Reviewerly/exHarmony huggingface dataset:
Design of a Stored-value Card Platform Based on Smart Contract

Stored-value cards are used more and more widely. But there are some problems in the present stored-value card platform: 1. The service agreement of stored-value card is not clear enough; 2. The refund process of users is inconvenient; 3. The transfer of stored-value can not be realized among users; 4. The security of accounts is not guaranteed. Smart contracts are computer contracts written in code, which are enforced directly by computers. This paper proposes a stored-value card platform in which the service contracts are expressed with smart contracts. The smart contract is executed on the blockchain. Blockchain technology, in which accounts are decentralized and data is resistant to modification, is applied to improving reliability in the consumption process. The platform is built on the private blockchains of Ethereum, which is designed for small merchants. ECDSA (Elliptic Curve Digital Signature Algorithms) is used to asymmetrically encrypt accounts. Some functions, such as stored-value issuance, stored-value consumption, commodity purchase, are designed in this system.
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

Handling missing data is one of the most fundamental problems in machine learning. Among many approaches, the simplest and most intuitive way is zero imputation, which treats the value of a missing entry simply as zero. However, many studies have experimentally confirmed that zero imputation results in suboptimal performances in training neural networks. Yet, none of the existing work has explained what brings such performance degradations. In this paper, we introduce the variable sparsity problem (VSP), which describes a phenomenon where the output of a predictive model largely varies with respect to the rate of missingness in the given input, and show that it adversarially affects the model performance. We first theoretically analyze this phenomenon and propose a simple yet effective technique to handle missingness, which we refer to as Sparsity Normalization (SN), that directly targets and resolves the VSP. We further experimentally validate SN on diverse benchmark datasets, to show that debiasing the effect of input-level sparsity improves the performance and stabilizes the training of neural networks.
Mapping the volcanic and postvolcanic activity and its revealing on thermal infrared satellite images of high spatial resolution

The article deals with revealing the volcanic and postvolcanic activity on the thermal infrared images of high spatial resolution (ASTER, TM, ETM +, TIRS) and mapping based on the results of their interpretation. Active volcanoes of the temperate and subpolar zones of the Northern Hemisphere are considered
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents

As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe the learned agents. Understanding the decision making process and its relationship to visual inputs can be very valuable to identify problems in learned behavior. However, this topic has been relatively under-explored in the research community. In this work we present a method for synthesizing visual inputs of interest for a trained agent. Such inputs or states could be situations in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insights for a variety of environments and reinforcement learning methods. We explore results in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify behavioural weaknesses with this technique, we believe this general approach could serve as an important tool for AI safety applications.
LOCUS OF CONTROL IN RELATION TO PSYCHOLOGICAL AND ENTREPRENEURIAL COMPETENCES OF UNIVERSITY STUDENTS

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 2086-2091ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0578Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Multi-Layers Saliency Detection Based on Spectral Density Peaks Clustering

Saliency detection obtains the whole salient object by simulating human visual system, but the detection accuracy is seriously influenced by the small-scale and highcontrast regions in foreground or background, especially when dealing with objects with complex construe. This problem is common in natural images and forms a fundamental challenge for prior methods. To solve the above problem, we propose the multi-layers approach based on spectral density peaks clustering, in which the information from different saliency layers are fused to extract saliency object in complex scene, and we propose a criterion of salient region merging in order to light whole salient object. The experimental results show that the proposed method improves saliency detection on many images that cannot be handled well traditionally.
A small object detection method based on local maxima and SSD

Small object detection in complex scene is a difficult task in image processing. Normally, small objects in images are also weak objects where the contrast between targets and background is so subtle which makes it difficult to perceive. SSD(Single Shot MultiBox Detector) is one of the object detection method proved to be effective for normal size object detection, otherwise, unable to handle the small target task. A new small object detection method based on SSD is brought up in this paper. At first, a local maxima detector is performed in the image to obtain local maxima points in the image, which would be considered as the center of prior boxes for the subsequent object detection in feature maps of different levels. Secondly, the object extraction would be performed in con2_2 and conv3_3, that assures small objects does not be disappear in high level feature maps. Finally, a method to mark small object is brought up. The proposed method is performed in in several videos, which prove this method is feasible and effective.
Multi-Agent Interactions Modeling with Correlated Policies

