Reviewerly/exHarmony huggingface dataset:
Remotely Establishing Polarization Entanglement by Entanglement Transfer

We experimentally establish polarization entanglement between photon pairs distributed over noisy polarization channels. We exploit energy-time entanglement for transmission, and then transfer the entangled state at the receivers to the polarization domain.
Simulating Phase Transition in Two-Dimensional Ising Model on Coherent Ising Machine

We show that two-dimensionally coupled optical parametric oscillator pulses capture the signature of the exact phase transition of the two-dimensional Ising model rather than that of a mean-field one.
Unbiased Quantum Random-Number Generation by Use of Photon-Number-Resolving Detection up to 30 Photons

We experimentally demonstrate an unbiased all-optical random number gen-erator using coherent light and photon-number-resolved detection of up to 30 photons. Our method requires no de-biasing algorithms and is robust to environmental noise and laser fluctuations.
Classification of EEG Signals for Brain-Computer Interfaces using a Bayesian-Fuzzy Extreme Learning Machine

In brain-computer interface (BCI) applications, classification of motor imagery electroencephalogram (EEG) using Extreme Learning Machine (ELM) theory dates back to 2006. Even though, it is relatively new, advances in ELM-based classification have demonstrated to be a robust methodology with strong generalization properties. In this study, a unified framework based on Bayesian and Fuzzy ELM theory referred to as Bayesian-Fuzzy Extreme Learning Machine (BFELM) is developed for EEG signals classification. The proposed methodology is a hybrid approach for the training of a class of Fuzzy Inference Systems (FISs) of Takagi-Sugeno-Kang (TSK). On the one hand, Fuzzy logic theory is applied to handle any bounded non-constant piecewise continuous membership functions (MFs). On the other hand, Bayesian ELM theory is used to calculate the consequent parameters of each fuzzy rule by estimating their likelihood while minimizing training error and improving associated model generalization. Performance comparison of BFELM with other existing ELM methods and Support Vector Machine (SVM) is implemented for the classification of EEG signals using two public data sets. The experimental results confirm the advantages of using a unified framework for an improved classification of EEG data associated with motor imagery (MI) in BCI applications.
A Modified Dynamic Time Warping (MDTW) and Innovative Average Non-self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings

ECGs objectively reflects the working conditions of the hearts as these signals contain vast physiological and pathological information. In this work, in order to improve the efficiency and accuracy of &quot;best so far&quot; time series analysis-based ECG anomaly detection methods, a novel method, comprising a modified dynamic time warping (MDTW) and an innovative average non-self-match distance (ANSD) measure, is proposed for ECG anomaly detection. To evaluate the performance of the proposed method, the proposed method is applied to real ECG data selected from the MIT-BIH heartbeat database. To provide a reference for comparison, two existing anomaly detection methods, namely, brute force discord discovery (BFDD) and adaptive window discord discovery (AWDD), are also applied to the same data. The experimental results show that our proposed method outperforms BFDD and AWD.
General Rules and Framework Resolution About Cybercrime Governance in the Era of Globalization

This is the resolution made at the 10th Session of the International Forum on Crime and Criminal Law in the Global Era (IFCCLGE), the participants of which agreed that although there are great differences in the theory and practice of cyber crime governance in the world, especially in Europe, America, China, Russia and other regions, due to the differences in cognition, the priorities of their respective goals are in conflict and dislocation, in the global era, the governance of cyber crime in the world and other countries should at least follow three general principles: First, protect basic human rights according to law; second: combat transnational cyber crime effectively; Third: Respect the network sovereignty and judicial sovereignty of all countries. In addition, the governance framework of cyber crime in the Global Era should include the following aspects: first, improving the ability of law enforcement and investigation of cyber crime; second, strengthening public–private cooperation; third, expanding the jurisdiction of cyber crime; fourth, applying and taking of electronic evidence.
BLOCK CHAIN-BASED BIOMETRIC AUTHENTICATION SCHEME

Data security is one aspect of the digital era, and it is recommended that biometrics and block chain be used together as an original and reliable method of protecting sensitive data. As high-level authentication methods, biometric technologies are often utilized in the modern world in place of passwords because they are more difficult for hackers to crack. At this point, the block chain can actually serve as a rapid access method while providing high degrees of security. Any authentication process generates digital signatures using a common hashing method. This paper describes the hashing method's use in the block chain system to process identical digital signatures.
INFORMATION MANAGEMENT SYSTEM FOR MONITORING THE EMOTIONAL STATE OF STUDENTS USING AI

