Reviewerly/exHarmony huggingface dataset:
User Interface Modelling of Sundanese Culture Introduction for Deaf Youth using User-Centered Design Method

Culture is a heritage that needs to be preserved and passed on to future generations. Cultural preservation has to be held for everyone without exception even for a person with disability, such as the deaf. Now education can not only be done using traditional way such as books, but it can be done using an application on a smartphone. The use of smartphone could make the process of education to be more efficient and effective. However not every user interfaces (UI) could meet the needs and limitations for the deaf. As with the sample of the existing application that already tested with USE Questionnaire, it still has low usability value. Therefore to design a user interface that meets the users' need, we implemented the user-centered design (UCD) to this research. The prototype was measured its usability by using USE Questionnaire method. The usability testing result showed that the value is 82.75%. This score shows that the design of the user interface already has a usability value with an excellent category, and it also could be said that the user interface has met the user's needs.
Recognition of Daily Human Activities Using Accelerometer and sEMG Signals

Human activity recognition (HAR) is anBiagetti,Giorgio important technology for ambient-assisted living, sport and fitness activities, and health care ofCrippa,Paolo elderly people. HAR is usually achieved in two steps: acquisition of body signals and classification of performed activitiesFalaschetti,Laura. This paper presents an investigation on the optimal setup forLuzzi,Simona recognizing daily activities using a wearable system designedTurchetti,Claudio to acquire surface electromyography (sEMG) and accelerometer signals through wireless sensor nodes placed on the upper limbs of the human body. To evaluate the optimal number of accelerometer and sEMG signals for detecting the user’s activities, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. In this evaluation, that was performed on eight different exercises executed by four subjects, the automatic classifier achieved an overall accuracy ranging from 10.6% to 93.0% according to different selections and combinations of the signals acquired from the sensing nodes.
Secret Sharing on Evolving Multi-level Access Structure

Secret sharing is a process that allows storing secret information in a distributed manner among several participants. In the original setting of secret sharing schemes, it was assumed that the total number of participants is fixed from the very beginning. However, to meet the state of the art needs, it is required to consider the scenario where any time a new participant can join and the total number of participants is (possibly) unbounded. Evolving secret sharing solves the problem. Secret sharing for evolving threshold access structure has been considered in the last few years. Here, we consider the Multi-level access structures. More specifically, we consider evolving compartmental and hierarchical access structures. We provide constructions with the estimation of share sizes.
The secure link prediction problem

Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form.We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of common neighbors for prediction. We present three algorithms for the secure link prediction problem. We design prototypes of the schemes and formally prove their security. We execute our algorithms in real-life datasets.
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings

The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations.Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models.We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality.In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases.Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only.Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements.Further, we publicly release our Poincaré embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.
MidSemI

The web diversification into the Web of Data and social media means that companies need to gather all the necessary data to help make the best-informed market decisions. However, data providers on the web publish data in various data models and may equip it with different search capabilities, thus requiring data integration techniques to access them. This work explores the current challenges in this area, discusses the limitations of some existing integration tools, and addresses them by proposing a semantic mediator-based approach to virtually integrate enterprise data with large-scale social and linked data. The implementation of the proposed approach is a configurable middleware application and a user-friendly keyword search interface that retrieves its input from internal enterprise data combined with various SPARQL endpoints and Web APIs. An evaluation study was conducted to compare its features with recent integration approaches. The results illustrate the added value and usability of the contributed approach.
Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations.

This paper presents a multilingual corpus with semantic annotation of collocations in English, Portuguese, and Spanish.The whole resource contains 155k tokens and 1, 526 collocations labeled in context.The annotated examples belong to three syntactic structures (adjective-noun, verb-object, and nominal compounds), and represent 60 lexical functions in the Meaning-Text Theory (e.g., Oper, Magn, Bon, etc.).Each collocation was annotated by three linguists and the final resource was revised by a team of experts.The resulting corpora, which are freely available, can serve as a basis to evaluate different approaches for collocation identification and classification in three languages, which in turn can be useful for different NLP tasks such as natural language generation or understanding.
Convolutional Genetic Programming

