Reviewerly/exHarmony huggingface dataset:
Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence

In recent years, edge intelligence (EI) has emerged, combining edge computing with AI, and specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed high-level synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%. This study provides two different optimization schemes for edge intelligence applications through design and experimentation and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.
Student’s Speaking Anxiety and Motivation in EFL Learning Process at Vocational High Schools in North Bintan

The research aims at exploring the relationship between speaking anxiety and motivation among vocational high school students in EFL learning in Bintan Islands. In conducting this research, the method used is descriptive analysis. There are 60 samples taken from four vocational high schools with 15 samples from each school. Those are SMKN 1 Bintan Utara, SMKN 1 Seri Kuala Lobam, SMK Perkapalan, and SMK Muhammadiyah. The data collected were in the form of questionnaire and interview. The results of this research found that there are some factors that influence students’ EFL learning process such as anxiety about mispronouncing, making mistakes, and regarding teacher interruption for mistake correction, the lack of vocabulary, and the less attractive and interactive teaching style in the classroom. Those factors cause the low motivational issue in speaking English matters.
The Cluster Analysis by Using Nonparametric Cubic B-Spline Modeling for Longitudinal Data

Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.&#x0D; In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.&#x0D; The longitudinal balanced data profile was compiled into subgroups by penalizing the pairwise distances between the coefficients of the cubic B-spline model using one of the common penalize functions, the Minimax Concave Penalty function (MCP). This method, in turn, works to determine the number of clusters through one of the model selection criteria, Bayesian information criteria (BIC), and we used optimization methods to solve their equations. Therefore, we applied the alternative direction method of the ADMM multiplier algorithm to reach approximate solutions to find the estimators of the nonparametric model using R statistical software.Longitudinally balanced data were generated in the simulation study, as the number of subjects was 60 and the number of repeats (time) was 10 for each subject. The simulation was iterated 100 times, and it showed that employing the MCP partial methods on the cubic model can group profiles into clusters, which is the aim of this paper.&#x0D; &#x0D; Paper type: Research paper.
Unveiling Advanced Computational Applications in Quantum Computing: A Comprehensive Review

The field of advanced computing applications could experience a significant impact from quantum computing, which is a rapidly developing field with the potential to revolutionize numerous areas of science and technology. In this review, we explore into the various ways in which complex computational problems could be tackled by utilizing quantum computers, including machine learning, optimization, and simulation. One potential application of quantum computers is in machine learning, where they could be used to improve the accuracy and efficiency of algorithms. Complex optimization problems, such as those encountered in logistics and finance, can be addressed using quantum computers as well. Furthermore, the utilization of quantum computers could enable the simulation of intricate systems, such as molecules and materials, leading to significant applications in fields like Physics and Material Technology. Although quantum computers are currently in the early stages of development, they possess the potential to propel numerous areas of science and technology forward in a significant manner. Further research and development are needed to fully realize the potential of quantum computing in the field of advanced computing applications.
ENHANCING USER EXPERIENCE THROUGH SENTIMENT ANALYSIS FOR KATSINA STATE TRANSPORT AGENCY: A TEXTBLOB APPROACH

Katsina State Transport Authority is the state's government-owned transportation provider, operating in all local governments. Because of its extensive reach, it faces a difficult problem in measuring customer satisfaction with the services it delivers. The purpose of this research is to improve the experiences of Katsina State Transport Authority users by using sentiment analysis and the TextBlob library to categorize comments as neutral, negative, or positive. The study begins with meticulous data collecting using Google Forms to provide a representative sample that captures an all-encompassing view of user opinions. The study used feature engineering and model fine-tuning to improve the process, tailoring TextBlob's performance to the complexities of transportation-related feedback. The results reveal that 71% of respondents are usually happy with the agency's services, while 9% offered negative comments with ideas for improvement. This study's findings, which included sentiment analysis and topic modeling, present a road map for enhancing services and planning.
Penerapan Metode Convolutional Neural Networks pada Pengenalan Gender Manusia berdasarkan Foto Tampak Depan

