Reviewerly/exHarmony huggingface dataset:
Characterizing Speed Performance of Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing advancements in MARL algorithms focus on improving the rewards obtained by introducing various mechanisms for inter-agent cooperation. However, these optimizations are usually compute- and memory-intensive, thus leading to suboptimal speed performance in end-to-end training time. In this work, we analyze the speed performance (i.e., latency-bounded throughput) as the key metric in MARL implementations. Specifically, we first introduce a taxonomy of MARL algorithms from an acceleration perspective categorized by (1) training scheme and (2) communication method. Using our taxonomy, we identify three state-of-the-art MARL algorithms - Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Target-oriented Multi-agent Communication and Cooperation (ToM2C), and Networked Multi-Agent RL (NeurComm) - as target benchmark algorithms, and provide a systematic analysis of their performance bottlenecks on a homogeneous multi-core CPU platform. We justify the need for MARL latency-bounded throughput to be a key performance metric in future literature while also addressing opportunities for parallelization and acceleration.
CRACS: Compaction of Rules in Anticipatory Classifier Systems

Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned. We thus propose CRACS (Compaction of Rules in Anticipatory Classifier Systems), a compaction algorithm for ALCS that aims to reduce the size of their environmental models without impairing these models or the ability of these systems to solve their tasks. CRACS relies on filters applied to classifiers and subsumption principles. The capabilities of our compaction algorithm have been studied with three different ALCS on a thorough benchmark of 23 mazes of various levels of environmental uncertainty. The results show that CRACS reduces the size of populations of classifiers while the learned models of environments and the ability of ALCS to solve their tasks are preserved.
Complex Natural Systems for Language

In this paper we want to show how complex systems theory can inform the theoretical development in natural phonology. This development leads us to propose complex natural systems modelling for language. We explain how complex systems help us understand both theoretical concepts from natural phonology, for example the concept of preference, as well as variation in language data, for example in English vowel realizations and Polish consonant clusters. We select from our respective research areas those exemplifications which clearly demonstrate substantial variation in language use.
ATAMAN M. I. PLATOV – DIALOGUE WITH DESCENDANTS

Objective of the study is to consider the applicability of the category “intangible heritage” to the personality and achievements of Ataman Platov, to assess its relevance in the modern socio-cultural context. The methodological basis of the research. The research is based on the analysis of historical sources, scientific research, archival documents and materials testifying to the life of ataman M.&nbsp;I.&nbsp;Platov. Research results. The relevance of the patriotic heritage of the leader of the Don Cossacks for modern society is indicated. Prospects of the study. The prospects of the research are connected with the definition of forms and methods of using the intangible heritage of M.&nbsp;I.&nbsp;Platov in patriotic education.
Upaya menumbuhkan karakter gemar membaca melalui gerakan literasi nasional

Reading is a useful activity to add information, knowledge, and insight. If from an early age the children are trained and accustomed to reading, than the character of liking to read will grow in the children. All components of society can participate to increase the communitiy’s literacy index by cultivating the character of liking to read. The purpose of this research is to identify various efforts that can be made to foster the character of liking to read in Indonesian society. The research method used is library research. The main data source used in this research is a collection of previous research journal articles and books that are relevant to the research discussion. The results of the research show that the literacy index of Indonesian people is still quite lacking, efforts that can be made to improve the literacy index can be through learning, exemplary, strengthening, and habituation. In addition, parents or teachers can use the storytelling method in order to attract children's attention and interest to read. These efforts can be done as early as possible in order to grow into a strong characters and not be carried away by the swift currents of technology and globalization.
Dispersive Photonic Time Crystals: Causality-Induced Limitations and Opportunities

The unique characteristics of the temporal dimension in photonic time crystals introduce both limitations and valuable opportunities. Here, we focus on several aspects, including limits on the modulation strength and opportunities related to light absorption.
Single-pass femtosecond parametric process towards continuous variables quantum networks

We demonstrate the generation of 21 squeezed spectral modes at 156 MHz, combining frequency- and time- multiplexing in multimode squeezing. This paves the way to the implementation of scalable and fully reconfigurable multipartite entangled states.
Siting of Photovoltaic (P.V.) Potential Station in the Borno State of Nigeria, Using Satellite Images

