Reviewerly/exHarmony huggingface dataset:
Implementation of a User Finger Movement Capturing Device for Control of Self-standing Omnidirectional Robot

Convenient systems and equipments to support humans are in great need for ever-growing populations of the elderly and those with disabilities caused by illness or injury. One of these system is the wheelchair, which can provide the user with many benefits such as maintaining mobility, continuing or broadening community and social activities, conserving strength and energy, and enhancing quality of life. However, when users use wheelchairs, they have to frequently stand and sit. This increases the physical burden on the user. For this reason, we proposed a self-standing omnidirectional robot. In order to support the user, the robot body must be able to flexibly make different movements and should be capable to deal with various control methods to meet diverse needs. In this paper, we present implementation of a user finger movement capturing device for control of self-standing omnidirectional robot.
Elderly in Outdoors: Naturalistic Observations in a Public Park

Assistive technology is a promising solution to improve the well-being of people, especially the elderly who are exposed to dangers/barriers that prevent them from being active and performing activities of daily living outdoors. Most environmental assistive life research focuses on indoor environments, using information from sensors in the environment and person. However, if the solutions can be implemented on an external scale, the elderly can perform more outdoor activities that can play a fundamental role in increasing their quality of life and autonomy. The main objective of the paper is to identify problems that the elderly face within a public park in Lisbon by naturalistic observations and categorize them according to the risk and undesirable situations before proposing smart solutions that can solve the problems. The results showed that the elderly are faced with 4 risks (outdoor exposition, noise, illumination, maintenance) and 3 undesirables (segregation, interaction, equipment location) situations.
Lawful Interception in WebRTC Peer-To-Peer Communication

Lawful interception is the act of giving law enforcement officials access to communication between private individuals or organizations. According to the European Telecommunications Standards Institute (ETSI), service providers are expected to ensure that the entire contents of communication associated with the target identity being intercepted can be intercepted during the entire period of the lawful authorization, and that the delivery of the interception related information is reliable. In traditional telephone networks, authorized surveillance takes place by duplicating the conversation data at the service provider premises and forwarding it to law enforcement agencies (LEA). The same approach is suitable for VoIP communication, as long as the data is transferred via a mediator located on the service provider’s premises. Today, direct VoIP communication between clients is the preferred approach due to better call quality and reduced network footprint. Although, VoIP service providers are obliged to provide lawful interception according to ETSI, the traditional model for lawful interception is no longer applicable for direct VoIP communication. In this article, we present a technique to intercept direct VoIP communication between two clients using the state of the art WebRTC technology. This paper addresses an important unmet need of service providers to enable lawful interception in P2P VoIP calls. The new approach maintains high performance without degrading the user experience.
Security in Optical Communication Systems: Data Encryption and Beyond

Optical communication systems (OCSs) are prone to a myriad of security threats, such as fiber cutting, tapping, and jamming. However, OCSs also provide appropriate conditions for the exchange of encryption keys by quantum-based approaches and for the emergence of signal encryption. Both these strategies may lead to unprecedented levels of security. In this paper, we present a brief review on the security menaces and opportunities related to OCSs. Encryption-based solutions receive special attention.
Selection of diverse features with a diverse regularization

Many embedded feature selection methods ignore the correlation among the important features. To reduce correlation, some models introduce constraints to impose sparsity on features, some try to exploit the similarity and group features without changing the objective function. In this paper, we propose diverse feature selection (DFS), which simultaneously performs feature clustering and selection. Given a dataset with known class labels, we separate the features into a set of feature clusters where the features in the same cluster have a higher correlation with each other than with the features in different clusters. A diverse regularization (DR) is proposed to reduce the linear and nonlinear correlations among important features. Using this regularization, DFS can select features that are both informative and diverse. The experimental results on seven image datasets, five gene datasets as well as four other datasets demonstrate the superior performance of DFS.
Doctor Chatbot – Smart Health Prediction

As in today’s world, the number of patients on usual is increasing apace with the amendment in life-style. Queues in hospitals and native doctor's residences are rapidly Increasing. Patients with hectic schedules must spend a significant amount of time waiting to meet the doctor. It takes a long time to heal certain diseases. Heart disease could be a quite common problem worldwide. Many people, both young and old, suffer and die from heart attacks every day. Chatbots will perform tasks such as reducing agent transfers, resolving issues more quickly, improving self-service, and so on. They need constant support to discuss their issues with and to provide them with factual data. This paper introduces a possible solution to provide them with what they're seeking for a chatbot. The projected chatbot would be a heart disease Predictor which is designed for individuals managing any kind of symptoms that connect to the heart. The bot is trained by data collected from numerous Heart Disease-related forums that have a good and wide range of knowledge regarding the heart.
Use of Blockchain for Ensuring Data Integrity in Cloud Databases

