Reviewerly/exHarmony huggingface dataset:
A New Transparent and Secured Transmission Routing Method for Blockchain Data in Management Systems

A significant quantity of information, particularly financial data, has now been growing in accordance with the advancement of information technologies. Due to the prevalence of fraud, it is impossible to identify the data sources for corporate financial data and the applicable employees. There are substantial issues with nonstandard behavior and a lack of critical financial data of these firms, as evidenced by the fact that the majority of employees are not capable of perform appropriate queries on the needed financial statements. It has consistently created financial information management for companies more difficult, posed a risk to the entire company's ecosystem, and hurt the interests of several parties, among other concerns, because many analogous concerns were not satisfactorily addressed. Blockchain has garnered a great deal of attention recently, and Crypto currency and other crypto currencies have gained popularity as a result. This is because of the characteristics of blockchain, like centralized control, confidentiality, truthfulness, and lack of courage, which make data difficult to predict and tamper with. According to current implementation and exploration, blockchain has emerged as a novel solution to issues relating to company financial information management since these features are connected to the data storage privacy and data transfer speed required by this type of management. In order to construct a transparent and secured transmission method for blockchain data, the study deployed blockchain for managing the financial management framework and information processing strategy. In terms of security level, throughput, run time, and scalability, the suggested Blockchain solution is contrasted with existing approaches.
A Study on the Appropriateness of Visual-Related EEG Electrodes for Cybersickness Measurement

As the virtual environment rapidly develops, cybersickness has been suggested as an issue that needs to be addressed. Cybersickness is an unpleasant sensation that a person feels during or after exposure to a virtual environment, causing physical and mental discomfort. Research is being conducted to measure cybersickness through the study of brain waves. In this study, we investigated whether the channel responsible for visual activity was suitable for measuring cybersickness by measuring the brain waves and analyzing each frequency band when the participants sat comfortably or viewed virtual reality (VR) content. Alpha wave analysis was performed on the 01 and O2 channels placed in the occipital lobe, and beta wave analysis was performed on the FPl and FP2 channels placed in the frontal lobe. Participants were found to be more visually stimulated when viewing VR content than when in a comfortable state. Therefore, this study provides evidence for the use of EEG electrodes responsible for visual activity to measure cybersickness. Also, this study can be used as a basic study to measure cybersickness using brain waves electrodes responsible for visual activity.
Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs

Solving Partially Observable Markov Decision Processes (POMDPs) with continuous actions is challenging, particularly for high-dimensional action spaces. To alleviate this difficulty, we propose a new sampling-based online POMDP solver, called A daptive D iscretization using V oronoi T rees (ADVT). It uses Monte Carlo Tree Search in combination with an adaptive discretization of the action space as well as optimistic optimization to efficiently sample high-dimensional continuous action spaces and compute the best action to perform. Specifically, we adaptively discretize the action space for each sampled belief using a hierarchical partition which we call a Voronoi tree. A Voronoi tree is a Binary Space Partitioning (BSP) that implicitly maintains the partition of a cell as the Voronoi diagram of two points sampled from the cell. This partitioning strategy keeps the cost of partitioning and estimating the size of each cell low, even in high-dimensional spaces where many sampled points are required to cover the space well. ADVT uses the estimated sizes of the cells to form an upper-confidence bound of the action values of the cell, and in turn uses the upper-confidence bound to guide the Monte Carlo Tree Search expansion and further discretization of the action space. This strategy enables ADVT to better exploit local information in the action space, leading to an action space discretization that is more adaptive, and hence more efficient in computing good POMDP solutions, compared to existing solvers. Experiments on simulations of four types of benchmark problems indicate that ADVT outperforms and scales substantially better to high-dimensional continuous action spaces, compared to state-of-the-art continuous action POMDP solvers.
Enhanced optoelectronic reservoir computation using semiconductor laser with double delay feedbacks

We numerically explored the enhanced performance and physical mechanism of semiconductor laser (SL) based reservoir computation (RC) with double optoelectronic feedback (DOEF). One-step and multistep Santa Fe time series predictions were used as standard test benchmarks in this work. We found that in the optimized parameter region the normalized mean square error (NMSE) of an SL-based RC under DOEF is smaller than an SL-based RC with single optoelectronic feedback (SOEF). In addition, the performance improvement is more obvious for multistep prediction, which is particularly suitable for more complex tasks that requires a higher memory capability (MC). The enriched node states (optical intensity of the virtual nodes for each sample) and the enhanced MC of the proposed DOEF were verified by a comparison to SOEF under the optimized feedback strength. The influence of the feedback strength and the delay difference on the NMSE and the MC was also investigated. Our study should be helpful in the design of a high-performance optoelectronic RC based on an SL.
Research Methodological Choice: Explaining Research Designs; Qualitative and Quantitative Sample Size Determination, Sampling, Data Collection, and Analysis Techniques

