Reviewerly/exHarmony huggingface dataset:
A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine

This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.
Similarity Based Filter Pruning for Efficient Super-Resolution Models

Recently, deep convolutional neural networks have achieved significant success on image super-resolution tasks. Models with more complex structure typically contribute to better accruacy while at higher computational cost, which impeded the deployment of them on edge devices with limited resource. In this paper, we propose a similarity based filter pruning method to obtain efficient super-resolution models. Specifically, for every two convolutional filters, we determine their similarity by calculating the Euclidean distance between them and obtain a correlation matrix for the entire filter set. By pruning filters that are highly similar to all other ones, we can obtain a compact model without significant accuracy drop. In addition, after the weights initialization, we set the filters to be pruned to zero, and complete training process according to the obtained mask, which reduce the dependence on the pre-trained model and eliminate the fine-tuning step. Compared with existing pruning method, our experiments achieve better performance on super-resolution benchmarks.
Imperative Process Algebra with Abstraction

This paper introduces an imperative process algebra based on ACP (Algebra of Communicating Processes). Like other imperative process algebras, this process algebra deals with processes of the kind that arises from the execution of imperative programs. It distinguishes itself from already existing imperative process algebras among other things by supporting abstraction from actions that are considered not to be visible. The support of abstraction of this kind opens interesting application possibilities of the process algebra. This paper goes briefly into the possibility of information-flow security analysis of the kind that is concerned with the leakage of confidential data. For the presented axiomatization, soundness and semi-completeness results with respect to a notion of branching bisimulation equivalence are established.
Evaluation of Novel Cognitive Assessment System for Testing Visual Memory of the Elderly

With the development of such technologies as embedded systems, cloud databases and hardware components, detection of cognitive in the elderly can be made more convenient and accurate. This work develops a novel cognitive assessment interactive embedded system, which consists of a micro-controller unit (MCU), organic light-emitting diode (OLED) modules and a client identification circuit. The reliability of the proposed system is demonstrated in visuospatial memory tests. With its combination of OLED modules and pressable components, the elderly can use the system to perform testing under a flashing light source sequence with tactile feedback. To identify the various pattern arrangements in the Corsi block tapping task (CBTT), the client identification circuit in the proposed system is implemented to compute the number of OLED lighting devices on each column of the wooden base. The data that are collected from each subject will be transferred to cloud servers through a smart phone APP via wireless networks to eliminate recording errors by the experimenter. The functionality of the spatial working memory is verified in experiments that involve the proposed OLED, a computerized version of the CBTT, and 24 participants. Experimental results reveal that the difference in memory span between young and elderly groups is significant, F(1, 92) = 214, p<0.001, indicating the feasibility of the proposed system in spatial working memory detection.
Poster: Practice of PBL Education in Collaboration with Printing Company

Implementation of PBL (Project Based Learning) is important for engineering education. However, PBL programs have the problem that it is difficult for teachers to manage class and to set the standard for student evaluation. Furthermore, students’ problem-solving conclusions may be weak and may not have educational effects. To solve these problems, project themes were limited to one set by the company. Furthermore, in order to improve the level of problem solving for students, the project aims to design products as an outcome of problem solving. After the project, about 60% to 90% of the students felt that the 12 abilities defined by the Ministry of Economy, Trade and Industry in the “Fundamental Competencies for Working Persons” improved their abilities. This paper describes the development project of paper toys and paper crafts using special stickers that can be freely attached and removed as many times as possible, by 4th grade students of Kanazawa International College of Technology, and its educational effects.
Method for Customizable Automated Tagging: Addressing the problem of over-tagging and under-tagging text documents

Using author provided tags to predict tags for a new document often results in the overgeneration of tags. In the case where the author doesn't provide any tags, our documents face the severe under-tagging issue. In this paper, we present a method to generate a universal set of tags that can be applied widely to a large document corpus. Using the IBM Watson's NLU service, first, we collect keywords/phrases that we call "complex document tags" from 8,854 popular reports in the corpus. We apply LDA model over these complex document tags to generate a set of 765 unique "simple tags". In applying the tags to a corpus of documents, we run each document through the IBM Watson NLU and apply appropriate simple tags. Using only 765 simple tags, our method allows us to tag 87,397 out of 88,583 total documents in the corpus with at least one tag. About 92.1% of the 87,397 documents are also determined to be sufficiently-tagged. In the end, we discuss the performance of our method and its limitations.
Analytical cryptanalysis upon N = p2q utilizing Jochemsz-May strategy

