Reviewerly/exHarmony huggingface dataset:
poliWeet — Election prediction tool using tweets

<h2>Abstract</h2> Twitter is a platform used to express opinions. These opinions are measured for different purposes. One such purpose is to measure the inclination of people toward political events and, thus, the outcome of elections. For this purpose, we propose <i>poliWeet</i>, a tool to predict the outcome of elections using Twitter. We evaluated our <i>poliWeet</i> on <i>2019 Indian general elections</i> dataset and found that our <i>poliWeet</i> gave excellent results.
Military AIs impacts on international strategic stability

Technological revolution brought major changes in the system framework of strategic stability. Artificial intelligence (AI) thrives internationally as a disruptive technology and is applied to many fields in the 21st century. This paper evaluates the strength, limitations, and impacts of AI-empowered military application on international strategic stability. Applications of AI technology for military purpose brings both positive and negative impact on nations defending and offending, so the international strategic stability. However, the impact of AI on international strategic stability is mainly negative. For facing stability challenges, nations shall formulate systematic governance of military AI; the global community shall promote friendly multilateral cooperation between each other. In the end, this view offers significant implications for maintaining international strategic stability and improving AI governance capabilities in the foreseeable future.
Deep Learning-based Sequence Labeling Tools for Nepali

A Part-of-Speech (POS) tagger and Chunker (or shallow parser) are sequence labeling tools, crucial for improving the accuracy of Natural Language Processing (NLP) tasks like parsing, named entity recognition, sentiment analysis, information extraction, and so on. Developing such tools for a low-resource language is an arduous task. Nepali is a relatively resource-poor Indian language and has not been able to evolve from a computational perspective. Therefore, we present effective part-of-speech tagging and chunking tools for the Nepali text using sequential deep learning models—Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer (BI-LSTM-CRF) and other LSTM-based models exploring both character and word embeddings of the Nepali texts. Word Embedding has been used to capture syntactic as well as semantic information whereas character embedding has been applied to capture the morphological as well as shape information of words and also to handle the out-of-vocabulary problem. The developed chunker is the first statistical chunker for the Nepali language. A baseline model with a Conditional Random Field has also been developed to identify the optimum feature set for the aforementioned tasks. The BI-LSTM-CRF model produced an accuracy of 99.20% and 98.40%, for Nepali POS tagging and chunking, respectively. This is the highest-ever accuracy for Nepali. Thorough error analysis and observations have also been reported with examples. The developed tools can help advance research in Nepali language processing, improve the accuracy of language technology applications, and contribute to the preservation and promotion of the Nepali language.
Steered discrete-time quantum walks for engineering of quantum states

We analyze the strengths and limitations of steered discrete-time quantum walks with position-independent coins in generating quantum states of bipartite quantum systems comprising of a qubit coupled to a qudit system. We demonstrate that not all quantum states in the composite space are accessible through such quantum walks, even under the most generalized definition of a quantum step, leading to a bifurcation of the composite Hilbert space into ``walk-accessible states'' and the ``walk-inaccessible states.'' We give an algorithm for generating any walk-accessible state from a simple-to-realize product state in a minimal number of walk steps, all of unit step size. We further give a prescription towards constructing minimal quantum walks between any pair of such walk-accessible states. Linear optics has been a popular physical system for implementing coin-based quantum walks, where the composite space is built up of spin and orbital angular momenta of light beams. We establish that in such an implementation, any generalized quantum step can be implemented up to a global phase using a single $q$-plate and a pair of homogeneous wave plates. We then give a quantum walk based scheme for realizing arbitrary vector beams consisting of a finite number of OAM components, using only $q$ plates and wave plates.
Review of computer vision in sports

