Reviewerly/exHarmony huggingface dataset:
Supply chain performance in the power distribution sector

Purpose In today’s highly competitive milieu, many organizations are entering into the international market to gain greater share and take advantage of higher production. However, the introduction of advanced technology has brought a significant amount of competition within the supply chain especially within the bounds of the power distribution sector. This study aims to examine how information and knowledge sharing influence supply chain performance in the power distribution sector. Design/methodology/approach The quantitative method and cross-sectional survey design were used in the study. A sample of 200 officers specifically selected from power distribution companies was used for the study. Data was analysed using descriptive, correlation, regression and structural equation models. Findings The relationship between information sharing, knowledge sharing and performance of the supply chain are positively mediated by supply chain collaboration. The findings indicate that technological innovation positively moderates the relationship between information sharing, knowledge sharing and supply chain performance. Practical implications Information sharing, knowledge sharing and technological innovation are critical indicators driving the supply chain operation of power distribution organizations. Originality/value This study presents a contemporary approach towards understanding knowledge and information sharing as antecedents of supply chain performance.
FNet: A Two-Stream Model for Detecting Adversarial Attacks against 5G-Based Deep Learning Services

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.
Telenursing in COVID-19 times and maternal health: WhatsApp®as a support tool

Abstract Objective: To describe the process of creating “Talk to the Midwife Recife – PE” as a telenursing service using WhatsApp®as a support tool to promote maternal health in the COVID-19 pandemic. Method: This is an experience report type study. The creation of “Talk to the Midwife Recife – PE” followed the recommendations of the Institute of Medicine and the Health Care Quality Committee of America in relation to the provision of health care. Remote and virtual work with construction [...]
Resource Allocation Based on Predictive Load Balancing Approach in Multi Cloud Environment

Cloud computing gives a support to purchasers to lessen their internal establishment, and for providers to extend wages, using their own specific system. The best possible burden adjusting and dynamic asset provisioning improves cloud execution and draws in the cloud clients. In this paper, we propose a mechanized asset provisioning calculation, Speculation asset provisioning, inciting load adjusting through theoretical methodology in asset provisioning. As an endeavor to measure asset portion we utilize two level versatile expectation component to check the computational examples of past asset distribution to the future necessity. The structure ensures reasonable assets required for the application, by avoiding over or under-provisioning of asset and supports vitality effectiveness in asset distribution. We use estimation system to address the fluctuation in the verifiable information to adjust the theory overhead. We have passed on our proposed work in an open source cloud structure and differentiated our results and other AI draws near. Our Experimental outcomes show versatile asset designation over client driven help the executives under the quickly changing necessities of cloud computing.
When is the Peak Performance Reached? An Analysis of RDF Triple Stores

With significant growth in RDF datasets, application developers demand online availability of these datasets to meet the end users’ expectations. Various interfaces are available for querying RDF data using SPARQL query language. Studies show that SPARQL end-points may provide high query runtime performance at the cost of low availability. For example, it has been observed that only 32.2% of public endpoints have a monthly uptime of 99–100%. One possible reason for this low availability is the high workload experienced by these SPARQL endpoints. As complete query execution is performed at server side (i.e., SPARQL endpoint), this high query processing workload may result in performance degradation or even a service shutdown. We performed extensive experiments to show the query processing capabilities of well-known triple stores by using their SPARQL endpoints. In particular, we stressed these triple stores with multiple parallel requests from different querying agents. Our experiments revealed the maximum query processing capabilities of these triple stores after which point they lead to service shutdowns. We hope this analysis will help triple store developers to design workload-aware RDF engines to improve the availability of their public endpoints with high throughput.
VR interactive game design based on unity3d engine