The main aim of this article is to discuss the factors a researcher should take into account when selecting the appropriate research design or method (i.e.qualitative, quantitative, and mixed-methods).The article also discusses sample size determination and sampling procedures in qualitative and quantitative research.Further, the paper has provided an examination of qualitative and quantitative data collection and analysis methods.The study used online desk research to collect data from online public access scholarly databases such as Google Scholar, ResearchGate, Academia, and many more freely accessible electronic books in research methods.Search terms that were used included, among many, the following: qualitative research, quantitative research, mixed-methods, qualitative and quantitative data collection, qualitative and quantitative data analysis; descriptive and inferential statistics.A wealth of relevant and timely scholarly literature was downloaded, read, and used to write this paper, and draw the conclusion provided.
Characterization of Arabic sibilant consonants

The aim of this study is to develop an automatic speech recognition system in order to classify sibilant Arabic consonants into two groups: alveolar consonants and post-alveolar consonants. The proposed method is based on the use of the energy distribution, in a consonant-vowel type syllable, as an acoustic cue. The application of this method on our own corpus reveals that the amount of energy included in a vocal signal is a very important parameter in the characterization of Arabic sibilant consonants. For consonants classifications, the accuracy achieved to identify consonants as alveolar or post-alveolar is 100%. For post-alveolar consonants, the rate is 96% and for alveolar consonants, the rate is over 94%. Our classification technique outperformed existing algorithms based on support vector machines and neural networks in terms of classification rate.
Node classification with graph neural network based centrality measures and feature selection

&lt;span lang="EN-US"&gt;Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs are used as important components for developing and training neural networks. GNN always learns the weight importance of the neighbor for perform message aggregation in which the feature vectors of all neighbors are aggregated without considering whether the features are useful or not. Using such more informative features positively affect the performance of the GNN model. So, in this paper i) after selecting a subset of features to define important node features, we present new graph features’ explanation methods based on graph centrality measures to capture rich information and determine the most important node in a network. Through our experiments, we find that selecting certain subsets of these features and adding other features based on centrality measure can lead to better performance across a variety of datasets and ii) we introduce a major design strategy for graph neural networks. Specifically, we suggest using batch renormalization as normalization over GNN layers. Combining these techniques, representing features based on centrality measures that passed to multilayer perceptron (MLP) layer which is then passed to adjusted GNN layer, the proposed model achieves greater accuracy than modern GNN models.&lt;/span&gt;
Predicting Multiple behaviors from the activity of Deep Neural Networks: Is one visual hierarchy enough?

Recent advances in computer vision have enabled machines to have high performance in labeling objects in natural scenes. However, object labeling constitutes only a small fraction of human daily activities. To move towards building machines that can function in natural environments, the usefulness of these models should be evaluated in more diverse and natural tasks. To achieve this, it is necessary to collect databases of a broader set of human behavior towards natural objects. Here, we collected a database of two different behaviors on a large set of 3D-printed objects: 1) a grasping task and 2) a similarity judgment task. For the grasping task, we recorded participants’ finger positions when they grasped the objects. For the similarity judgment task, we asked participants to perform an odd-one-out task on triplets of objects and obtained a similarity matrix based on these judgments. Comparing these matrices across the two tasks suggests distinct features of objects are used for each. We next explored if the features extracted in different layers of the state-of-the-art deep convolutional neural networks (DNNs) could be useful in deriving both outputs. These networks are pre-trained to perform categorization tasks, yet it has been suggested that they could be adopted for other tasks. For similarity judgments, the accuracy of the predictions for similarity judgments increased from low to higher layers of the networks, while those for grasping behavior increased from low to mid-layers and then dropped dramatically at higher layers. These results suggest that for building a system that could perform these two tasks the hierarchy may need to be split starting at the mid-layers. These results could inform future models that can perform a broader set of tasks on natural images.
Optimizing resource allocation in flex-grid entanglement distribution networks

We introduce a model for the distribution of frequency-polarization hyper-entangled photon pairs in a flexible-grid optical network. In order to optimize entanglement fidelity and entangled bit rate, we apply a genetic algorithm to determine the proper spectrum allocation.© 2022 The Author(s)
Enhancing students’ productive skills in English Language Teaching through Project-Based Learning at Foreign Trade University – HCMC campus