This paper presents a cryptanalytic approach on the variants of the RSA which utilizes the modulus N = p2q where p and q are balanced large primes. Suppose [Formula: see text] satisfying gcd(e, ϕ(N)) = 1 where ϕ(N) = p(p - 1)(q - 1) and d < Nδ be its multiplicative inverse. From ed - kϕ(N) = 1, by utilizing the extended strategy of Jochemsz and May, our attack works when the primes share a known amount of Least Significant Bits(LSBs). This is achievable since we obtain the small roots of our specially constructed integer polynomial which leads to the factorization of N. More specifically we show that N can be factored when the bound [Formula: see text]. Our attack enhances the bound of some former attacks upon N = p2q.
Trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model

Voting systems are the tool of choice when it comes to settle an agreement of different opinions. We propose a solution for a trustless, censorship-resilient and scalable electronic voting platform. By leveraging the blockchain together with the functional encryption paradigm, we fully decentralize the system and reduce the risks that a voting provider, like a corrupt government, does censor or manipulate the outcome.
Ontology-Based Decision Support System for the Nitrogen Fertilization of Winter Wheat

Digital technologies are already used in several aspects of agriculture. However, decision-making in crop production is still often a manual process that relies on various heterogeneous data sources. Small-scale farmers and their local consultants are particularly burdened by increasingly complex requirements. Regional circumstances and regulations play an essential role and need to be considered. This paper presents an ontology-based decision support system for the nitrogen fertilization of winter wheat in Bavaria, Germany. Semantic Web and Linked Data technologies were employed to both reuse and model new common semantic structures for interrelated knowledge. Many relevant general and regional data sources from multiple domains were not yet available in RDF. Hence, we used several tools to transform relevant data into corresponding OWL ontologies and combined them in a central knowledge base. The GUI application of the decision support system queries it to parameterize requests to external web services and to show relevant information in an integrated view. It further uses SPARQL queries to automatically generate recommendations for farmers and their consultants.
360Cast+: Viewport Adaptive Soft Delivery for 360-Degree Videos

The existing viewport-adaptive 360-degree video streaming schemes encode tiled 360-degree videos with digital-based compression. However, these schemes cause a cliff effect wherein the headset video quality drops when the channel signal-to-noise ratio (SNR) falls below a certain threshold. To realize high-quality wireless 360-degree video streaming, we propose a novel viewport-adaptive soft delivery scheme for 360-degree videos, called 360Cast+. 360Cast+ skips the non-linear operations in digital-based streaming and adopts power allocation and analog modulation to achieve graceful video quality improvement in unstable wireless links. In particular, 360Cast+ integrates the human vision system (HVS) and projection distortion as a perceptual weight in power allocation operations. A near-optimal low-complexity subcarrier matching algorithm was also adopted to extend 360Cast+ to fading channel environments. To reduce the effect of the prediction error, 360Cast+ uses dynamic linear regression (DLR) to predict the future orientation and future prediction error to extract the extension area around a viewport. The evaluation results demonstrated that the proposed 360Cast+ provides sophisticated video quality irrespective of the prediction error and channel conditions in orthogonal frequency-division multiplexing (OFDM)-based systems.
Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters

In order to provide context-aware services such as health monitoring and customized energy consumption, smart environment designers need to design robust systems for recognizing the Activities of Daily living (ADL). Once these activities are recognized, the data collected can be used for prediction. For example, energy consumption and other characteristics in the home can be predicted. This is possible if human activity in a smart home can be forecasted. The aim of this research work is to "find the best machine learning algorithm to predict human activities and to use hyperparameters tuning through performance optimization to improve the accuracy of the algorithm" The results of our initial experiments using default hyper-parameters show that Random Forest, compared to four other algorithms (MLP, SVM, Naïve Bayes, and Decision Tree), has the best accuracy at 65.32% for all features and 62.54% for reduced number of features. Tuning four Random Forest hyperparameters through optimization increases the accuracy to 97.9777% for all features and 98.287% for reduced features respectively. Using the optimized Random Forest hyperparameters, 20,000 data points are forecasted with MAE of 0.0098 compared to 0.0445 for Support Vector Machine (SVM).
Reconstruction of the Sculpture Surface in the Process of Optical 3D Scanning

