Reviewerly/exHarmony huggingface dataset:
Optimizing Data Availability and Utilization in Deep Learning Accelerator SoCs

Deep learning accelerators are pivotal in accelerating computation-intensive tasks in modern AI applications. Optimizing the utilization of system resources, including shared cache, on-chip SRAM, and data movement mechanisms, is crucial for achieving superior performance and energy efficiency. In this study, we propose an efficient system architecture specifically tailored for deep learning workloads. Our architecture enables to reconfigure the last level cache as a scratchpad with prefetch capability, which eliminates cache misses and thereby offers resource efficiency, improved performance, and energy efficiency. By implementing a strategy to overlap accelerator execution with data movement, we achieved remarkable results, including a $14\times$ speedup and %5 reduction in energy consumption for the ResNet50 benchmark when compared to the base system configuration. These findings demonstrate the substantial benefits of incorporating prefetch support and scratchpad reconfiguration in the last level cache, leading to enhanced performance and energy efficiency in real-world deep learning accelerator applications.
Lookupx: Next-Generation Quantization and Lookup Techniques for Empowering Performance and Energy Efficiency

Long Short Term Memory (LSTM) networks as one of the most used Recurrent Neural Networks (RNN) structures offer high accuracy for sequence learning tasks. However, it is challenging to offer low latency and high throughput while satisfying the low power constraints at the same time for computationally expensive LSTM operations. This work offers a two-pronged approach to accelerate inference in RNN networks. First, linear quantization technique is applied to reduce the complexity of operations, power consumption and required memory resources. Then, a new activation implementation method is proposed, called lookupx, to accelerate sigmoid function computation during inference. It is shown that lowering precision to 4-bit integer numbers for inputs causes only 2% accuracy loss and the lookupx activation methodology has 1.9x better performance and 50x lower power consumption while decreasing the required chip area 1.2x compared to integer domain activation functions with the same accuracy result.
Light-Controlled Switching of Gait Patterns in a Central Pattern Generator: Circuit Design and Emulation

Central pattern generators (CPGs) are a subject of great interest in the fields of bio-inspired circuits and robotics. They enable the generation of rhythmic patterns, which are involved in the control of locomotion. This paper presents an electrical model of a self-organized CPG with the ability of switching between different gait patterns based on external light signals. Here, we introduce leaky FitzHugh-Nagumo oscillators to mimic the basic functionality of pacemaker neurons with reciprocal inhibition. Furthermore, we introduce a novel wiring mechanism utilizing light-responsive conductances to allow for the production of different gait patterns through dynamic topology formation. A wave digital (WD) model is derived for emulation and testing and we provide results to demonstrate the light-dependent switching between gait patterns.
HLS-based acceleration of the BIKE post-quantum KEM on embedded-class heterogeneous SoCs

An effective transition to post-quantum cryptography mandates its deployment on embedded-class devices, guaranteeing adequate performance while satisfying their strict area constraints. This work accelerates BIKE, a QC-MDPC code-based post-quantum KEM, through HLS on embedded-class heterogeneous SoCs that couple a CPU with FPGA programmable logic. The proposed methodology implements HLS-generated accelerators to compute the most time-consuming operations of BIKE, identified by analyzing the software-only execution. The mix of accelerators instantiated in hardware and operations executed in software, as well as the configurable architectural parameters of the former, are then determined, depending on the resources available on the target SoC, to minimize BIKE’s execution time. Experiments on AMD Zynq-7000 SoCs highlight a speedup of up to 3.34 times compared to the reference software execution and up to 1.98 times over state-of-the-art HW/SW implementations targeting the same chips.
FPGA Implementation of Area-Time Aware ECC Scalar Multiplication Core*

Elliptic curve cryptography (ECC) is capable of providing higher security per key bit which makes it a great choice for ensuring different security services in many resource-limited environments. Scalar multiplication is the most frequent and performance-limiting primitive required in all security protocols developed using ECC. This paper presents an area-time-aware scalar multiplier architecture and the results are shown after its implementation on different field programmable gate array (FPGA) platforms. It is based on unified finite field arithmetic (UFFA) units, capable of computing basic field operations such as field addition/subtraction, multiplication, and inversion. In these units, efficient parallelism techniques are adopted to increase achievable frequency while novel resource-sharing strategies are employed to reduce the hardware resource consumption cost. Moreover, an efficient scheduling strategy is presented to schedule several field operations required to execute the Montgomery ladder algorithm. Due to these efforts, the proposed scalar multiplier produces better area-time product and efficiency results. It is implemented on Xilinx Virtex-7 and Virtex-6 FPGA platforms for different operand sizes. It provides $3.8 \times$, $3.3 \times$ better results in terms of latency and efficiency as compared to its counterparts.
3D-INSEG: A 3D Instance Segmentation Algorithm for Extended Object Tracking

