Reviewerly/exHarmony huggingface dataset:
Overview of key technologies of human-computer interaction

In the 21st century, computers are developing faster and faster. Human-computer interaction(HCL) is defined as a multidisciplinary field of study which focuses on the interaction between humans and computer technology. The advancement of HCL makes a great contribution to nowadays’ society as human beings gradually perceive themselves in a technological world that is in a time of high-speed development.The situation of human-computer interaction is becoming more and more common, and computers have more and more intelligent factors. The latest computer technology has been applied to various disciplines, which have developed rapidly and achieved remarkable research results. In this paper, we summarizes the core technologies involved in human-computer interaction (HCI), mainly including human-computer interaction psychology, flexible screen technology, target recognition and classification, and expounds the current situation and development trend of these technologies.
A Comparative study on QoS and QoE in Modern Networking

This paper presents a brief review of some existing correlation models Quality of service (QoS) is the description or measurement of the overall performance of a service in modern networking, particularly the performance is seen by the users of the network and Quality of Experience (QoE) for multimedia services. The hugely demanding network resources on media services, with release of complex cellular multimedia services such as video gaming, UHD video, and VR, requires the enhancement of end-users perceived quality of experience (QoE). QoE has generated much research interest in recent years and it has emerged as a key component in the evaluation of network services and operations. Due to these requirements QoE-aware network planning approach is getting attention
Semantic Relation Extraction from Cultural Heritage Archives

Digital preservation technologies are now being increasingly adopted by cultural heritage organizations. This cultural heritage data is often disseminated in the form of digital text through a variety of channels such as Wikipedia, cultural heritage archives, etc. To acquire knowledge from digital data, the extraction technique becomes an important part. However, in the case of digital text, which has characteristics such as ambiguity, complex grammar structures such as the Thai language, and others, it makes it more challenging to extract information with a high level of accuracy. We thus propose a method for improving the performance of data extraction techniques based on word features, multiple instance learning, and unseen word mapping. Word features are used to improve the quality of word definition by concatenating parts of speech (POS) and word position is used to establish the accurate definition of a word and convert all of this into a vector. In addition, we use multiple instance learning to solve issues where words do not fully express the meaning of the triple. We also cluster the particular word to find the predicate word by removing words that are irrelevant between the subject and the object. The difficulty of having a new set of words that have never been trained before can be overcome by using unseen word mapping with sub-word and nearest neighbor word mapping. We conducted several experiments on a cultural heritage knowledge graph to show the efficacy of the proposed method. The results demonstrated that our proposed technique outperforms existing models currently utilized in relation to extraction systems. It can achieve excellent accuracy since its precision, recall, and F1 score are 0.89, 0.88, and 0.89, respectively. Furthermore, it also performed well in terms of unseen word prediction, precision, recall, and F1 score, which were 0.81, 0.87, and 0.84, respectively.
Reflective class observation of Korean language teachers during the pandemic, crisis of public health

Korean teachers observe and analyze their classes from a reflective point of view. They directly observed videos of their classes and interpret the class. Through this process, the teacher plays the role of a subject who interprets his/her class, and in the process, he/she can look at his/her class as a whole. In this study, 50 self-class observation reports were collected, and analyzed using the NVivo 12 program. As a result of the analysis, 362 contents of self-reflection by teachers were found, and these were categorized into teacher factors, learner factors, class factors, and environmental factors. Among them, the most reflective content was on class factors. The content was about the problems and difficulties that the teacher encountered in the process of preparing and conducting the class. In order to improve a Korean teacher's class expertise and class quality, it is necessary for the teacher to view and interpret his/her own class from a reflective point of view. Therefore, it is necessary to continuously provide opportunities for class reflection so that teachers can improve their own classes.
A Study on Effectiveness of Smart Classrooms through Statistical Analysis

Smart Classroom concept is a way of digital learning. From the beginning it was an imagination of the student. This type of classroom has digital equipped with a huge variety of teaching learning methods using technology. In these days many more classrooms are doing or using audio and visual components to a lesson. A smart classroom helps a teacher through technology as well. The purpose of this study is to compare the effect of smart classroom through statistical analysis. For study,10th grades students were selected. Teacher taught 10th grades student between two ways which were traditional way and other one smart teaching learning process. From this analysis getting that students are more interested to join smart class than traditional classes. The sample of the study comprised of 120 students of 10th grade students. The analysis was conducted the way of pre-test score and post test score of two groups. As a result smart classroom have more positive effect on education than traditional class.
Insight Evaluation on Traditional and CNN Features

