Reviewerly/exHarmony huggingface dataset:
FLB2: Layer 2 Blockchain Implementation Scheme on Federated Learning Technique

The usage of the federated learning (FL) concept in the artificial intelligence (AI) field has increased. The main concept of FL is to tackle the centralized-based approach, which requires the model to update training data to the cloud server by creating a decentralized deep learning (DL) model. However, the current FL model is still not completely decentralized, as each client needs to upload the training data to a centralized aggregator. Thus, this paper proposed an implementation of the FL scheme by using blockchain to tackle this problem. The proposed system uses the blockchain as the place to exchange training data instead of sending the training data immediately to the aggregator. In addition, this paper also tried to implement the layer 2 blockchain to minimize the time needed to exchange training information between each client and aggregator. The simulation result of this paper shows that we are able to implement the layer 2 blockchain in the FL system successfully. Also, it is shown that by using the layer 2 blockchain, training data exchange time is able to be reduced by around 50% compared to the layer 1 blockchain. In addition, this paper shows that the implementation of the layer 2 blockchain does not affect the performance of the FL model in terms of accuracy.
A 28nm 11.2TOPS/W Hardware-Utilization-Aware Neural-Network Accelerator with Dynamic Dataflow

With the rapid evolution of AI technology, various neural network structures have been developed for diverse applications. As a typical ease, Fig. 22.4.1 shows that the convolution (Conv) layer used in the convolutional neural networks (CNNs) features distinct shapes and types. Neural network accelerators with high peak energy efficiency have been demonstrated [1–4]. However, they usually suffer decreased hardware (mainly multiply-accumulate (MAC) units) utilization for various network structures, which reduces the attainable energy efficiency accordingly. To improve the MAC utilization, the Nvidia deep learning accelerator (NVDLA) [5] applies hardware parallelism along the channel direction, but the MAC utilization is still low for the shallow layers. According to our experiments, NVDLA achieves 23% MAC utilization in the worst case. A Scatter-Gather scheme [4] is utilized to mitigate the utilization drop for shallow layers by rearranging the input features (IF), but the improvement is limited. As depthwise convolution (Dwcv) has been widely used, the accompanying low MAC utilization also needs to be considered. Taking MobileNetV2 as an example, NVDLA only achieves 0.4% utilization for Dwcv. To address these critical issues, this work presents a utilization-aware neural network accelerator, which can dynamically change the level of parallelism along multiple dimensions to maximize the MAC utilization. The chip achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 97.3{\%}$</tex> MAC utilization on benchmark networks while delivering <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.7\times$</tex> higher attainable energy efficiency than state-of-the-art designs [1–4].
Implementation of Real-Time Adversarial Attacks on DNN-based Modulation Classifier

In this paper, we provide a hardware implementation for over-the-air (OTA) adversarial attack on a deep neural network (DNN)-based modulation classifiers. Although Automatic modulation classification (AMC) using the DNN-based method outperforms the traditional classification, it has been proven that the machine learning (ML) approaches lack robustness against adversarial attacks. Therefore, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. The case study presented evaluates the adversarial attack performance and its effects on the accuracy of the DNN-classifier OTA using a universal software radio peripheral (USRP) B210. Firstly, we develop an intelligent AMC system using USRPs to classify four digitally modulated signals, namely, BPSK, QPSK, 8PSK, and 16QAM, in real-time. We consider a wireless communication system that consists of three software-defined radios (SDRs), namely, transmitter, receiver, and adversarial attack. While the Rx classifies the received signal, using a DNN-based classifier, the adversarial attack node intends to misclassify the DNN-based classifier by perturbing the input data of with an adversarial example. The developed adversarial node implements the Fast-Gradient Sign method (FGSM) to generate the needed perturbation. The results of the conducted experiment show that the DNN-based classifier achieves 97% accuracy in the absence of an adversarial node. However, after deploying the adversarial attack the classifier accuracy drops to 42%.
AI-based RF-Fingerprinting Framework and Implementation using Software-Defined Radios

Radio frequency (RF) fingerprinting is considered to be a promising security solution for wireless communications at the physical layer. RF fingerprinting is still in its infancy, and much research is needed to further improve the detection capabilities. To address this problem, in this paper, we propose utilizing software-defined radios (SDRs), which have proven to be extremely beneficial to the RF research community. We demonstrate the capability of RF fingerprinting by identifying the transmit radios that are in the pre-selected whitelist (authorized) and reject any other transmit radios not found in the whitelist. We have experimented with four different universal software-radio peripherals (USRPs) models with a total of fourteen USRPs for our RF fingerprinting solution. Deep learning models and transfer learning are used to train the RF fingerprinting models. Experimental results reveal that the ability of RF fingerprinting the USRPs drops as the hardware quality of USRPs improves. For low-end USRPs an accuracy of 99% is achieved; however, for high-end radios, the accuracy decreased to as low as 43%. This is due to the difficulty of finding anomalies with high-quality hardware, which is essential for successful RF fingerprinting.
Suspicious Activity Trigger System using YOLOv6 Convolutional Neural Network

Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically home surveillance CCTV. Therefore, this paper introduces a security system known as Suspicious Activity Trigger System (SATS) that able to automatically trigger an alarm or an alert message whenever suspicious activity is detected from the CCTV video image. The activity will be detected in a video image using Deep Learning technique which is YOLOv6 Convolutional Neural Network (CNN) algorithm. The algorithm will detect an object which is a person in the video and classify it as a suspicious activity or not. If the activity is classified as the suspicious activity, the system will automatically display a trigger message to alert SATS user. The user can therefore take whatever appropriate measure to prevent being a victim. Experiments have been conducted using a dataset taken from Google Open Image. We also implemented the experiments on the self-obtained dataset. Based on the experiment, 92.53% for precision and 96.6% of the accuracy is obtained using this algorithm. Therefore, YOLOv6 can be implemented in the security system to prevent crimes in residency areas.
2.7 MetaVRain: A 133mW Real-Time Hyper-Realistic 3D-NeRF Processor with 1D-2D Hybrid-Neural Engines for Metaverse on Mobile Devices

A neural radiance field (NeRF) [1] uses a deep neural network (DNN) to create 3D models by training the DNN to memorize 3D scene geometry from a few photos. Prior work uses conventional computer graphic algorithms, such as ray-tracing or SLAM, for the same purpose. With NeRF, the generated model can display hyper-realistic 3D content on the metaverse, with quality better than or the same as 3D images rendered by complicated ray-tracing. The 3D model can also be shared with other metaverse users via low-bandwidth communication because the transaction requires <1MB of parameters. NeRF is promising for 3D reconstruction, but also for a wide range of applications from depth estimation to 3D style transfer [2], however, its heavy computational demands stand in the way of its applicability for mobile and wearable applications.
16.1 MuITCIM: A 28nm $2.24 \mu\mathrm{J}$/Token Attention-Token-Bit Hybrid Sparse Digital CIM-Based Accelerator for Multimodal Transformers

Human perception is multimodal and able to comprehend a mixture of vision, natural language, speech, etc. Multimodal Transformer (MuIT, Fig. 16.1.1) models introduce a cross-modal attention mechanism to vanilla transformers to learn from different modalities, achieving excellent results on multimodal AI tasks like video question answering and multilingual image retrieval. Transformers require specialized hardware for efficient inference [1]. Prior work demonstrates that a Compute-In-Memory (CIM) accelerator with attention sparsity can efficiently process vanilla transformers [2]. Multimodal signals like video and audio exhibit diverse token significance, providing new opportunities for token sparsity via runtime pruning [3]. Additionally, activation functions like GELU and softmax produce many near-zero values that expose bit sparsity in the most-significant bits (MSB). In utilizing attention-token-bit hybrid sparsity, there are three challenges: 1) For attention sparsity, irregular patterns result in long reuse distance, which requires CIM to hold infrequently used weights, lowering CIM utilization. 2) Although token sparsity reduces computation, MuIT's cross-modal attention processes tokens from two modalities with different token lengths (N) and embedding dimensionality <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{d}_{\mathrm{m}})$</tex> , causing high latency in cross-modal switch. 3) At the bit level, since token sparsity reduces value locality, a CIM macro has more variance in effective bitwidth for the same group of inputs. In a conventional CIM's bit-serial MAC scheme, computation time is defined by the longest bitwidth.
Convolutional neural network optimisation to enhance ESPI fringe visibility

The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI). The proposed approach is based on quick and light trainings to select the architecture parameters (network depth and kernel sizes) to maximize the performance of the neural network improving the visibility of ESPI images. To measure the performance, the structural similarity index (SSMI) will be the lead indicator, and the need for large datasets to train neural networks, unavailable for ESPI images, forces the use of a simulated ESPI image dataset along the process. This dataset is computed using Zernike polynomials to simulate local surface deformations in the specimen under test and simulated true speckle fields for the reference and object field involved in ESPI techniques.
Do you have too much security?

Organisations seem to accumulate cyber security solutions, to the point where they become difficult to use and it becomes impossible to ensure that they are operating efficiently. The result can be unnecessary and expensive duplication as well as unseen gaps that leave you vulnerable to attack.
Ideas about the meanings of education among students: socio-cultural peculiarities of self-regulation

The article reveals the correlation of terminal (development of abilities, need for knowledge, general culture) and instrumental (diploma, opportunity to make a career, prestige) meanings of education. It based on a comparative analysis of the ideas of higher education among students and other groups of young people in education. The methodological basis of the study is the concept of self-regulation of the life of young people. An analysis of the connections between the terminal and instrumental meanings of education and other elements of the socio-cultural mechanism of self-regulation of life activity (archetypes, mental traits, value orientations towards knowledge, habitual attitudes, subcultural meanings), belonging to the basic types of cultures made it possible to determine the trends in attitudes towards education of student and student youth. The tendency to instrumentalize the value attitude to the education of university students and young people in general is stable. It was revealed that the meanings of education correlate less significantly with archetypes and mentality. Subcultural meanings as properties of a generation, containing various kinds of “activities” and elements of demonstrative behavior, have significant connections with the instrumental meanings of education and, on the contrary, in some cases their denial – with the terminal ones. A similar paradoxical relationship between the terminal and instrumental meanings of the education of student youth is also recorded in correlations with the denial of belonging to the innovative and spiritual types of basic culture, which significantly distinguishes it from student youth. As sociocultural factors in the formation of ideas about the meanings of education of student youth, the type of settlement, the level of income, the level of education of parents are singled out. Ideas about the meanings of education of university students are considered in relation to the institutional regulation of higher education. It is concluded that the steady trend of instrumentalizing the attitude of student youth to education is consistent with such a direction of purposeful regulation of higher education as the practice orientation of educational programs. Education is understood as a means of increasing the manageability of one’s life, which means increasing the level of social certainty.
Development of Learning Independence: Cooperation Between a Teacher and a Schoolchild