In large quantities, solar energy is released from the sun's surface. However, we can convert only some part of its reaching the earth's surface into valuable forms of energy for utilization such as thermal, electrical, or mechanical. As proposed in this research to determine the most suitable site for the installation of Photovoltaic power solar station in Borno state, Nigeria using satellite images. Annual global solar irradiance for satellite images was obtained and validated with annual global irradiance calculated from meteorological data. It can also determine Slope, Aspect, Road, Grid network, and temperature. We used an analytical hierarchy process to determine the percentage weight of each factor. We have also applied the Weighted Overlay tool to multiply the input raster, producing a final constraints layer of the most suitable area. The Annual global solar irradiance was obtained as 2240.10 Kilo-Watt-hour/meter<sup>2</sup>/year for meteorological station data and 2244.98 Kilo-Watt-hour/meter<sup>2</sup>/year for satellite images data with the validation difference of 0.88 Kilo-Watt-hour/meter<sup>2</sup>/year. The coefficient of determination R<sup>2</sup>=0.778. For Slope = 0 to 89.18-degree rise, Aspect =-1 to 359 degrees, Road & Grid network = 0 to 1.31km and Temperature = 22.20 to 29.80 degrees Celsius. The weight of each factor was determined as: Slope=13%, Aspect=13% Road & Grid =5%, Temperature=5% and Global solar irradiance =64%. The most suitable area for installing a solar station was an estimated 39,426 square kilometers, mostly in the northern and some central parts of the study area, which is about 54.5% of the total land mass of Borno state. The annual global solar irradiance obtained from satellite images was converted into electric energy by different solar cell technologies: Single crystalline Silicone=1101Mega Watt, Multi crystalline Silicone=1019 Mega Watt, amorphous Silicone=440.5 Megawatt and Cadmium Tellurium=798.5 Megawatt. It found that the main factors for installing the solar station were global solar irradiance, temperature, topographical Slope and aspect, and nearest to the road and grid network. The global solar irradiance estimated in the study area is adequate to generate sufficient electrical energy for the Borno state. We found the satellite imagery data method to effectively identify suitable sites for the solar station to install a Photovoltaic power plant.
Activities and personal qualities of military personnel

The article discusses the features of the personal qualities of military personnel. For any profession, it is important to pay attention to the professional suitability of a person for the same type of activity. The armed forces are an integral part of society and thestate, therefore, the personality and psychology of actions considered in the article are reflected in every action of military personnel. The most important element of the combat capabilities of the army is the high moral and psychological state of military personnel. It is wellknown that "morale... is the most powerful weapon known to man; more powerful than the heaviest tank, than the most destructive bomb. High morale is a means that can turn defeat into victory. The moral and psychological state is a combination of psychological capabilities, the degree of mobilization and mental attitude of each serviceman for active and selfless actions in a combat situation. The most important factor in the formation and strengthening of the fighting spirit of soldiers is scientifically based psychological work with military personnel aimed at forming their mental resistance to the powerful stressors of modern combat and their psychological readiness to effectively solve combat tasks in accordance with their regular purpose.
Video Abnormal Behavior Recognition and Hierarchical Warning System Based on Markov Model

In this paper, a video crowd anomaly detection and classification method based on Markov model is proposed. The anomaly detection in crowd is defined as different abnormal targets in dense crowd, and on this basis, a human feature model or a sample describing human features is established. In order to verify the effectiveness of this method, ShanghaiTech, UMN and UCSD human behavior database are used to analyze and verify the performance of the proposed Markov model feature extraction algorithm. The experimental results of video abnormal behavior recognition and detection in the above three databases using the algorithm proposed in this paper show that: The recognition rates of ShanghaiTech, UMN and UCSD human behavior databases are 94.65%, 93.46% and 90.31%.
Performance Comparison Analysis of Classifiers on Binary Classification Dataset

In this study, we compared the performance of Random Forest Classifier and Decision Tree using classification evaluation methods. The results showed that Random Forest Classifier had a higher overall accuracy rate but also produced more outliers. On the other hand, Decision Tree demonstrated consistency in classification with fewer outliers. These findings provide insights into the trade-off between accuracy and consistency when selecting the appropriate classification method. Furthermore, further research is needed to understand the impact of outliers on classification performance and to take appropriate steps in addressing them.
Research on Teaching Method of Children’s Image Cognition Based on AR Technology

