Reviewerly/exHarmony huggingface dataset:
The Critical Link Between Knowledge Management and Succession Management at Higher Education Institutions

The purpose of this paper is to provide the growing range of literature review and offers a comprehensive review for new researchers with a specific focus on the relationship between knowledge management and succession planning at a higher education institution.This study will get a better idea of how higher education institutions deal with the danger of face the risk of knowledge loss due to long absence at the workplace. Most of the literature on succession planning was focused on healthcare and limited literature for the education sector.This paper is based on an extensive traditional literature review search of published articles that primarily address the concept of knowledge management and succession management. The study has categorized the literature representing knowledge management introduction, challenges facing by higher education for knowledge retaining, succession management, and linking relationship of knowledge management and succession planning.The changing role of the higher education system is all part of the globalization process. This study sets the global context which can help for new leadership processes in the higher education system. Today, higher education institutes are playing a big role to create university leaders.There are some challenges faced by knowledge management in terms of retaining leadership within the organization, which includes culture, knowledge retaining, technology, and directive leadership. This article approaches the topic of knowledge management in the context of succession management. The relationship between knowledge management and succession planning can improve the decision-making capabilities of academic leaders.
Multilevel Attention Networks and Policy Reinforcement Learning for Image Caption Generation

The analysis of large-scale multimodal data has become very popular recently. Image captioning, whose goal is to describe the content of image with natural language automatically, is an essential and challenging task in artificial intelligence. Commonly, most existing image caption methods utilize the mixture of Convolutional Neural Network and Recurrent Neural Network framework. These methods either pay attention to global representation at the image level or only focus on the specific concepts, such as regions and objects. To make the most of characteristics about a given image, in this study, we present a novel model named Multilevel Attention Networks and Policy Reinforcement Learning for image caption generation. Specifically, our model is composed of a multilevel attention network module and a policy reinforcement learning module. In the multilevel attention network, the object-attention network aims to capture global and local details about objects, whereas the region-attention network obtains global and local features about regions. After that, a policy reinforcement learning algorithm is adopted to overcome the exposure bias problem in the training phase and solve the loss-evaluation mismatching problem at the caption generation stage. With the attention network and policy algorithm, our model can automatically generate accurate and natural sentences for any particular image. We carry out extensive experiments on the MSCOCO and Flickr30k data sets, demonstrating that our model is superior to other competitive methods.
The Impact of Accounting Information System on Organizational Performance through Good University’s Private Governance in Indonesia

The purpose of this research is to analyze and test the impact of accounting information systems on good university governance, the research designed used to test the hypothesis causal-comparative (Causal-Comparative Research method), universities in Medan, North Sumatera Indonesia. a sample of 25 private universities in the city of Medan. Data collection techniques are how to arrange interview instruments, perform data triangulation. Interviews were carried out later in this study, data will be analyzed using statistical analysis. The data were processed using Smart PLS data processing tools. The results of the study were the impact of accounting information system on organizational performance through Good University’s private governance in Medan City, North Sumatera, Indonesia.
Test Points for Online Monitoring of Quantum Circuits

Noisy Intermediate-Scale Quantum (NISQ) computers consisting of tens of inherently noisy quantum bits (qubits) suffer from reliability problems. Qubits and their gates are susceptible to various types of errors. Due to limited numbers of qubits and high error rates, quantum error correction cannot be applied. Physical constraints of quantum hardware including the error rates are used to guide the design and the layout of quantum circuits. The error rates determine the selection of qubits and their operations. The resulting circuit is executed on the quantum computer. This study explores the risk of unexpected changes in the error rates of NISQ computers post-calibration. We show that unexpected changes in error rates can alter the output state of a quantum circuit. To detect these changes, we propose the insertion of test points into the quantum circuit to enable online monitoring of the physical qubit behavior. We utilize classical, superposition, and uncompute test points. Furthermore, we use a gate error coverage metric to assess the quality of the tests. We verify the effectiveness of the proposed scheme on different IBM quantum computers (IBM Q), in addition to a noisy simulation that shows the scalability of the proposed approach.
The use of game-based learning in English class

