Reviewerly/exHarmony huggingface dataset:
Supporting Observing-on-the-Move with Proximity-Based Technology: Designing for Children’s Scientific Observations Outdoors

This research employs mobile, proximity-based technologies to create opportunities for children to engage in place-based science learning in an arboretum. We present data from a mobile, iBeacon-enabled learning experience to support wayfinding and observing the biodiversity of tree fruit with 84 youth working in 27 small groups (4th graders on a field trip). Data collected include log files, observational field notes, and learners’ photographic artifacts of children’s scientific observations. Our findings show that (a) learners personalized their own routes to navigate the space, (b) made accurate observations of tree fruit, cones, and seeds, and (c) began to express productive dispositions to support their work as scientific observers. We provide insights into how to support short-term, child-centered mobile computing experiences in outdoor gardens that support observations of natural objects in informal spaces.
Polynomial time deterministic identity testing algorithm for Σ <sup>[3]</sup> ΠΣΠ <sup>[2]</sup> circuits via Edelstein–Kelly type theorem for quadratic polynomials

In this work we resolve conjectures of Beecken, Mitmann and Saxena [BMS13] and Gupta [Gupta14], by proving an analog of a theorem of Edelstein and Kelly for quadratic polynomials. As immediate corollary we obtain the first deterministic polynomial time black-box algorithm for testing zeroness of Σ[3]ΠΣΠ[2] circuits.
Hybrid Machine Translation System for the Translation of Simple English Prepositions and Periphrastic Causative Constructions from English to Hindi

Functional and content words are highly expressive. The functional words like adpositions and auxiliary verbs, modal auxiliary verbs, and helping verbs are polysemic and synonymic in nature. They play a vital role in determining the syntactic and semantics of natural languages. In the context of machine translation (MT), the issues related to them still remain a major problem. This study aims to develop a sample knowledge-based method of MT system, which is able to handle the linguistic specificities of the source (SL) and target (TL) languages.
Iris Recognition for Biometrics Based on CNN with Super-resolution GAN

Biometric technologies can realize high-security systems. Although camera performance and photographic environment cause a low signal-to-noise ratio, super-resolution (SR) techniques, such as generative adversarial networks (SRGANs) and deep convolutional neural networks (DCNNs), can improve image quality. This study aimed to investigate the effect of the SRGANs on individual identification by DCNNs, assuming external image noise and a prefiltering process in actual cases of iris recognition. After downgraded iris images were improved by the SRGANs, a DCNN classifier predicted the individuals from the restored images. The accuracies of the DCNN classifier were higher in the SR images using the Bicubic method or squared mean errors than the SRGANs focusing on perceptual loss. This result suggests that it may be easier for the DCNN classifier to create image features based on the pixel-based differences (i.e., high peak signal-to-noise ratio), rather than on the perceptual image differences. In the future, a robust security system based on SR methods may be capable of assessing a health condition using the images obtained for individual certification.
Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods.
Practice and Challenges of (De-)Anonymisation for Data Sharing

Personal data is a necessity in many fields for research and innovation purposes, and when such data is shared, the data controller carries the responsibility of protecting the privacy of the individuals contained in their dataset. The removal of direct identifiers, such as full name and address, is not enough to secure the privacy of individuals as shown by de-anonymisation methods in the scientific literature. Data controllers need to become aware of the risks of de-anonymisation and apply the appropriate anonymisation measures before sharing their datasets, in order to comply with privacy regulations. To address this need, we defined a procedure that makes data controllers aware of the de-anonymisation risks and helps them in deciding the anonymisation measures that need to be taken in order to comply with the General Data Protection Regulation (GDPR). We showcase this procedure with a customer relationship management (CRM) dataset provided by a telecommunications provider. Finally, we recount the challenges we identified during the definition of this procedure and by putting existing knowledge and tools into practice.
Task differentiation: Constructing robust branches for precise object detection