This paper proposes a novel method for using a PoW-based Blockchain to ensure data integrity in cloud database management systems. The use of cloud platforms for storing data or even hosting databases is incredibly huge, and in many cases, there is no convenient way for a client to check the integrity of the data stored in the cloud database. To solve this, we propose a technique based on an interaction between the cloud platform and a PoW-based Blockchain. This interaction exploits a Distributed Hash Table and lightweight software agents, which are monitoring changes done to cloud database storage nodes. Data update operations are published by the agents as Blockchain log/audit transactions that propagate deep into the Blockchain network until they become immutably and cryptographically protected by it. The proposed method enables the Cloud Provider to manage metadata so that it will be able to easily detect deliberate or accidental corruptions of transactions and to recover the transactions in case such a data corruption incident occurs.
The Effects of Agricultural Experience Program on Agricultural Literacy and Hand Function Improvement of Adolescents Living in Self-reliance Residence Hall

Background and objective This study was conducted to increase understanding of agriculture through agricultural experience programs for adolescents living in self-reliance residence hall, and to investigate changes in work performance ability through changes in hand function. Methods There were 11 subjects, and the average age was 18.2 years, all males without disabilities. The agricultural experience program consists of a total of 10 sessions including orientation and watching videos on future agriculture, creating vegetable gardens, planting, managing each crop, harvesting, visiting the processing room, and selling at a local food store. Results The change in agricultural literacy by the agricultural experience program positively improved from a score of 113.73 to 127.91 (&lt;italic&gt;p&lt;/italic&gt; = .008). The changes by sub-item are as follows. The value and safety of agri-foods (&lt;italic&gt;p&lt;/italic&gt; = .020) and agriculture and natural environment (&lt;italic&gt;p&lt;/italic&gt; = .007) were significantly improved. The function and value of rural areas (&lt;italic&gt;p&lt;/italic&gt; = .050), production of agricultural products (&lt;italic&gt;p&lt;/italic&gt; = .160), processing and distribution of agricultural products (&lt;italic&gt;p&lt;/italic&gt; = .248), and agricultural policies (&lt;italic&gt;p&lt;/italic&gt; = .058) were not significantly changed. The simple function of the hands was measured by the number of pegs inserted during 30 seconds, and the assembly function was measured by the number of pegs inserted during 60 seconds. In the case of simple function, the dominant hand was improved from 14.82 to 15.83 (&lt;italic&gt;p&lt;/italic&gt; = .014), and the non-dominant hand was also significantly improved from 13.79 to 15.01 (&lt;italic&gt;p&lt;/italic&gt; = .002). There was no significant improvement in the simple function (&lt;italic&gt;p&lt;/italic&gt; = .153) and assembly function (&lt;italic&gt;p&lt;/italic&gt; = .770) of both hands. Conclusion It is considered that the agricultural experience program will enable youths living in self-reliance residence halls to enhance their understanding of agriculture as an occupation, and enable them to play a role as wise consumers by positively affecting improvements in their agricultural literacy and simple hand functions.
PERCEPTIONS OF EFL LEARNERS REGARDING EFFECTIVENESS OF TWITTER IN ENGLISH LANGUAGE LEARNING PROFICIENCY AT UNIVERSITY LEVEL

Purpose of the study: Social media and online social media sources play a vital role in our daily routine life. This study's main purpose was to highlight the role of Twitter in English language learning proficiency development at the university level.&#x0D; Methodology: In the study, the researcher analyzed the perception of EFL learners regarding the effectiveness of Twitter in English language learning proficiency at the university level by taking data from sampled respondents through a self-developed questionnaire on a 5-Points Likert scale. John Curry's (1984) sample size rule of thumb, convenient sampling technique, validity, and reliability of the research instrument were ensured in the study.&#x0D; Main Findings: The key findings of the current study revealed that in social media, Twitter has a high positive role in promoting, polishing, and developing English Language learning by using computers, laptops, or smartphones for the teaching-learning process in this regard. The study's main findings indicated that Twitter is very helpful to facilitate teachers and learners regarding the development of the English language as a second language.&#x0D; Applications of this study: The current study may be highly significant and effective for students, parents, teachers, the public, policymakers, and the government to apply it for developing language proficiency via the practical use of Twitter.&#x0D; Novelty/Originality of this study: The study will be a quite novel and original way of promoting and developing language proficiency by using media resources, especially Twitter, to attain the desired objectives, i.e., developing language proficiency via Twitter.
A validation &amp; verification driven ontology: An iterative process