Currently, 3D scanning technology is used for high-precision measurements and fixing the geometric shape of various objects. However, when creating a computer 3D-model as a result of processing the array of data obtained from the scanning process, may contain errors. Errors may related to the features of the studied object (material, weight, size, location), functional properties of the software used or they may be the low qualification of the software engineer involved in processing 3D scan data. The task of this work was a finding the technical solutions, which allows one to reconstruct the objects surface, recorded using 3D scanning in the process of creating their computer 3D models. We proposed to use the inverse distance method, which is used in biometry to improve the quality of reconstruction of the 3D surface of a human face. Experiments have shown that the result of the reconstruction makes it possible to increase the accuracy of creating 3D models by recovering gaps in the surface of an object that were made during the 3D scanning process. The proposed approach allows one to construct a 3D surface without solving the triangulation problem, which leads to a reduction in computational costs.
Enhancing the identification of web genres by combining internal and external structures

Automating the identification of the genre of web pages becomes a promising research area in web pages classification, as it can be used to improve the quality of the web search result and to reduce search time. Many studies have been proposed to identify the genre of web pages. These studies differ with respect to three main factors which are the features used, the classification algorithm and the list of genres used for the evaluation. The main idea of this paper is to combine the predictions produced by different classifiers using the internal and external structures of a web page. To combine the predictions of the different classifiers we used different OWA operators and the Dempster-Shafer (DS) combination rule. Moreover, we proposed an improved DS combination method based on the ranks of the predicted genres. The experiments conducted using the two known datasets (KI-04 and SANTINIS), show that our study achieves better results in comparison with other ensemble classifiers and genre identification works as well.
SMART environment control in virtual and mixed reality based on cognitive user abilities

This paper focuses on the description of the interface design of a backend server and the design implementation of a user interface suitable for the visualization of virtual and real objects/appliances controlled in a SMART environment, for example in the home, with regard to the cognitive abilities of the user. Client applications will be able to use the REST API and communicate through this API using requests, use implemented logic, and store objects and their states in the database. The server is deployed in the cloud to be accessible outside the local network. The solution is universal and offers a generic standard to support different types of appliances using a generic form. Task scheduling is also an important function. In virtual reality, it is possible to quickly test the cognitive abilities of the user and thus adapt the user interface to his needs.
High-Utility Pattern Mining in Hadoop Environments

In this article, we present an Efficient High Utility Pattern Mining framework to mine high-utility patterns with a reasonable pruning strategy to speed up the mining performance. Concurrently, for solving the problem of excessive data volume in the current era, we applied the developed framework to the MapReduce architecture used for improving the feasibility in practical applications. Our in-depth work in this paper culminates with some experimental results that clearly show that our proposed framework can perform well to mine the required pattern in a big-data dataset and shows great performance in a Hadoop computing cluster.
Sentiment analysis of hate speech as an information tool to prevent riots and environmental damage

Abstract Twitter is currently one of the most popular social media in the world. Indonesia ranked in 8th the most Twitter users from all over the world with the 10.65 million users. President of the Republic of Indonesia, Joko Widodo, was ranked 8th leader with the most followers on Twitter, with 11 million users. Everyone can express or give an opinion on something or what they thought through social media including create or spreading the hate speech. One result of this hate speech can lead to mass demonstrations which can result in environmental destruction. Our research focused on sentiment analysis of hate speech towards Indonesian president by comparing the performance between Random Forest algorithm and Support Vector Machine algorithm to prevent the negative result of hate speech that can be happen in the future. This research obtained 550 tweets and labelled manually whether it is categorized as a hate speech or not and divided into each of 275 data for positive and negative sentiments. Random Forest classifier has the best performance by combining gini criterion with the number of trees 128 with the result of the accuracy score is 76.7%, precision score 82.7%, recall score 64%, and F1 score 73.3%. Support Vector Machine classifier has the best performance by combining the linear kernel type and set the C parameter to 100 with the result of the accuracy score at 74.7%, precision score 100%, recall score 49.3%, and F1 score 66.1%. Random Forest classifier performs its best in scoring F1 score, accuracy, and recall while Support Vector Machine classifier performs its best in scoring precision. Based on the results, Random Forest classifier for hate speech detection can be applied for hate speech detection to prevent damaging the environment as it shows better score on accuracy and F1 score than Support Vector Machine Classifier.
Automated generation of terminological dictionary from textual business rules