Most prevailing object detection methods share networks and features between localization and classification components, which easily leads to sub-optimal learning for the two separate tasks. In this paper, we propose a conception of task differentiation and design specialized sub-networks for both localization and classification components based on SSD framework. A novel probability based localization method is introduced into the one-stage framework and combined with bounding box regression for precise object localization. Furthermore, a new feature fusion strategy, together with a global attention mechanism, is proposed to learn more robust features. Experimental results on PASCAL VOC and MS COCO data sets indicate that our method has impressive performance compared with other state-of-the-art object detection approaches.
Collision-free path planning based on new navigation function for an industrial robotic manipulator in human-robot coexistence environments

(2020). Collision-free path planning based on new navigation function for an industrial robotic manipulator in human-robot coexistence environments. Journal of the Chinese Institute of Engineers: Vol. 43, No. 6, pp. 508-518.
Vestiges of Past Generation: Threats to 5G Core Network

Due to the commercialization of 5G NSA network service and high-performance smartphones, mobile service usage has rapidly increased these days. Accordingly, the communication network has been changed into an environment where different kinds of abnormal traffic can more easily flow in and people have now started paying attention to security threats related to previous telecommunication. In particular, 5G NSA network uses GTP (GPRS Tunneling Protocol) and Diameter Protocol used in LTE communication, so it is naturally exposed to the attacks based on these protocols. This paper aims to describe different kinds of threats existing in 5G NSA networks and measures to cope with the threats.
Effect of Curriculum Planning for Physical Education in Colleges on Innovation Ability

With poor integrity and unclear goals, the curriculum planning for physical education (PE) in colleges cannot effectively promote the innovation ability of students. To solve the problem, this paper attempts to clearly evaluate the effect of curriculum planning for college PE on the innovation ability of PE majors. Based on the defects of the current curriculum planning, the authors put forward several strategies and suggestions to enhance the promoting effect of college PE curriculum planning on innovation ability. Following the fuzzy theory, an index system and a fuzzy evaluation model were put forward to quantify the effect of college PE curriculum planning on innovation ability. The research results have great theoretical and practical significance.
Investigation of the Helmholtz Motion of a Violin String: A Finite Element Approach

Abstract In the context of this work, a violin string motion is examined using a finite element approach. The string is formulated via ideal string elements and is bowed at one point on the string; hence, there is a nodal contact between the bow and the string. The bow movement induces the stick-slip effect, which is the cause for the violin string sound. The present paper aims at the investigation of the stick-slip phenomenon of bowed strings, considering well-known bowed string effects like the Helmholtz corner modulation, the Schelleng ripples, and the flattening effect. One key element that is used in this work is the Schelleng diagram, which indicates the “perfect” bow force depending on the bowing position. Within these parameters, the Helmholtz motion is carried out. Additionally, different friction characteristic curves are applied in order to study the impact of the rosin on the string motion.
A Survey on Challenges in Designing Cognitive Engines

The primary goal of cognitive computing is to design a digitalized model that is able to mimic human thinking processes. The cognitive engine is in charge of implementing the functionality of a cognitive system. Nowadays, cognitive engines are used as a self-organized management mechanism in different fields such as computer networks, Internet of Things (IoT), and Robotics. This is because the management algorithms of these fields are going to be very complex and therefore human thinking models as digitalized models are required for fast and accurate decision making. In this paper, we summarize challenges in designing cognitive engines. Then, a set of challenges in designing the cognitive engine for body-mind operating system in the digitalized healthcare system is obtained. In the literature, our survey and also suggested case study in the healthcare system have not been considered yet.
Ensuring the Correctness and Well Modeling of Intelligent Healthcare Management Systems

Recent research focus more and more on IoT systems and their applications in order to make people life easier and controllable. The main aim is to expand IoT applications and services into various domains while ensuring communication and automated exchange between them. Recent research handles many issues related to IoT especially implementation, modeling, and deployment. However, many challenges need more deep and thorough analysis especially in terms of flexible modeling, extensible implementation, with respect to the privacy issue. This work focuses principally on modeling IoT systems dedicated to smart healthcare case. We attempt to address the emergency service by initiating a modeling mechanism for Healthcare Management System (HMS) by using UML diagrams, and propose an appropriate access control in order to reinforce it. Then, we ensure the correctness of the developed HMS by relying on the verification and validation based on a formal analysis that showed significant results by using Alloy tool.
Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints

In this paper, we address the problem of synthesizing optimal path plans in 2D subject to spatio-temporal and thermal constraints. Our solution consists of reducing the path planning problem to a Mixed Integer Linear Programming (MILP) problem. The challenge is in encoding the “implication” constraints in the path planning problem using only conjunctions that are permitted by the MILP formulation. Our experimental analysis using an implementation of the encoding in a Python toolbox demonstrates the feasibility of our approach in generating the optimal plans.
Analysing Customers’ Opinions Towards Product Characteristics Using Social Media

With the ever-increasing number of social media messages posted daily, millions of users express opinions on various subjects, including opinions concerning the characteristics of products and services that they have already bought or they intend to buy in the near future. Accurately knowing the opinions of such a large number of users in near real time would be invaluable for the companies marketing those products. Thus, in the present paper, we propose an approach based on Semantic Web technologies, natural language processing and machine learning for accurately analysing the social media messages posted on Twitter. Compared to existing approaches, which mainly focus on determining the opinion of the user concerning the entire product, the approach proposed in the present paper offers deeper insights, by taking into consideration the fact that a user might have different and sometimes even contradictory opinions concerning the various characteristics of a single product. We start by creating an ontology for representing the relationships between the products and their characteristics, ontology that is also used for performing named entity recognition, given the fact that various users can employ different terms for referring to the same concept. The ontology is afterwards used in order to filter from the huge number of tweets published every minute only the ones that can prove relevant for the analysis. In the next step, aspect-based sentiment analysis is employed in order to determine the sentiment expressed by the social media user regarding one or several characteristics of the analysed product. The results of the analysis are stored as semantically structured data, thus making it possible to fully exploit the possibilities offered by Semantic Web technologies, such as inference and accessing the vast knowledge in Linked Open Data, for further analysis.
Named Entity Recognition for Icelandic: Annotated Corpus and Models

Named entity recognition (NER) can be a challenging task, especially in highly inflected languages where each entity can have many different surface forms. We have created the first NER corpus for Icelandic by annotating 48,371 named entities (NEs) using eight NE types, in a text corpus of 1 million tokens. Furthermore, we have used the corpus to train three machine learning models: first, a CRF model that makes use of shallow word features and a gazetteer function; second, a perceptron model with shallow word features and externally trained word clusters; and third, a BiLSTM model with external word embeddings. Finally, we applied simple voting to combine the model outputs. The voting method obtains an \(F_{1}\) score of 85.79, gaining 1.89 points compared to the best performing individual model. The corpus and the models are publicly available.
An Eye-Tracking Project in Industrial Design Education: A case study for Engaging in the Research Process

The practice of design is constantly evolving; new technologies have become a support for the implementation of disruptive proposals in diverse disciplines, including design products. A Paradigm shift are present in the design and engineering education related with support technologies and developing new products. The objective of this work is to present the novel process of a design project that incorporates a creative and objective process for designing and validating products in order to attract, engage and retain talent in design and engineering courses for research and technology implementation. The challenge was a project for create an novelty industrial tool board with a minimum number of tools validated by Eye-tracking (ET) technology. The ET technology is based on the study of eye movement, which provides an objective indicator of where a person's overt, and typically centered, attention is focused. Twenty-eight students from the third year of Industrial Design Bachelor’s program were involved in this academic course and used a product design methodology to implement the technology and dynamics of the ET. The results of this education project revealed a novel dynamic in design education. The results reveled an improved interest in research and technology implementation. Students perceived the relevance of ET technology in a fundamental phase of product design. In addition, the students shared their enjoyment and interest in reusing this technology in similar processes. The students' perception of factors, such as utility, novelty and relevance of this technology, in their design processes was positive. Finally, the novel process became familiar to the students, even if it was their first-time using ET technology. This work reveals how technology becomes a fundamental part of the process and how to guide students to integrate rigid and meticulous processes in design products without neglecting the creative process.
NeuroGIFT: Using a Machine Learning Based Sat Solver for Cryptanalysis