In this current changing era, everything has become digitalised, education is conducted online. Teachers use online platforms to teach their coursework, but it has recently been realised that the classes were not conducted efficiently. The challenge here was to know if the students were understanding the concepts or not. In an offline class, the teacher could get the information just by looking at the students. However, in an online class, it is not possible to look into each student and get collective data. So, in order to cope with these changing demands of the era, we have attempted to build a system that can be helpful in analysing the emotions of students. A framework is built with the convolutional neural network (CNN). The network is trained on the FER2013 dataset and the emotions are analysed and live feedback is given to the teachers by showing the statistics. That represents the proportion of feelings that are angry, disgusted, afraid, joyful, neutral, sad, and surprised. In addition to that, the recording of the complete online class with the label of individual emotions is displayed in the recorded video, and the videos are compressed, so whenever the teachers wish to see the recorded emotions of the class. They need not view the 1-hour video for a 1-hour class, the video is compressed to a few minutes. This saves both time and space.
Explainability of Image Semantic Segmentation Through SHAP Values

The introduction of Deep Neural Networks in high-level applications is significantly increasing. However, the understanding of such model decisions by humans is not straightforward and may limit their use for critical applications. In order to address this issue, recent research work has introduced explanation methods, typically for classification and captioning. Nevertheless, for some tasks, explainability methods need to be developed. This includes image segmentation that is an essential component for many high-level applications. In this paper, we propose a general workflow allowing for the adaptation of a state of the art explainability methods, especially SHAP, to image segmentation tasks. The approach allows for explanation of single pixels as well image areas. We show the relevance of the approach on a critical application such as oil slick pollution detection on the sea surface. We also show the applicability of the method on a more standard multimedia domain semantic segmentation task. The conducted experiments highlight the relevant features on which the models derive their local results and help identify general model behaviours.
An Efficient Computation of Discrete Orthogonal Moments for Bio-Signals Reconstruction

Abstract Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete Orthogonal Moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram-Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error (MSE ) and peak signal to noise ratio ( PSNR). The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram-Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.
Deep Learning on Graphs

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'22)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.
Anak Berbakat (Jenius Atau Gifted Children)

Talent is an ability which is something that is inherent (has existed and is integrated) in a person, carried from birth and related to the structure of the brain. Other factors that play a role on intelligence are nutritional and neurological factors. Gifted children are described as children who have more potential than children of the same age in general. In general, there are two ways to identify gifted children, namely, gathering information in an objective way and gathering information that is subjective in nature. The characteristics of the genius / gifted children, namely children who have a high iq above the average iq in the population, namely&gt; 130. This study aims to present the gifted children (genius or gifted children). This research was conducted by analyzing library research (Library Research). Based on the results of data analysis through literature study obtained from the compilation of data by carrying out a search of books, literature, notes, as well as various relevant information by showing that gifted children (geniuses or gifted children) should know as early as possible.
Fire-Fighting Drones: A Use Case for Tactile Internet

Fire-fighting drones will play a key role in aiding fire-fighting efforts, not only in forest surveillance but also in adopting an agile aid in wildfire fire extinguishing. One of the key challenges in scaling-up the use of fire-fighting drones is their ability to autonomously carry out their tasks, so that swarms of them could be rapidly dispersed. However, in certain situations, it is inevitable that a remote controller needs to be involved in steering/adjusting their operation due to the highly volatile operation environment, especially with heat columns. We build on the potential of the Tactile Internet (TI) in delivering near real-time haptic interaction with drones. We build on the IEEE P1918.1 standard TI architecture and present a deterministic state machine following detailing remote interaction with such drones, and present the message exchanges to support their remote operation. Furthermore, we present a fire-fighting drone use case focused on flight autonomy, remote TI control, and identifying the characteristics of drone in service.
Cyber-Security Enhanced Network Meta-Model and its Application

