Reviewerly/exHarmony huggingface dataset:
A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

Abstract With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user’s behaviour, which means a user’s interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn’t consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.
Understanding Contexts Inside Robot and Human Manipulation Tasks through a Vision-Language Model and Ontology System in a Video Stream

Manipulation tasks in daily life, such as pouring water, unfold intentionally under specialized manipulation contexts. Being able to process contextual knowledge in these Activities of Daily Living (ADLs) over time can help us understand manipulation intentions, which are essential for an intelligent robot to transition smoothly between various manipulation actions. In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner. Furthermore, we propose a scheme to generate a combination of visual attentions and an evolving knowledge graph filled with commonsense knowledge. Our scheme works with real-world camera streams and fuses an attention-based Vision-Language model with the ontology system. The experimental results demonstrate that the proposed scheme can successfully represent the evolution of an intended object manipulation procedure for both robots and humans. The proposed scheme allows the robot to mimic human-like intentional behaviors by watching real-time videos. We aim to develop this scheme further for real-world robot intelligence in Human-Robot Interaction.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL algorithms are rarely applied to real vehicles for two predominant problems: behaviours are unexplainable, and they cannot guarantee safety under new scenarios. This paper presents a safe RL algorithm, called Parallel Constrained Policy Optimization (PCPO), for two autonomous driving tasks. PCPO extends today's common actor-critic architecture to a three-component learning framework, in which three neural networks are used to approximate the policy function, value function and a newly added risk function, respectively. Meanwhile, a trust region constraint is added to allow large update steps without breaking the monotonic improvement condition. To ensure the feasibility of safety constrained problems, synchronized parallel learners are employed to explore different state spaces, which accelerates learning and policy-update. The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
Entropy Evaluation Method for Sandstone Uranium Reservoir Characteristics Based on Convex Hull Search

The continuity and homogeneity of sandstone uranium deposits are inherent characteristics that affect in situ leaching performance indexes and have important guiding effects on the optimization of mining design. In this work, we propose a spatial information entropy calculation method based on a convex hull search strategy to quantitatively evaluate the geological characteristics of uranium deposits. We use the 3-D block model of sandstone-hosted uranium deposits as hard data. First, we use the Alpha-Shapes algorithm to calculate the convex hull of the space area where various types of unit blocks are located. Second, we formulate a suitable multi-point configuration based on the convex hull size. Then, we use the multi-point density function. Scanning the convex hull point cloud establishes a probability density function to calculate the local information entropy of various sub-regions. Finally, the local spatial information entropy is aggregated using addition and multiplication to obtain the joint spatial information entropy of the entire evaluation area. A series of parameter sensitivity and performance analysis experiments prove the stability of the method and the accuracy of the quantitative and homogeneous evaluations of the complex geological conditions. In order to solve the problem of the difference between the designed production capacity and actual production capacity of a uranium deposit, we use a new method to evaluate the homogeneity and continuity of the lithology and grade of the deposit. The new method and evaluation conclusions can be used for the automation and understanding of future engineering optimization processes.
New anisotropic diffusion operator in images filtering

Abstract The anisotropic diffusion filters have become in the fundamental bases to address the medical images noise problem. The main attributes of these filters are: the noise removal effectiveness and the preservation of the information belonging to the edges that delimit the objects of an image. Due to these excellent attributes, through this article, a comparative study is proposed between a new diffusion operator and the Lorentz operator, proposed by the pioneers of anisotropic diffusion. For this, a strategy consisting of two phases is designed. In the first, called operator construction, the composition of functions is used to generate a new diffusion operator that meets with the conditions reported for this kind of the mathematical object. In the second phase, denominated filtering, a synthetic cardiac images database, based on computed tomography, is filtered using the aforementioned operators. According with the value obtained for the peak of the signal-to-noise ratio, the new operator shows similar performance to the Lorentz operator. The implementation of this new operator contributes to the generation of new knowledge in digital image processing context.
Factors Affecting Implementation of Blockchain in Retail Market in Malaysia

