Reviewerly/exHarmony huggingface dataset:
Perceived ‘optimal efficiency’: theorization and conceptualization for development and implementation

We recently advanced the study of positive psychology by introducing the theory of optimization, which explains the underlying process of optimal best. Our continuing research interest has led us to a newly developed concept, termed as ‘optimal efficiency’. Optimal efficiency, we contend, focuses on the utilization of resources as well as the amount of time and effort that a person would have to expend during the course of his/her learning. How much time and effort, for example, should a student expend before it is perceived as being ‘inefficient’? Optimal efficiency, in this analysis, is concerned with an important relationship – namely: the minimization of expenditure of time, effort, resources, etc. versus the maximization in productivity.Perceived efficiency is related to the teaching and training of judgment, decision making, autonomy, and self-determination – for example, in terms of successful schooling, a student has to decide whether it is worthwhile to expend so much time and effort on a given task when he/she may not necessarily pass. In our conceptual analysis and proposition of optimal efficiency, we consider the impact of cognitive load theory, which places emphasis on calculated investment and subsequent use of cognitive resources to process information for the purpose of achieving effective learning in a subject matter. Using cognitive load theory as a basis, we attempt to validate the concept of optimal efficiency by taking into account three main types of cognitive load imposition: extraneous, intrinsic, and germane. For example, we consider the possibility that a reduction in extraneous cognitive load imposition could instill a perception of efficiency, resulting in a person's achievement of optimal best. Emphasis on encouragement of germane cognitive load, in contrast, could be perceived as being more efficient, likewise yielding exceptional outcomes in a subject matter.
A New WoT Cryptography Algorithm Based on GOST and Novel 5d Chaotic System

The concept Web of Things (WoT) goes well beyond the emphasis on the Internet as a means of sharing data, instead of introducing all resources and connections involving computers, data, and people to the Web. It, therefore, focuses on a range of problems and opportunities, thus paving the way for several exciting industries applications. In cryptography a range of main characteristics of chaotic systems such as non-linearity, initial condition sensitivity, and mixing properties are available. These characteristics provide an essential connection between cryptography and chaos. GOST block cipher is based on secret key secrecy. However, when the encryption process with the same key is used for plaintext, the same cipher text is created. Message replication can be easily detected by an adversary who is a bad link in every communication. In this paper, propose to use a 5d chaotic system combined with GOST block cipher to create a new secure Web of Things (WoT) cryptography system. The 5D chaotic system was used to generate chaotic random keys used in GOST algorithm to provide proper security with as high hardness randomly enhances the NIST fifteen statistical tests and modifies key schedule as security operations.
Considerations for Quality Assurance of E-Learning Provision

Today’s learning landscape differs greatly from that of a few years ago (Bates, 2015; Daniel, 2016; Mathes, 2019; Ossiannilsson, Williams, Camilleri, Brown, 2015). The main global challenges highlighted so far and affecting and influencing education are globalization, changing demographics and increasing digitization (Schwab, 2016). The United Nations’ UNESCO sustainability goals aim to meet these challenges (2015a; 2015b). In the field of education, the SDG4 focuses on education for all through its core areas of access, equity, equality, equality, inclusion and lifelong learning. Mobile learning is the first choice for most people today. The way learners learn is changing dramatically, due to the fourth industrial revolution, not just the way we learn, but also the way we live, perform, communicate, and interact (Schwab, 2016). Today, learning is possible anywhere, anytime and through any type of device. In the context of globalization and the knowledge-based economy, the quality of higher education is increasingly seen as strategically important for national economic development and competitiveness. High quality and relevant higher education provide students with the knowledge, skills and transferable core competences they need to succeed after graduation, in a high-quality learning environment that recognizes and supports good learning and teaching. The combination of educational technologies is both natural and demanding, so quality aspects must be taken into account (Bates, 2017, Daniels, 2016, Heurtas et al., 2018).
InCell VR: A Virtual Reality-based Application on Human Cell Division for Mobile Learning