All industries employ machine learning extensively, and one of the most promising fields is computer vision. Computer vision is a simulation of the human visual system that uses cameras and computers to take the role of the human eye to find the target, follow it, and gather data from it so that a decision may be made on whether to take further action or provide recommendations. The various uses of computer vision in sports are covered in this paper. Currently, computer vision is mostly utilized for broadcast enhancement, tracking and detection of players and balls. Although the games graphics has been substantially improved by this technology, there are still several flaws. For instance, some areas are not suited to employ this technology. Another is the issue of players being blocked in multiplayer sports. For broadcasters, computer vision has significant commercial value. For athletes, this technique can improve their performance.
Review of variational autoencoders model

Variational autoencoder is one of the deep latent space generation models, which has become increasingly popular in image generation and anomaly detection in recent years. In this paper, we first review the development and research status of traditional variational autoencoders and their variants, and summarize and compare the performance of all variational autoencoders. then give a possible development direction of VAE.
The current situation and potential development of face recognition

Face recognition has received more attention in the recent past. It refers to using biometric technology to identify individuals from a captured image by comparing it to the images in the database. There are three face recognition techniques: 2D, 2D-3D and 3D. Face recognition occurs in three processes. Firstly, face recognition begins with face detection, where an image is identified as having a face. That is followed by face extraction, which involves identifying the various faces within an image. The final stage is face classification which entails face verification or face identification. Depending on the type of system, face recognition can either occur in verification or identification mode. Additionally, face recognition has various applications in the current global environment. Face recognition can be used in security systems, hospitals, schools, and retail industries. It allows easier verification and identification of individuals. However, despite the development of the technology, there are still some challenges, such as plastic surgery, illumination, aging, occlusion and pose variation.
A deep learning model for accurate and robust internet traffic classification

Network traffic classification is significant due to the fast growth of the number of internet users. The traditional way of classifying the large number of traffic generated by these users is becoming less effective. Therefore, many researchers made a network traffic classifier based on deep learning. However, those classifiers do not provide far better results and perform poorly when dealing with encrypted information. This paper tries to approach highly accurate and robust results in both encrypted and unencrypted networks by using machine learning algorithms. The algorithm used is the convolutional neural network (CNN). The performance of the proposed CNN is compared with that of the classical LeNet-5 network. Experimental results show that the classifier based on the proposed CNN performed better when dealing with both encrypted and unencrypted datasets, achieving a maximum average accuracy of 83.55%. Moreover, it is not sensitive to hyper-parameter choices, indicating its superiority in robustness. Compared with traditional network classifiers, the network classifier based on CNN can improve accuracy and improve stability.
Daeng AMANG: A Novel AIML Based Chatbot for Information Security Training

Security awareness is considered as the main key to maintain institutional cyber security during digital transformation. As digital assets become more diverse and increasingly vulnerable to various cyber security threats, every organization (public and private) require information security training to enhance security awareness among their employees. This study presents the creation of a specific chatbot aimed at delivering fundamental information security training. Program-O as an application framework based on Artificial Intelligence Markup Language (AIML) is used in developing the chatbot called Daeng AMANG. Daeng AMANG chatbot able to respond questions related to information security by providing human like communications in Indonesian language. Finally, Blackbox and System Usability Scale (SUS) evaluations are employed to evaluate Daeng AMANG chatbot which show positive results.
CONCEPT MAPS, KNOWLEDGE GRAPHS, ONTOLOGIES AND INTELLIGENT SEMANTICS - BASED APPROACHES FOR PERSONALIZED LEARNING

CONCEPT MAPS, KNOWLEDGE GRAPHS, ONTOLOGIES AND INTELLIGENT SEMANTICS - BASED APPROACHES FOR PERSONALIZED LEARNING
A Weberized Total Variance Regularization-based Image Multiplicative Noise Model

This paper considers Weber's law and proposes a new non-convex model for images contaminated by Gaussian noise and Rayleigh noise. The alternating direction method of multipliers (abbreviated as ADMM) is a recent popular method that can handle convex and non-convex problems well. This paper compares denoising effect between ADMM and the Euler-Lagrange equation method applied to the non-convex model. The numerical experimental results show that ADMM performs better and has a higher Peak Signal to Noise Ratio.
Data-Sharing Across Otolaryngology: Comparing Journal Policies and Their Adherence to the FAIR Principles