In this paper, we expand the traditional graph model to include security information in cyberspace. And a meta-model in networks that contain both typical network elements and security information is proposed. Detailed definitions of nodes, edges, structures, and paths in a network are given to present security elements from both macroscopic and microcosmic perspectives. Meanwhile, two remarkable case studies, AI-driven penetration testing and cascading failures in routing networks are shown to demonstrate that our model is prevailing in solving frontier issues of security in cyberspace.
An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types

Edge detection is a widely used low-level operation done in computer vision applications and image processing tasks. The primary purpose of edge detection is to discover and distinguish sharp discontinuities from an image. Since edges give boundaries between different regions in the image, these boundaries can be used to classify objects for segmentation and matching purposes. This is the first step in many computer vision applications. Edge detection remarkably reduces the amount of data and filters out undesired or less significant information and gives useful information in an image. There are some widely used algorithms for edge detection, some of which perform better than others but might require higher computational power. But all of them face several difficulties like false edge detection, issues due to noise, and missing of low contrast boundaries. Such difficulties are handled with other algorithms, if the data set has few images. But when the data set is enormous, removing noises and detecting edges simultaneously is very costly. So, a general idea regarding how noise resistant different edge detection algorithms are is necessary. In this paper, we have compared the performances of the most common edge detection methods on a publicly available dataset, provided experimental results on how sensitive each of them is to different kinds of added noises to the original images, and finally come to a conclusion on which one of them is most resistant to noises.
Policy Making Process of Regulatory Reform Policy in Science and Technology Sector: A Case of Regulatory Sandbox Policy for ICT Convergence Industry

This study analyzes the policy making process of ICT convergence industry’s regulatory sandbox in order to review characteristics and contexts of scientific technology regulatory reform policy that was caused by the process of the fourth industrial revolution. For the analysis, the enactment period of ｢Special Act on Promotion of Information and Communication and Revitalization of Convergence, etc.｣, which is a base law of ICT convergence industry’s regulatory sandbox, into Period 1 and Period 2, and each period is analyzed with the use of Kingdon’s multiple streams model.&#x0D; The results show that in the presidential elections of the two periods, the window of policy was open to a strong candidate’s campaign and election. The political tendency of the Period 1 showed irrational policy making with the combination of policy alternative and policy problem tendency, and no policy leader appeared. Nevertheless, in Period 2, the president of Korea appeared as a policy leader.&#x0D; Regarding the theoretical implications, the exactness of multiple streams model was identified in analyzing decision making process of ICT convergence industry regulatory reform policy. There was irrational policy making that political mainstream involved policy problems and alternatives, and the political mainstream was confirmed to become a basis and background of the advent of policy leader and the exertion of competence, so the importance of political mainstream highlighted by multiple streams model was recognized. Regarding the policy implications, it was confirmed that there could be an effectiveness problem of scientific technology regulatory reform policy unless the importance of horizontal and vertical governance in the scientific technology regulatory reform policy, and value acceptance of relevant ministries in the policy making process are considered.
Machine translation as a means of communication between police and foreigners.

This study considered the possibility of machine translation as a means of communication between police and foreigners. 203 sentences frequently used by the police were analyzed and error analysis was conducted through domestic translator Papago and non-domestic translator Google. There were 206 errors in Papago and 153 errors in Google. Looking at specific cases, there was a case of misinterpreting the detainee as a child, and there was a case where the word petition (陳情), meaning 'to state the facts or circumstances', was misinterpreted as sedation because it is a homonym but the context could not be grasped. Due to the nature of the Korean language, if the subject is omitted, it is sometimes misinterpreted as ‘the police will take the suspect on their own’ in fact in a situation where the police asks the foreign suspect to accompany him voluntarily. According to the performance analysis results of Papago and Google, in the case of the Korean-Russian language pair, the accuracy of machine translation was low and serious errors were included in many cases. As a means of communication with foreigners, machine translation should be used limitedly if necessary, and even if it is notified by machine translation, it is necessary for the human interpreter to check it again later. It is because the police's questions or notices to the subject are directly related to the fairness and effectiveness of the investigation.
Text Criticism of Hyangka in Samkwukyusa: Focusing on Missing Letters, Superfluous Letters and Inverted Lines