Many teaching and learning tools incorporate current technologies for the learners to gain new knowledge thus increasing the efficiency of learning. Some of the technologies adopted in the mobile-based education systems are virtual reality, augmented reality, and mixed reality. In the Biology field, one of the problems faced by the learners is the difficulties in visualizing the human cell division process in the topic of mitosis and meiosis. Thus, many innovations which adopt these technologies are created to improve the understanding and interest of the learners. In this work, an interactive mobile learning application with virtual reality technology has been designed and developed to help learners visualize the human cell division process. The mobile application which is known as &lt;em&gt;InCell VR&lt;/em&gt; also includes mini games which can help to test and improve the learners’ understanding. The &lt;em&gt;InCell VR&lt;/em&gt; was developed using Unity, a game engine platform for developing high performance commercial game. The final evaluation assesses two parts: the learners’ knowledge gain after using the application and usability evaluation of the application. The usage of the proposed application was seen to give positive impact on the development of learners’ knowledge from the pre- and post-test testing sessions. The usability study showed that &lt;em&gt;InCell VR&lt;/em&gt; application is useful, easy to be used and learnt, and give satisfactions to the users.
The Impact of Employee Empowerment on Job Engagement: Evidence from Jordan

The objective of this study was to investigate the impact of employee empowerment (i.e., structural and psychological empowerment) on job engagement (i.e., physical, emotional, and cognitive engagement). A quantitative approach was adopted in order to achieve such a study aim, and the data was collected via an online questionnaire. The sample of this study consisted of 300 employees from the Aqaba Special Economic Zone Authority (ASEZA), one of the biggest Jordanian organisations in Aqaba, with a total of 293 questionnaires retrieved and 7 excluded due to their being invalid for statistical analysis. This yielded a total of 286 accepted questionnaires, or 95.3% of the total questionnaires provided. Moreover, the Statistical Analysis Package for Social Science (SPSS) was used for data analysis, the results of which indicating that the level of implementation of employee empowerment dimensions had a medium rate amongst ASEZA&amp;rsquo;s employees, as the structural empowerment was applied more than the psychological empowerment. Furthermore, the level of present job engagement dimensions also had a medium rate amongst the surveyed ASEZA employees. The results also indicated there to be a significant statistical impact by structural and psychological empowerment on physical engagement and cognitive engagement, whilst there was no significant statistical impact made by structural empowerment on emotional engagement compared to psychological empowerment, which had a significant statistical impact on it. In light of the findings of this research, ASEZA managers must pay more attention to the role of empowerment of employees in promoting work engagement, since this enhances the organisation&amp;rsquo;s ability to achieve the appropriate strategy and gain a comparative advantage.
From Auto-encoders to Capsule Networks: A Survey

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.
A classification method for social information of sellers on social network

Abstract Social e-commerce has been a hot topic in recent years, with the number of users increasing year by year and the transaction money exploding. Unlike traditional e-commerce, the main activities of social e-commerce are on social network apps. To classify sellers by the merchandise, this article designs and implements a social network seller classification scheme. We develop an app, which runs on the mobile phones of the sellers and provides the operating environment and automated assistance capabilities of social network applications. The app can collect social information published by the sellers during the assistance process, uploads to the server to perform model training on the data. We collect 38,970 sellers’ information, extract the text information in the picture with the help of OCR, and establish a deep learning model based on BERT to classify the merchandise of sellers. In the final experiment, we achieve an accuracy of more than 90%, which shows that the model can accurately classify sellers on a social network.
Weak Keys in Reduced AEGIS and Tiaoxin

