Reviewerly/exHarmony huggingface dataset:
PruVer: Verification Assisted Pruning for Deep Reinforcement Learning

Active deployment of Deep Reinforcement Learning (DRL) based controllers on safety-critical embedded platforms require model compaction. Neural pruning has been extensively studied in the context of CNNs and computer vision, but such approaches do not guarantee the preservation of safety in the context of DRL. A pruned network converging to high reward may not adhere to safety requirements. This paper proposes a framework, PruVer, that performs iterative refinement on a pruned network with verification in the loop. This results in a compressed network that adheres to safety specifications with formal guarantees over small time horizons. We demonstrate our method in model-free RL environments, achieving 40–60% compaction, significant latency benefits (3 to 10 times), and bounded guarantees for safety properties.
Enhancing Keyphrase Generation by BART Finetuning with Splitting and Shuffling

Keyphrase generation is a task of identifying a set of phrases that best represent the main topics or themes of a given text. Keyphrases are dividend int present and absent keyphrases. Recent approaches utilizing sequence-to-sequence models show effectiveness on absent keyphrase generation. However, the performance is still limited due to the hardness of finding absent keyphrases. In this paper, we propose Keyphrase-Focused BART, which exploits the differences between present and absent keyphrase generations, and performs finetuning of two separate BART models for present and absent keyphrases. We further show effective approaches of shuffling keyphrases and candidate keyphrase ranking. For absent keyphrases, our Keyphrase-Focused BART achieved new state-of-the-art score on F1@5 in two out of five keyphrase generation benchmark datasets.
Prediction of the Number of Graduates Absorption by the Field of Expertise

The study program accreditation process also includes reporting forms filled in with the number of graduates working according to their field of expertise. A low conformity rate will indicate a lack of training and skills for graduates. If the conformity rate is high, it will affect the results of accreditation to increase and make the level of public trust in the study program higher, as well as from the perspective of graduate staff users. The study program needs to know the data of its graduates earlier as a step to increase the compatibility rate of the field of work with the field of lectures for its graduates. These problems can be minimized by carrying out a prediction process on the suitability of graduate fields. The variable used to predict is a data series of the number of graduates working in the right field over a certain period of time. The use of linear regression for predictions in the case of determining the exact field really helps make it easier for study programs to help predict the number of students who graduate by working in the right field. This study uses a linear regression method.
Medi-Block Record Secure Data Sharing in Healthcare System: Issues, Solutions and Challenges

With the advancements in the era of artificial intelligence, blockchain, cloud computing, and big data, there is a need for secure, decentralized medical record storage and retrieval systems. While cloud storage solves storage issues, it is challenging to realize secure sharing of records over the network. Medi-block record in the healthcare system has brought a new digitalization method for patients’ medical records. This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital. It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval. Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record. This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records. It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing, such as using Blockchain technology, Access Control, Privacy-Preserving, Proxy Re-Encryption, and Service-On-Chain approach. Finally, we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.
KELL: A Kernel-Embedded Local Learning for Data-Intensive Modeling

Kernel methods are widely used in machine learning. They introduce a nonlinear transformation to achieve a linearization effect: using linear methods to solve nonlinear problems. However, typical kernel methods like Gaussian process regression suffer from a memory consumption issue for data-intensive modeling: the memory required by the algorithms increases rapidly with the growth of data, limiting their applicability. Localized methods can split the training data into batches and largely reduce the amount of data used each time, thus effectively alleviating the memory pressure. This paper combines the two approaches by embedding kernel functions into local learning methods and optimizing algorithm parameters including the local factors, model orders. This results in the kernel-embedded local learning (KELL) method. Numerical studies show that compared with kernel methods like Gaussian process regression, KELL can significantly reduce memory requirements for complex nonlinear models. And compared with other non-kernel methods, KELL demonstrates higher prediction accuracy.
Features of Designing a Low-Speed Generator for the Accumulation of Gravitational Energy

The article is devoted to the study of increasing the efficiency of low-speed synchronous generators with permanent magnets as part of a system with a gravitational energy storage. Features analysis of the gravitational energy storage devices use was carried out. The shortcomings of pumped-storage power plants in comparison with solid-state storage power plants are considered. Practical significance: the research results were used in the development of low-speed generators with permanent magnets for use in a system with a gravitational energy storage. They can significantly reduce the weight and size of the system as a whole.
Enhanced Real-Time Fuzzy PID Controlled WNCS Over TCP/UDP Protocol

