Reviewerly/exHarmony huggingface dataset:
A Low-Latency Hardware Accelerator for YOLO Object Detection Algorithms

Object detection is an important computer vision task with a wide range of applications, including autonomous driving, smart security, and other domains. However, the high computational requirements poses challenges on deploying object detection on resource-limited edge devices. Thus dedicated hardware accelerators are desired to delever improved performances on detection speed and latency. Post-processing is a key step in object detection. It involves intensive computation on the CPU or GPU. The non-maximum suppression (NMS) algorithm is the core of post-processing, which can eliminate redundant boxes belonging to the same object. However, NMS becomes a bottleneck for hardware acceleration due to its characteristics of multiple iterations and waiting for all predicted boxes to be generated. In this paper, we propose a novel hardware-friendly NMS algorithm for FPGA accelerator design. Our proposed algorithm alleviates the performance bottleneck of NMS by implementing the iterative algorithm into an efficient pipelined hardware circuit. We validate our algorithm on the VOC2007 dataset and show that it only brings 0.27% difference compared to the baseline NMS. Additional, the exponential function and sigmoid function are also extremely hardware-costly. To address this issue, we propose an approximate exponential function circuit to calculate the two functions with minimum logic cost and zero DSP cost. We deploy our post-processing accelerator on Xilinx’s Alveo U50 FPGA board. The final design achieves a end-to-end detection latency of 283us for YOLOv2 model, According to the user guide provided by Xilinx and Intel, we converted the logic resources of different implementations on the FPGA into LUT resources. After that, we compared the resource utilization of acceleration module in the current state-of-the-art object detection system deployed on Intel with ours. Compared with it, we consumed 13.5 $$\times $$ lower LUT resources and used much fewer DSP resources.
The Effect of Leadership and Teamwork on Employee Turnover and Team Performance

This study aims to investigate the influence of leadership and teamwork on team performance while exploring the potential effects of team turnover. Our methodology involved distributing questionnaires to 60 individuals who met our specific criteria. The data collected was analyzed using the advanced statistical analysis software SPSS 20.0. Our findings indicate that leadership and teamwork are crucial in team performance, with a significant impact observed. Additionally, our research indicates that team turnover does not affect team performance. For a company to achieve optimal success and productivity, it is imperative to thoroughly examine and analyze the influence of leadership and teamwork on team performance and turnover. By doing so, organizations can better understand the underlying factors contributing to these outcomes and make informed decisions to enhance team dynamics and drive positive results.
Pengaruh Variasi Ukuran Ketebalan Material terhadap Karakteristik Termoelektrik Komposit CuO/Karbon Aktif Kulit Pisang Kepok

Thermoelectric is a device that can convert heat energy into electrical energy. One thing that influences the quality and characteristics of a thermoelectric material is the size of the thermoelectric material. In this research, we will look at the effect of the size of the thermoelectric material which has been varied in thickness. The thermoelectric material itself is made from a composite of active carbon from kepok banana peel with CuO with a mass ratio of 3:7. The composite was then printed with varying mold thickness sizes and the results were that a mold with a thickness of 0.5 cm was the best mold with an electrical conductivity value of 0.2 MS/cm, a heat conductivity value of 0.0680394 J/s, a value the Seebeck coefficient is 1.13229 mV/oK. The band gap value of the activated carbon and CuO composite resulting from the DR-UV test is 1.27 eV
PENINGKATAN MINAT BACA SISWA KELAS XII TATA BUSANA SMK DARUSSALAM DENGAN PROGRAM AKSI BACA PAKSA INDONESIA (ABPI) TAHUN 2021/2022

Learning to read has a very important role in learning Indonesian. All educators hope that their students can master reading skills. By reading students can broaden their horizons of knowledge. With the holding of the Indonesian Forced Reading Action program directs a student so that reading can become a culture and even a hobby. The hypotheses in this study are: 1) By holding the Indonesian Forced Reading Action program, students develop more interest in reading and students' interest in reading it without being told, 2) Before implementing the Indonesian Forced Reading Action program students had no interest in reading. knowing the world of reading, this is due to a lack of encouragement for oneself and the local environment, 3) after the Indonesian Forced Reading Action program was held, students became more interested in the world of reading. In this quantitative descriptive research, researchers will describe something. Descriptive research can involve the use of a questionnaire that will be a sample of increasing students' interest in reading. The hope is that students can develop their interest in reading by holding the Indonesian Forced Reading Action program. The questionnaire results from the researcher tested the validity, reliability, and normality at a significance level of 0.05 in the r table for 28 respondents with a result of 0.374. The Cronbach Alpha value of the X variable is 0.789, while the Y variable is 0.780, each of these variables shows a high increase in reading interest.
THE IMPLEMENTATION OF MEMRISE IN IMPROVING ENGLISH VOCABULARY MASTERY