In recent years Convolutional Neural Networks (CNN) have come to dominate many machine learning tasks, specially those related to image analysis, such as object recognition. Herein we explore the possibility of developing image denoising filters by stacking multiple Genetic Programming (GP) syntax trees, in a similar fashion to how CNNs are designed. We test the evolved filters performance in removing additive Gaussian noise. Results show that GP is able to generate a diverse set of feature maps at the ’hidden’ layers of the proposed architecture. Although more research is required to validate the suitability of GP for image denoising, our work set the basis for bridging the gap between deep learning and evolutionary computation.
Concept theory in library and information science: an epistemological analysis

Purpose The purpose of this paper is to discuss the literature on concept theory in library and information science (LIS) from an epistemological perspective, ascribing each paper to an epistemological family and discussing their relevance in the context of the knowledge organization (KO) domain. Design/methodology/approach This paper adopts a hermeneutic approach for the analysis of the texts that compose the corpus of study following contingency and categorical analyses. More specifically, the paper works with Bardin’s contingency analysis and follows Hjørland’s families of epistemologies for the categorization. Findings The analysis corroborates the observations made for the last ten years about the scarcity of studies on concept theory in LIS and KO. However, the study also reveals an epistemological turn on concept theory since 2009 that could be considered a departure from the rationalist views that dominated the field and a continuation of a broader paradigm shift in LIS and KO. All analyzed papers except two follow pragmatist or historicist approaches. Originality/value This paper follows-up and systematizes the contributions to the LIS and KO fields on concept theory mainly during the last decade. The epistemological analysis reveals the dominant views in this paradigm shift and the main authors and trends that are present in the LIS literature on concept theory.
Intruder Detection with Alert Using Cloud Based Convolutional Neural Network and Raspberry Pi

In this paper, an intruder detection system has been built with an implementation of convolutional neural network (CNN) using raspberry pi, Microsoft's Azure and Twilio cloud systems. The CNN algorithm which is stored in the cloud is implemented to basically classify input data as either intruder or user. By using the raspberry pi as the middleware and raspberry pi camera for image acquisition, efficient execution of the learning and classification operations are performed using higher resources that cloud computing offers. The cloud system is also programmed to alert designated users via multimedia messaging services (MMS) when intruders or users are detected. Furthermore, our work has demonstrated that, though convolutional neural network could impose high computing demands on a processor, the input data could be obtained with low-cost modules and middleware which are of low processing power while subjecting the actual learning algorithm execution to the cloud system.
FROM COMPUTER LITERACY TO DIGITAL TRANSFORMATION OF EDUCATION

The publishing of the 300th issue of the INFO magazine coincided with the approval of the Passport of the National Digital Economy Program. One of the Program' task is the development of the promising Digital Educational and Methodological Complexes (DEMC) that should transform the informatics’ classes at the public school. This event is considered as a step to the digital transformation of the Russian school. Digital transformation is considered as harnessing digital technologies to increase the productivity and value of an enterprise. Digital transformation of education is considered as the controlled and/or spontaneous process of the transformation of the content, methods and organizational forms of educational activities, which is aimed at improving the quality of schools in a rapidly developing digital environment to meet the requirements of the digital economy. The development of Digital Educational and Methodological Complexes, smart simulation and training apps and virtual laboratories provided by the Passport of the National Digital Economy Program for the implementation of general secondary and vocational education programs for areas of “Mathematics”, “Informatics” and “Technology” is аn important step towards the digital transformation of education. The DEMC for informatics curriculum that will be created under the Digital Economy Program has to predetermine the formation of digital literacy of the younger generation in the next decade. The features of the promising DEMC for informatics, which the author would like to see in it, are discussed.
Logic programming as a service in multi-agent systems for the Internet of Things