Classical multiple object tracking (MOT) algorithms were based on the assumption of single targets yielding single measurements. Recently there has been significant work which relaxes this assumption by addressing extended target tracking in which it is assumed that a single target can yield multiple measurements. To provide these multiple measurements, robust detections are a central challenge in Multiple Extended Object Tracking (MEOT). In this article, we propose the 3D INStance SEGmentation (3D-INSEG) algorithm to address this problem by harnessing the capabilities of stereo cameras and Convolutional Neural Networks (CNNs) for both depth estimation and segmentation in 3D space. By utilizing stereo vision, we obtain depth information, which complements the 2D segmentation produced by a CNN-based model. The segmentation results are projected into the 3D space using the depth information. To validate the effectiveness of the approach, we conduct comparisons with traditional clustering techniques, specifically with Velodyne LIght Detection And Ranging (LIDAR) data points. Depth values are estimated from the RAFT-stereo algorithm, and the Mask-RCNN model performs 2D segmentation in the image plane. The robustness of the approach is currently demonstrated based on single human tracking and its potential extension to general MEOT is discussed.
Development of electric drive system for solar battery system based on artificial intelligence

In this article, the authors present research on the influence of the drive system that controls the solar battery system according to sunlight, to maximize the source of incoming solar radiation PV panels. This drive system tracking operates according to the changing solar radiation light entering the PV panel; When the sun changes its position and shines on the solar battery system, the motor will rotate, causing the panel to move according to the sun’s light. The system is researched to build a model and calculate the position tracking control algorithm, according to [12, 15] and combined with reinforcement learning technology with Q-learning algorithm and MPPT (Maximum Power Point Tracker) control). These studies will bring maximum capacity to the grid-connected solar power system. These research results will be the basis for establishing control algorithms and designing solar power systems in industry and military.
Performance Analysis of an Autoencoder-based Communication System with Numerous Transmitter-Receiver Pairs

With the significant increase in the number of users, there is a high demand to design a shared communication system that allows multiple users to communicate at the same time. In addition, the autoencoder model has recently emerged as a promising solution to automatically modulate the transmitted symbols. Therefore, in this work, we study an autoencoder-based communication system when multiple users share the same communication channel. Specifically, transmitters jointly modulate the generated symbols using the autoencoder model and send the encoded data to the receivers over the shared communication channel. The optimal constellation diagram is learned by minimizing the difference between the transmitted and reconstructed symbols. We have conducted extensive experiments to evaluate the performance of the autoencoder-based communication system sharing between multiple users. With the same data throughput, the AE-based shared communication system for multiple transmitters and receivers results outperforms traditional time-division and frequency-division multiple access control methods in terms of symbol error rate.
Tracking of Maneuvering Targets with Poisson Labeled multi-Bernoulli Tracker

In this paper, the Poisson labeled multi-Bernoulli (PLMB) tracker is exploited to solve the problem of maneuvering target tracking. For avoiding tracking divergence resulting from a single motion model, multiple models (MM) strategy is applied to the PLMB tracker. Specifically, each state of target is coupled with all possible motion models and the model transition is considered throughout the tracking process. Simulation results demonstrate that the proposed MM-PLMB tracker strikes a favorable balance between limited computational resources and high tracking accuracy when compared to state-of-the-art approaches.
An Efficient System for Personal Information Search in Cyberspace Using Facial Recognition Technology

The paper presents an artificial intelligence (AI) system, MTAFaceSearch, which finds images or videos containing a specific person’s face among an extensive collection of online data. The proposed system integrates three main components: a Data Storage Mechanism, a Data Collection Module, and a Search Module. The system employs state-of-the-art deep learning models that can detect and recognize faces with high accuracy and robustness. The system also leverages advanced processing technologies such as parallel and cloud computing to speed up the face search process and handle large-scale data. The experiments show that our system achieves high accuracy and speed.
Multi-UAV Coverage Strategy Using a Balanced Decomposition Approach

This paper presents a multi-UAV coverage strategy employing a balanced decomposition approach for patrol and surveillance tasks. The balanced decomposition method, based on coverage paths utilizing the Rotating Caliper Path Planning (RCPP) algorithm, facilitates the partitioning of a Region of Interest (ROI) into convex sub-regions with uniform size. Subsequently, the Hungarian algorithm is applied to assign these sub-regions to the UAVs, taking into account estimated travel distances and energy reserves, to ensure that the UAVs accomplish their designated coverage mission at an optimal cost. Simulation experiments conducted in various ROIs with varying numbers of UAVs validate the high performance of the proposed approach. This is evidenced by the precise balanced decomposition of ROIs, resulting in workload equilibrium among UAVs, nearly absolute coverage rates, and enhanced energy efficiency.
Understanding LMS Use in Saudi Higher Education: A Case Study of Blackboard at King Abdulaziz University