Vocabulary has a big role in learning English, because the understanding of English vocabulary affects to the other skills. unfortunately, the lack of English vocabulary is often founded in Indonesian students, and it is a serious problem. The researcher found the problems in an Islamic Senior High School in Majalengka west Java. The first problem is most of students were lack of vocabulary, and the second problem is the limited of technology in learning English language. Research methodology in this research was qualitative method with Classroom Action Research as data collection. The instruments of the research were test, observation sheet, and questionnaire. The research questions were; How does the teacher implement MEMRISE in teaching vocabulary? What is the students’ perception of their experiences before using MEMRISE and after using MEMRISE, and what is the result of teaching English vocabulary after using MEMRISE? The aims of The Study were to Investigate the teacher in the implementation of MEMRISE in teaching English vocabulary, to investigate the students' improvement of English vocabulary after using MEMRISE in cycle I, to investigate the students' improvement of English vocabulary after using MEMRISE in cycle II, to investigate the students’ perception of their experiences before and after using MEMRISE. This research used qualitative method through Classroom Action Research as data collection. In this research the researcher used purposive sampling, and the sample is a class of x IPS 2 that consists of 18 students. In this research the researcher as a teacher. The result show in the pre-test the mean score is 54,06, in the post-test 1 the mean score is 72,06, in the post-test 2 the mean score of pre-test is 52,048, the mean score of post-test 1 was 69,43, and the mean score of post-test 2 was 80,86. The conclusion was there was improvement of students’ vocabulary score through MEMRISE application.
Quantum logical controlled-NOT gate in a lithium niobate-on-insulator photonic quantum walk

Abstract The two-qubit controlled-NOT gate is one of the central entangling operations in quantum information technology. The controlled-NOT gate for single photon qubits is normally realized as a network of five individual beamsplitters on six optical modes. Quantum walks (QWs) are an alternative photonic architecture involving arrays of coupled waveguides, which have been successful for investigating condensed matter physics, however, have not yet been applied to quantum logical operations. Here, we engineer the tight-binding Hamiltonian of an array of lithium niobate-on-insulator waveguides to experimentally demonstrate the two-qubit controlled-NOT gate in a QW. We measure the two-qubit transfer matrix with 0.938 ± 0.003 fidelity, and we use the gate to generate entangled qubits with 0.945 ± 0.002 fidelity by preparing the control photon in a superposition state. Our results highlight a new application for QWs that use a compact multi-mode interaction region to realize large multi-component quantum circuits.
The Implementation of Team-Based Learning for English Department Students

The purpose of this study is to explore English Education Department students’ perceptions and experiences about the implementation of Team-Based Learning for their class. The study was carried out using a qualitative methodology, and five students were used as the sample population for an interview. The total number of participants who took part in this study was 33 students who came from the second grade from semester 6. Participants were given a close-ended questionnaire to collect their perceptions about team-based learning. According to the survey, the majority of students believed that group projects enhanced learning, although some challenges persisted. As a consequence of the interview findings, it was discovered that several of the issues raised by the students were brought on by the group's members. Some students preferred working by themselves had varying levels of skill, found it challenging to speak with others, did not respect differing viewpoints, did not want to collaborate (students who study more slowly), and did not want to learn.
Generalised Predictive Control of Nonlinear Systems Using the Echo State Network and the Nelder-Mead Method

Although generalised predictive control (GPC) has been widely attractive in the industry as a popular model predictive control, its control performance could sometimes decrease because linear transfer function models in the GPC could become hard to estimate system states precisely when the object plant process/dynamic systems are affected by nonlinearity effects. Therefore, to overcome this problem and achieve optimal predictive control of nonlinear systems, this study explored a more comprehensive approach to the GPC, using an echo state network (ESN) as the nonlinear model, after which we investigated its capability in controlling nonlinear dynamic systems. By optimisation using a Nelder-Mead method without gradient computations of the objective function, the predicted quadratic error function based on the future system output estimated using the ESN was then minimised, resulting in optimal control input. Finally, we conducted computational experiments on controlling discrete-time nonlinear plants to assess the feasibility of the ESN-based GPC, followed by simulations to clarify the characteristics of the proposed method.
Design and Development of Automated Self Recoverable Drone