The widespread diffusion of low-cost computing devices, along with improvements of cloud computing platforms, is paving the way towards a whole new set of opportunities for Internet of Things (IoT) applications and services. Varying degrees of intelligence are required for supporting adaptation and self-management: yet, they should be provided in a light-weight, easy to use and customisable highly-interoperable way. In this paper we explore Logic Programming as a Service (LPaaS) as a novel and promising re-interpretation of distributed logic programming in the IoT era. After introducing the reference context and motivating scenarios of LPaaS as an effective enabling technology for intelligent IoT, we define the LPaaS general architecture, and discuss two different prototype implementations - as a web service and as an agent in a multi-agent system (MAS), both built on top of the tuProlog system, which provides the required interoperability and customisation. We finally showcase the LPaaS potential through two case studies, designed as simple examples of the motivating scenarios.
Internet of Things Platforms Based on Blockchain Technology: A Literature Review

The implementation of Blockchain Technology (BT) within Internet of Things (IoT) systems has numerous benefits, whereas if it is not correctly adapted to the specific use case, it can present some disadvantages. In the design of IoT Blockchain-based platforms, to identify where the use of BT may present a drawback and search the alternatives that overcome that flaw it is needed to perform a systematic analysis of the platform needs. In order to being capable of face the design of IoT Blockchain-based systems at a later stage, in this work it is performed an analysis of the key characteristics that BT has to take into account when working with it and a literature review of different Blockchain-based IoT platforms.
Virtualization, Containerization, Composition, and Orchestration of Cloud Computing Services

Cloud Computing is a dynamic concept which applies virtualization cum allied techniques to facilitate the provision of services to users. To support provision of resources to users by the service and deployment models, core technologies such as virtualization, containerization and orchestration are used on the cloud. However, the task of having to determine a research focus is challenging and rigorous. A systematic map enables a synthesis of a scheme for categorizing data in a domain that interests researchers. This work conducts a systematic mapping study of virtualization, containerization and orchestration of cloud computing services. The results indicated that articles on virtualization in the area of valuation research and experience papers were 8.56% and 3.28% respectively. In addition, many articles discussed deployment based on validation and solution research with 4.92% and 13.93% respectively. There were more papers published that discussed orchestration in terms of philosophical papers with 2.45%. The lowest publications on models were on the topic of orchestration which was 1.9%. Also, the lowest number of papers on evaluation research was on deployment which was 3.28%. Furthermore, the lowest numbers of articles on validation research were on composition enabler which was 0.82%, while that of solution proposal were on orchestration with 0.82%. The result of this research reveals the gaps that will be beneficial to the trio of researchers, industries, and providers.
Homography‐based traffic sign localisation and pose estimation from image sequence

This study proposes a vision-based method for traffic sign attribute estimation, i.e. 3D position and pose, from image sequences by binocular or monocular cameras. The method starts with acquiring robust feature correspondences based on homography constraints from image pairs. Then the objective function is designed to integrate the feature correspondences to optimise the parameters of the traffic sign plane in the 3D coordinate. Finally, the sign plane is utilised for attribute estimation. In addition, the authors provide an extension for the raw KITTI dataset, which can be utilised for 3D tasks of traffic sign localisation and pose estimation. In the experiments, three popular methods are employed for comparisons based on the publicly available BelgiumTS and KITTI datasets. The results show that the authors’ method based on SIFT and SURF features can locate the traffic signs with a mean error of ∼0.44 and 0.51 m in the BelgiumTS and KITTI datasets, respectively, and estimate the pose with a mean error of ∼14.45° in the KITTI dataset.
Interactions and Sentiment in Personal Finance Forums: An Exploratory Analysis

The kinds of interactions taking place in an online personal finance forum and the sentiments expressed in its posts may influence the diffusion and usefulness of those forums. We explore a set of major threads on a personal finance forum to assess the degree of participation of posters and the prevailing sentiments. The participation appears to be dominated by a small number of posters, with the most frequent poster contributing even more than a third of all posts. Just a small fraction of all possible direct interactions actually take place. Dominance is also confirmed by the large presence of self-replies (i.e., a poster submitting several posts in succession) and rejoinders (i.e., a poster counter-replying to another poster). Though trust is the prevailing sentiment, anger and fear appear to be present as well, though at a lower level, revealing that posts exhibit both aggressive and defensive tones.
Efficient label ordering for improving multi-label classifier chain accuracy