English teachers may come across challenges within the process of English language teaching, especially now, when technologies and innovations grow rapidly. Teachers who trapped in traditional comfort zone of teaching might left behind. Nevertheless, an English teacher of Sekolah Tunas Bangsa is familiar with new technological media by applying Game-Based Learning to teach English in the classroom. This research aims to explore the using of GBL to increase students’ interest in learning English. This research was conducted in Primary level of Sekolah Tunas Bangsa where an English teacher became the focused participant of this study. The Narrative Inquiry method was used to conduct this research by following the research procedure taken from Creswell (2012). The findings show that the teacher used some game-based platforms, such as Kahoot!, Quizizz, ESL Games Plus, and Duolingo, because they are rich of visuals and easy to play. In this study, the teacher conducted GBL by using structured schedule consisted of instruction, presentation, and reflection helped the students to engage with the materials being learnt. The way the teacher managed the using of GBL by following a structured schedule has also promoted fun and interesting learning. Then, the teacher’s reason on using GBL in the classroom was exposed as her way to engage and to motivate the students in learning English. She believes that when the students feel challenged as well as having fun during the learning, the level of interest increases. The students’ achievements have also increased when the teacher uses GBL in the classroom. In conclusion, the using of GBL has successfully increased the students’ interest in learning English.
Locust Inspired Algorithm for Cloudlet Scheduling in Cloud Computing Environments

Cloud computing is an emerging paradigm that offers flexible and seamless services for users based on their needs, including user budget savings. However, the involvement of a vast number of cloud users has made the scheduling of users’ tasks (i.e., cloudlets) a challenging issue in selecting suitable data centres, servers (hosts), and virtual machines (VMs). Cloudlet scheduling is an NP-complete problem that can be solved using various meta-heuristic algorithms, which are quite popular due to their effectiveness. Massive user tasks and rapid growth in cloud resources have become increasingly complex challenges; therefore, an efficient algorithm is necessary for allocating cloudlets efficiently to attain better execution times, resource utilisation, and waiting times. This paper proposes a cloudlet scheduling, locust inspired algorithm to reduce the average makespan and waiting time and to boost VM and server utilisation. The CloudSim toolkit was used to evaluate our algorithm’s efficiency, and the obtained results revealed that our algorithm outperforms other state-of-the-art nature-inspired algorithms, improving the average makespan, waiting time, and resource utilisation.
A Generic Construction of CCA-Secure Attribute-Based Encryption with Equality Test

Attribute-based encryption with equality test ( $$\textsf{ABEET}$$ ) is an extension of the ordinary attribute-based encryption ( $$\textsf{ABE}$$ ), where trapdoors enable us to check whether two ciphertexts are encryptions of the same message. Thus far, several CCA-secure $$\textsf{ABEET}$$ schemes have been proposed for monotone span programs satisfying selective security under q-type assumptions. In this paper, we propose a generic construction of CCA-secure $$\textsf{ABEET}$$ from delegatable $$\textsf{ABE}$$ . Specifically, our construction is an attribute-based extension of Lee et al.’s generic construction of identity-based encryption with equality test from hierarchical identity-based encryption. Even as far as we know, there are various delegatable $$\textsf{ABE}$$ schemes. Therefore, we obtain various $$\textsf{ABEET}$$ schemes with new properties that have not been achieved before such as various predicates, adaptive security, standard assumptions, compact ciphertexts/secret keys, and lattice-based constructions.
The Qualification of Digital Assets according to Swiss Law, with Particular Reference to Stable Coins

What is the qualification of a digital token? This article will help the reader understanding the legal analysis required in order to qualify a digital token according to Swiss Law, with comparative analysis with US and EU Law. The authors also use stable coins as a practical example in order to understand when a token is to be considered as a security and when not.
TWO-STEP MODEL FOR EMOTION DETECTION ON TWITTER USERS: A COVID-19 CASE STUDY IN MALAYSIA