This paper presents a smart self-recoverable drone equipped with advanced sensors, computing capabilities, and a fault detection and self-recovery system that enables it to operate autonomously and recover from unexpected situations. The system involves the integration of various advanced technologies and sensors, such as Spring assistance ejection, Manual disbalancing propeller, Solar recharge and Manual thread control, to enable the drone to avoid getting stuck in real-time. The paper evaluates the execution of the drone through extensive simulations and concludes that it is highly effective in completing difficult tasks with high precision and efficiency. Future research includes developing a drone recovery and damage control technology that will save humankind from suffering a severe loss.
Clustered Control of Transition-Independent MDPs

This work studies efficient centralized solution methods for cluster-based control policies of transition-independent Markov decision processes (TI-MDPs). We focus on control of multi-agent systems, whereby a central planner (CP) influences agents to select desirable group behavior. The agents are partitioned into disjoint clusters whereby agents in the same cluster receive the same controls but agents in different clusters may receive different controls. Under mild assumptions, this process can be modeled as a TI-MDP where each factor describes the behavior of one cluster. The action space of the TI-MDP becomes exponential with respect to the number of clusters. To efficiently find a policy in this rapidly scaling space, we propose a clustered Bellman operator that optimizes over the action space for one cluster at any evaluation. We present Clustered Value Iteration (CVI), which uses this operator to iteratively perform “round robin” optimization across the clusters. CVI converges exponentially faster than standard value iteration (VI), and can find policies that closely approximate the MDP's true optimal value. A special class of TI-MDPs with separable reward functions are investigated, and it is shown that CVI will find optimal policies on this class of problems. Finally, the optimal clustering assignment problem is explored. The value functions TI-MDPs with submodular reward functions are shown to be submodular functions, so submodular set optimization may be used to find a near optimal clustering assignment. We propose an iterative greedy cluster splitting algorithm, which yields monotonic submodular improvement in value at each iteration. Finally, simulations offer empirical assessment of the proposed methods.
Multimodal Multiobjective Differential Evolutionary Optimization With Species Conservation

Multimodal multiobjective optimization problems (MMOPs) have attracted wide attention in recent years. This kind of problem is very challenging since they need to locate different Pareto-optimal solution sets (PSs) that correspond to the same Pareto front. To resolve it, this article proposes a novel multimodal multiobjective differential evolution (DE) algorithm with species conservation, which develops a new way of locating different PSs. Specifically, the proposed algorithm adopts species conservation to determine different PSs in known areas, while it uses a variant of DE as the basic optimizer to explore new areas. There are three operators in species conservation: 1) species division; 2) seed determination; and 3) seed conservation. Species division mainly partitions the joint population of parents and children into various species in the decision space for retaining different PSs. Seed determination selects superior solutions from each species as seeds that need to be kept in the next generation. Seed conservation is to ensure that all species seeds are retained in the new generation by substituting no promising solutions with them, thereby guarantee not missing some known areas that may contain different PSs. Besides, the DE variant is utilized to produce diverse solutions to find new areas in the decision space where PSs may exist. The comparative experiments with ten state-of-the-art algorithms have been performed on the CEC 2019 MMOPs test set and two real-world problems. The experimental results have verified that the proposed algorithm has a competitive performance for MMOPs.
EASpace: Enhanced Action Space for Policy Transfer

Formulating expert policies as macro actions promises to alleviate the long-horizon issue via structured exploration and efficient credit assignment. However, traditional option-based multipolicy transfer methods suffer from inefficient exploration of macro action’s length and insufficient exploitation of useful long-duration macro actions. In this article, a novel algorithm named enhanced action space (EASpace) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available suboptimal expert policies. Specifically, EASpace formulates each expert policy into multiple macro actions with different execution times. All the macro actions are then integrated into the primitive action space directly. An intrinsic reward, which is proportional to the execution time of macro actions, is introduced to encourage the exploitation of useful macro actions. The corresponding learning rule that is similar to intraoption Q-learning is employed to improve the data efficiency. Theoretical analysis is presented to show the convergence of the proposed learning rule. The efficiency of EASpace is illustrated by a grid-based game and a multiagent pursuit problem. The proposed algorithm is also implemented in physical systems to validate its effectiveness.
Research on Professional Competence and Enhancement of International Chinese Language Teachers in Shaanxi, Xi’an