Abstract To support decision making, organizations tend to operate according to Business Rules, which are usually represented in a natural language format easily understood by all intervenors. According to the business rules manifesto by the Business Rules Group (OMG), rules build on facts, and facts build on concepts as expressed by terms. To avoid ambiguity and misunderstanding, the standardization of the terminology used at the business level becomes a persistent need. However, doing so manually is error prone and time consuming, especially that the Business Rules are the subject of continuous updating. In this paper, we present an automated approach to generate the Business Vocabulary from textual statements of Business Rules. Our approach is distinguished from existing works in that it extracts the Terminological Dictionary as described by the Semantic of Business Vocabulary and Rules (SBVR) standard to provide a more comprehensive meaning for each concept. Accordingly, an in‐depth Natural Language Processing (NLP) is used to extract not only flat list of terms and relations, but also extra specifications and implicit knowledge. With a satisfactory result, our approach has proved its capability to automatically generate the SBVR Terminological Dictionary from large number of natural language business rules statements.
Blockchain Consensus Protocols, Energy Consumption and Cryptocurrency Prices

Cryptocurrencies employ different consensus protocols to verify transactions. While the Proof-of-Work consensus protocol is the most energy consuming protocol, Proof-of-Stake and Hybrid consensus protocols have been introduced which consume considerably less energy. We employ portfolio analysis to explore whether energy is a fundamental economic factor affecting cryptocurrency prices. Surprisingly, our results suggest that, on average, cryptocurrencies employing Proof-of-Work consensus protocols do not generate returns that are significantly different from those that incorporate Proof-of-Stake consensus protocols. Even more surprising is that our results show that cryptocurrencies that incorporate Hybrid consensus protocols generated significantly higher average return than the other groups. A possible explanation for that phenomenon may be that investors’ demand for cryptocurrencies that they perceive as offering more trust is larger than for those that carry potential risks of blockchain manipulation.
Smart War on COVID-19 and Global Pandemics

The present global pandemic, COVID-19, has exceeded many other health crises in recent times, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MARS), and Ebola. In the intervening period of nearly two decades since SARS, artificial intelligence (AI) has altered our society in unthinkable ways. But how could we utilize it to the fullest extent to fight against COVID-19? With the very high global fatality (&gt;1,100,000 compared to 774 for SARS), frankly, the answer is “Not enough.” So, what is holding us back? Despite the astounding success of modern science and technology, why we looked so vulnerable in front of a non-intelligent creature? Why our strategies and tactical responses lacked perfect coordination and synchronization? Clearly, we shackled our technologies and clipped its wings by creating monolithic centralized technological architectures, which are simply incapable of handling such a large-scale crisis. This study attempts to survey prominent research directions that will shape the integrated future technological paradigm by breaking shackles and creating “trust” in the digital space. In particular, we sketch how blockchain and artificial intelligence will be the agent of this transformation and create a robust first line of defense against future catastrophes of similar or even of bigger magnitude.
Generating and Exploiting Semantically Enriched, Integrated, Linked and Open Museum Data

The work presented in this paper is engaging with and contributes to the implementation and evaluation of Semantic Web applications in the cultural Linked Open Data (LOD) domain. The main goal is the semantic integration, enrichment and interlinking of data that are generated through the documentation process of artworks and cultural heritage objects. This is accomplished by using state-of-the-art technologies and current standards of the Semantic Web (RDF, OWL, SPARQL), as well as widely accepted models and vocabularies relevant to the cultural domain (Dublin Core, SKOS, Europeana Data Model). A set of specialized tools such as KARMA and OpenRefine/RDF-extension is being used and evaluated in order to achieve the semantic integration of museum data from heterogeneous sources. Interlinking is achieved using tools such as Silk and OpenRefine/RDF-extension, discovering links (at the back-end) between disparate datasets and other external data sources such as DBpedia and Wikidata that enrich the source data. Finally, a front-end Web application is developed in order to exploit the semantically integrated and enriched museum data, and further interlink (and enrich) them (at application run-time), with the data sources of DBpedia and Europeana. The paper discusses engineering choices made for the evaluation of the proposed framework/pipeline.