In order to enable users to get an immersive experience in the game environment, a VR interactive game design method based on Unity3D engine is proposed. According to the VR game design process and the interactive methods in the game, analyze the VR interactive game design concept, according to the data structure of the VR interactive game, combined with the state space search method to traverse, use the gravity acceleration component of the multi-axis acceleration sensor to calculate the pitch of the VR device Angle, according to the user's pitch angle, acceleration and gravity sensing information, real-time positioning of the user's head movement, controlling the position of the VR device in the game engine, and optimizing the rendering result of the VR game space structure, completing the Unity3D engine-based VR interactive game design. The experimental results show that the experience satisfaction of the VR interactive game design is higher than that of the traditional VR interactive game design, and it fulfills the requirements of obtaining an immersive experience in a virtual reality game environment.
Auxiliary Sequence Labeling Tasks for Disfluency Detection

Detecting disfluencies in spontaneous speech is an important preprocessing step in natural language processing and speech recognition applications. Existing works for disfluency detection have focused on designing a single objective only for disfluency detection, while auxiliary objectives utilizing linguistic information of a word such as named entity or part-of-speech information can be effective. In this paper, we focus on detecting disfluencies on spoken transcripts and propose a method utilizing named entity recognition (NER) and part-of-speech (POS) as auxiliary sequence labeling (SL) tasks for disfluency detection. First, we investigate cases that utilizing linguistic information of a word can prevent mispredicting important words and can be helpful for the correct detection of disfluencies. Second, we show that training a disfluency detection model with auxiliary SL tasks can improve its F-score in disfluency detection. Then, we analyze which auxiliary SL tasks are influential depending on baseline models. Experimental results on the widely used English Switchboard dataset show that our method outperforms the previous state-of-the-art in disfluency detection.
IBM Watson Assistant and Node-RED-Based Movie Ticketing Bot Design

The present work is intended to provide a solution through the development of a chatbot using the Watson assistant and Node-RED UI for booking movie tickets. It ensures error-free booking of tickets without human agent intervention smoothly even during peak hours. It saves time and makes the process much more easy for the customer as well as for the service provider by eliminating the elongated process starting from waiting in the queue of booking counter to getting the answers to repetitive queries. A web-based conversational flow is generated by only clicking on an URL from any electronic gadget with internet access.
Social analysis of young Basque-speaking communities in twitter

In this paper, we take into account both social and linguistic aspects to perform demographic analysis by processing a large amount of tweets in Basque language. The study of demographic characteristics and social relationships are approached by applying machine learning and modern deep-learning Natural Language Processing (NLP) techniques, combining social sciences with automatic text processing. More specifically, our main objective is to combine demographic inference and social analysis in order to detect young Basque Twitter users and to identify the communities that arise from their relationships or shared content. This social and demographic analysis will be entirely based on the automatically collected tweets using NLP to convert unstructured textual information into interpretable knowledge.
Examining the Tech Stacks of Czech and Slovak Untrustworthy Websites

The burgeoning of misleading or false information spread by untrustworthy websites has, without doubt, created a dangerous concoction. Thus, it is not a surprise that the threat posed by untrustworthy websites has emerged as a central concern on the public agenda in many countries, including Czechia and Slovakia. However, combating this harmful phenomenon has proven to be difficult, with approaches primarily focusing on tackling consequences instead of prevention, as websites are routinely seen as quasi-sovereign organisms. Websites, however, rely upon a host of service providers, which, in a way, hold substantial power over them. Notwithstanding the apparent power hold by such tech stack layers, scholarship on this topic remains largely limited. This article contributes to this small body of knowledge by providing a first-of-its-kind systematic mapping of the back-end infrastructural support that makes up the tech stacks of Czech and Slovak untrustworthy websites. Our approach is based on collecting and analyzing data on top-level domain operators, domain name Registrars, e-mail providers, web hosting providers, and utilized website tracking technologies of 150 Czech and Slovak untrustworthy websites. Our findings show that the Czech and Slovak untrustworthy website landscape relies on a vast number of back-end services spread across multiple countries, but in key tech stack layers is nevertheless still heavily dominated by locally based companies. Finally, given our findings, we discuss various possible avenues of utilizing the numeral tech stack layers in combating online disinformation.
Behavior of Keyword Spotting Networks Under Noisy Conditions