The study investigates the development of professional competence of international Chinese language teachers in Shaanxi, Xi’an. The study adopts two methods, Comparative Research Method and Investigation and Research Method, to examine both comparisons of professional competence between international Chinese language teachers and English language teachers and the current situation of literacy of international Chinese language teachers and provide treatment and strategies to enhance the literacy: establishing a professional development framework for teachers in Chinese language Education, enhancing the establishment of information ethics frameworks for Chinese language teachers, fostering self-reflective awareness and competence among international Chinese language teachers, promoting the practice of lifelong learning among Chinese teachers.
Channel Hopping Steganography For Color Images

Internet has spread to quite large area and has a great share in communication systems. The protection of data during transfer from one point to another has also gained importance because of attacks. Data can be sent and received as sound, image, text etc. Structure of digital image offers us advantages for hiding while transferring data. Therefore, the use of steganography, known as confidentiality, by means of digital images will reduce violations from any external environment and consequently allow us to communicate reliably. Least significant bit (LSB) is well known method for image steganography where channel to be used is predefined. Thus, it is weak approach basically. In this study, channel hopping strategy where the channel to be used to store data bits are changed with a pseudo-random number generator (PRNG). A chaotic logistic map was used as PRNG, and its parameters were assigned as key vector. Consequently, confidentiality of data hiding has been increased.
Patient data management using blockchain technology

&lt;span&gt;The patient data management is an essential component of healthcare systems, the secure and efficient data processing is important for the medical data. Data security, interoperability and privacy are the key requirements of data storage systems of healthcare organizations. The electronic medical records have become a key technique to maintain patient information in hospitals due to the technology revolution. Some hospital systems are also using server-based patient detail management systems, they require considerable storage to record all of the patient's medical reports, limiting scalability. They are facing difficulties, including interoperability, security and privacy worries, cyberattacks on centralized storage, and maintaining medical policy compliance simultaneously. The blockchain technology has come up with solution having decentralized and irreversible data storage. A distributed secure ledger of blockchain is the solution, enabling safe storage and retrieval of data. The proposed work yields effectively deployed smart contracts based on the system's functions, real-time patient health monitoring. The main goal of this system is to bring the whole medical data together on a single platform, employing a secured decentralized approach to store and retrieve medical information effectively.&lt;/span&gt;
Intelligent photovoltaic system to maximize the capture of solar energy

The large consumption of electricity worldwide has an impact on the environment which can be said to alter climate change, the degradation of the ozone layer and acid rain. A house has an average daily consumption of 270kWh, this is why solar panels are very useful and can help to have a renewable energy at low costs. To create an intelligent photovoltaic system, different electronic sensors can be applied to follow the sunlight through a series of instructions in some programming software. The article proposes to prototype an intelligent photovoltaic system, based on artificial intelligence with a neural network library "propet" having a positive impact on the optimization of power generation by allowing a more accurate tracking of the sun and a greater collection of photovoltaic energy throughout the day. performing an integration between Arduino and machine learning algorithms such as artificial neural networks in prediction of time series. Different practical experiments were performed to illustrate the effectiveness of the proposed method.
A Decision Tree Induction Algorithm for Efficient Rule Evaluation Using Shannon’s Expansion

Decision trees are one of the most popular structures for decision-making and the representation of a set of rules. However, when a rule set is represented as a decision tree, some quirks in its structure may negatively affect its performance. For example, duplicate sub-trees and rule filters, that need to be evaluated more than once, could negatively affect the efficiency. This paper presents a novel algorithm based on Shannon’s expansion, which guarantees that the same rule filter is not evaluated more than once, even if repeated in other rules. This fact increases efficiency during the evaluation process using the induced decision tree. Experiments demonstrated the viability of the proposed algorithm in processing-intensive scenarios, such as in intrusion detection and data stream analysis.
Using Relational Dialectics to Better Understand the Impact of Computer-Mediated Communication from the Perspective of Online Teaching

The present study aims to understand the relational dialectics between faculty and students during online teaching. Moreover, the study uses a relational dialectic framework- RDT (Baxter & Montgomery, 1996) to analyze the computer-mediated communication- CMC process and relationship building between faculty and students. The study employed a mixed methodology of quantitative and qualitative methods. A total of 158 surveys were shared with university-level students exposed to online learning for at least one year and previously exposed to in-class learning. Principle component factor analysis with a varimax rotation was conducted for each scale. Open-ended questions are analyzed through a thematic analysis. Three main themes derived from the qualitative analysis were accountability vs. unreliability, low anxiety vs. low motivation, and conventional vs. CMC-delivered practical (lab) classes. Findings support the need for a more specific online learning pedagogical discussion on low-level motivation, group project difficulties, and a more interactive lab-oriented class delivery.