Many students at Foreign Trade University - Ho Chi Minh City Campus (FTU HCMC Campus) are challenging with productive skills. Therefore, in response to the calls for involving students in the learning process, various approaches and methods based on the constructivist approach of learning are being adopted, such as Project-Based Learning, Task-Based Learning, Cooperative-Learning and Problem-Based Learning (Ali &amp; Kassem, 2018). This article presents the research findings of the current application of Project-Based Learning (PBL) in the teaching of English productive skills to students at FTU HCMC Campus and how students can improve them. The research also provides insights into the expectations of students and entrepreneurs on that matter. The research group used the 5-point Likert scale in a questionnaire to collect data from students and interviewed FTU - HCMC’s English teachers and entrepreneurs to give further suggestions. Wilcoxon signed-rank test was used to analyze whether or not there is a significant difference in students’ English productive skills before and after using PBL in English language learning. This study found a significant difference in learners’ speaking skills before and after participating in PBL activities. The students improved their comprehension, range and the ability to deliver their ideas to the target reader for writing skills. However, no difference was found in their content, cohesion, and accuracy. The interviewed teachers and entrepreneurs supported the implementation of PBL in schools. Overall, PBL is encouraged in the settings of higher education institutions. The research findings may vary in different contexts. They also provide further reliable information for the instructors in adapting the curriculum and the teaching and learning strategies to enhance Vietnamese students’ productive skills.
Data Shape and Response Modalities Can Bias Estimations of Average Data Location in Visualizations

Our visual system is powerful at extracting summary statistics from data visualizations (Alvarez & Oliva, 2008; Szafi et al., 2016). Yet, our visual recall of summary statistics, for example, recall of average y-position value of line charts, can be systematically biased (Xiong et al., 2020). Potential factors that drive this bias might include data distribution, which changes the resulting shape of the chart, and the modality of the memory representations, which can be visual (e.g., remembering where the values are spatially located), or verbal (e.g., remembering a specific number). To determine how these factors might bias the recall of data, we asked participants (N = 13) to view line charts with a labeled y-axis. The lines were randomly generated following either a positive or negative parabolic data distribution (U-shaped ‘bowl’ versus inverted U-shaped ‘hat’). After a brief delay, participants estimated the average y-position value of the line either verbally, by reporting a number, or visually, by moving a horizontal line representing the average on a blank chart. We found main effects of data shape and response modality, but no interaction between the two factors (F(1, 12) = 0.01, p = 0.9). Positive parabolic ‘bowl’ charts were significantly more underestimated than negative parabolic ‘hat’ charts (t(12) = 4.26, p < 0.01). Verbal reports of average values were significantly overestimated compared to visual reports (t(12) = 5.41, p < 0.01). These results suggest that both the shape of the line and the response modality can bias a viewer’s recall of average positions in visualizations. Future researchers should consider these factors when designing data visualizations and visualization experiments.
Norm-referenced neural mechanism for the recognition of facial expressions across fundamentally different face shapes

Humans recognize facial expressions from highly different facial shapes, including comic figures that never have been seen before. Even though they are trained with huge numbers of pictures of faces (REF), this task is a challenge for popular deep neural network models of face recognition, while humans effortlessly accomplish such transfer. For the encoding of facial identity two different encoding mechanisms have been contrasted, norm-referenced and example-based encoding (Tsao et al., 2017, Leopold et al., 2006; Koyano et al, 2021). We demonstrate that norm-referenced encoding is suitable to account for facial expression recognition from fundamentally different head shapes without extensive training. METHODS: We propose a neural model consisting of two modules: 1) a standard deep neural network, which models the initial parts of the visual pathway and extracts a sparse set of facial landmarks that support optimally the classification of the learned facial expressions; 2) a simple neural network for norm-referenced encoding of expressions based on these landmark features. We compare this new model to a standard deep neural model with a stimulus set that presents the same 7 basic facial expressions on very different head shapes (human avatar, comic figure, etc.). RESULTS: Our model recognizes reliably the facial expressions across all tested head shapes, requiring training of the expressions only of one head shape and of the neutral expressions of all other head shapes. In contrast, a state-of-the art CNN architecture (ResNet50) trained with 400k+ facial images fail to classify expressions robustly on this data set. CONCLUSIONS: We presented a physiologically-inspired neural model for the ‘vectorized encoding’ (Beymer & Poggio, 1995) of facial expressions that accounts for transfer across very different head shapes, which is difficult to obtain with standard models.
Analyzing Grammatical and Lexical Cohesion in A Legendary Short Story