Human emotion is often a reflection of one’s behaviour that leads to awareness of one’s mental health. Emotion detection on Twitter users specifically gains attention as it generates information that is useful in the field of psychology and linguistics. However, most existing works related to the subject analysed the emotion of a tweet based on the number of keywords or phrases in it, thus resulting in false annotations and eventually false judgement of the user’s emotion. This paper proposes a two-step model with high and reliable accuracy to detect the emotion of Twitter users based on the semantic meaning of the tweets they posted. The first step classified the emotion of each tweet using four different deep learning techniques. The second step detected the emotion of the user based on the proposed statistical post-level features and a boosting technique. Then, Kappa’s agreement score method was implemented to validate both models. The first step achieved 0.9482 in accuracy while the second step achieved 0.9683 in accuracy. To further validate such highly accurate result, a case study was conducted, and a web-based system was developed to analyse the emotion of college students during the COVID-19 pandemic. Additionally, our analysis corresponded to most of the current reports on the pandemic which further proved the reliability of the developed model.
Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques

Handwritten signature has been considered as one of the most widely accepted behavioral personal trait in Biometric security system; and It contains various dynamic and innate behavioral traits like shapes and patterns which can certainly find a person’s soft characteristics like age, gender, Personality, handedness and many more. Person’s signature or handwriting determines the state of the person’s mind or personality characteristics at the time of writing. This paper provides a personality prediction system of different characteristics determining the personality of a person based on offline handwritten signature Images. Experiments are carried out using supervised learning techniques. Results shows a significant recognition rate and validates the effectiveness against the state-of-art techniques in comparison to similar works.
Internet of Things (IoT) Security and Forensics

Internet of Things (IoT) devices are increasingly found around, in, and on us (e.g., smart home and other consumer devices) in applications ranging from environmental monitoring to healthcare (e.g., healthcare or medical IoT devices) to surveillance to industry (e.g., industrial IoT IIoT), and battlefields / military (e.g., Internet of Battlefield / Military Things). Such devices are also generally capable of capturing a broad range of information, including digital artifacts that can be used for cyber threat intelligence and inform security mitigation strategy formulation. There are, however, a number of challenges associated with designing IoT cyber security and threat intelligence solutions. In addition to the technical challenges, there are also associated legal and policy challenges that need to be considered in the design and deployment of such solutions in practice. For example, how do we use machine/deep learning to facilitate detection of real-time attacks against IoT devices and systems, and how can we automatically identify and collect digital evidence in a forensically sound manner which can be subsequently used for cyber threat intelligence? In the event that the attackers use sophisticated tools to obfuscate their trails, can we design machine/deep learning techniques to unobfuscate and/or identify and exploit vulnerabilities to get access to digital evidence? What are the potential legal implications and challenges? Can we also design explainable AI techniques to facilitate the explanation and inclusion of such digital evidence and cyber threat intelligence in court proceedings or presentations to C-level or boards in organizations? Based on these discussed challenges, we will identify potential opportunities for stakeholders in academia (e.g., students and researchers), industry and government.
Social Virtual Reality: Implementing Non-verbal Cues in Remote Synchronous Communication

Social Virtual Reality (SVR) platforms allow remote, synchronous interaction and communication between individuals immersed in shared virtual worlds. Such platforms commonly implement full-body motion and real-time voice communication, but often lack complete non-verbal cues support. This work presents the development process and preliminary usability evaluation results of an SVR platform, incorporating non-verbal cues such as finger motion, gaze direction, and facial expressions, while allowing inter-communication between remotely located interlocutors.
Digital technology to control COVID-19 in China: issues, challenges and implications for the healthcare system

In the current era, public health crises are presenting new systematic and cross-border characteristics and uncertainty. Public health crises are challenges for governments and health systems. The development of digital technology has changed the world and connected it as a “village”, and digital technology has played a critical role in providing support during public health crises over the past three decades. From the perspective of empowerment theory, we explore the role of digital technology in responding to the COVID-19 pandemic and digital technology approaches to controlling COVID-19 in China. Accordingly, we identify the challenges of using digital technology to control public health crises, including the imbalance of the rights and responsibilities of governance subjects, the incompatibility of the governance model and digital technology and the inadequate application of digital technology. Considering implications for the successful prevention and control of COVID-19, we suggest that the government should improve the balance of rights and responsibilities for coordinated crisis governance, link digital technology and the governance system and broaden the grassroots governance community.
Will You Ever Become Popular? Learning to Predict Virality of Dance Clips

