Choosing essential features by removing irrelevant information is a significant step in feature engineering. Perceptual Linear Predictive (PLP) modeling concentrates on understanding the speech signals by focusing on the features perceived at the listener end. They have been used successfully in many speech processing applications. The selection of the order of PLP coefficients for efficient classification of spoken units plays a crucial role in the recognition task. A conventional speech processing system requires a huge training process to develop an Automatic Speech Recognition system. Such systems are efficient for the languages that have enough resources i.e. data. But, low-resource languages especially Asian languages haven't been developed to provide the data sufficient for such tasks. In this context, alternative methods and techniques are encouraged to enhance or optimize the development process with less amount of data. This paper proposes a pre-clustering technique to improve the classification rate with low resources.
Transition of Learning Physics Technology Using Virtual Practicum to High School Physics Teachers in Aceh Barat District

Abstract Practicum is one of the essential learning aspects of physics learning that is required in the 2013 curriculum. The problem of High School Physics teachers in West Aceh Regency is the limited facilities and infrastructure for the Physics laboratory. The development of Information Technology (IT) enables the Virtual Laboratory of Physics is implemented using a smartphone device. Technology transfer using IT in learning is carried out for High School Physics teachers in the West Aceh district. The method used is physics training using virtual experiments. The training participants are all High School Physics teachers in West Aceh District. The aim of the activity is to train physics teachers’ ability of IT which can be applied in learning activities at High Schools in the West Aceh district. The results of the activity were that 100% of the participants could master IT technology using a Smart Phone for a virtual practicum on Wave material. Therefore, they feel that there is an increase in their pedagogical ability in carrying out their duties and the optimists can apply this in the learning process in their place of duty. 75% of trainee teachers believe that the application of virtual practicum methods in learning can improve students’ understanding of physics subject matter; 12.5% of participants were not sure and 6.25% of participants were not sure that learning Physics using the virtual experiment method could improve students’ understanding. The benefits of the activity are expected to increase the human resources of High School Physics teachers in West Aceh district in utilizing the Virtual Laboratory so that it supports the physics learning process using practicum methods and online learning.
Gender Classification beyond Visible Spectrum using Shallow Convolution Neural Network

Gender classification contributes promising position in a spectrum of utilities in digital platforms like digital purchase, content based retrieval, decision making, searching, forensics and demographic studies. Face recognition system is highly used in cyber forensics especially for criminal identification where gender classification reduces the suspects list of searches. Gender is one of the soft biometrics which provide some evidence about the users' identity that could be beneficial. Also, it could be easily collected at the time of enrollment. In order to tackle with the problem of illumination variation, face images are captured in the NIR spectrum. A shallow CNN has been proposed to perform gender classification in the NIR spectrum. The proposed shallow CNN model has given good performance accuracies when tested on challenging NIR datasets namely: IITKh, Oulu CASIA, HITSZ and CBSR.
Correlated Proportions Test under Indeterminacy

The existing <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Z</mi> </math> -test for correlated proportions under classical statistics is applied when observations in the data are exact and precise. In the current paper, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>Z</mi> </math> -test for correlated proportions is proposed to deal with the data having indeterminate observations. The statistic of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>Z</mi> </math> -test for correlated proportions under neutrosophy is introduced in the paper. The testing procedure of the proposed test is given. The application of the proposed test is given using the price of oil data. From the application, it is clear that the proposed test outperforms the existing <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>Z</mi> </math> -test for correlated proportions under classical statistics in terms of flexibility, adequacy, and information. The simulation study also shows the effect of indeterminacy on the statistic of the proposed test.
Anomaly Mining: Past, Present and Future

Anomaly mining finds high-stakes applications in various real-world domains such as cybersecurity, finance, environmental monitoring, to name a few. Therefore, it has been studied widely and a large body of detection techniques exists [1]. Today, many real-world settings necessitate detection at speed for streaming/evolving data, and/or detection at scale for massive datasets stored in a distributed environment [2]. Despite the plethora of detection algorithms, selecting an algorithm to use on a new task as well as setting the values for its hyperparameter(s), known as the model selection problem, is an open challenge for unsupervised anomaly detection. This issue is only to be exacerbated with the recent advent of detectors based on deep neural networks that exhibit a long list of hyperparameters. The challenge stems from two main factors: the lack of labeled data and the lack of a widely accepted anomaly loss function. Toward automation, one can explore internal evaluation strategies [3], or capitalize on the experience from historical detection tasks through meta-learning [4]. However, the problem remains far from solved. In deployment, many real-world use cases of anomaly detection require the flagged anomalies from a detector to be screened or audited by a human expert, typically for vetting purposes, where taking automatic actions can be costly (e.g. directly charging a flagged medical provider with fraud). While a vast majority of the literature focuses on novel detection algorithms, as humans are often involved with(in) the process, anomaly mining also concerns various human-centric problems that are beyond mere detection, namely explanation [5, 6], human interaction [7], and fairness [8]. These aspects of the field are under-studied and pose many open challenges.
A first polynomial non-clausal class in many-valued logic