Certain types of blockchains were made and developed for various purposes. Despite the development of cohort systems, the blockchain needs more development from the perspective of Malaysia. In addition, blockchain is the only cryptographic block currency that improves retail marketing. This study has critically analyzed the variables of the current research Framework of the blockchain implementation in retail market. Thus, this study aims to implement blockchain in retail market for increaseing customer benefits in order to improve the retail industrial supply chain activities. In order to achieve the main aim, this study comprises of two main phases, which are theoretical study, development of proposed blockchain factors and its implementation. The blockchain experience of variables, blockchain management, blockchain transaction policy, blockchain adaption and implications were discussed gradually. Finally, we demonstrate the underlying theory, the theoretical framework and the hypotheses were discussed in detail.
Does Data-Driven Recruitment Lead to Less Discrimination? – A Technical Perspective

Due to its large cost-saving potential, data-driven recruitment is becoming increasingly popular across various industries. However, several cases were reported where the use of corresponding technologies had caused systematic discrimination against certain candidate groups. While existing approaches to discover and prevent discrimination in data classification mostly perform well within a specific context, it remains unclear to what extent datadriven recruitment can be conducted discrimination-free in real-world business applications, where the respective context-specific assumptions do not necessarily hold. Hence, we first define a generic discrimination model that allows for arbitrary descriptions of job candidate characteristics, before applying two sophisticated discrimination-prevention algorithms on a sample data set generated from our model to evaluate the algorithms’ performance. Our analysis shows that the amount of removed discrimination highly depends on the application context and its underlying definitions and assumptions, making it hard to provide a holistic answer to our research question.
A Method of Fusing Gesture and Speech for Human-robot Interaction

Aiming at the problem of using the unimodal model in human-robot interaction, in order to improve the operator's ability to control and interact with the robot and thus realize the natural and friendly interaction between human and robot, we propose a novel multimodal fusion architecture based on gesture and speech. Firstly, the convolutional neural network and Baidu API are used to recognize gesture and speech respectively. Secondly, the result of gesture prediction probability and the result of speech forward matching are normalized. Finally, the proposed multimodal fusion algorithm is used to fuse the two results, and the output results are filled into the intention slot. The operator's intention is determined by judging the fill integrity of intents the intention slot. The experimental results show that the proposed multimodal fusion architecture is superior to the unimodal model in recognition accuracy and interaction efficiency.
Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification

Video-based person re-identification (reID) aims at matching the same person across video clips. It is a challenging task due to the existence of redundancy among frames, newly revealed appearance, occlusion, and motion blurs. In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation. In order to determine the contribution/importance of a spatial-temporal feature node, we propose to learn the attention from a global view with convolutional operations. Specifically, we stack its relations, i.e., pairwise correlations with respect to a representative set of reference feature nodes (S-RFNs) that represents global video information, together with the feature itself to infer the attention. Moreover, to exploit the semantics of different levels, we propose to learn multi-granularity attentions based on the relations captured at different granularities. Extensive ablation studies demonstrate the effectiveness of our attentive feature aggregation module MG-RAFA. Our framework achieves the state-of-the-art performance on three benchmark datasets.
Multi-object Tracking via End-to-end Tracklet Searching and Ranking

Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets. However, the forced exposure to ground-truth in the training stage leads to the training-inference discrepancy problem, i.e., exposure bias, where association error could accumulate in the inference and make the trajectories drift. In this paper, we propose a novel method for optimizing tracklet consistency, which directly takes the prediction errors into account by introducing an online, end-to-end tracklet search training process. Notably, our methods directly optimize the whole tracklet score instead of pairwise affinity. With sequence model as appearance encoders of tracklet, our tracker achieves remarkable performance gain from conventional tracklet association baseline. Our methods have also achieved state-of-the-art in MOT15~17 challenge benchmarks using public detection and online settings.
Corpus-based Data for Determining Specialised Language Features