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.Cover :Leatherback (a), green (b - adult &amp; f - hatchling), hawksbill (c) and olive ridley (d) turtles who nest in Sri Lankan beaches, green turtle crawl marks (e) and by-catch of sea turtles in fisheries (c &amp; d)Photo credits: SJ Perera (a &amp; c), WPN Perera (b &amp; e), K Ekaratne (d &amp; f)
Speculative Execution Attack Methodologies (SEAM): An overview and component modelling of Spectre, Meltdown and Foreshadow attack methods

Speculative execution has made a significant impact in the processing speed of computing devices over the last circa 20 years. The functionality of modern CPU's to be able to `speculatively' execute instructions ahead and `out-of-order' of the normal process has increasingly optimized performance. However, vulnerabilities inherent in the speculative execution design are being increasingly discovered. This paper will provide an overview of some of the published works of these CPU vulnerabilities. In addition to providing an overview, this paper presents SEAM: Speculative Execution Attack Methodologies. Representing the attacks via Speculative Execution Attack Schemas (SE Attack Schemas) of the attack types provides further granularity to the attacks. The SE Schemas of existing exploits can potentially be used in future research to identify specific attack approaches and enable scrutiny of the underlying complexity in a more organized manner.
Enterprise security risk management

This chapter explores the processes and tasks that have helped to develop enterprise security risk management (ESRM). ESRM is a holistic approach to covering all security risks to an enterprise.
Examining Security and Usability Aspects of Knowledge-based Authentication Methods

Graphical passwords are considered to be one of the promising alternatives to conventional textual passwords. However, while offering potential theoretical improvements over their textual counterparts, it is important to evaluate how these authentication methods would fare in practice. In this study, we were interested in the user-generated passwords from the security and usability perspective. We conducted an experiment in which the participants were tasked to create and memorize three types of passwords: a textual password, a chess-based graphical password, and an association-based hybrid textual-graphical password. Two weeks after the initial registration, the users were prompted to login using their previously created passwords. By comparing the authentication methods, we showed that despite the graphical passwords' advantages, the user-created chess passwords were the weakest, and the users had the most difficulty remembering them after the two-week period. On the contrary, the association-based passwords were just as strong and memorable as the textual passwords. The conclusions drawn from this paper are therefore two-fold: firstly, alternative authentication methods should be evaluated and compared against textual passwords in reallife scenarios to determine their practical value; and secondly, association-based approaches have the potential to augment both the security and memorability of the existing and novel authentication mechanisms.
C <sup>3</sup> PO

In this paper, we ask a question whether evolutionary algorithms can evolve cryptographic algorithms when no precise design criteria are given. Our strategy utilizes Cartesian Genetic Programming in the bi-level optimization setting with multiple populations trying to evolve a cryptographic algorithm and break it. To challenge our design paradigm, we consider a number of scenarios with varying criteria on the system and its security. We are able to obtain interesting results in several scenarios where the attacker is not able to understand the text with more than a random chance. Interestingly, our system is able to develop various versions of one-time pads, which are the only systems that ensure perfect secrecy. Although our system is far from practical, we consider it interesting since it gives good results that are also human-readable.
Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques

The objective of this research is to apply a new approach to estimate arrival azimuth of seismic events using seismological records of the “El Rosal” station, near to the city of Bogota – Colombia, by applying support vector machines (SVMs). The algorithm was trained with time signal descriptors of 863 seismic events acquired from January 1998 to October 2008; considering only events with magnitude ≥ 2 ML. The earthquake signals were filtered in order to remove diverse kind of low and high frequency noise not related to such events. During training stages of SVMs, several combinations of kernel function exponent and complexity factor were applied to time signals of 5, 10 and 15 seconds along with earthquake magnitudes of 2.0, 2.5, 3.0 and 3.5 ML. The best classification of SVMs was obtained using time signals of 5 seconds and earthquake magnitudes greater than 3.0 ML with kernel exponent of 10 and complexity factor of 2, showing accuracy of 45.4 degrees. This research is an improvement of previous works related to earthquake arrival azimuth determination from data of one single seismic station employing machine learning techniques.