Real-Time wireless networked control systems (WNCS) are commonly used in various applications, including wheelchairs. However, these systems often suffer from a delay issue caused by the randomly generated delay accompanying the network, which can make the system response sluggish and unstable in some cases. This delay issue is a significant problem that needs to be addressed because it affects the system's performance and reliability. Therefore, this paper proposes a solution to the delay issue by utilizing a Fuzzy Proportional-Integral-Derivative (PID) controller to control a high-torque Real-Time Direct Current motor, commonly used in wheelchairs while also checking for wireless network connectivity. To transfer the data from Arduino Microcontroller to MATLAB, Transmission Control Protocol/Internet Protocol (TCP/IP) or User Datagram Protocol (UDP) with Node MCU will be used since MATLAB Truetime, which will be used to simulate Wireless Network, does not work with Direct Serial Communication. The simulation results show that the proposed system can efficiently control the Real-Time DC motor in real-time with the best settling time of 0.5371 for 0.01 sec sampling time and 0.5371-sec settling time with 0.001 sampling time. The proposed solution addresses a significant problem in WNCS and can contribute to developing more reliable and efficient Real-Time wireless networked control systems.
Age Estimation from Human Facial Expression Using Deep Neural Network

Human age estimation from a single image has been significant for researchers, yet challenging tasks in computer vision. Age estimation is becoming more complicated as the vast diversity of individuals’ faces includes those with diverse races, genders, and geographical locations. As a result, the accuracy of age determination depends on a large image dataset. Therefore, an attention module can be used to give extra attention to important patches in the image. Whereas a conventional Convolutional Neural Network model treats every pixel of an image equally. This paper proposes a CNN model with a self-attention module, and two different datasets are merged for evaluation. However, an attention mechanism is regarded as a dynamic weight adjustment process. In this process, scaled dot-product is used as an attention-scoring function for the query and key vector element-wise multiplication. It adjusts weight dynamically through back-propagation while training the model. Moreover, transfer learning is utilized to get optimal performance faster. However, the proposed model achieves state-of-the-art results, outperforming other models by attaining the lowest mean absolute error, 3.515.
Political polarization and politainment: Methodology for analyzing crypto hate speech on TikTok

TikTok has become an international benchmark: In 2022 it was the most downloaded application in all of Europe. Political discourse has not stayed on the sidelines, and its rhetoric has been adapted to a young electorate who feels at home with this new platform. Given this social network’s explosion, the main objective of this research is to analyze which topics and approaches the parties with the greatest parliamentary representation use most frequently by looking at their activity on TikTok and determine how they have leveraged a time of polarization and political confrontation to a network geared toward entertainment. To achieve these objectives, a methodological triangulation based on three analyses –quantitative content, discourse, and qualitative content– is used. The sample consists of 250 posts from the official profiles of the following Spanish political parties: Partido Socialista Obrero Español (PSOE), Partido Popular (PP), Ciudadanos (C’s), Unidas Podemos (UP), and Vox. Specifically, a non-random stratification system was used, selecting the 50 videos from each party that had with the highest number of views since they launched their profiles on this social network. From a theoretical standpoint, this study proposes a new concept, the aggressive critical message (ACM), which is presented as a further contribution to the conceptual framework of emerging studies on crypto hate speech. From an empirical standpoint, the results obtained reveal that the partisan rhetoric on TikTok is not homogeneous, and substantial differences are seen between the topics addressed by the different parties. It also is concluded that the parties are implementing techniques used in entertainment alongside polarization itself, which trivializes politics, aiming to go viral rather than to provoke thought.
Frameproof codes, separable codes and $$B_2$$ codes: Bounds and constructions

Abstract In this paper, constructions of frameproof codes, separable codes, and $$B_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>B</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> codes are obtained. For each family of codes, the Lovász Local Lemmais used to establish lower bounds for the codes. The obtained bounds match all best known bounds in the literature. Our strategy allows us to present constructions of the aforementioned codes, by using the variable framework for the Lovász Local Lemma.
Face Recognition Based on Windowing Techniques, with Compressed Hybrid Domain Features

Face recognition is used in human-machine interfaces for day-to-day activities in real-time applications. This paper proposes face recognition based on windowing techniques with compressed hybrid domain features. The face images are resized to 224×160 and are segmented into windows of sizes 4 × 4, 8 × 8, and 16 × 16. The hybrid features are extracted using Discrete Wavelet Transform (DWT) and the covariance concept on each window. The average covariance of each window is computed to obtain compressed final features. Artificial Neural Network (ANN) is used for classification to identify a person effectively. The obtained results of average recognition are around 98% for four benchmarked public face databases. It is observed that the proposed model attains a higher recognition rate with a reduced number of features compared with the existing methods.
SegMatic: A Deep Neural Network Learning Model for Semantic Segmentation