In multi-agent systems, complex interacting behaviors arise due to heavy correlations among agents. However, prior works on modeling multi-agent interactions from demonstrations have largely been constrained by assuming the independence among policies and their reward structures. In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents’ policies. Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution. Various experiments demonstrate that CoDAIL can better fit complex interactions close to the demonstrators and outperforms state-of-the-art multi-agent imitation learning methods.
A Novel Ontology Construction and Reasoning Approach Based on the Case Investigation

The big data has become a key component for intelligent systems and it is very important about data mining and cognitive reasoning in the field of criminal data analysis.Modeling of investigation knowledge is very important to realize the semantic retrieval, knowledge discovery, information push and classification for case data.Ontology modeling combined with the characteristics of the case in the investigation process, a method of ontology construction based on investigation knowledge is proposed in this paper.It builds an organization system of the investigation process at the first, which is described in stages by collecting terminology.Then the ontology of investigation knowledge is constructed.In addition, an instance is added for verification to describe the investigation process in detail.The method has a good advantage of describing the detection process quickly and integrate knowledge according to different investigation stages, formulating a standardized organization mode and providing standardized knowledge assistance in the investigation process.
A Low Cost Automated Medicine Unit

This paper describes an automated medicine unit in which pills and medicines can be delivered at a lower cost than those provided at the medical stores. The implemented system also provides a centralized database which stores the medical history of each and every patient. It is possible to recover the data for further analysis and obtain the information of all the illness affected by the patient. In future, the system will be expanded to an ‘Anytime Medicine Kiosk’ where the common man can avail medicines at any time at a lower cost and the medical history of all the people in a given area can be obtained easily.
A Fuzzy Min-Max Neural Network based Classification of Histopathology Images

Classification of histopathology images can provide support towards diagnosis of breast cancer. In this study, a Fuzzy Min-Max (FMM) network based approach is studied and used to classify the magnification independent breast cancer histopathology images. Gray level co-occurrence matrix (GLCM) is used to extract the features from images, and are then feed into FMM network to classify the dataset. BreaKHis database is used in this work, which contains microscopic benign and malignant breast tumor images at four different magnifying factors. Experimental results show that the magnifying factor of 200× gives better recognition rate than other magnification rates.
Expanding the Use of Linked Data Value Vocabularies in PCC Cataloging

The Program for Cooperative Cataloging's 2018–21 strategic plan includes as one of its goals to “expand [the] use of linked data value vocabularies to augment, and where feasible to replace, existing metadata practices”. This paper sets out the reasons for pursuing this goal and considers some of the implications for MARC cataloging practice.
Integrating management science into the HPC research ecosystem

How management science benefits from High Performance Computing.
Constructing Faithful Homomorphisms over Fields of Finite Characteristic.

We study the question of algebraic rank or transcendence degree preserving homomorphisms over finite fields. This concept was first introduced by Beecken et al. [Malte Beecken et al., 2013] and exploited by them and Agrawal et al. [Manindra Agrawal et al., 2016] to design algebraic independence based identity tests using the Jacobian criterion over characteristic zero fields. An analogue of such constructions over finite characteristic fields were unknown due to the failure of the Jacobian criterion over finite characteristic fields. Building on a recent criterion of Pandey, Saxena and Sinhababu [Anurag Pandey et al., 2018], we construct explicit faithful maps for some natural classes of polynomials in fields of positive characteristic, when a certain parameter called the inseparable degree of the underlying polynomials is bounded (this parameter is always 1 in fields of characteristic zero). This presents the first generalisation of some of the results of Beecken, Mittmann and Saxena [Malte Beecken et al., 2013] and Agrawal, Saha, Saptharishi, Saxena [Manindra Agrawal et al., 2016] in the positive characteristic setting.
Transformation of sound cipher complexes in the history of musical art and their specific manifestations in the work of composers of the XVII-XXI centuries

The article discusses the features of sound cipher complexes in the works of famous composers - representatives of different styles and trends. The phenomenon of monograms in the baroque art of J.S. Bach, the semantics of musical anagrams are revealed in the works of R. Schumann and the peculiarities of the manifestation of onamophony in postmodernism. The transformation of separate sound cipher complexes in the historical development and development of musical art is traced. It is emphasized that when studying musical material containing onamophony, the performer traces the patterns of stylistic changes in composer's thinking, analyzes the semantic features of the musical language, reveals the author's idea in the process of creating the original interpretation of the work.
A Groupware Usability-Oriented Evaluation Methodology Based on a Fuzzy Linguistic Approach