Feature extraction serves as the prerequisite for any intelligent-based applications. There are various methods to extract features from the initial data sets, namely traditional filters and deep learning components. However, current traditional feature extraction methods still have troubles in dealing with automatically updating the weights. This paper will propose a new feature representation method that shows more promising results than any previous methods. This proposed method investigates the benefits of features based on Convolutional Neural Network (CNN) to solve the above problem. CNN’s purpose is to process multi-dimensional data, such as image and time-series data and crucial features are noticed and picked automatically without the need of being supervised by human-being. The proposed method has demonstrated significant improvement in terms of accuracy and stability compared to traditional methods of feature extraction. From that, we can easily understand why CNN-based algorithms have become a part of state-of-the-art algorithms these days.
Intertemporal Linkages between Government Expenditure and Corruption in a Digital Economy

Dubious rent-seeking behaviour or corruption is a major economic development challenge for less developed countries as it diverts government expenditure from the dire needs of these countries to unproductive, wasteful, and illegal activities. It is widely acknowledged by policymakers and experts that information and communication technology (ICT) can play a dual role in enabling governments to manage their national resources and corruption more efficiently. This study examines the short- and long-run associations between ICT infrastructure, corruption control, and government expenditure in poorer countries over 2005–2019 using the vector error-correction model and the Granger causality test. Our results show that ICT infrastructure, corruption control, and government expenditure have strong endogenous links among each other in the short run. Empirical results also display that in the end ICT infrastructure and government expenditure are the important drivers of controlling corruption. These results suggest that a well-developed ICT infrastructure and favourable government expenditure are keys to managing and mitigating the risks associated with corrupt practices in these countries.
GCN-based graph anomaly detection

Graph data structures are now widely used and detecting graph anomalies is a challenging task. Traditional anomaly detection methods can achieve good results in the case of low-latitude data, but in the face of today's unstructured graph structure data, they often seem to be powerless. Fortunately,GCN provides an effective method for processing graph data. Based on the idea of GCN network and traditional support vector data description, this paper proposes a graph anomaly detection method and makes corresponding experiments. Compared with traditional methods, better results have been achieved.
Decentralized Blockchain based Voting System

The voting technique is the process for putting the people's opinion into action in order to better govern the system. However, the voting system in India, as well as in many other countries, has indeed been defective in recent years and may be easily manipulated and hampered by figures of authority for their own personal gain. They aren't fully secure because ballots are easy to tamper with. It also raises concerns about voter security and transparency. Furthermore, counting the votes takes an excessive amount of time. To address these issues, citizens in many nations use digital technology during the voting process. Digitalization would not be enough to solve every one of the problems. Digital technology can also be manipulated or altered in a number of ways. The foundation of our product is built on blockchain technology. This research work combines digitalization with blockchain technology to develop a voting mechanism by analyzing the aforementioned challenges. Our voting mechanism's key goals are to ensure voter integrity, anonymity, privacy, and security. The system can be used to highlight a number of popular block chain frameworks that provide blockchain as a service as well as an electronic E-voting system. Based on the results of this study, we propose a decentralized e voting protocol based on blockchain technology that removes a need for a trusted third party
Multimodal deep learning model for human handover classification

Giving and receiving objects between humans and robots is a critical task which collaborative robots must be able to do. In order for robots to achieve that, they must be able to classify different types of human handover motions. Previous works did not mainly focus on classifying the motion type from both giver and receiver perspectives. However, they solely focused on object grasping, handover detection, and handover classification from one side only (giver/receiver). This paper discusses the design and implementation of different deep learning architectures with long short term memory (LSTM) network; and different feature selection techniques for human handover classification from both giver and receiver perspectives. Classification performance while using unimodal and multimodal deep learning models is investigated. The data used for evaluation is a publicly available dataset with four different modalities: motion tracking sensors readings, Kinect readings for 15 joints positions, 6-axis inertial sensor readings, and video recordings. The multimodality added a huge boost in the classification performance; achieving 96% accuracy with the feature selection based deep learning architecture.
Crying Sea, The Sound Installation: Artistic Considerations for Coexistence between Human and Technology

