Reviewerly/exHarmony huggingface dataset:
Nanophotonics for Quantum Information Processing and Artificial Intelligence

Compute requirements for state-of-the-art artificial intelligence applications are growing at a rate that is unprecedented in the field of high performance computing. New hardware technologies and architectures are being developed towards closing the gap between traditional compute advancement cycles, typically bound by an 18 month cadence, and what is required to power increasingly large parameter count neural networks. Silicon photonics is typically viewed as a communications platform. Here, we will discuss a brief history of optical computing in both the classical and quantum regimes, what's changed, and how silicon photonics can be applied to the problem of accelerating artificial intelligence algorithms. We will also discuss a novel, wafer-scale, switchable photonic fabric implemented in a hybrid CMOS photonics process with reticle-stitched transistors and photonic components and integrated III-V lasers.
XPM-Based Reservoir Computing Using Membrane MZM Integrated with SOAs on Si

A membrane MZM integrated with SOAs on Si is utilized for reservoir computing. The SOAs are asymmetrically driven to exploit cross-phase modulation (XPM), which enhances the reservoir responsivity and improves the pulse train lifetime.
Photonic Decision Making by Chaotic Mode Dynamics in Multi-mode Semiconductor Laser with Optical Feedback

We propose a decision-making method for solving the multi-armed bandit problem by controlling chaotic mode dynamics in a multi-mode semiconductor laser. Chaotic mode dynamics can accelerate the decision-making performance for a large number of choices.
The Effects of Self-directed Learning through an AI Speaking Application in an EFL Speaking Class on Learners’ Self-directed Learning Ability and Speaking Skills

The current study investigated the effects of self-directed learning through implementing Artificial Intelligence (AI)-integrated smartphone speaking application (app) into an EFL classroom on undergraduate learners’ self-directed learning ability and speaking skills. A total of 76 students participated in this study and they were assigned to an experimental group 1 (n=26), an experimental group 2 (n=25) and a control group (n=25). Over a semester, the experimental group 1 engaged in self-directed learning through AI speaking apps and the experimental group 2 participated in AI speaking app-assisted language learning without self-directed learning while the control group participated in traditional speaking activities without the aid of AI speaking apps and self-directed learning. To collect data for this study, 1) a pre- and post-questionnaire on self-directed learning abilities and 2) a pre- and post-test on speaking skills were administered for each group. The results showed that self-directed learning through AI speaking app was effective in increasing the participants’ self-directed learning ability and speaking proficiency. However, the other two groups show no significant improvement in their self-directed learning abilities. This study suggests that self-directed learning through AI speaking apps offers many opportunities to practice speaking and is useful to develop students’ self-directed leaning abilities and speaking skills.
Environmentally-Based Orientation of Higher Education as a Condition for the Formation of Students’ Experience of Emotional-Value Attitude to the Reality around Them

The aim of the study is to substantiate the inclusion of an environmentally-based component in the content of higher education as a necessary condition for the formation of students’ personal experience of an emotional-value attitude to the reality around them: to nature, oneself, health, work/profession, other people, the Fatherland. The article shows the options for an environmentally-based filling of education content composition, presented, according to the cultural theory of education content selection (I. Ya. Lerner, N. M. Skatkin, V. V. Kraevsky) by adapted social experience. From the standpoint of the social and personal components, the leading role of the emotional-value attitude in the structure of social experience is substantiated. Pedagogical conditions for the formation of students’ personal experience of the emotional-value attitude are determined. The scientific novelty of the study lies in the following: the little-studied aspects of applying the theoretical provisions of the cultural theory of education content selection in higher education are considered, with the emphasis on the leading role of the experience of the emotional-value attitude; the ways to include an environmentally-based component in the content of education, in the content of academic disciplines/modules, training materials and practical activity of participants in the educational process are identified. As a result, the priority in the structure of social experience is given to the leading role of the experience of the emotional-value attitude, which is a basic element of the culture of society and the most important element of the general culture of the individual. The possibility of the environmentally-based filling of the education content, specified in the content of academic disciplines/modules, based on their and specifics, is shown by the example of academic disciplines/modules of the mandatory part of the curriculum in the enlarged group of training areas 44.00.00 “Education and Pedagogical Sciences”. The pedagogical conditions for the organization of the educational process are determined, which allow changing the personal system of values, forming value orientations and beliefs that correspond to the co-evolution of nature, man, and society.
Integration of Augmented Reality (AR) Technology in Education: Perceptions and advantages