AEGIS-128 and Tiaoxin-346 (Tiaoxin for short) are two AES-based primitives submitted to the CAESAR competition. Among them, AEGIS-128 has been selected in the final portfolio for high-performance applications, while Tiaoxin is a third-round candidate. Although both primitives adopt a stream cipher based design, they are quite different from the well-known bit-oriented stream ciphers like Trivium and the Grain family. Their common feature consists in the round update function, where the state is divided into several 128-bit words and each word has the option to pass through an AES round or not. During the 6-year CAESAR competition, it is surprising that for both primitives there is no third-party cryptanalysis of the initialization phase. Due to the similarities in both primitives, we are motivated to investigate whether there is a common way to evaluate the security of their initialization phases. Our technical contribution is to write the expressions of the internal states in terms of the nonce and the key by treating a 128-bit word as a unit and then carefully study how to simplify these expressions by adding proper conditions. As a result, we find that there are several groups of weak keys with 296 keys each in 5-round AEGIS-128 and 8-round Tiaoxin, which allows us to construct integral distinguishers with time complexity 232 and data complexity 232. Based on the distinguisher, the time complexity to recover the weak key is 272 for 5-round AEGIS-128. However, the weak key recovery attack on 8-round Tiaoxin will require the usage of a weak constant occurring with probability 2−32. All the attacks reach half of the total number of initialization rounds. We expect that this work can advance the understanding of the designs similar to AEGIS and Tiaoxin.
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference

Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in input-adaptive multi-exit architectures, such as MSDNets or Shallow-Deep Networks. These architectures enable faster inferences and could bring DNNs to low-power devices, e.g., in the Internet of Things (IoT). However, it is unknown if the computational savings provided by this approach are robust against adversarial pressure. In particular, an adversary may aim to slowdown adaptive DNNs by increasing their average inference time—a threat analogous to the denial-of-service attacks from the Internet. In this paper, we conduct a systematic evaluation of this threat by experimenting with three generic multi-exit DNNs (based on VGG16, MobileNet, and ResNet56) and a custom multi-exit architecture, on two popular image classification benchmarks (CIFAR-10 and Tiny ImageNet). To this end, we show that adversarial example-crafting techniques can be modified to cause slowdown, and we propose a metric for comparing their impact on different architectures. We show that a slowdown attack reduces the efficacy of multi-exit DNNs by 90–100%, and it amplifies the latency by 1.5–5× in a typical IoT deployment. We also show that it is possible to craft universal, reusable perturbations and that the attack can be effective in realistic black-box scenarios, where the attacker has limited knowledge about the victim. Finally, we show that adversarial training provides limited protection against slowdowns. These results suggest that further research is needed for defending multi-exit architectures against this emerging threat. Our code is available at https://github.com/sanghyun-hong/deepsloth.
Word prediction in computational historical linguistics

In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.
On the segmentation of Chinese incremental words.

In the present article, we report two eye-tracking experiments on how Chinese readers segment incremental words while reading Chinese. Incremental words are multicharacter words containing a subset of characters that constitute another word (referred to as the embedded word). For example, in a word containing three characters ABC (e.g., "," meaning landlady), the first two characters AB ("," meaning boss) constitute an embedded word. In the two experiments, Chinese readers read sentences with 3-character incremental words. In Experiment 1, the incremental words were always plausible, and the verbs prior to the target words were manipulated so that the embedded word of the incremental word was either plausible or implausible as an independent word. The eye-movement data showed that the plausibility manipulation did not influence the reading times on the incremental words nor their embedded words. In Experiment 2, we manipulated the plausibility of both incremental and embedded words. No plausibility effect of the embedded word was observed when the incremental words were plausible. However, the plausibility effect of the embedded word was obtained when the incremental words were implausible. These results suggest that Chinese readers tend to segment and integrate incremental words as a whole unit when they are plausible. However, when incremental words are implausible, the embedded words are segmented as independent words. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Megalite: A New Spanish Literature Corpus for NLP Tasks

In this work we introduce the Spanish Literary corpus MegaLite, a new corpus well adapted to Natural Language Processing (NLP), Computational Creativity (CC), Text generation and others studies. We address the creation of this corpus of literary documents to evaluate or design algorithms in automatic text generation, classification, stylometry and rhetorical analysis, sentiment detection, among other tasks. We have constituted this corpus manually in order to avoir genre classification errors. Near of 5 200 works on the genres narrative, poetry and plays constitute this corpus. Some statistics and applications of MegaLite corpus are presented and discussed. The MegaLite corpus will be available to the community as a free resource, under several adequate formats.
Location, Proximity, Affinity – The key factors in FaaS