Blackboard is the most recognized integrated e-learning management system. It has been proven especially during the COVID-19 pandemic when all businesses shifted online. It manages the educational process synchronously and asynchronously and provides a safe learning environment for students and faculty. This survey-based study aims to understand the use of learning management systems (Blackboard) among higher education students and faculty at King Abdulaziz University (KAU) by exploring different aspects of the system features and determining whether mobile devices and social media influence the use of Blackboard. The findings confirm the importance of improving the quality of LMS already in use by providing more knowledge about effective utilities of LMS tools and technical support for LMS use with mobile devices. Furthermore, this study validates the importance of making the most of LMS, especially in situations like the COVID-19 pandemic. The study’s implications indicate a positive correlation between mobile blackboard use and social media if solely used for the learning process rather than for distractions.
Changing employee competencies to meet business needs

The article examines the main trends in the demands of modern organizations for the key competencies of future university graduates. The main problems are identified in the discrepancy between business demands and the real competencies of young specialists. Key competencies are identified and analyzed that allow one to adapt to the instability of the external environment of modern business. Positive domestic and foreign experience in solving this problem is considered.
UV decontamination of materials v1

Protocol for reducing DNA contamination on tubes, bottles, zip lock bags and other reaction vessels and containers used in the ancient DNA cleanroom by UV treatment.
Team work as an important component in the process of training in the educational organization of higher education

Аt present, enterprises require not only in highly qualified specialists who, during their training, master certain skills for a particular profession, but also in specialists who have the ability to quickly respond to changing circumstances, are not afraid of the challenges of the time and are able to work in a team. The responsibility for the training of such specialists lies with the educational organization of higher education, which must build the learning process in such a way that everyone of the students was aimed not only at personal performance, but also to achieve high results in teamwork.
Science, technology and innovation foresight methodology and outcomes

This foresight exercise employed a tailor-made methodology and tools designed to achieve a comprehensive analysis within a relatively short time. The methodology and outcomes are discussed together in this chapter.
UPAYA MENINGKATKAN KETERAMPILAN MEMBACA DENGAN MEDIA AUDIO VISUAL PADA SISWA KELAS V SD NEGERI 13 PESING

This research was motivated by the low reading skills in class V of SD Negeri 13 Pesing, Sekayam District, Sanggau Regency. The formulation of the problem is “What the use of audio-visual media improved students' reading skills at SDN 13 Pesing?”. The purpose of this research is to improve reading skills by using audio-visual media. The type of research is Classroom Action Research (CAR). The location is in class V SD Negeri 13 Pesing. The subjects were 36 students of class V. Data collection techniques used test techniques and non-test techniques. The research instrument is a test and non-test. The results of the research on reading skills using audio-visual media have increased. Cycle I is students' reading skills completed 27 students (75%) and 9 students (25%) did not complete. As for the results of observations of student activity with a rating of 27 students (75%) in Good (B) category, 6 students (16.67%) in Enough (C) category, and 3 students (13.63%), in Poor (D) category. In cycle II, 32 students (88.87%) completed their reading skills and 4 students (11.11%) did not complete them. Student activity with a rating of Very Good (A) 32 students (88.87%), Good (B) 2 students (2.78%) and 2 students (2.78%) Fair (C).
Development of Augmented Reality (AR) Learning Media Integrated with STEM Learning

This research is motivated by the development of science and technology in the 21st century, especially in the fields of Augmented Reality (AR) and STEM (Science, Technology, Engineering, and Mathematics) learning which provides opportunities for students to develop work skills and mathematical abilities which lead to the formation of life competencies in the future that is the competence of Education for Sustainable Development Goals (ESDG). This AR Learning Media was developed with a 4D development design: Define, Design, Development, and Dissemination. The subjects of this research were 7th-grade science students and teachers at SMPN 1 Dewantara. Media and content validation was carried out by 2 material experts and a media expert. Instruments used Media validation sheets, materials, and student and teacher response questionnaires. Analysis was done using percentage techniques on validation sheets, teacher response questionnaires, and student responses. The results of this research are AR media prototypes that are very feasible according to material experts and appropriate according to media experts with excellent student responses and the teacher's response was very practical
Effectiveness of learning media with diverse applications for mental impairment pupils' science knowledge