Recognition is one of the many problems encountered today, this problem has several ways to be solved. This research used Convolutional Neural Networks (CNN), which is a deep neural networks method as a means of face recognition, which has been proven to be widely used in face classification, using a dataset of male and female facial photos totaling 27,167 photos, of which 17,678 are male and 9,489 are male. woman. To avoid unbalanced data processing, the researchers disguised the photos of women and men so that the total photos used for the training amounted to 18,978 photos. Besides that, the researcher also added dropout as a test parameter. The author uses python to implement gender differences in the images in the data that has been prepared. For the preparation of the Convolutional Neural Networks model architecture the authors use several layers. Then the data will be trained before being tested with new data that has been prepared where the new data for testing is divided into two datasets to see if there are differences in accuracy results. What distinguishes the two datasets is the position of the photo and the background of the photo. Of the two existing datasets, the first dataset produces an average of 73.33%, while the second dataset produces the highest 84.34%.
ADMINISTRASI PEMBIAYAAN PENDIDIKAN DI MIS NURUL HIDAYAH KECAMATAN MEDAN DENAI

The purpose of this observation is to explain how Education Funding Administration at the Elementary School level is carried out at MIS Nurul Hidayah, Medan Denai District. This observation is included in a descriptive study with a qualitative approach. Data was collected through interviews, observations, and documentation. The validity of the data was tested through triangulation techniques and sources. Data were analyzed using Miles Huberman’s interactive model. The results of observations show that the sources of education funding at MIS Nurul Hidayah Medan Denai consist of student parents’ contributions, Central BOS/Regular BOS, and assistance from foundations. These funds are used to fulfill National Education Standards, including Graduate Competency Standards, Content Standards, Process Standards, Facilities and Infrastructure Standards, Management Standards, Education Financing Standards, and Educational Assessment Standards, except for Educator and Education Personnel Standards. However, there is a discrepancy between the budget stated in the Activity Plan and School Budget (RKAS) and the results achieved. This is due to limited funds received from the government and the small number of students.
Easy Travelogue: A Travelogue Editor with Automatic Image Recommendation and Insertion

Travelogues are a common media form that incorporates both text and images. Typically, they are composed after the completion of a travel period. Creating a travelogue demands substantial time and effort, particularly in the curation of suitable images from the extensive collection of photos taken during the journey to complement the text. Consequently, we have developed and implemented Easy Travelogue, a travelogue editor that utilizes visual and language models. It offers real-time image suggestions while writing the text and can automatically insert fitting images into the finished content. The editor is versatile and can be readily utilized for personal travelogues, travel blogs, and various social media platforms, facilitating users in effortlessly sharing and showcasing their travel experiences.
Exploring the Capabilities of a General-Purpose Robotic Arm in Chess Gameplay

As the field of human-robot collaboration continues to grow and autonomous general-purpose service robots become more prevalent, robots need to obtain situational awareness and handle tasks with a limited field-of-view and workspace. This paper addresses these challenges using the game of chess as a testbed with a general-purpose robotic arm. To achieve successful robotic manipulation in playing board games like chess, the robot must follow game rules and prioritize human safety, all while operating in a limited workspace with field-of-view constraints. The proposed solution uses a 6-DOF general-purpose robotic arm with a parallel gripper attached to a tea table. A pipeline is proposed that considers the rules of the chess game, the constraints of the robot, and environmental conditions to generate valid moves and countermoves. To validate the algorithm, experiments were conducted with the robotic platform, and the parameters of the system were tuned. The proposed algorithm achieved competitive performance, with an average robot turn success rate of 91.94%. This research demonstrates the potential of a general-purpose robot with a single camera to engage safely in competitive tasks with humans, even with these constraints.
UrduSpeakXLSR: Multilingual Model for Urdu Speech Recognition

Speech recognition, a disruptive technology, has revolutionized human-machine interaction. While numerous Automatic Speech Recognition (ASR) models are publicly available via HuggingFace, the majority cater to English language. For Urdu, however, models are scarce or closed-source, with open-sourced ones often lack the robustness. Our research addresses this scarcity, focusing on the challenges posed by dialects, slangs, and accents. In this work, we introduce an innovative ASR model leveraging the pretrained XLS-R model based on Wav2Vec2.0 architecture, trained on CommonVoice corpus V11. Our approach outperforms other deep learning-based techniques both qualitatively and quantitatively, offering promising results for an under-resourced language.
UltraCytomic: A cross-scale exploration to improve risk estimation of the malignant thyroid nodule