As the discourse on Anthropocene grows, this exploratory research investigates the interrelationship and interconnectivity between humanity and technology by analyzing a sound art installation created by the author.Crying Sea is a sound installation that uses plastic wastes collected from the shore to create symbolic narratives and artistic experience connecting humans, objects, and nature through interactive digital technology.In this installation, the audiences are guided to walk over the wastes, and the sounds created by the footsteps are recorded in real-time, which then are distorted and amplified into disturbing sounds through speakers filling up the room.In analyzing this artwork, three theories from technological, philosophical, and ecological backgrounds were used; specifically, Bernard Stiegler's pharmakon theory, Dona Haraway's cyborg manifesto, and Timothy Morton's dark ecology theory.A common factor revealed from all three theories by analyzing the Crying Sea is that humans, technologies, and all other entities within nature are interconnected and resonated.The awareness of this recursive relationship allows us to consider sustainable balancing.
Cytology should create structured data sets without using synoptic reporting

Structured data sets can be created from cytology reports without the addition of synoptic reports using either natural language processing or minor changes to laboratory information systems structure.
Graph Influence Network

Due to the extraordinary abilities in extracting complex patterns, graph neural networks (GNNs) have demonstrated strong performances and received increasing attention in recent years. Despite their prominent achievements, recent GNNs do not pay enough attention to discriminate nodes when determining the information sources. Some of them select information sources from all or part of neighbors without distinction, and others merely distinguish nodes according to either graph structures or node features. To solve this problem, we propose the concept of the Influence Set and design a novel general GNN framework called the graph influence network (GINN), which discriminates neighbors by evaluating their influences on targets. In GINN, both topological structures and node features of the graph are utilized to find the most influential nodes. More specifically, given a target node, we first construct its influence set from the corresponding neighbors based on the local graph structure. To this aim, the pairwise influence comparison relations are extracted from the paths and a HodgeRank-based algorithm with analytical expression is devised to estimate the neighbors’ structure influences. Then, after determining the influence set, the feature influences of nodes in the set are measured by the attention mechanism, and some task-irrelevant ones are further dislodged. Finally, only neighbor nodes that have high accessibility in structure and strong task relevance in features are chosen as the information sources. Extensive experiments on several datasets demonstrate that our model achieves state-of-the-art performances over several baselines and prove the effectiveness of discriminating neighbors in graph representation learning.
Gaze Capture based Considerate Behaviour Control of Virtual Guiding Agent

Agents in VR have wide application like guidance. Most current agents are passive, so that people should suspend their current tasks and request agents with explicit demand. It is necessary to make agent more actively open the interaction naturally but without being bothering. We propose a virtual guidance agent which provide voice explanation in appropriate timing, using gaze tracking, attention amount estimation and attention driven state machine. We used time-decayed moving average of angle between gaze direction and face front direction. We implemented the method in VR and evaluated effectiveness in virtual guiding tour experimentally.
Searching Intrinsic Dimensions of Vision Transformers

It has been shown by many researchers that transformers perform as well as convolutional neural networks in many computer vision tasks.Meanwhile, the large computational costs of its attention module hinder further studies and applications on edge devices.Some pruning methods have been developed to construct efficient vision transformers, but most of them have considered image classification tasks only.Inspired by these results, we propose SiDT, a method for pruning vision transformer backbones on more complicated vision tasks like object detection, based on the search of transformer dimensions.Experiments on CIFAR-100 and COCO datasets show that the backbones with 20% or 40% dimensions/parameters pruned can have similar or even better performance than the unpruned models.Moreover, we have also provided the complexity analysis and comparisons with the previous pruning methods.
A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses

The statistical inference of high-order Markov chains (MCs) for biological sequences is vital for molecular sequence analyses but can be hindered by the high dimensionality of free parameters. In the seminal article by Bühlmann and Wyner, variable length Markov chain (VLMC) model was proposed to embed the full-order MC in a sparse structured context tree. In the key procedure of tree pruning of their proposed context algorithm, the word count-based statistic for each branch was defined and compared with a fixed cutoff threshold calculated from a common chi-square distribution to prune the branch of the context tree. In this study, we find that the word counts for each branch are highly intercorrelated, resulting in non-negligible effects on the distribution of the statistic of interest. We demonstrate that the inferred context tree based on the original context algorithm by Bühlmann and Wyner, which uses a fixed cutoff threshold based on a common chi-square distribution, can be systematically biased and error prone. We denote the original context algorithm as VLMC-Biased (VLMC-B). To solve this problem, we propose a new context tree inference algorithm using an adaptive tree-pruning scheme, termed VLMC-Consistent (VLMC-C). The VLMC-C is founded on the consistent branch-specific mixed chi-square distributions calculated based on asymptotic normal distribution of multiple word patterns. We validate our theoretical branch-specific asymptotic distribution using simulated data. We compare VLMC-C with VLMC-B on context tree inference using both simulated and real genome sequence data and demonstrate that VLMC-C outperforms VLMC-B for both context tree reconstruction accuracy and model compression capacity.
THE CONCEPT OF USING THE NUMBER SYSTEM IN THE RESIDUAL CLASSES FOR BUILDING ARTIFICIAL INTELLIGENCE SYSTEM