Designing an ontology that meets the needs of end-users, e.g., a medical team, is critical to support the reasoning with data. Therefore, an ontology design should be driven by the constant and efficient validation of end-users needs. However, there is not an existing standard process in knowledge engineering that guides the ontology design with the required quality. There are several ontology design processes, which range from iterative to sequential, but they fail to ensure the practical application of an ontology and to quantitatively validate end-user requirements through the evolution of an ontology. In this paper, an ontology design process is proposed, which is driven by end-user requirements, defined as Competency Questions (CQs). The process is called CQ-Driven Ontology DEsign Process (CODEP) and it includes activities that validate and verify the incremental design of an ontology through metrics based on defined CQs. CODEP has also been applied in the design and development of an ontology in the context of a Mexican Hospital for supporting Neurologist specialists. The specialists were involved, during the application of CODEP, in collecting quality measurements and validating the ontology increments. This application can demonstrate the feasibility of CODEP to deliver ontologies with similar requirements in other contexts.
Certifiably robust interpretation via Rényi differential privacy

Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of interpretation robustness from a new perspective of Rényi differential privacy (RDP). The advantages of our Rényi-Robust-Smooth (RDP-based interpretation method) are three-folds. First, it can offer provable and certifiable top-k robustness. That is, the top-k important attributions of the interpretation map are provably robust under any input perturbation with bounded ℓd-norm (for any d≥1, including d=∞). Second, our proposed method offers ∼12% better experimental robustness than existing approaches in terms of the top-k attributions. Remarkably, the accuracy of Rényi-Robust-Smooth also outperforms existing approaches. Third, our method can provide a smooth tradeoff between robustness and computational efficiency. Experimentally, its top-k attributions are twice more robust than existing approaches when the computational resources are highly constrained.
Simultaneous estimation and modeling of nonlinear, non-Gaussian state-space systems

This paper presents a framework for simultaneous estimation and modeling of nonlinear, non-Gaussian state-space systems. In the proposed approach, uncertainty in motion model parameters is incorporated to avoid overconfidence in state prediction and better account for modeling inaccuracies. The additional original contribution of a model correction stage improves nonlinear model parameter estimates in order to enhance the accuracy of state estimation. The presented nonlinear/non-Gaussian Simultaneous Estimation And Modeling (SEAM) approach was compared with contemporary estimation techniques using a Monte-Carlo simulation study. This study showed that the proposed method successfully reduces estimation error relative to existing approaches even when substantial model parameter uncertainty and multi-modal sensor noise are present. The framework has potential use in a wide range of applications where state-space estimation is employed, including robotics, signal processing, and controls.
Analysis of Weather Forecasting Techniques

Weather forecasting is one of the important science application in our daily planning activities. This application has played a significant role to humans from a long time. Every human relied on their philosophical experience and other weather phenomenon to predict the weather and infer what was coming their way. This was the knowledge gathered over many years observations and has been passed from one generation to another. To predict the future’s weather condition, the variation in the conditions in past year must be utilized. The probability that the weather condition of the day in consideration will match the same day in previous year is very less. But the probability that it will match within the spam of adjacent fortnight of previous year is very high. In this paper, we analyse the use of various data mining techniques in forecasting maximum temperature, rainfall and wind speed.
Arbitration of Digital Fingerprint-based Digital Resource Copyright

Academic document plagiarism is the most common infringement in the copyright protection of digital resources. Plagiarism detection is based on document similarity detection. This paper presents a copyright arbitration algorithm of digital resources. The algorithm combines the digital fingerprint generation algorithm with other factors to extract the key words of documents, such as word relevance, word semantics, TF-IDF features and position factor. These headings are processed with the SHA-1 encoding dimensionality reduction method to form a digital fingerprint based on document content, and then the source of a plagiaristic document is accurately tracked and positioned through a comparison between local digital fingerprints. In this paper, 55 academic documents as samples underwent an experiment. The experiment shows that the digital fingerprints generated by the algorithm presented in this paper are highly robust.
Understanding and Reviewing RDA Vocabularies in RDF: A Multi-Layer Framework Approach

This paper proposes a framework to understand and review the present and possible RDA Vocabularies in RDF after the 3 R Project, and also examines RDA Vocabularies under this framework to confirm the validity and usefulness of the framework. Further, it proposes an alternate for the vocabularies at each layer of the framework. (1) The proposed framework consists of four layers: (a) layer 1 constructs RDA Vocabularies in line with the RDA conceptual model; (b) layer 2 constructs vocabularies in RDF representation that accommodate the recording methods prescribed in RDA for individual elements; (c) layer 3 constructs them by reconstructing the relationship elements; and (d) layer 4 accommodates additional ways to enforce the structural representation of RDA metadata. RDA Vocabularies are examined at each layer to demonstrate the framework’s validity and usefulness. (2) Alternate vocabularies in RDF are proposed at layers 2 and 3 of the framework. Those proposed vocabularies could be useful, but the examination of their usefulness is limited. Extensions at layer 4 would also be useful for the schema for RDA metadata creation. Therefore, the proposed framework is valuable for understanding and reviewing RDA Vocabularies, while some points need further consideration.
VR-ISLAND