Initial estimation of the thyroid nodule malignancy mainly relies on the analysis of ultrasound images, but cytology confirmation is always required. Despite clinical decision guides, final criteria evaluation is still prone to subjectivity in both, ultrasound and cytology. Recently, artificial intelligence has been applied to identify malignant thyroid nodules with outstanding results in both image modalities. However, these automatic models have been separately developed either to ultrasound or cytology, ignoring coupled relations which are commonly used by physicians in clinical practice. Malignancy prediction is herein improved by concatenating relevant cytology and ultrasound image features and then used to obtain coupled models. The proposed approach is challenged with 314 patients, coming from two independent databases containing either only ultrasound or coupled ultrasound-cytology images. Basically, the most relevant ultrasound features were selected from the first image database with only ultrasound information (299 patients and 650 thyroid nodules). Afterward, malignancy prediction was improved in a second independent database composed of patient-coupled thyroid/ultrasound images (40 nodules, eight malignant, from 15 patients). The obtained results show that the model trained with coupled information outperformed the one trained with only ultrasound, i.e. accuracy and fl-score were respectively 0.80 and 0.77 when ultrasound and cytology were combined, compared to the respective 0.67 and 0.63 obtained with only ultrasound.
Optimizing Strategy Games: Ant Colony Optimization vs. Minimax Algorithm

This article proposes an Ant Colony Optimization (ACO) algorithm, an optimization method to find paths in graphs, adapted to solve strategic games. The games of study are Tic-Tac-Toe (also known as noughts and crosses, three in a row, or Xs and Os), and Chess. The algorithms' performance is contrasted by contending ACO against the Minimax algorithm, in different setups of Tic-Tac-Toe and Chess. The performance is explained in terms of average time response, correctness of the move choice, and memory used when executing the function. Results reveal a slightly better average performance by the ACO algorithm compared to Minimax. These findings highlight the ability of ACO in decision-making algorithms without requiring knowledge of previous games. Furthermore, the results suggest that the ACO-based path optimization approach can be an effective alternative to improve the efficiency of decisions made by intelligent systems in environments that require rapid response.
A comparative analysis between two convolutional networks architectures for semantic segmentation of histopathology breast cancer images

This study presents a comparative analysis efficacy between Fully Convolutional Networks (FCN) and U-Net architectures for semantic segmentation of tissues in histopathology breast cancer images. The imperative nature of precise and reliable image segmentation within the realm of computational pathology is highlighted, given its pivotal role in automating tasks involving structure detection within Whole-Slide Images (WSIs). Leveraging a publicly available dataset of breast cancer histopathology images from the Breast Cancer Semantic Segmentation (BCSS) challenge, the research evaluates the performance of FCN and U-Net models. The experimental results show that the best model was U-Net, under a binary cross entropy training, which achieves a performance among tissue classes (tumor and stroma) for semantic segmentation in terms of average accuracy of 0.8162, a Dice score of 0.6785 and Jaccard index of 0.5140. In contrast, FCN achieves an average accuracy of 0.7556, a Dice score of 0.6625 and a Jaccard index of 0.4956. Thus, the U-Net’s superior segmentation performance is evident, wherein its predicted masks exhibit a noticeable resemblance to actual segmented masks for both tumor and stroma classes.
Quantum Teleportation from Photon to Matter at Telecom Band

We experimentally demonstrate quantum teleportation from a telecom photonic time-bin qubit to a solid-state quantum memory at telecom band. The demonstration is compatible with fiber communication infrastructure, which is important for future quantum networks.
Timeslot-Aware Shared Protection Scheme with Dynamic Request Adjustment in QKD Optical Networks

In this paper, we proposed a timeslot-aware shared protection scheme with dynamic request adjustment (TAPS-DRA) to enhance timeslot utilization and success ratio of requests within quantum key distribution (QKD) optical networks. Simulation results show that the proposed TAPS-DRA can achieve higher success ratio of requests and greater security requirement adaptability.
Training Data Leakage via Imperceptible Backdoor Attack