Corpus-based data have been used extensively to describe language use.Studies into specialised languages have adopted this approach to describe the English language used in different disciplines, such as Engineering English and Business English.Corpus-based analysis has also been used to determine the characteristics of specialised languages used by writers in RAs.Serving to contribute to the body of knowledge on characteristics of language use in RAs, this study presents the word lists analysis of research articles (RA) from two different disciplines -engineering and, business technology and innovation.The findings provide insights into the distinct features of these specialised languages in RAs.The RAs for both corpora were obtained from the Scopus database, and the frequency word lists for both were generated using the Wordsmith Tool 6.0.This study demonstrates not only the different word lists, but also empirical evidences in describing the two specialised languages.To do this, the analyses of the corpora involve the comparison of the general statistical details, the high frequency word lists, and the function vs. content word distributions.Insights into the characteristics of the specialised languages, such as provided in this study, are helpful in assisting students, researchers, writers, language practitioners to be more well-informed and more effective in using the specialised language.
Spesies Mollusca di Ekosistem Mangrove Kecamatan Baitussalam Kabupaten Aceh Besar sebagai Referensi Pendukung Materi Keanekaragaman Hayati

Mollusca is the second largest phylum of invertebrate animals that has high adaptability. One of Mollusca's habitats is the mangrove ecosystem in Baitussalam District, Aceh Besar. There were very little data regarding the type and diversity of Mollusca in the mangrove ecosystem area of Baitussalam Subdistrict, Aceh Besar, after the tsunami in 2004. A study is needed considering that the data can be used as a database and supporting reference for biodiversity material in schools. This study was carried out to; 1) Identifies the types of Mollusca in the mangrove ecosystem in Baitussalam District, Aceh Besar; 2) Determines the output of this study as a reference for supporting biodiversity learning; and 3) Finds out the feasibility of the output as supporting reference for biodiversity material. Data collection was performed by line transect and quadrant transect method with purposive sampling. Data was analyzed with qualitative and quantitative analysis. The results of the study revealed that there are 29 types of Mollusca consisting of Gastropods and Bivalves. The output is in the form of a national standard textbook that can be used as a reference for supporting biodiversity learning. The book feasibility test obtained a total score of 95.6%. The conclusion is that there are 29 types of Mollusca and the output is a national standard textbook that are very feasible to be recommended as a supporting reference for biodiversity material.
A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.
Pengaruh Persepsi Kegunaan, Kemudahan Penggunaan dan Risiko Terhadap Minat Mahasiswa Menggunakan Sistem Blockchain

The purpose of this study was to determine the effect of perceived usefulness, perceived ease of use and risk perception of interest in using the blockchain system in financial technology. The population of this study was undergraduate students majoring in accounting FEB Udayana University class of 2015-2018. Methods Determination of samples in this study using saturated sampling techniques. The sample in this study amounted to 294 people. The data collection method used is the survey method with a questionnaire. The data analysis technique used is multiple linear regression analysis. Based on the results of the analysis it was found that perceived usefulness and perceived ease of use had a positive effect on the interest in using the blockchain system in financial technology. But the perception of risk has a negative effect on interest in using the blockchain system in financial technology.&#x0D; Keywords: Perception of usability, perception of ease of use, perception of risk and interest in using the blockchain system.
News Media Robot: Theoretical Aspects of Intellectual System for Content Generation