Analyzing Grammatical and Lexical Cohesion in A Legendary Short Story Noprival Sekolah Tinggi Ilmu Kesehatan Harapan Ibu Jambi, Jambi, Indonesiae-mail: noprival@gmail.com (corresponding author) Alfian Universitas Islam Negeri Sulthan Thaha Saifuddin Jambi, Jambi, Indonesiae-mail: alfian@uinjambi.ac.id Robi SomaUniversitas Jambi, Jambi, Indonesiae-mail: robisoma858@unja.ac.id Abstract:Despite the existence of many studies on cohesion in discourse, little scholarly work reports grammatical and lexical cohesion in a short story. To fill this gap, the present study investigates these two aspects of cohesion in a legendary short story entitled “After Twenty Years.” Halliday and Hasan’s (1976) theoretical framework of cohesion was used to guide the current study. We applied textual analysis as the appropriate approach to find out grammatical and lexical cohesion within the forms of text. The findings in the grammatical cohesions showed that reference had the highest frequency of the total cohesive devices. Also, under grammatical cohesion, conjunctions are found to exist. However, other aspects were not found within this short story, including substitution and ellipsis. On the other hand, repetition was dominantly used lexically among other aspects of lexical cohesion and sequentially followed by repetitions, hyponyms, synonyms, and antonyms. Only one aspect of lexical cohesion, the meronym, was not found. The overall results of the research indicated that the short story was a coherent discourse because both grammatical and lexical cohesion exist properly.
System and Structure of English Classroom Discourse at Junior High School in Medan

This study aims to investigate the system and structure of English classroom interaction at Medan's Jabal Rahmah Mulia Boarding School. The records of a discussion in communication a video recording by Zoom Meeting among understudies and an English educator are viewed as information to be concentrated on utilizing a talk examination approach in light of Martin's Conversation Analysis (CA) model inside Halliday's Systemic Functional Linguistics structure (SFL). The foundation of the framework and construction of the English study hall connection are the challenges to be addressed. The examination saw movement types, temperament types, and the discourse capability of different provisions. The discoveries show that understudies act as initiators, while English educators work as allies, with optional knower (k2k1) moving sort for the most part; the communication is one of data exchange instead of labor and products, as conversationalists favor decisive and utilize basic sparingly. As per the discoveries, Question (Q) appears to generally satisfy the data given. Inquisitive asking is most ordinarily used to exhibit that a design's relational significance doesn't be guaranteed to compare to its lexico-sentence structure. This examination likewise shows how a study hall conversation happens in a characteristic set of mindset and discourse capability. It likewise demonstrates that understudies are on the requesting end of the scale, bringing about an absence of criticism in the communication.
Continual Recognition with Adaptive Memory Update

Class incremental continual learning aims to improve the ability of modern classification models to continually recognize new classes without forgetting the previous ones. Prior art in the field has largely considered using a replay buffer. In this article, we start from an observation that the existing replay-based method would fail when the stored exemplars are not hard enough to get a good decision boundary between a previously learned class and a new class. To prevent this situation, we propose a method from the perspective of remedy after forgetting for the first time. In the proposed method, a set of exemplars is preserved as a working memory, which helps to recognize new classes. When the working memory is insufficient to distinguish between new classes, more discriminating samples would be swapped from a long-term memory, which is built up during the early training process, in an adaptive way. Our continual recognition model with adaptive memory update is capable of overcoming the problem of catastrophic forgetting with various new classes coming in sequence, especially for similar but different classes. Extensive experiments on different real-world datasets demonstrate that the proposed model is superior to existing state-of-the-art algorithms. Moreover, our model can be used as a general plugin for any replay-based continual learning algorithm to further improve their performance.
Expressway danger warning ICV based on multi-sensor information fusion

The expressway has the phenomenon of high speed and large traffic flow, which is easy to cause secondary traffic accidents when dealing with expressway traffic accidents.An ICV (Intelligent Connected Vehicle) that can be loaded by vehicles is studied.ICV detects vehicle information and lane lines in real time through onboard detectors, and realizes the application of automatic tracking, vehicle distance detection, wireless communication, accident handling assistance and other scenarios of expressway ICV through linear control and remote wireless communication.The ICV is used to test the section of the scene through the micro expressway scene built to verify the application effect of the system.
Design of a smart lighting system based on electric power cat technology

Based on the power cat technology, a smart lighting system design is presented, which can be used for the smart upgrading of the old lighting system in buildings without wiring operation. The system consists of upper microcomputer unit, lighting management unit, power cat communication unit and smart switch with WIFI communication function. They constitute the bigger unit named as lighting management unit and control unit; the power cat communication unit consists both of them. Through power cat unit acting as convertor, the smart switches are controlled by the upper computer, in order to turn on/off the light. The circuits related to the system are given, the functions of both power line carrier communication and WIFI connection are tested successfully.