Nowadays, many organizations or groups of persons must carry out group work effectively. Groupware typically provides a shared workspace, which is displayed through shared user interfaces by group members, with appropriate resources to accomplish a common task. Consequently, an adequate design and development of groupware requires that the shared user interfaces offer a high level of usability for the users’ interaction and the utilization of the shared resources. In this paper, a groupware usability evaluation methodology, specifically for the shared user interfaces, is proposed. This methodology provides an evaluation instrument that collects the users’ perceptions through four dimensions—effectiveness, efficiency, learnability and satisfaction—In addition, the gap theory of quality service, and a fuzzy linguistic approach using aggregation operators—which operate directly with words—are applied. Accordingly, the methodology shows to be a significant, innovative contribution to the research area on groupware usability evaluation. A case study to validate methodology, is presented.
Collaborative Fault Diagnosis Decision Fusion Algorithm Based on Improved DS Evidence Theory

DS evidence theory has in obtaining a correct diagnosis when confronted with highly conflicting evidence, a collaborative fault diagnosis decision fusion algorithm based on an improved version of DS evidence theory is proposed. The algorithm builds upon the closeness of certain kinds of evidence produced by existing DS evidence theory algorithms. According to the importance of the diagnostic information, weights are assigned to reduce the conflicting information while retaining the important diagnostic information. Simulated example shows that the algorithm could reduce the impact of conflicts in diagnostic information and improve the accuracy of the decision fusion process.
A New Approach For Rating Prediction by Using Trust Computation

With the advent of the era of big data, personalized recommendation is becoming more and more important. However, there is a problem of data sparsity in recommender system. The sparseness of data leads to the inaccuracy of recommendation. With the rapid development of social network, researchers begin to add social relationship into recommender system. Trust relationship is a very important kind of relationships. In this paper, we bring up a new approach for rating prediction by using trust computation. First, we describe a method for trust computation. Then, we give the strategy of prediction by using trust and user’s rating preference. Finally, we compare our model with four related methods and conclude that our model can not only improve the prediction effect but also recommend more kinds of items accurately.
Remembering Eric McLuhan

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Developing the Way of Thinking in Pre-school Children in the Conditions of Computer Games

The present article considers the impact of information technologies on the development of pre-school children, which, we believe, is relevant owing to the rise of interest showed by young children in digital technologies. Computer and mobile games firmly hold the lead among the top entertainments for children. The present study presents the theoretical and experimental basis of the idea that computer games contribute to the development of practical thinking. Drawing on the classical ideas expressed by L. S. Vygotskiy who believed that human mental processes can be changed the same way as practical activities, we analyze some modern approaches and policies aimed at the development of higher mental functions. The article discusses the results of the educational experiment, which explored the impact of educational computer games on the development of the way of thinking in children as young as 6–7 years. The investigation consisted of three stages: the first one introduced the diagnosis of the current development, particularly, such types of thinking as conceptual speech thinking, abstract thinking, and practical thinking and constructive thinking. The second stage included the educational experiment with realization of specific programs. The selected programs are focused on the age group between 5 and 7 years, they are designed taking into account the peculiarities of the children’s attention. The third stage performed a repeated control diagnosis. Comparing the data of two diagnosis allowed us tracing the changes, which happened after introduction of educational computer games.
TOM: Twitter Opinion Mining

We present an opinion mining approach whose aim is to perform sentiment classification over microblogs in Spanish; since we use the Twitter microblog as a case study, this approach receives the name of Twitter Opinion Mining or TOM. To classify acomment as positive, negative, or neutral, TOM uses a term-counting strategy that sums the individual polarities of words and phrases contained in the comment. These polarities are obtained with an opinion lexicon that consists of weighted terms and valence shifters. Our lexicon not only includes generic terms translated from an English repository, but also more specific vocabulary from Twitter; this vocabulary is extracted by detecting adjectives and nouns from tweets with emoticons and trigrams that follow the “is-a” pattern. To assess TOM’s quality, we measured precision, recall, and F1 using a set of manually-classified tweets. Our results show high averages for each of these metrics, which were also used for comparing TOM against Sentitext, a tool for opinion mining in Spanish. The results for this comparison show that our approach outperforms this state of the art method.
Experimental Comparative Research on Clustering Algorithm Based on Spark Platform