The Function-as-a-Service paradigm emerged not only as a pricing technique, but also as a programming model promising to simplify developing to the cloud. Interestingly, while placing functions across hosts under the service platform is believed to be flexible, currently the available platforms pay little attention to co-locate connected functions, or data with the respective processing function in order to improve performance. Even though the local function invocation and data access might be an order of magnitude faster than their remote intra-cloud counterparts. In this paper, we therefore propose a Function-asa- Service platform design that reaps the performance benefits of co-location. We build the platform on WebAssembly, a secure and flexible tool for efficient local function invocations, and on a distributed in-memory database, which allows arbitrary data placement. On top we advocate smart placement strategies for function executions and data, decoupled from the functions. Hence we envision good horizontal scaling of functions while keeping the experienced processing latency to that of a single machine case.
Abnormal behavior detection using sparse representations through sequential generalization of k-means

The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems.This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors.In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences.The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene.The sequential generalization of k-means (SGK) algorithm was applied in this study to generate the dictionary set that can sparsely represent each signal; in addition, the orthogonal matching pursuit algorithm was utilized to recover high-dimensional sparse features when referring to a few numbers of noisy linear measurements.Using the SGK allows gaining a less complex and quicker implementation compared to other dictionary learning methods.We conducted comprehensive experiments to analyze and evaluate the ability of our framework in detecting abnormalities using several public benchmarks, which contain different abnormal samples and various contextual compositions.The experimental results show that the proposed framework achieved high detection accuracy (up to 95.33%) and low frame processing time (31 ms on average) compared to the relevant related work.
Secondary School Teacher Support and Training for Online Teaching during the COVID-19 Pandemic

In Italy, due to the Covid-19 pandemic, all schools were closed on March 5, 2020 and it was mandatory to switch to “distance learning”, in order not to interrupt the teaching continuity and to guarantee the right to education for all students. The Ministry of Education suggested several initiatives to teachers, including the PPS national project. In the paper we analyse why and how the PPS project, intended for teachers of secondary schools of the STEM disciplines but opened in the emergency to teachers of all disciplines, was able to provide much needed support and not only. Following this opening and the consequent registration of many new teachers, three different phases were carried out: an analysis phase of the teachers’ needs for online teaching, a phase of support and initial help, and a training phase. The results show an extremely high participation of the teachers and a wide online collaboration. All the teachers of the PPS will certainly have an advantage in the post-covid teaching, since they can take advantage of the work done and the experience and skills gained in the past.
NEXUS BETWEEN QUALITY OF EDUCATION, STUDENT SATISFACTION AND STUDENT LOYALTY: THE CASE OF DEPARTMENT OF ENGLISH TEACHER EDUCATION AT UNIVERSITAS ISLAM NEGERI IN INDONESIA

The objective of the study is to examine the effect of quality of education on the students' satisfaction and loyalty in the universities of Indonesia.The study has collected 384 responses from the students of Indonesian HEIs using fivepoint Likert scale self-administered questionnaire.The sample was gathered using nonprobability convenience sampling technique and analysis was based on PLS-SEM using SmartPLS version 3.2.8.The results have shown that quality of education has significant effect on student satisfaction while student satisfaction has significant effect on student loyalty.From a practical point of view, as a cumulative body work arises on the quality of educational service, but this study's conceptual model provides greater acknowledgement if such improvement of image and ranking are addressed by the institution of tertiary level.A strong understanding of the relationship between program quality, industrial link, student satisfaction, image and service quality that allows to make better policy and strategic decisions, as well as finding ways to be strengthened by organization management.This makes it possible to recruit high-quality foreign students in the world's leading universities for academic research and development.