This study attempted to solve the two problems in Text criticism of Hyangka in Samkwukyusa. One is the problem to find missing letters, superfluous letters and inverted lines. The other is the problem to discover the cause that missing letters, superfluous letters and inverted lines occurred. The first problem could be solved by finding the letters and lines through the consideration of grammar and context in text of Hyangka. Meanwhile, I attempted to solve the second problem in accordance with two step’s arguments. One is that preceding transcriber add missing letters(羅, 羅, 沙, 伊, 於, 矣, 羅) and missing line(耆郞矣皃史是史藪邪 or 沙是八陵隱汀理也中) between lines in order to complement missing letters(羅, 羅, 沙, 伊, 於, 矣, 羅) and missing line(耆郞矣皃史是史藪邪 or 沙是八陵隱汀理也中) in transcription. The other argument is that posterior transcriber misunderstand the location that preceding transcriber add between lines, as the wrong location of missing lines. Because of this misunderstanding, missing letters, superfluous letters and inverted lines happened.
Chinese Research Article Introductions: Move Analysis and Linguistic Features

Research on academic genre has received widespread attention from linguists around the world. In the big data era, the demand of knowledge mining promotes the emergence and development of bibliometrics, and formal description of scientific knowledge is also put on the agenda. The application of RA move analysis to the field of knowledge mining is a new perspective of bibliometrics. At present, there are few studies on move analysis of Chinese RA introductions, and fewer studies are oriented to discourse computing. This study analyzes the move structures of Chinese RA introductions and finds out some linguistic features corresponding to each move. It is found that nearly half of the Chinese RA introductions follow the CARS model, but there are also unconventional structures. The linguistic features, including citation, negation, connectives and sentence subjects, are helpful for the automatic identification of move structures of Chinese RA introductions.
Socio-Political Activism: Content Analysis of Selected Hash Tags

Social media is the means to register people’s participation in the virtual world and to organise, coordinate and/or drive social change, mass protest or any social movement which brings commendable changes in and around the world. The concept and practice of hashtag activism are relatively new. They are very user friendly, by just liking or retweeting the post on Twitter many social drives and socio-political movements have gone popular and successful in recent times. It has gained significance quick as a wink, due to the participation of a large number of people pan the world, for divergent issues like justice for victims, human rights abuse, political drama, fundraising, campaigns etc. This research study which has never been conducted in its current form clearly showed the use of the hashtag and socio-political activism on tweeter. The primary focus of this research was a detailed study of the selected popular hashtag in recent times.
Land-Atmosphere Energy Exchange Characteristics in Ali of Tibetan

Based on the comprehensive data from the land-atmosphere interaction observation station in Ali of Tibetan in 2019, the characteristics of land-atmosphere energy exchange processes in Ali were analyzed. The results indicated that the timing of the mean intraday net radiation peak in Ali over the past 20 years has been delayed, and the month when the maximum monthly mean net radiation occurred has been delayed by about 2 months; the maximum daily mean, maximum monthly mean, minimum monthly mean, and annual mean sensible heat were 99.63 w/m2, 76.53 w/m2, 17.47 w/m2, and 46.74 w/m2, respectively, and the maximum daily mean, maximum monthly mean, minimum monthly mean, and annual mean latent heat flux were 73.27 w/m2, 36.13 w/m2, 0.67 w/m2, and 8.32 w/m2, respectively; and the monthly mean sensible heat was greater than the latent heat in all months.
Comparing GPT-2 and Humans in Processing Garden Path Sentences

This study is to compare the GPT-2-based neural-network language model (NLM) and humans in processing sentences with three different types of garden-path structure: NP/S(noun phrase/sentential complement); NP/Z(noun phrase/zero complement); MV/RR(main verb/reduced relative clause). It is to see whether the surprisal values calculated from the GPT-2 NLM display a similar pattern as human reading times in processing the three types of garden-path construction at issue; the surprisal of a sentence-internal word input, measured as the negative log-likelihood of the current observation according to the autoregressive language model, is used as a measure of input difficulty. It is found in this study that like humans, the GPT-2 NLM effectively distinguishes ambiguous from unambiguous sentences in each of them. However, the GPT-2 NLM deviates drastically from humans in recognizing garden-path effects, namely, the magnitude of cognitive load induced by processing a particular type of garden-path structure. Pending further articulations on the parallelism between reading time and surprisal, the GPT-2 NLM as a language learner is yet to attain a human-like ability to discern different types of garden-path structure in a fine-grained way.