Dance challenges are going viral in video communities like TikTok nowadays. Once a challenge becomes popular, thousands of short-form videos will be uploaded within a couple of days. Therefore, virality prediction from dance challenges is of great commercial value and has a wide range of applications, such as smart recommendation and popularity promotion. In this article, a novel multi-modal framework that integrates skeletal, holistic appearance, facial and scenic cues is proposed for comprehensive dance virality prediction. To model body movements, we propose a pyramidal skeleton graph convolutional network (PSGCN) that hierarchically refines spatio-temporal skeleton graphs. Meanwhile, we introduce a relational temporal convolutional network (RTCN) to exploit appearance dynamics with non-local temporal relations. An attentive fusion approach is finally proposed to adaptively aggregate predictions from different modalities. To validate our method, we introduce a large-scale viral dance video (VDV) dataset, which contains over 4,000 dance clips of eight viral dance challenges. Extensive experiments on the VDV dataset well demonstrate the effectiveness of our approach. Furthermore, we show that short video applications such as multi-dimensional recommendation and action feedback can be derived from our model.
Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network

Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language.Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalisation and rule-based models of this system.We perform analyses including behavioural experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the models rely on features that are also key predictors in rule-based models of German plurals.However, the models also display shortcut learning, which is crucial to overcome in search of more cognitively plausible generalisation behaviour.
Image Colouration in Adobe Photoshop: A Digital Painting Technique for Transforming Grayscale Photographs into Colour Mode

Digital Photography as a modern trend in the photographic industry offers a wide range of software for editing and enhancing visual qualities of hue, colour saturation, vibrancy, colour balance, exposure and gamma correction among many others in images.Most of this graphic application software is also equipped with tools for transforming grayscale photographs into colour.This photographic exercise, however, requires artistic and technological expertise to accomplish.Adobe Photoshop, one of the most popular software for digital imaging among graphic artists in Ghana, offers a flexible scheme for transforming grayscale photographs into colour, even though many users of the software in Ghana are not conversant with this scheme.This paper, which is a practice-led research, is written to demonstrate the fundamental editing procedures and skills, to help equip digital photographers in Ghana with the capacity and the technical know-how for grayscale image colouration.The authors have illustratively explained the techniques using the design-based and descriptive methods to simplify the procedures for easy grasping by all categories of digital photographers.
Convergent Ontologization of Collective Scientific Discoveries

This paper addresses the acceleration of scientific discoveries by developing logical and cognitive ontologies in a special convergent way that ensures the acceleration and purposefulness of collective scientific discussions. It is noted that modern information systems, which have been created to support scientific activities, focus on information and reference work. They help analyse retrospective data to predict the development of events and reveal hidden relationships in big data and knowledge. At the same time, the known methods of creating ontologies are used to build artificial intelligence (AI) models by explicating the knowledge of experts and big data. It is shown in the paper that modern approaches to creating ontologies excessively reduce non-formalized and uncaused cognitive processes of scientific discoveries such as free will, consciousness, unconsciousness, feelings, thoughts, and experience. Attempts to formalize these processes lead to an incorrect and incomplete replacement of infinite-dimensional and continuous cognitive phenomena by finite-dimensional spaces, digital numbers, and discrete symbols. To compensate for the resulting cognitive distortions, a special approach has been developed to represent convergent scientific creativity processes in AI models more correctly and completely by using the capabilities of hybrid (human-machine) AI, the cognitive semantics of AI models, and the method of inverse problem-solving in topological spaces. The idea of using a nonlocal approach to enrich the cognitive semantics of AI models that considers the subatomic structure of the human mind's biological tissue is also considered. Currently, the proposed approach is successfully used for accelerating collective strategic planning in the real digital economy.
Model-Independent Error Mitigation in Parametric Quantum Circuits and Depolarizing Projection of Quantum Noise

Finding ground states and low-lying excitations of a given Hamiltonian is one of the most important problems in many fields of physics.As a novel approach, quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices offers the prospect to efficiently perform such computations and may eventually outperform classical computers.However, current quantum devices still suffer from inherent quantum noise.In this work, we propose an error mitigation scheme suitable for parametric quantum circuits.This scheme is based on projecting a general quantum noise channel onto depolarization errors.Our method can efficiently reduce errors in quantum computations, which we demonstrate by carrying out simulations both on classical and IBM's quantum devices.In particular, we test the performance of the method by computing the mass gap of the transverse-field Ising model using the variational quantum eigensolver algorithm.