The relevance of polynomial classes of formulas to deductive efficiency motivated their further research, and currently, a great number of such classes is known. Nevertheless, they have been exclusively sought in the setting of propositional logic and clausal form, which is of course expressively limiting for real-world applications. Along these lines and towards making tractability applicable beyond propositional clausal logic, firstly, we define the Regular many-valued Horn Non-Clausal (NC) class, or RH, obtained by suitably amalgamating both regular classes: Horn and NC. Then we show that recognizing whether any NC formula is Horn-NC takes only linear time. Secondly, we demonstrate that the relationship between RH and: (1) its subclass of regular Horn formulas is that syntactically RH subsumes the Horn class but semantically both classes are equivalent; and (2) its superclass of regular non-clausal formulas is that RH contains all non-clausal formulas whose clausal form is Horn. Thirdly, we define Regular Non-Clausal Unit-Resolution, or RURNC, and prove that RURNC is complete for RH and also checks its satisfiability in polynomial time. Altogether, RH is a class recognized and solved polynomially, which shows that our intended goal is reached since RH is many-valued, non-clausal and tractable. Fourthly, based on RH, we characterize extensive classes of Horn-NC-like formulas, super-classes of RH, which are composed of formulas that are logically equivalent to some formula in RH. We furnish syntactical patterns of such Horn-NC-like formulas, being outside RH and whose satisfiability test is also decidable in polynomial time. As RH and RURNC are, both, basic in the DPLL scheme, the most efficient in propositional logic, and can be extended to some other non-classical logics, we argue that they pave the way for efficient non-clausal DPLL-based approximate reasoning. Field: Tractable Approximate Automated Reasoning.
Airborne hyperspectroscopy using AVIRIS- NG data for detecting surface signatures of kimberlite and kimberlite clan of rocks in Bundelkhand Craton, India

We have utilized spectral bands of Advanced Visible Infrared Imaging Spectroradiometer Next Generation sensor (AVIRIS-NG) to identify surface signatures of new kimberlite. The main focus of the work is to identify the surface signatures of probable kimberlite occurrence and to substantiate these surface signatures as the prospective areas of detailed exploration. Since, all the discovered kimberlites were reported within ferruginous quartzite (Fe-quartzite), we prepared an AVIRIS-NG image composite to demarcate the spatial extension of ferruginous quartzite. The AVIRIS-NG image spectrum of a known kimberlite was also used, as a reference, to identify the surface signatures of new kimberlite within Fe-quartzite. We applied matched filtering and adaptive coherence estimator algorithms to detect surface signatures of kimberlite. The detected surface anomalies of kimberlite in each spectral map are consistent in shape, number and spatial extent. Subsequently, we integrated these maps into a unified surface anomaly map of kimberlite using multiplication operator for identifying the surface anomalies of kimberlite. We observed that the image spectra of these surface anomalies are similar to the laboratory and image spectra of a known pipe. Most of the detected new anomalies are circular and associated with geological lineaments delineated in the residual gravity anomaly map derived from the EIGEN 6C4 gravity model. The surface anomalies of kimberlite, identified in this research, substantiate the importance of detected anomalies for further exploration.
Cyber Safety Against Social Media Abusing

In few years of development Social Media has been one of the remarkable innovations, which have made communication a lot easier and accessible to all. But, it has some drawbacks associated with it as every other useful appliance has. Cyberbullying is one of its major drawbacks in which a group or individuals are embarrassed or harassed with messages from people. To deal with it, this research firstly surveys the state of social media networks. Then, a system is proposed where cyberbullying will be controlled also there is a knowledge intelligence to gather information about trollers and report them accordingly. The system uses JavaScript and deep learning technology to detect abusive texts and images. This paper proposes the simplest and new technology to spot social media abuse which will help in solving all the cyberbullying issues of all age groups.
Sign Language Recognition Using Convolutional Neural Network

A condition in which an individual has trouble forming sounds is called speech impairment, and a condition in which an individual cannot completely receive sounds through their ears is called hearing impairment. Both of these impairments affect an individual’s ability to communicate with others. Those affected by these impairments use alternative forms of communication, such as sign language. However, it is difficult for non-sign language speakers to communicate with sign language speakers. This is because most of the sign language recognition solutions are not lightweight or portable and require considerable hardware like a computer for usage. This work focuses on developing a computer vision-based application that translates sign language into text using a Convolutional Neural Network, thus enabling signers and non-signers to communicate. This application will support mobile use and work without an internet connection as well, thus serving as a ubiquitous communication aid.
Explaining a Black-Box Sentiment Analysis Model with Local Interpretable Model Diagnostics Explanation (LIME)

With the increase in usage of social media, sentiment analysis has emerged as an area of upcoming inter-disciplinary research. Twitter, a popular micro-blogging site has a rich source of text data in the form of tweets, which can be used to understand public perception on various domains like news, pandemics, motilities, diverse policies, legislations, personalities, and many more. This paper aims at understanding the sentiment of people using twitter data using Bi-directional Long Short-Term Memory (LSTM) networks with 72% accuracy and decrypt the black box deep learning model with Local Interpretable Model Diagnostics Explanation (LIME) to extract the important features and their interaction taken during prediction. In contemporary research work, LIME is used to decrypt the sequential neural network models mostly. This study takes a step forward in using LIME to decrypt the recurrent neural networks (LSTM to be specific) of a sentiment classification model.