Augmented Reality (AR) has become one of the 21st-century technology to be used in education to enhance the pedagogical process. Due to its handiness and effectiveness, it may visualise abstract context into digital elements, making this mechanism one of the preferred learning trends today. Therefore, this study aims to examine the use of AR technology in Malaysian education from the insights of the educators. The significant outcome from the study has shown that educators were captivated by AR technology due to its novelty. Keywords: Augmented Reality; Malaysian Education; Educators Perspective; Pedagogy Enhancement eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3804
Double-inputs Illumination Pattern Recognizing Model with Automatic Shadow Detection Network in a Single Face Image

Illumination pattern recognition of face image has always been a hot research topic in the field of human-computer interaction, and has been widely used in lighting recovery, virtual scene construction and other multimedia fields. Most of the traditional methods achieve this task by analyzing the illumination components from the image texture and structure information, which is often considered to be indirect, complex and time-consuming. This paper introduces a Double-inputs Illumination Pattern Recognizing Model (DIPRM) with Automatic Shadow Detection Network (ASDN) based on Convolution Neural Networks (CNNs). In the proposed coherent system framework, we first annotate the shadow regions of face images with uneven illumination to obtain the face shadow detection dataset. Second, an ASDN which has the encoder-decoder structure is designed. The main architecture of the ASDN is based on the nested U-Net, and for this nesting, the attention mechanism is applied to fuse the output of each sublayer. Third, a double-inputs Facial Illumination Pattern Recognizing Network (FIPRN) following the ASDN is organized, which consists of AlexNet and the attention module. As the double-inputs, the binary image after the shadow segmentation from the ASDN and the original image are input into the FIPRN to make the whole network converge to a good collaboration state eventually. For shadow detection task, the ASDN was evaluated in comparisons with U-Net and UNet++. Experimental results demonstrated that the ASDN achieved an average IoU and Dice gains of 1.9 and 1.2 points over the base-line model with best results. Moreover, the FIPRN was tested in comparison with some baseline models in illumination pattern recognizing task, where the results demonstrated that it achieved an accuracy rate gain of 1.0 points than the AlexNet with a signal-input.
A Study on Grounded Theory for Teachers’ Experience in Art Appreciation Education Using Metaverse

Objectives This study aims to examine the teachers' experience in art appreciation education using metaverse and to explore the possibility of using metaverse in the field of art appreciation education.&#x0D; Methods In order to achieve the research objectives, qualitative research data were collected through in-depth interviews with ten teachers who had a class experience using metaverse. In-depth interviews with ten learners were simultaneously conducted to complementally explain the teacher's experience. The results of the interviews were analyzed with grounded theory presented by Strauss and Corbin (2015).&#x0D; Results As a result of analyzing the transcribed data from in-depth interviews with teachers and learners, 95 concepts, 23 sub-categories, and 12 upper-categories were derived.&#x0D; Conclusions In the art appreciation education using metaverse, teachers said that they were interested but did not satisfy their needs. Therefore, in order to increase the possibility of educational use of metaverse in the field of art appreciation education, it was concluded that the metaverse class ecosystem should be improved. As an improvement plan, an establishment of class support system was proposed to promote teachers and students' awareness of using metaverse and to provide metaverse art appreciation education.
Financial accounts reconciliation system using enhanced mapping algorithm

Account reconciliation has become a daunting task for many financial organisations due to the heterogeneity of data involved in the accounts' reconciliation process - coupled with the recent data deluge in many accounting firms. Many organisations are using a heuristic-based algorithm for their account reconciliation process while in some firms the process is completely manual. These methods are already inundated and were no longer efficient in light of the recent data explosion and are prone to lots of errors that could expose the organisations to several financial risks. In this regard, there is a need to develop a robust financial data analytic algorithm that can effectively handle the account reconciliation needs of financial organisations. In this paper, we propose a computational data analytic model that provides an efficient solution to the account reconciliation bottlenecks in financial organisations. Evaluation results show the effectiveness of our data analytic model for enhancing faster decision making in financial account reconciliation systems.
Dispersion Vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes

Visualization research often focuses on perceptual accuracy or helping readers interpret key messages. However, we know very little about how chart designs might influence readers' perceptions of the people behind the data. Specifically, could de- signs interact with readers' social cognitive biases in ways that perpetuate harmful stereotypes? For example, when analyzing social inequality, bar charts are a popular choice to present outcome disparities between race, gender, or other groups. But bar charts may encourage deficit thinking, the perception that outcome disparities are caused by groups' personal strengths or deficiencies, rather than external factors. These faulty personal attributions can then reinforce stereotypes about the groups being visualized. We conducted four experiments examining design choices that influence attribution biases (and therefore deficit thinking). Crowdworkers viewed visualizations depicting social outcomes that either mask variability in data, such as bar charts or dot plots, or emphasize variability in data, such as jitter plots or prediction intervals. They reported their agreement with both personal and external explana- tions for the visualized disparities. Overall, when participants saw visualizations that hide within-group variability, they agreed more with personal explanations. When they saw visualizations that emphasize within-group variability, they agreed less with personal explanations. These results demonstrate that data visualizations about social inequity can be misinterpreted in harmful ways and lead to stereotyping. Design choices can influence these biases: Hiding variability tends to increase stereotyping while emphasizing variability reduces it.
Nonclassicality and entanglement as a quantifiable measure for phase estimation

We establish an approach to measure the nonclassicality of a two-mode quantum state by extending the method of quantifying nonclassicality for a single-mode quantum state. We then discuss the nonclassicality and entanglement properties of several different quantum states, and determine the optimal phase estimation for entangled coherent states (ecs) in the form of nonclassicality and concurrence. Accordingly, a new interferometer (linear and nonlinear) scheme is proposed by modifying a traditional interferometer. Specially, we specify a new normal ordering form of the evolution operator of nonlinear interferometer (NI) using the techniques of integration within an ordered product of operators (IWOP), and obtain the parity signal based on representation of the coherent state. By inputting several common quantum states, we further study the phase sensitivity of the linear interferometer (LI) and NI with parity detection, and perform a detailed comparison among the different input states schemes. Furthermore, we quantitatively investigated the effect of nonclassicality and entanglement on the phase sensitivity of two interferometers. These results show that nonclassicality or entanglement is very crucial but not a necessary condition for improving the phase sensitivity of interferometers.
A Physical-Layer Watermarking Scheme Based on 5G NR

Based on the existing 5G NR system, a physical-layer watermarking scheme is proposed to enhance the physical-layer security in 5G communication systems. A new scheme for watermark generation is proposed to improve the robustness of the authentication. The watermark signal is embedded in the phase of the demodulation reference signal (DMRS), and the influence of the watermark on the demodulation reference signal is reduced by designing the encoder of the watermark. Simulation results show that the watermarking scheme proposed in this paper has good anti-noise and anti-frequency-offset performance, and has good feasibility both in the Gaussian channel and Rayleigh fading channel.
Analysis of Study Habits and Their Relationship with Biology Learning Outcomes of Class XI MIPA Students at SMA N 8 Padang

This study aims to analyze study habits and their relationship with biology learning outcomes for students of class XI MIPA at SMA Negeri 8 Padang. This type of research is descriptive, with a total sample of 45 students in the odd semester of the 2021/2022 academic year. The sampling technique was simple random sampling using a questionnaire. The results showed that the highest average study habits were habits in student preparation (28.82&#x0D; ± 3.36), reading textbooks (23.84 ± 3.05) making notes and summaries (24.15 ± 3.14 ) repeating lessons (17.06 ± 3.04) facing e xa ms (23.60 ± 2.94) and completing assignments (16.35 ± 2.02). Study habits contribute to students' biology learning outcomes with a t count of 2,794. The relationship between study habits and learning outcomes in general shows a low relationship (r = 0.161). Furthermore, the relationship between variable habits showed a low relationship between taking notes and summaries (r = 0.216), with study preparation (r = 0.174), reading habits of textbooks (r = 0.128), repeating lessons (r = 0.103), in facing exams ( r = 0.102) and the relationship between habits in completing tasks with learning outcomes (r = 0.000) is in the very low category. From this research, it can be concluded that the relationship between study habits and learning outcomes in taking notes and summaries with learning outcomes is in the low category, while the relationship between learning preparation habits, reading textbooks, repeating lessons, facing exa ms and the relationship between habits in completing assignments is in the very low category.
Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system