Objective: Data-sharing plays an essential role in advancing scientific understanding. Here, we aim to identify the commonalities and differences in data-sharing policies endorsed by otolaryngology journals and to assess their adherence to the FAIR (findable, accessible, interoperable, reusable) principles. Methods: Data-sharing policies were searched for among 111 otolaryngology journals, as listed by Scimago Journal &amp; Country Rank. Policy extraction of the top biomedical journals as ranked by Google Scholar metrics were used as a comparison. The FAIR principles for scientific data management and stewardship were used for the extraction framework. This occurred in a blind, masked, and independent fashion. Results: Of the 111 ranked otolaryngology journals, 100 met inclusion criteria. Of those 100 journals, 79 provided data-sharing policies. There was a clear lack of standardization across policies, along with specific gaps in accessibility and reusability which need to be addressed. Seventy-two policies (of 79; 91%) designated that metadata should have globally unique and persistent identifiers. Seventy-one (of 79; 90%) policies specified that metadata should clearly include the identifier of the data they describe. Fifty-six policies (of 79; 71%) outlined that metadata should be richly described with a plurality of accurate and relevant attributes. Conclusion: Otolaryngology journals have varying data-sharing policies, and adherence to the FAIR principles appears to be moderate. This calls for increased data transparency, allowing for results to be reproduced, confirmed, and debated.
CLASSIFICATION OF EXTRACTION PROCESSES BIOLOGICALLY ACTIVE SUBSTANCES FROM PLANT RAW MATERIALS

The article considers the processes of extraction of biologically active substances from plant materials, classifications are given. Methods for activating the extraction process have been identified, which make it possible to carry out work to improve the technological process of extraction and improve the design of installations for obtaining plant extracts. An extended classification of processes for the extraction of biologically active substances from plant materials has been proposed. Ways of activation of extraction processes are revealed. Classification of extraction preparations for plant materials can be carried out on the basis of various extraction systems and production technologies. The following classification schemes for the technological process of extraction are compiled and presented in the article: a schematic representation of extraction as a physical and chemical process; graphical representation of the extraction process; features of extraction of biologically active substances from raw materials with a cellular structure; stages of the extraction process; types of diffusion of biologically active substances from the internal structures of the material into the extractant; factors determining the efficiency of the extraction process; classification of extraction preparations from plant materials according to the production technology, taking into account extraction systems; parameters characterizing the extraction process; methods for determining the distribution coefficient of a substance in a plant extract; ways to activate the extraction process. Each type of extraction has its own advantages and disadvantages, and can be more or less effective depending on the type of plant material and the purpose of using the extract. Therefore, the choice of an extraction system should be based on a thorough analysis of the properties of plant materials and the requirements for the final product. Thus, the identified methods for activating the extraction process make it possible to carry out work to improve the technological process of extraction and the design of installations for obtaining plant extracts.
Payload-Independent Direct Cost Learning for Image Steganography

Recent research has shown that architectures utilizing reinforcement learning (RL) are effective in cost-based image steganography. However, these architectures only learn embedding probabilities rather than costs, and are trained for a specific embedding payload, making it difficult to extend the trained model to serve other payloads. In this paper, we propose a payload-independent cost learning framework using RL called PICO-RL. This framework directly learns universal costs that can be applied to any payload. PICO-RL incorporates an optimal probability approximation (OPA) module that can calculate the required probability map for embedding simulation directly from a learned cost map for any payload, eliminating the need for time-consuming searches for a valid probability scaling parameter. Additionally, PICO-RL uses an advanced steganalysis environment network to provide more effective reward feedback for learning. During RL training, the learned cost maps of different payloads converge and eventually become similar under the OPA constraint, resulting in payload independence. Experimental results demonstrate that a well-trained PICO-RL model, which acts as a universal cost function, defines costs with superior security performance against steganalysis and has better coding compatibility when encoding with practical steganographic codes.
Towards a Dereversibilizer: Fewer Asserts, Statically