The subject of the article is the consideration of the concept of constructing an artificial intelligence (AI) system based on the use of a non-positional system of residual classes (RNS). This concept is based on the hypothesis of the holographic principle of building the memory of biological systems. The purpose of the article is to consider a method for constructing an information model of the process of information processing by the human brain, based on the ass umption that the storage and processing of information is carried out in the RNS. Tasks: to consider a model of the process of information processing by the human brain; consider the proposed model of information processing by the human brain in the RNS; to study the influence of RNS properties in the creation of intelligent computing systems. Research methods:methods of analysis and synthesis of computer systems, data analysis, number theory, coding theory in RNS. The following results are obtained. The article considers a model of the process of information processing by the human brain, based on the assumption that the storage and processing of information is carried out in the RNS. When accepting the hypothesis of the holographic principle of information processing by the human brain, the expediency and efficiency of building AI systems based on the information processing model in RNS is obvious. This is due to the fact that the principles and methods of information processing in the RNS are in good agreement with modern ideas about the process of information processing by the human brain. The accuracy of the description (representation) of the information object G depends on the number and value of RNS bases. So, the greater the number of RNS bases and the greater their value, the more accurately the information object G is described using frames. This fact confirms the feasibility of using RNS. Conclusions. The main idea of the study is to consider the hypothesis of the holographic principle of building the memory of biological systems. In this case, the failure of one or more memory cells does not affect the normal functioning of the biological model of the brain, i.e. each unit of initial information is distributed over the entire surface of the hologram. In the article, AI is presented as a model of computational processes operating in RNS. Thus, the expediency and efficiency of building AI systems based on the information processing model functioning in the RNS is assessed as obvious.
Measuring Virtual Object Location with X-Ray Vision at Action Space Distances

Accurate and usable x-ray vision is a significant goal in augmented reality (AR) development. X-ray vision, or the ability to comprehend location and object information when it is presented through an opaque barrier, needs to successfully convey scene information to be a viable use case for AR. Further, this investigation should be performed in an ecologically valid context in order to best test x-ray vision. This research seeks to experimentally evaluate the perceived object location of stimuli presented with x-ray vision, as compared to real-world perceived object location through a window, at action space distances of 1.5 to 15 meters.
Robust Tangible Projection Mapping with Multi-View Contour-Based Object Tracking

Dynamic projection mapping for moving objects can greatly enhance an augmented reality representation by using projected images because the target object can be freely moved. However, the interaction between the target object and the user has not been sufficiently considered. In this research, we propose tangible projection mapping, with which a user can grasp an object with their hands and move it freely. It uses a simple configuration of two infrared cameras to realize dynamic projection mapping that is robust to hand occlusion.
PENINGKATAN KEMAMPUAN LITERASI DIGITAL GURU NON-FORMAL DENGAN PEMANFAATAN MEDIA HAND PUPPET DALAM DONGENG

The development of information technology and the internet affect the abundance of digital information flows. This reflection also indicates that students in Indonesia, who are the digital native generation, have a strong dependence on the internet. In addition, the weak information literacy that plagues educators affects unattractive learning and the quality of students is not adaptable. Therefore, teachers as educators and school library staff as educational staff are required to be competent incapable of information literacy so that they can teach digital literacy continuously. The method in this study uses a Participatory Action Research (PAR) approach, which is located at Al-Jannah Jambon KB/TK, Pakis District, Malang Regency. The purpose of this literacy improvement training is to improve teachers' understanding and abilities related to digital literacy competencies, provide insight into alternative learning methods that can be used using technology, improve teacher skills in making interactive learning media to build classroom activity. The PAR results show that with the digital literacy workshop on hand puppets, these non-formal teachers feel helped in their efforts to improve digital literacy. On the other hand, teachers also find it easier to be creative.