In this paper, the research on the teaching method of children’s image cognition based on AR technology is carried out. By analyzing the principle of AR technology to recognize images, we understand that AR technology can promote children’s image cognition, and this teaching method is in line with the Tower of Experience theory. It further analyzes the current situation of children’s image cognition teaching with simple teaching methods, backward AR teaching tools, and poor perception of teaching objects. Teachers’ traditional image teaching methods cannot effectively and efficiently improve children’s image cognition. Therefore, based on AR technology, two commonly used image cognition teaching methods are proposed: AR interactive picture book education and AR interactive game education. Both of these educational methods can improve children’s ability to recognize images.
Anomaly Detection via Real-Time Monitoring of High-Dimensional Event Data

Modern technological developments, such as smart chips, sensors, and wireless networks, have revolutionized data-collection processes. One type of data that can be highly beneficial is event data due to the general conceptualization of an event. Monitoring event data enables real-time monitoring since an observation becomes available as soon as the event happens. Most of the available literature on monitoring event data is focused on vector-based time between events (TBEs) data. Methods for this type of data incorporate monitoring delays either due to overseeing temporal dependencies between variables or the need to wait until a complete vector is observed. To tackle these issues, we propose a multivariate monitoring scheme of event data that signals in real time. Our contribution is twofold: Our proposed method can monitor high-dimensional event data and it is computationally quick and easy to implement, thereby, outperforming existing methods that are only feasible to implement up to ten dimensions.
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing

The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.
Double Dual Convolutional Neural Network (D2CNN) for Copy-Move Forgery Detection

In recent years, the problem of fake image diffusion is on the rise mainly on social networks because of the development of different tools for image editing. Copy-move forgery (CMF) is one of the image forgeries types used for manipulating the image content. In CMF, the region in an image is copied and placed in a different location in the same image. In this paper, an algorithm for CMF detection based on a Double Dual Convolutional Neural Network (D2CNN) is proposed. A novel concatenation of two Dual Convolutional Neural Networks (DCNN) is used, where each DCNN is composed of two CNN networks. A fully connected network (FCN) is taking the result of the D2CNN and hence classifying the input images into either original or forged. The features extracted from the two DCNN and fusion of these features (D2CNN) have achieved good results according to the following metrics: Accuracy, f1-score, precision, and recall. Two standard datasets namely MICC F-220 and MICC F-2000 have been used to evaluate the proposed approach. Experimental analysis shows that the proposed approach achieves accuracy higher than 98.48% on the MICC F-220 dataset and 97.83% on the MICC F-2000 dataset.
Case Study of Mathematics Class Using Graphic Calculators at Technical College within Industry Company

The purpose of this study is to improve the teaching-learning competency required in the industrial field by identifying the parts where classes using graphic calculators are helpful to students of in-house technical colleges located in the provinces. As a case study, this study was conducted targeting first-year and second-year students attending in-house technical colleges in small and medium-sized cities using TI-Nspire. Most of the students were active and passionate about learning, but they were adult students who had to drop out of school due to difficulties in mathematics or change career paths because of studying mathematics. In addition to taking notes and using a camera while observing the class since March 2017, the researcher collected questionnaires and transcribed data after an interview. In July 2020, additional interviews were conducted with the instructor three times. The case study results are as follows.&#x0D; First, the participating students were cut off from their math studies for a long time, and most of them were afraid of math and had a low level of math knowledge. Second, nevertheless, most of the study participants showed a very positive reaction to the use of TI-Nspire after the end of the lecture, and it was found that the positive view was somewhat improved in terms of saving time and obtaining accurate information when dealing with math problems. Third, it was found that cooperative classes using technology help to deal with mathematical concepts along with self-reflection. Participating students were given the opportunity to learn from each other through cooperative learning in the industrial field by pressing function keys while handling the graphic calculator and re-recognizing the calculation process.
Single-station multi-view global calibration based on the concentric circle 3D target

In view of the disadvantage that traditional tracking 3D scanners can only use global binocular camera to complete single-station multi-view global calibration, a concentric circle 3D target (CC3DT) is designed in this paper. For the designed CC3DT, a single-station multi-view global calibration algorithm is proposed. This method only needs one camera to complete the global calibration function of global binocular camera. The designed CC3DT has simple structure and low cost. In this paper, the validity and feasibility of the proposed global calibration algorithm are verified by real experiments. It has widely application prospect and practical theoretical research value.
Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion

Deep speech classification has achieved tremendous success and greatly promoted the emergence of many real-world applications. However, backdoor attacks present a new security threat to it, particularly with untrustworthy third-party platforms, as pre-defined triggers set by the attacker can activate the backdoor. Most of the triggers in existing speech backdoor attacks are sample-agnostic, and even if the triggers are designed to be unnoticeable, they can still be audible. This work explores a backdoor attack that utilizes sample-specific triggers based on voice conversion. Specifically, we adopt a pre-trained voice conversion model to generate the trigger, ensuring that the poisoned samples does not introduce any additional audible noise. Extensive experiments on two speech classification tasks demonstrate the effectiveness of our attack. Furthermore, we analyzed the specific scenarios that activated the proposed backdoor and verified its resistance against fine-tuning.
EFL Students’ Business Writing Performance and Writing Anxiety

A cover letter is an excellent way of establishing a conversation with a potential employer. Therefore, the letter must provide the best possible initial impression. The objectives of this study are to 1) investigate students’ performance in business English writing after implementing Bhatia’s genre analysis, 2) examine the relationship between students’ writing achievement and their writing anxiety, and 3) explore students’ attitudes toward implementing Bhatia’s genre analysis. The total number of participants in this study was 27. Three research instruments were used to collect the data: pretest and posttest, writing anxiety inventory, and student attitude survey. The quantitative data were analyzed and interpreted using descriptive statistics and paired sample t-tests. The findings showed that the students significantly improved their business English writing after the intervention. Findings from the writing anxiety inventory revealed a negative correlation between the students’ writing achievement and their writing anxiety before and after implementing Bhatia’s model. Furthermore, the students in this study had positive attitudes toward the intervention. Recommendations and implications are provided in this study. Keywords: business English writing, genre analysis, second language writing anxiety, EFL writing instruction. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.61.46
Analyzing the Semantic Network of Simulated Teaching Demonstrations in Early Childhood Movement Education through Team-Based Learning

Objectives This study examined the meaning and impact of team-based learning (TBL) on early childhood movement education by analyzing the reflective materials on teaching practices written by prospective early childhood teachers participating in TBL.&#x0D; Methods The participants of this study were 45 students enrolled in a 4-year early childhood education program in G City. The movement education class they took involved the design and demonstration of simulated lessons, which were conducted using the Team-Based Learning (TBL) approach. After completing a semester of classes, the learners wrote self-reflection materials on TBL and simulated lessons in the context of movement education. The qualitative data from these materials were analyzed using KrKwic and UCINET to extract keywords and perform centrality and semantic network analysis.&#x0D; Results Firstly, the common theme keywords that emerged from the TBL activities and simulated teaching demonstration experiences were ‘lesson plan’, ‘activity’, ‘movement’, ‘class’, ‘children’, ‘simulated teaching’, ‘feedback’, and ‘interest’. Secondly, the keywords associated with TBL activities included ‘time’, ‘team members’, ‘groups’, ‘opinions’, ‘active participation’, ‘challenges’, ‘parts’, ‘materials’, ‘expression’, ‘practice’, ‘modification’, and ‘utilization’. The semantic network analysis of TBL activities revealed a meaningful structure related to simulated teaching and the experiences of TBL activities. Thirdly, the keywords identified in relation to simulated teaching experiences were ‘teacher’, ‘thoughts’, ‘progress’, ‘experience’, ‘field’, ‘assistance’, ‘application’, ‘methods’, ‘process’, ‘rules’, ‘improvement’, and ‘demonstration’. The keyword ‘teacher’ showed high values in terms of linkages, degree centrality, closeness centrality, and betweenness centrality. The proximity centrality of ‘progress’, ‘experience’, and ‘application’ was high, while the betweenness centrality was low. The semantic network structure reflected the effectiveness of learning and application through the preparation and implementation phases of simulated teaching.&#x0D; Conclusions The semantic network analysis of early childhood movement education simulated teaching experiences conducted through TBL revealed that prospective early childhood educators perceive TBL activities and simulated teaching experiences as highly significant. These findings underscore the importance of learner-engaged instructional approaches in enhancing the teaching competencies of prospective early childhood educators.