Keyword spotting (KWS) is becoming a ubiquitous need with the advancement in artificial intelligence and smart devices. Recent work in this field have focused on several different architectures to achieve good results on datasets with low to moderate noise. However, the performance of these models deteriorates under high noise conditions as shown by our experiments. In our paper, we present an extensive comparison between state-of-the-art KWS networks under various noisy conditions. We also suggest adaptive batch normalization as a technique to improve the performance of the networks when the noise files are unknown during the training phase. The results of such high noise characterization enable future work in developing models that perform better in the aforementioned conditions.
Hahn polynomials and the Burnside process

We study a natural Markov chain on $\{0,1,\cdots,n\}$ with eigenvectors the Hahn polynomials. This explicit diagonalization makes it possible to get sharp rates of convergence to stationarity. The process, the Burnside process, is a special case of the celebrated `Swendsen-Wang' or `data augmentation' algorithm. The description involves the beta-binomial distribution and Mallows model on permutations. It introduces a useful generalization of the Burnside process.
Perceptions of the Skills of Graduates in the Library and Information Science and Technology Degree of P. Porto, Portugal: A Statistical Data Analysis of the Alignment Between Students, Teachers, and Employers

Objective – The objective of this research was to identify the four professional competences of graduates in Library and Information Sciences and Technologies (LIST) considered most pertinent, from the points of view of students, teachers, and employers. We also sought to compare the perceptions of the different groups. The study was based on the premise that alignment of these perceptions may enhance the employability of LIST graduates. Methods – A questionnaire, used and validated by Arias-Coello et al. (2014), was further translated by Martins and Carvalho (2018). The questionnaire consisted of a set of questions regarding four dimensions: Information Management; Communication and Interpersonal Relationships; Domain and Application of Information Technologies; Organization Management. We sent the survey by email to the target audience; it was available to complete in April and May 2018. Data analysis included calculating mean and standard deviation, as well as Shapiro Wilk normality tests, statistical tests for multiple comparisons, ANOVA, Kruskal Wallis test, Friedman test, Wilcoxon test, and Pearson's correlation. Results – In relation to certain dimensions, one could think that age would be a determining factor, but this has not been proved. In fact, results showed that age is not a factor that influenced the importance attributed to different competences in the several dimensions. The respondents' academic degrees and areas of knowledge were linked to significant differences transversally. The Kruskal Wallis test indicated that students, teachers, and employers perceived the importance of Information and Communication Technologies (ICT) skills equally. As for the other competences, overall there were significant differences between students and employers, and there were significant differences between students and teachers regarding the perceived importance of organization management skills. There were also significant differences between teachers, students, and employers regarding the perceived importance of communication skills. We also found that responses within the teacher group had less dispersion of answers, therefore there was greater internal agreement. The opposite occurred in the employer group. Conclusion – The differences detected in the perception of the different groups were minor. However, it is necessary to create initiatives for the alignment of the perceptions of students and employers, because if all groups have the same perception, they will develop and value the same skills, responding to the needs of the labor market, thus promoting the employability of LIST graduates. The inclusion of a curricular internship, even one of short duration, in the first year of the degree could also be a way of endeavoring to bring together the expectations of both groups. These suggestions are part of a proposal to change and update the study plan and enhance the performance of course management.
Removing education barriers for deaf students at the era of Covid-19

Nowadays where Covid-19 threatens human lives, it is essential to adapt to this situation and try to continue our lives even though some activities are almost inevitable. Education system is one of the most affected daily activities. New technological means had to be adopted to overcome the distance and to continue education process. Although these new adopted means brought new perspectives, also many problems were arised. Deaf or hard hearing students, faced more difficulties than in ordinary situations. Deaf students in order to communicate are either using lip reading and must see their interlocutor’s whole body. In the not likely scenario that the schools are open, teachers and students must wear a face mask, while in distance learning only a small window of the participants is visible and most probably only their faces. Thus, communication at the era of Covid-19 for deaf and hard hearing students is almost inevitable. This paper makes a presentation of the special education system of deaf people in Greece, addresses the difficulties these students face and presents a novel educational tool that with the proper use should help them overcome this difficult situation.
Higher-order uncertainty bounds for mixed states