In the era of big data, data mining has become the focus of research in various fields. Cluster analysis is one of its core processing methods. A large number of iterative calculations make cluster analysis computation extremely time-consuming. At the same time, data mining based on Spark platform is not yet mature at home and abroad, and the research on clustering algorithm based on Spark is still very scarce. Based on the above background, this paper focuses on the Spark framework, and conducts research in-depth on it. Using the advantages of its elastic distribution data set and the characteristics of parallelization, this paper improves the processing speed of clustering analysis, and compares the clustering results of clustering algorithms - K-means, K-means++, PIC and GMM through experimental comparison.
The deployment of Machine Learning in eBanking: A Survey

Thanks to the machine learning algorithms revolution, several organizations will be able to transform their services, automate functions and predict their customer's behaviors. The fact that digital has begun a huge change in the world of finance such as the traditional bank with its physical breaches and advisers; the digital bank is one of the financial organizations integrates machine learning into its services. In order to properly guide future research and development, it will be extremely beneficial to carry out an integral and up-to-date study, focusing on banking fields integrating (or able to integrate) the machine learning techniques. This paper will allow managers and developers to carefully evaluate and embrace machine learning techniques to solve e-baking issues.
Remembering Eric McLuhan

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Design and Development of an Incident Reporting Mobile Application

Reporting actual incidents through short messaging systems and telephone calls are tedious,prone to errors and misinterpretations. Developing a mobile application that can be utilized totransmit incident type, location, and images among other details can provide assistance inemergency situations. The software development method followed in this study was theprototyping model, where initial versions of the software were shown to the end users for it tobe refined to their needs. The target users of the mobile application are a group of 38Adventist motorcycle riders from Silang, Cavite, Philippines who are trained to be firstresponders. Interviews with end users and benchmarking on related systems were conductedto meet results. The end users’ feedback on the developed application can be summarized ashaving high accuracy, less prone to errors, and ease-of-use. Through Global PositioningSystems available on modern smart phones, reporting a specific incident location can be asaccurate as within five meters. The report receiver can view the location in a map.Minimizing text entry and utilizing control elements not only improve reporting time but alsoreduce chances of committing errors. Further, the application can provide map location andcontact information of the emergency facilities near the incident site. Reported information isalso centrally stored for references to improve response processes. Once substantial data isgathered from this system, it can be data-mined to predict possible hotspots, peak times andother information on the occurrences of incidents. To implement the system to a largergeographical territory, the proponents suggest employing artificial intelligence which canautomatically identify and request help from the nearest possible medical facilities.
A Study on the Order of Teaching about Ba-construction

Although the theory of preposition of object is the oldest and most widely known explanation about Ba-construction and many grammar books introduce it. However this explanation causes misunderstanding that it equates Ba-construction with SVO construction, resulting in the most learning errors.BR In this paper, we believe that Korean students should be taught in the A category at least from diachronic process of this construction and a theory of acquisition of Korean, Chinese, Chaoxianzu.BR Furthermore, In the case of Ba+O+V+le/zhe, the students must fully understand resultative feature of ‘le’ and ‘zhe’ according to a particular verb.BR Finally, non-disposal type is a special type of Ba-construction, so it is necessary to explain to students the differences of these disposal and non-disposal construction.BR Through this step-by-step learning process on Ba-construction, we expect students will reduce many errors about this special construction.
Single image dehazing using a novel criterion based segmenting dark channel prior

Single image dehazing has become a hot issue in image processing. Almost all the dehazing algorithms suffer from two problems: bringing out the halo artifacts or high computational complexity. In this paper, we propose a novel single image dehazing algorithm with a modified criterion based segmenting dark channel prior (SDCP), which is simple and effective. Based on the estimated global atmospheric light, the transmission map (TM) is obtained by a novel region segmenting logic. Then, the TM is smoothed by guided image filtering (GF) with the edge information being preserved. Experiments reveal that the proposed algorithm eliminates the halo artifacts more effectively than SDCP or GF based dark channel prior (DCP) both subjectively and objectively. Also, it is faster than the soft matting based DCP.