Recently, deep neural networks (DNNs) have been widely used and proven successful in many real-world tasks. There are many third-party DNN services available for data holders who want to develop custom DNN applications for their data and tasks. To ensure data privacy, it is crucial to safeguard the data holder's training data. This paper explores a unique attack paradigm where a hostile third-party DNN model supplier subtly obtains training data from the data holder. Prior attacks which can steal training data typically use augmented datasets to memorize the information of the data that the attacker intends to steal. However, these attacks are easily identified since the augmented datasets are visually different from the original dataset and rendered ineffective. In this attack, we generate an augmented dataset by modifying a portion of the training data using the DNN-based image steganography technique. This approach creates an augmented dataset that is visually identical to the original training dataset, making it difficult for humans to detect. Through extensive experiments, we have successfully and quietly accessed the confidential training data of data holders.
Zero-padding on Connected Directed Acyclic Graphs for Spectral Processing

Directed acyclic graphs (DAGs) are often utilized for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this setting because the eigendecomposition of the adjacency matrix yields all eigenvalues equal to zero. This inherent property of DAGs results in an inability to differentiate between frequency components of signals on such graphs. To address this limitation, we propose, in this paper, a zero-padding approach for connected DAGs. This approach involves augmenting the original connected DAG with additional nodes that are connected to the existing structure. These added nodes are characterized by signal values set to zero and does not interfere with the original DAG except at the source and sink. The proposed technique enables the spectral evaluation of system outputs on DAGs, that is the computation of vertex-domain convolution (output of a graph filter) in without the adverse effects of aliasing due to the added nodes (added nodes do not change the original output of a filter on a DAG).
Analysis of Graph Matched Filters for Higher-Order Diffusion Signals

Matched filters for signals residing on directed graphs are analyzed in this paper. In this case, the adjacency matrix is asymmetric, therefore commonly leading to nonorthogonal eigenvectors. The concept of matched filters is crucial for signal analysis, as well as for understanding convolutional neural networks (CNN) for data on graphs. We analyze the performance of graph-matched filters for diffusion signals, particularly focusing on the case when the optimal order of the matched filter and the initial vertex of signal diffusion are unknown.
Search of Highly Selective Cells in Convolutional Layers with Hebbian Learning

Deep Convolutional Neural Networks (ConvNets) have demonstrated successful implementations in various vision tasks, including image classification, segmentation, and image captioning. Despite their achievements, concerns persist regarding the explainability of these models, often referred to as black-box classifiers. While some interpretability papers suggest the existence of object detectors in ConvNets, others refute this notion. In this paper, we address the challenge of identifying such neurons by utilizing Hebbian learning to discover the most associated neurons for a given stimulus. Our method focuses on the VGG19 and ResNet50 networks with the Dogs-vs-Cats dataset. During experimentation, we found that the most associated hidden neurons to the labels are not object detectors. Instead, they seem to encode relevant aspects of the category. By shedding light on these findings, we aim to improve the understanding and interpretability of deep ConvNets for future advancements in the field of computer vision.
Exploiting Pattern Recognition using Chimp Optimization Algorithm with Machine Learning for Financial Crisis Prediction

Financial crises are typically the result of a combination of economic, political, and market factors, and they can be triggered by events that are challenging to foresee. Financial Crisis Prediction (FCP) has played a most important part in the financial industry. While predicting financial crises is challenging, some attempts have been made by researchers and organizations to use Machine Learning (ML) techniques to find potential risks and patterns that could be related to impending financial crises. ML techniques can analyze enormous quantities of data and recognize complex relationships that mayn't be instantly evident to human analysts. Therefore, this study offers the design of Enhancing Financial Crisis Prediction using the Chimp Optimization Algorithm with Machine Learning (EFCP-COAML) algorithm. The primary objective of the EFCP-COAML technique is to predict the automated and accurate prediction of the financial crisis. For accomplishing this, the EFCP-COAML technique comprises two major processes such as classification and hyperparameter tuning. At first, the EFCP-COAML method uses a Kernel Extreme Learning Machine (KELM) based prediction procedure. In the next phase, the COA is employed for the parameter selection of the KELM model which in turn enhances the predictive performance. An extensive set of tests were accomplished to demonstrate the superior accomplishment of the EFCP-COAML technique. The simulation results inferred the advancement of the EFCP-COAML approach over other present techniques with greatest accuracy of 94.01%.
Continuous- Variable Quantum Key Distribution with Practical Unbalanced Heterodyne Detection

We report a continuous-variable quantum key distribution protocol with unbalanced heterodyne detection considering practical imperfection factors, where a higher secret key rate is achieved compared with the balanced protocols.