Students with cognitive impairments possess intellectual capacities, or IQs, that fall below the average range of typical youngsters, resulting in challenges comprehending abstract subjects like science. Nevertheless, there exist methods that can enhance their comprehension, specifically by employing educational resources that align with their aptitudes. This study aims to assess the efficacy of utilising diverse educational tools from multiple applications to enhance the comprehension of science subjects among students with intellectual disabilities. This study employs the systematic literature review (SLR) approach, utilising online article databases such as SINTA, GARUDA, ERIC, and Google Scholar. The reviewed publications encompassed a total of eight articles, focusing on the areas of "Effectiveness of Learning Media," "Students with Mental Impairment," and "Science Learning for Students with Mental Impairment." The studies were published between 2013 and 2023. The review findings indicate that using instructional media during the educational process for students with intellectual disabilities significantly enhances their comprehension of scientific content. The primary learning materials utilised are predominantly derived from diverse apps that integrate teaching and entertainment, encompassing films, games, visual media like photographs, and other resources tailored to the requirements of individuals with intellectual disabilities. The effectiveness of using learning media in science education for mentally disabled students to implement the 2013 curriculum has been demonstrated. This is particularly true when the learning medium is tailored to the needs of mentally disabled students, resulting in improved comprehension of scientific concepts
Solving The Students’ Problems In Writing Narrative Essay Through Pentigraf Writing Technique

This study looks into how an English lecturer used the Pentigraf writing technique to teach writing during the second semester of English pre-service lecturers. The employment of the Pentigraf writing approach by the English lecturer and the students' perceptions of it were the subjects of a narrative inquiry, which was chosen as the research design. The study's findings show that: 1) The professor employs Pentigraf writing because it is straightforward, entertaining, and efficient. 2) The preservice lecturer's poor language competence was the biggest issue the lecturer faced when employing the Pentigraf writing approach. 3) Before implementing the Pentigraf writing approach, teaching materials must be prepared. 4)Students in classes three and four demonstrated positive attitudes toward the Pentigraf writing style by acknowledging that it made writing easier for them to learn, motivated them more, and made it more pleasant. This study's implications are meant to aid in the development of writing instruction methods.
Investigating The Effects of Twitter on Students’ English Writing Skill

With a limited number of characters per tweet, writing on Twitter requires creativity. It is unique media format has not only revolutionized online communication but also created a distinct style of writing. In this case, Twitter stands as a platform that reflects the way we write and communicate in English. Researcher wants to investigate the effects of twitter on students’ English writing skill. This is a case study method. This method helps the researcher to identify the problem in detail information. The data collected from questionnaire in English writing learning process. The lecturer becomes the observer or researcher of the research. On the other hand, students become the object of the research. As the conclusion, Twitter had a significant impact on students' writing skills in English. It challenges them to express their thoughts succinctly and effectively within a limited number of characters, encouraging creativity. However, it can also lead to errors and the use of informal language. Meanwhile, Twitter provides exposure to a variety of content and interactions with people from diverse backgrounds, which improves grammatical understanding. It also helps expand vocabulary by offering different words in context.
Developing a chatbot in quantities, denomination, and measurement: an artificial intelligence-based teaching materials

The material of quantities, denomination, and measurement which are considered less interesting and difficult to understand. This is due to the content and structure of the material which does require prior knowledge to be understood so it seems difficult and a lot of prior knowledge and many abstract concepts. Based on this study, the solution that can be given is to develop a digital-based teaching material for science subjects in the form of a Chatbot. This study aims to develop a chatbot as a digital teaching material based on artificial intelligence in quantities, denomination, and measurement material. This research is development research using the 4D model. Data were collected using test and student response questionnaires. Data analysis using descriptive and inferential statistic. The developed chatbot validated by experts in terms of material, software, and language. The chatbot developed was then disseminated to 65 PGMI IAIN Pontianak students. Based on the results, it can be concluded that the chatbot of quantity, denomination, and measurement material has high validity. The result showed that students’ give the positive responses about the display, content and utilization of the chatbot. Based on the results of the calculation of N-gain obtained the result of 0.3. This means that the effectiveness of using chatbot is in the medium category. Therefore, this chatbot quantity, denomination, and measurement material is suitable for use as teaching material in college level.
No more buying cats in a bag? Literary Translation in the age of language automation

This paper is an introduction to the special Issue on Computer-Aided Literary Translation. It explores varying degrees of automation and the role of humans in automated processes, be it as experts or novices. Our contention is that automation can realize noble ideals, such as a more accessible and diverse global book market, but it can also have profound negative effects, especially in a cultural context. This introduction provides an overview of the complex interplay between technology and human involvement in literary translation.