Semantic segmentation enables vehicles to accurately identify and categorize objects in their surroundings, such as pedestrians, other cars, road signs, and obstacles. Semantic segmentation, object detection, and deep learning have emerged as critical pillars, enabling machines to perceive and understand the visual world with unprecedented precision. This paper introduces SegMatic, a novel deep-learning model specifically tailored to address the unique demands of autonomous vehicles. SegMatic is a novel deep model for semantic segmentation and precise object detection. This model harnesses the power of deep learning to transform raw images into pixel-wise semantic maps, providing detailed insights into object boundaries and category-specific regions. SegMatic employs a two-stage approach. It uses a modified U-Net as the first stage to extract feature maps. Mask R-CNN is used as the second stage for post-processing. Experiments are conducted on the Pascal VOC 2012 dataset. SegMatic outperformed traditional models with remarkable precision and pixel accuracy scores. It achieved superior results in both semantic segmentation and object detection. This success is evidenced by achieving a mIoU of 94.6 and PA of 95.7 across various object categories. These results substantiate the significance of SegMatic’s contributions to computer vision and deep learning.
Integration of Timbrel, Cepstral Domain and Linear Prediction-Based Features for Replay Attack Detection

The automatic speaker verification system is vulnerable to several spoofing attacks. Among these spoofing attacks, detecting replay attacks is challenging as attackers do not need any expertise to mount replay attacks. Many efforts from the research community have focused on anti-spoofing solutions against the reply attack. Such efforts are classified as one focusing on feature extraction and others concentrating on classifiers. This work evaluates the performance of feature extraction schemes CQCC, LFCC, and MFCC. The success of Linear Prediction analysis has been demonstrated in the past. This work evaluates the performance of LPC and LPCC features. The recent work in the literature has focused on using multiple features and combining these features for improved performance. In this work, numerous components of CQCC, MFCC, LFCC, LPC and LPCC are integrated considering various combinations and evaluated. In literature, the success of Timbrel features has been demonstrated for speaker identification. The feature vector formed using various Timbrel features is integrated with cepstral and linear prediction-based features. Finally, Timbrel features zero cross rate are combined with these multiple features. Among all experiments carried out on the ASVspoof 2017 version 2 database, EER 5.44% is achieved for the integration of zero cross rate and LPC on the development set and 17.79% EER is conducted for the integration of zero cross rate, MFCC, CQCC, LFCC, and LPCC features on evaluation set.
Real-time compensation of projector lens distortion using a linear-grid model in fringe projection profilometry

In fringe projection profilometry, applying pre-distortion to fringe patterns reduces the errors caused by projector lens distortion. However, it is important to note that discontinuous fringe patterns, such as binary fringe patterns, introduce additional errors when using pre-distortion methods. While post-undistortion methods are applicable for discontinuous fringe patterns, the computation is typically time-consuming. We propose a linear-grid model for correcting lens distortion. First, we select multiple equidistant points within the grid to calculate the linear parameters and store them as look-up tables (LUTs). Second, by rounding down the captured distorted point to the nearest integer point, we obtain the index value for LUTs. Finally, we achieve real-time compensation for distortion error through linear expressions. The experimental results show that the proposed effectively mitigates the distortion by a factor of 6 × in terms of root mean squared error. Additionally, it exhibits a computational speed of 409.50 fps, which is an improvement compared to the traditional iterative model at 39.48 fps and the scale-offset model at 264.48 fps.
ASIC design of LZ77 compressor for computational storage drives

Abstract The advent of cloud computing, 5G, and artificial intelligence technology has spurred the exponential growth of big data and the need for data compression. To alleviate the CPU burden, computational engines for data compression integrated into network cards and storage devices, giving rise to computational storage. However, computational storage faces several challenges, such as limited memory resources for large hash tables, data dependency for parallel encoding and decoding, and stringent requirements for bandwidth, latency, and power efficiency. Here, an application‐specific integrated circuit‐based LZ77 solution that aims to address these challenges is proposed. Our solution reduces the hash table resource consumption and the bandwidth fluctuations, while achieving high compression ratios. The solution is implemented using Taiwan Semiconductor Manufacturing Company Limited 12 nm technology and demonstrate that a single LZ77 engine can achieve a 4 GB/s throughput at 1 GHz clock frequency. By using four engines, a computational storage drive to achieve 16 GB/s compression bandwidth is enabled. The solution achieves a comparable compression ratio as the standard LZ77 algorithm with a negligible compression ratio loss of 5%, balancing efficiency and effectiveness.
Intelligent Multi-Agent Systems for Advanced Geotechnical Monitoring