Uncertainty lower bounds for parameter estimations associated with a unitary family of mixed-state density matrices are obtained by embedding the space of density matrices in the Hilbert space of square-root density matrices. In the Hilbert-space setup the measure of uncertainty is given by the skew information of the second kind, while the uncertainty lower bound is given by the Wigner-Yanase skew information associated with the conjugate observable. Higher-order corrections to the uncertainty lower bound are determined by higher-order quantum skew moments; expressions for these moments are worked out in closed form.
Quantum direct portation

Quantum state that carries classical information, 0 or 1, can be safely and reliably transmitted using quantum secure direct communication. How to transmit an arbitrary quantum state is a wider issue and has important applications. One way is to use quantum teleportation, namely, first distribute a large number of Einstein-Podolsky-Rosen pairs, and then perform entanglement purification to obtain a near-perfect pair, and make quantum teleportation using the pair. In this article, we propose a method that directly port the quantum state with security and reliability using quantum secure direct communication. After sufficient number of copies of the same state have been directly ported, single-particle purification is performed to obtain a near perfect single particle state. This is important because it offers a new method for sending an arbitrary single particle state securely and reliably without using quantum teleportation. It is also an important extension of quantum secure direct communication to send an arbitrary quantum state. Quantum direct portation will have great potential in quantum internet.
Digitising English Language Learning for Early Childhood Educators in Malaysian Context: The Rationale

Nowadays, many kindergartens in Malaysia are striving toward using English in their daily activities with their children and some even use English language as their medium of instruction. However, some of the early childhood educators were not well equipped with the knowledge of English language. This paper aims to report the rationale for digitising English language learning through the development of Kindy Talk Application in terms of approach, content and design. Kindy Talk Application is a mobile application for Malaysian early childhood educators to utilise accurate language in their daily routine interactions and instructional language typically used at daycare centres. This development of the application stemmed from a series of workshops with 2 modules, namely Communication English and Storytelling techniques. These workshops aimed at increasing 30 Pahang early childhood educators’ professional development. To ensure the sustainability of the training, the Kindy Talk Application, which uses augmented reality technology was developed to allow users to practise the daily routine and instructional language using their mobile phones in a safe autonomous learning environment. This enhances the language proficiency and confidence of Malaysian early childhood educators.
Gathering Data with Python

Information is an essential piece of data analysis. It comes from diverse sources in different formats. Some information requires a lot of effort to obtain; other data comes clean and structured. Python is a great tool to gather and manipulate any data in any format.
Evolutionary NAS in Light of Model Stability for Accurate Continual Learning

Continual learning, the capability to learn new knowledge from streaming data without forgetting the previous knowledge, is a critical requirement for dynamic learning systems, especially for emerging edge devices such as self-driving cars and drones. However, continual learning is still facing the catastrophic forgetting problem. Previous work illustrate that model performance on continual learning is not only related to the learning algorithms but also strongly dependent on the inherited model, i.e., the model where continual learning starts. The better stability of the inherited model, the less catastrophic forgetting and thus, the inherited model should be elaborately selected. Inspired by this finding, we develop an evolutionary neural architecture search (ENAS) algorithm that emphasizes the Stability of the inherited model, namely ENAS-S. ENAS-S aims to find optimal architectures for accurate continual learning on edge devices. On CIFAR-10 and CIFAR-100, we present that ENAS-S achieves competitive architectures with lower catastrophic forgetting and smaller model size when learning from a data stream, as compared with handcrafted DNNs.
Introducing Gestural Interaction on the Shop Floor: Empirical Evaluations

This paper describes two empirical evaluations we carried out in the context of an Industry 4.0 project, where we explored the use of touchless gestural interaction on the shop floor.