In order to help patients monitor their personal health in real time, this paper proposes an intelligent mobile health monitoring system and establishes a corresponding health network to track and process patients' physical activity and other health-related factors in real time. Performance was analyzed. The experimental results show that after comparing the accuracy, delay time, error range, efficiency, and energy utilization of Im-HMS and existing UCD systems, it is found that the accuracy of Im-HMS is mostly between 98 and 100%, while the accuracy of UCD is mostly between 98 and 100%. Most of the systems are between 91 and 97%; in terms of delay comparison, the delay of the Im-HMS system is between 18 and 39 ms, which is far lower than the lowest value of the UCD system of 84 ms, and the Im-HMS is significantly better than the existing UCD system; the error range of Im-HMS is mainly between 0.2 and 1.4, while the error range of UCD system is mainly between −2 and 14; and in terms of efficiency and energy utilization, Im-HMS values are higher than those of UCD system. In general, the Im-HMS system proposed in this study is more accurate than UCD system and has lower delay, smaller error, and higher efficiency, and energy utilization is more efficient than UCD system, which is of great significance for mobile health monitoring in practical applications.
DEVELOPMENT OF DIGITAL COMPETENCIES OF SPECIALISTS IN THE CONDITIONS OF FORMATION OF DIGITAL TRANSFORMATION OF THE LABOR MARKET

The article is devoted to the significant changes in life that modern digital technologies have made. The active and rapid ability to master and involve digital technologies in the process of working activity implies adaptation to the innovative conditions of the professional environment. Today digital literacy is a basic skill of any person, the ability to effectively and safely use the most convenient digital solutions, services and products. It is no coincidence that lessons on the study of digital literacy have been introduced in primary school, starting to develop digital competencies. Digital literacy is based on digital competencies that contribute to solving various tasks in the field of using information and communication technologies, which will definitely ensure gaining a competitive advantage in the global market. Measures aimed at the development of digital competencies contribute to the transformation and transformations in the field of "new" specialties. That is why modern schoolchildren choose in advance a reference point for the professions of the future, which undoubtedly arouse interest, despite the fact that there are questions about "new" specialties in modern society. The research methodology was based not on individual methods, but on their system. The scientific novelty lies in the development of theoretical, methodological and practical proposals for the development of digital competencies of specialists in the conditions of the formation of the digital transformation of the labor market.
Efficient Hash Code Expansion by Recycling Old Bits

Deep hashing methods have been intensively studied and successfully applied in large-scale multimedia retrieval. In real-world scenarios, code length can not be set once for all if retrieval accuracy is not satisfying. However, when code length increases, conventional deep hashing methods have to retrain their models and regenerate the whole database codes, which is impractical for large-scale retrieval system. In this paper, we propose an interesting deep hashing method from a brand new perspective, called Code Expansion oriented Deep Hashing (CEDH). Different from conventional deep hashing methods, our CEDH focuses on the fast expansion of existing hash codes. Instead of regenerating all bits from raw images, the new bits in CEDH can be incrementally learned by recycling the old ones. Specifically, we elaborately design an end-to-end asymmetric framework to simultaneously optimize a CNN model for query images and a code projection matrix for database images. With the learned code projection matrix, hash codes can achieve fast expansion through simple matrix multiplication. Subsequently, a novel code expansion hashing loss is proposed to preserve the similarities between query codes and expanded database codes. Due to the loose coupling in our framework, our CEDH is compatible with a variety of deep hashing methods. Moreover, we propose to adopt smooth similarity matrix to solve "similarity contradiction" problem existing in multi-label image datasets, thus further improving our performance on multi-label datasets. Extensive experiments on three widely used image retrieval benchmarks demonstrate that CEDH can significantly reduce the cost for expanding database codes (about 100,000x faster with GPU and 1,000,000x faster with CPU) when code length increases while keeping the state-of-the-art retrieval accuracy. Our code is available at https://github.com/IIE-MMR/2022MM-CEDH.
Research on sensing node location technology based on AOA and fingerprint recognition

Abstract In order to meet the needs of intelligent operation and maintenance of distribution networks, a large number of wireless sensors are deployed inside and outside the power grid for digital sensing and immediate control of the grid. However, once these wireless sensors fail, they will generate erroneous data, which affects the assessment of grid security and brings great hindrance to the automation of power system operation and maintenance. Therefore, we propose an AOA and fingerprint recognition-based sensing node location method to achieve accurate positioning of wireless sensing nodes to facilitate rapid troubleshooting. The method first enhances and denoises the signal characteristics of wireless sensing nodes to achieve pre-processing of wireless sensing fingerprint information and solve the problem of fingerprint feature recognition of data samples. After that we combine the node fingerprint information through deep learning models to achieve device type recognition of wireless sensing nodes. Finally, we design the AOA node localisation method, which uses the AOA coordinate system measurement model to perform distance measurement and coordinate conversion on the fingerprint features of wireless sensing nodes to achieve accurate and fast localisation of wireless sensing devices. The experiments prove that the method has a high accuracy rate in the identification and location of wireless sensing node devices, which can effectively improve the efficiency of grid automation operation and accurate troubleshooting.