Geotechnical monitoring, essential for ensuring the safety and longevity of infrastructures, has predominantly relied on centralized systems. However, as computational capabilities soar and advancements in Artificial Intelligence (AI) burgeon, the potential for decentralized solutions comes to the fore. This chapter intricately weaves the principles and applications of Multi-Agent Systems (MAS) into the fabric of geotechnical monitoring. It delves deep, elucidating the decentralized approach to monitoring aspects like soil quality and groundwater levels. Through a seamless interplay between agents, we witness real-time data acquisition, intricate analysis, and informed decision-making. While anchoring itself in theoretical foundations, the chapter also illuminates the real-world challenges and proffers potential solutions in geotechnical engineering, thereby mapping the past, present, and future of MAS in this domain.
ACMEOLOGICAL ASPECTS OF THE DEVELOPMENT OF INTELLIGENCE

It is shown that the increase in the specific weight of mental work and its creative nature increases the needs of society for the development of the intelligence of specialists. At the same time, the requirements for the content of the phenomenon of intelligence are changing. This refers to a systematic combination of professionalism and the general and professional culture of an intellectual and his professionally and socially significant personal traits and qualities. The article reveals the system of direct and inverse relationships of intelligence with the acme of a person as the highest level of his life achievements. This applies to both professional competence and personal self-realization. The need for the development and purposeful use of innovative pedagogical technologies, focused on the formation of students' desire to become real intellectuals and master the highest level of professionalism, is substantiated.
Navigating Workload Compatibility Between a Recommender System and a NoSQL Database: An Interactive Tutorial

In this tutorial, the issue of compatibility between a big data storage technology and an analytic workload is explored using a fictitious streaming company as an example. The tutorial offers an interactive approach to help students understand the importance of considering workload compatibility when adopting new technologies. We provide instructors with the following: two Jupyter Notebooks that analyze compatibility, a detailed instructor guide on how to execute these notebooks, lessons learned, and appendices containing solutions and explanations. This tutorial provides a valuable resource for instructors teaching courses in database systems, big data, and analytic concepts, helping students develop practical skills to navigate effectively the complexities of big data technologies.
Generating Robust Adversarial Examples against Online Social Networks (OSNs)

Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep neural networks (DNNs) are found to be fragile against the inevitable lossy operations conducted by OSNs. As a result, the AEs would lose their attack capabilities after being transmitted over OSNs. In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities. To this end, we first propose a differentiable network termed SImulated OSN (SIO) to simulate the various operations conducted by an OSN. Specifically, the SIO network consists of two modules: (1) a differentiable JPEG layer for approximating the ubiquitous JPEG compression and (2) an encoder-decoder subnetwork for mimicking the remaining operations. Based upon the SIO network, we then formulate an optimization framework to generate robust AEs by enforcing model outputs with and without passing through the SIO to be both misled. Extensive experiments conducted over Facebook, WeChat and QQ demonstrate that our attack methods produce more robust AEs than existing approaches, especially under small distortion constraints; the performance gain in terms of Attack Success Rate (ASR) could be more than 60%. Furthermore, we build a public dataset containing more than 10,000 pairs of AEs processed by Facebook, WeChat or QQ, facilitating future research in the robust AEs generation. The dataset and code are available at https://github.com/csjunjun/RobustOSNAttack.git .
BdSL47: A Complete Depth-based Bangla Sign Alphabet and Digit Dataset

Sign Language Recognition (SLR) is crucial for enabling communication between the deaf-mute and hearing communities. Nevertheless, the development of a comprehensive sign language dataset is a challenging task due to the complexity and variations in hand gestures. This challenge is particularly evident in the case of Bangla Sign Language (BdSL), where the limited availability of depth datasets impedes accurate recognition. To address this issue, we propose BdSL47, an open-access depth dataset for 47 one-handed static signs (10 digits, from ০ to ৯; and 37 letters, from অ to ँ) of BdSL. The dataset was created using the MediaPipe framework for extracting depth information. To classify the signs, we developed an Artificial Neural Network (ANN) model with a 63-node input layer, a 47-node output layer, and 4 hidden layers that included dropout in the last two hidden layers, an Adam optimizer, and a ReLU activation function. Based on the selected hyperparameters, the proposed ANN model effectively learns the spatial relationships and patterns from the depth-based gestural input features and gives an F1 score of 97.84%, indicating the effectiveness of the approach compared to the baselines provided. The availability of BdSL47 as a comprehensive dataset can have an impact on improving the accuracy of SLR for BdSL using more advanced deep-learning models.