The workshop “VR-ISLAND: Virtual Reality, Inclusion and Special Language Needs” is about the application of Virtual Reality (VR) and Augmented Reality (AR) tools and environments to inclusive language teaching. In fact, all the contributions included in the workshop speculate upon language teaching to students with Special Language Needs (SLN), that is students with language differences, language disorders, cognitive and sensorial disabilities, specific and diverse socio-linguistic backgrounds. Based on the discussion, VR and AR technologies have proved to have a positive impact on language inclusion because they increase students’ engagement and motivation, enhance certain language skills, reduce anxiety and affective filter.
The Combination of CNN, RNN, and DNN for Relation Extraction

Relation extraction, which is a subtask of NLP (natural language processing) field, its target is to identify the entities in texts and extract the relation between entities. Previous works prove that neural networks are feasible for relation extraction. CNN (convolutional neural networks) and LSTM (long short-term memory) are two majority models used in relation extraction. Further research shows that the combination of CNN and LSTM has a better performance. Inspired by the solution of LVCSR (Large-Vocabulary-Continuous-Speech-Recognition), another task in the NLP field, we propose adding DNN after the combination of CNN and LSTM. This model achieves a better effect on the precision-recall curve than the previous model.
Solving nonlinear equation systems via clustering-based adaptive speciation differential evolution

<abstract> In numerical computation, locating multiple roots of nonlinear equations (NESs) in a single run is a challenging work. In order to solve the problem of population grouping and parameters settings during the evolutionary, a clustering-based adaptive speciation differential evolution, referred to as CASDE, is presented to deal with NESs. CASDE offers three advantages: 1) the clustering with dynamic clustering sizes is used to set clustering sizes for different problems; 2) adaptive parameter control at the niche level is proposed to enhance the search ability and efficiency; 3) re-initialization mechanism motivates the algorithm to search new roots and saves computing resources. To evaluate the performance of CASDE, we select 30 problems with different features as test suite. Experimental results indicate that the speciation clustering with dynamic clustering sizes, niche adaptive parameter control, and re-initialization mechanism when combined together in a synergistic manner can improve the ability to find multiple roots in a single run. Additionally, our method is also compared with other state-of-the-art methods, which is capable of obtaining better results in terms of peak ratio and success rate. Finally, two practical mechanical problems are used to verify the performance of CASDE, and it also demonstrates superior results. </abstract>
Uncovering the Iceberg from Its Tip: A Model of Publication Bias and p-Hacking

Harvey, Liu, and Zhu (2016) argue that a large proportion of published asset-pricing factors are likely false. Researchers may try many variables and report only the significant ones, so-called p-hacking. Some recent work challenges the prevalence of p-hacking and argues that the amount of shrinkage necessary for reported results is trivial. We present a model where there are true anomalies and false anomalies. Our model does a good job of fitting the observed population. Our evidence is consistent with the idea that a large proportion of anomalies are false and reinforces the need to raise the thresholds for statistical significance.
The evolution of drum modes with strike intensity: Analysis and synthesis using the discrete cosine transform

The synthesis of convincing acoustic drum sounds remains an open problem. In this paper, a method for analysing and synthesising pitch glide in drums is proposed, whereby the discrete cosine transform (DCT) of an unwindowed drum sound is modelled. This is an extension of the scheme initially proposed by Kirby and Sandler [(2020). Proceedings of the 23rd International Conference on Digital Audio Effects, Vienna, Austria, pp. 155–162], which was able to reproduce key components of drum sounds accurately enough that they could not be distinguished from the reference samples. Here, drum modes were analysed in greater detail for a tom-tom struck at 67 different intensities to investigate their evolution with strike velocity. A clear evolution was observed in the DCT features, and interpolation was used to synthesise the modes of intermediate velocity. These synthesised modes were evaluated objectively through null testing, which showed that a continuous blending of strike velocities could be achieved throughout the data set. An AB listening test was also performed, where 20 participants attempted to distinguish between pairs of real and synthesised sounds. Exactly 50% accuracy was achieved overall, which demonstrates that the synthesised samples were deemed to sound as realistic as genuine samples. These results demonstrate that the DCT representation is a valuable framework for analysis and synthesis of drum sounds. It is also likely that this approach could be applied to other instruments.