The article discusses a new phenomenon of digital media sphere, i.e. news media robots, which are capable of creating content on a technological platform of intellectual systems. Interdisciplinary approaches to understanding this phenomenon, which attract theoretical concepts and cases from artificial intelligence, knowledge engineering and natural language technologies are considered. It is stated, that in the core of intellectual systems lie computational models of natural human intelligence and knowledge. The phenomenon of imitation as a characteristic of social and technical systems general ability to self-study relying on a sample is viewed as a key mechanism media robots adaptation to social environment. The case study of generating news content within the context of frames theory is provided as an illustration of universal formalism for representation of knowledge and social actions. The structure of general human intelligence, provided by Cattell — Horn theory, is considered as a possible reference model for comparison of a robotic intellectual system with natural human intelligence. The similarity between a general intelligence structure and an “intellectual” structure of a media robot is noted. The article defines the technology of modelling a natural language as the most prospective means of broadening the verbal intellect of a media robot. It is stated in conclusion, that one can observe trends for convergence in the methodology of knowledge engineering and analytical journalism. In a long-term perspective serious progress in creating new generation of media robots requires reproduction of such human abilities, as learning and “sense” of social responsibility. For this reason joint efforts in interdisciplinary studies in digital media, artificial intelligence and cognitive sciences are required.
Collaborating communities revisited

Purpose: This article follows up “Collaborating communities: the RDA experience and its implications for common information environments”, published in 2007 in the proceedings of the 11th seminar on Archives, Libraries, Museums held in Poreč, Croatia. That paper was written before the publication of RDA: Resource Description and Access, the successor to the Anglo-American Cataloguing Rules (AACR). The article outlines what has happened since, with a focus on the development of RDA in collaboration with related standards groups.Methodology/approach: This is a chronological review of the development of documents, which lead to the publication of RDA. Cooperation and harmonization of description of information objects among different concerned communities is also presented.Research limitations: The thorough review of RDA and related documents covers the period from 2008 to 2018.Originality/Practical implications: The strategic development of RDA is dependent on continuing collaboration with international communities, cultural heritage communities, and linked data communities. All of these communities have a strong interest in linked open data and the Semantic Web, and RDA has played a significant role in initiating and stimulating collaborative development of library and cultural heritage metadata in RDF format.
Data Preprocessing for des-knn and Its Application to Imbalanced Medical Data Classification

Learning from imbalanced data is a vital challenge for pattern classification. We often face the imbalanced data in medical decision tasks where at least one of the classes is represented by only a very small minority of the available data. We propose a novel framework for training base classifiers and preparing the dynamic selection dataset (dsel) to integrate data preprocessing and dynamic ensemble selection (des) methods for imbalanced data classification. des-knn algorithm has been chosen as the des method and its modifications base on oversampled training and validations sets using smote are discussed. The proposed modifications have been evaluated based on computer experiments carried out on 15 medical datasets with various imbalance ratios. The results of experiments show that the proposed framework is very useful, especially for tasks characterized by the small imbalance ratio.
Algorithm for exact area-weighted antialiasing of discrete circular apertures

Accurately calculating diffraction from geometrical shapes such as circular apertures is important in computational Fourier optics. In this paper, we present an algorithm for exactly generating a discrete representation of a circular aperture whose pixel values are given by the integral of the true aperture within each pixel. We characterize the accuracy and runtime of the presented algorithm in comparison to approximate techniques such as binning high-resolution arrays, relative to the analytical Airy pattern.
Development of iOS Software n-Queens Problem for Education and Its Application for Promotion of Computational Thinking

In this study, the authors develop an iOS software “n-Queens Problem for Education” and discuss its application in the promotion of computational thinking. As it has already been pointed out in [1] and [2], playing games and solving puzzles are effective in cultivating computational thinking, and our gaming software can be a good tool to cultivate skills regarding “Generalization (Pattern Recognition),” “Abstraction;’ and “Algorithms” of computational thinking. Our software “n-Queens Problem for Education” is based on the well-known “The n-Queens Puzzle,” which involves solving a puzzle of placing eight chess queens on an n × n chessboard so that they do not attack one another. This puzzle is a good way for students to learn computational thinking. Since this software shows the queens' moves and evaluates solutions, you can concentrate on using your brain without writing down the moves or erasing wrong solutions. In addition, this software has features such as the symmetry operations of rotation and reflection of the board that help users study their solutions.