Reversible programming is an unconventional paradigm that offers new ways to construct software. When programs have inherent inverse counterparts, such as compression/decompression, the invertibility of reversible implementations enables a “write once, verify once” approach. The nature of today’s computers is, however, irreversible. This work-in-progress contribution explores the dereversibilization of reversible source programs into irreversible target programs. As a first step into this space, we explore the use of state-of-the-art Satisfiability-Modulo-Theories (SMT) solvers to statically remove redundant assertions. We divide the problem space into two parts: High-level dereversibilization of Janus-like source programs and classical compilation to machine code. In this contribution, we focus on the semantics-preserving removal of assertions from reversible control-flow statements. Our prototype reduces the size of the assembly code and marks the first step towards automatic dereversibilization and new opportunities of using reversible programs.
Punish or reward? How to strengthen supervision of nucleic acid testing

Purpose Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud. Design/methodology/approach This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments. Findings The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision. Originality/value Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.
Atmospheric Pedagogies: Everyday Ethnographies of the (Post) Pandemic Classroom

This paper offers a series of (auto)ethnographic reflections on COVID-19 and the ways it changes how we, as educators, practice, perform and inhabit the spaces of higher education. Using a phenomenological framework based on the concept of atmosphere, which constitutes an embodied relation in space, we explore pedagogical relations in our felt university classrooms and lectures theatres. We focus our shared attention on the unexpected and unplanned political possibilities and emotional opportunities that arise from teaching in (post) COVID atmospheres. Thinking atmospherically about pedagogy shows how the dislocating pandemic may open onto a felt politics of disruption and transgression.
Intellectual Property and the Blockchain Sector, a World of PotentialEconomic Growth and Conflict

This chapter reviews current developments in the blockchain ecosystem whose outcome will impact the intellectual ownership of key aspects of the technology. The players include governments, legislators, and law makers attempting to make sense of an opaque, impenetrable, and baffling technological and social environment where myth and reality get mixed up. The interested parties are: on the one hand, the IP owners of blockchain-related technological developments. On the other hand, late comers hoping to deny these rights to use the said developments in their own businesses for free and in an unlimited manner. At stake: billions of US dollars and the real capacity for a new industrial revolution. But also, conceptions of the highest importance for human societies such as veracity, recognition and knowledge of the truth. In the background a world of spectators with an extremely unequal understanding of the technology, the implications of the intellectual property protections and what is being elucidated in this case. Focusing on specific developments, the authors navigate these matters to shed light on the events, the background, and the economic and social implications of the legal decisions around IP ownership that are being determined by the courts of law about the said developments.
Hybrid Blended Learning Solution for Teacher Education Innovation

The study reports data from the implementation of hybrid blended learning solutions (HBLS) in initial teacher training programmes for pre-school and primary school teachers. The design of the relevant university courses were revised in a transformative way, using innovative and digitally integrated approaches (DM 289/2021). Twenty teacher educators and 364 students were involved through the administration of semi-structured questionnaires whose dimensions investigated organizational flexibility to facilitate work-life balance processes, methodological quality and the role of teachers in HBLS.
UAV Mission Planning Based on Fish Swarm-ant Colony Optimization

Addressing the problem of how multiple drones can quickly reconnoiter target points in a complex environment. A task planning algorithm to improve the fish-colony hybrid algorithm is proposed. First of all, according to the reconnaissance requirements of different importance targets in complex environments, a mission planning model with the greatest reconnaissance benefit as the objective function is established. Second, the data is pre-searched by a simplified artificial fish swarm algorithm, the initial pheromone distribution is generated, and the pheromone is passed into the improved ant colony algorithm to solve the model. Experimental simulation proves that the algorithm can solve the problem of task allocation and path planning in an integrated manner, and the convergence speed and global optimization ability of the traditional ant colony algorithm are improved.