A recent trend in machine learning is the implementation of machine learning based solvers, such as the sat solver NeuroSat. The main limitation of NeuroSat is its scaling to large problems. We conjecture that this lack of scaling is due to learning an all-purpose SAT solver, and that learning to solve specialized SAT problems instead should yield better results. In this article, we evaluate our hypothesis by training and testing NeuroSat on SAT problems for differential cryptanalysis on the block cipher GIFT, and present the resulting classifier NeuroGift. We show that on these highly structured problems, our models are able to perform orders of magnitude better than the original NeuroSat, potentially paving the way for the use of specialized solvers for cryptanalysis problems.
Statistical insights into deep neural network learning in subspace classification

Deep learning has benefited almost every aspect of modern big data applications. Yet its statistical properties still remain largely unexplored. It is commonly believed nowadays that deep neural networks (DNNs) benefit from representational learning. To gain some statistical insights into this, we design a simple simulation setting where we generate data from some latent subspace structure with each subspace regarded as a cluster. We empirically demonstrate that the performance of DNN is very similar to that of the two‐step procedure of clustering followed by classification (unsupervised plus supervised). This motivates us to ask: Does DNN indeed mimic the two‐step procedure statistically? That is, do bottom layers in DNN try to cluster first and then top layers classify within each cluster? To answer this question, we conduct a series of simulation studies, and to our surprise, none of the hidden layers in DNN conduct successful clustering. In some sense, our results provide an important complement to the common belief of representational learning, suggesting that at least in some model settings, although the performance of DNN is comparable with that of the ideal two‐step procedure knowing the true latent cluster information a priori, it does not really do clustering in any of its layers. We also provide some statistical insights and heuristic arguments to support our empirical discoveries and further demonstrate the revealed phenomenon on the real data application of traffic sign recognition.
Hybrid and Secure E-Health Data Sharing Architecture in Multi-Clouds Environment

Healthcare is among the sectors showing efforts in adopting cloud computing to its services considering the provided cost reduction and healthcare process efficiency. However, outsourcing patient's sensitive data increases the concerns regarding security, privacy, and integrity of healthcare data. Therefore, there is a need for building a trust relationship between patients and e-health systems. In this paper, we propose a privacy-preserving framework, called Hybrid and Secure Data Sharing Architecture (HSDSA), to secure data storage in e-health systems. Our approach improves security in healthcare by maintaining the privacy and confidentiality of sensitive data and preventing threats. In fact, in the upload phase, Multi-cloud environment is used to store Rivest–Shamir–Adleman (RSA) encrypted medical records. We adopt a Shamir's secret sharing approach for the distribution of shares to different independent cloud providers. In the retrieval phase, the reconstruction operation is based on the (t, n) strategy. To check the requester identity and to prove the hash possession, we used a zero-knowledge cryptography algorithm, namely the Schnorr algorithm. The patient has a total control over the generation and management of the decryption keys using Diffie-Hellman algorithm without relying on a trusted authority.
Exploiting the optical reflectance behaviour of carbon fibre composites for low-cost inspection and orientations analysis

This work focusses on understanding and exploiting the optical reflectance behaviour of carbon fibre reinforced polymer composites for the non-destructive determination of fibre orientation and surface defects. By taking a series of images under different lighting conditions, fibre reflections have been isolated and MATLAB image analysis has been used to calculate the fibre orientations across the surface of the composite. An average peak method has been developed and validated against manual orientation measurements for two different applications: a machined scarf repair surface and a discontinuous fibre composite. The same experimental method and analysis code showed good results for both cases without modification, when at least 16 images with different directional lighting conditions are used. Hence, this low-cost approach can be applied to a diverse range of quality assurance and research applications where fibre orientations or other surface details need to be characterised.
Risks of Critical Infrastructure Adoption of Cloud Computing by Government

The purpose of this article is to discuss the risks and reasons of adopting the cloud computing in the critical infrastructure within the government context of cloud computing adoption. The article will also present examples of cloud computing adoption in the critical infrastructure domain. The data used in the paper was gathered from different academic, governmental, and online sources. It was found that, although there are risks involved in the cloud computing adoption, governments are deploying cloud computing using different deployment models and reaching high level of deployment within the critical infrastructure. The findings of this study suggest that it is not a question of adopting or not anymore, but the question of how to mitigate the risks involved after the deployment.