The ability of a younger schoolchild to show independence in solving practical and educational problems is one of the results of primary education. Obviously, many schoolchildren find it difficult to show independence due to individual differences: personal qualities, attitudes towards learning, purposefulness in cognition, and even the pace of learning activities. However, a teacher who has knowledge of age-related physiology and psychology, using modern teaching methods and technologies, can help a student become more proactive, teach him to plan and control his actions when studying training courses and performing creative tasks. Learning operations that the student masters under the guidance of a teacher and begins to apply first with the help of an adult, and then without outside help, fall into the treasury of his independent actions. The article discusses some approaches to the organization of cooperation between a teacher and a schoolchild, stimulating the development of a student’s independence in learning. Examples of tasks on the mathematical content are given, illustrating these approaches.
ESA-FedGNN: Efficient secure aggregation for federated graph neural networks

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve O(logNlog(logN)) improvements compared to ON2 in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.
Teaching Chatbot Development as a Tool to Motivate High School Students for IT Professions

The article shows the importance of professions in the field of IT. The problems that arise when applicants choose professions in this area are noted. In particular, difficulties in mastering the exact sciences at school, underestimation of the importance of information technology in solving professional problems, low motivation. It is noted that such a direction of informatics as "Social informatics" can help in choosing professions in the field of IT, but there are not enough teaching hours on this topic and the material is mostly theorized. To solve this problem, it is proposed to include an elective course “Fundamentals of Social Informatics. Creation of a chat bot”, where students, along with theoretical questions, would learn practice, namely, programming chat bots. Two online constructors are highlighted that can be used for learning - BorisBot and Botmother, and the basics of working in them are shown. The development of an elective course with an emphasis on the module "Practical issues of creating chat bots" is given, which provides classes in which high school students learn to program chat bots.
On the security of multivariate-based ring signature and other related primitives

The multivariate public-key cryptography (MPKC) provides a promising class of post-quantum signature schemes. Its theoretical security comes from the intractability of the multivariate quadratic problem (MQ problem), which is an NP-hard problem [1]. Nevertheless, not every instance of MQ-problem is hard [2], [3], [4], [5], [6] while the problem itself is NP-hard. Thus, it is important to understand and analyze the nature of the quadratic systems used in existing MPKC so that the security of the multivariate schemes can be properly assessed. Herein, we present a detailed cryptanalysis of the multivariate based ‘ring’ signature scheme presented by Wang et al. [7]. We utilize the attacks on the underdetermined system of multivariate quadratic equations. We prove both theoretically and experimentally that the scheme (Wang et al. [7]) can be broken in polynomial time even for rings of small size. Additionally, we observed that the attack strategy we proposed in this work is not only limited to [7], but can also be applied to break the unforgeability of other multivariate based schemes such as verifiable ring signature scheme [8], multivariate group signature scheme [9], and Gui and GeMSS based ring signature scheme [10], [11].
Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation

Although data have been extensively leveraged for process monitoring and control in advanced manufacturing, it still suffers from the connection issues among sensors, machines, and computers, which may lead to significant data loss, i.e., missing region in the collected data, in the application of data-driven monitoring. To address the missing region issues, one popular way is to perform missing data imputation. With the advances of machine learning, many approaches have been developed for the missing data imputation, such as the popular Generative Adversarial Imputation Network (GAIN), which is based on the Generative Adversarial Network (GAN). However, the inherent shortcomings of generative adversarial architecture may still lead to unstable training. More importantly, the collected online sensor data in manufacturing are in sequential order whereas GAIN considered the input data independently. Hence, to address these two limitations, this work proposes a novel approach termed transformer-enabled GAIN with selective generation (SGT-GAIN). The contributions of the proposed SGT-GAIN consist of three aspects: (i) the architecture for transformer-enabled generation is developed to capture the sequential information among the data; (ii) a selective multi-generation framework is proposed to further reduce the imputation bias; and (iii) an ensemble learning framework is applied to enhance the imputation robustness. Both the numerical simulation study and a real-world case study in additive manufacturing demonstrated the effectiveness of the proposed SGT-GAIN.
Designing innovative digital platforms from both human and nonhuman perspectives

Abstract Digital platforms are becoming critical infrastructures for supporting a variety of innovative services that enhance our everyday lives. These platforms need to offer not only rational services but also ludic or slow services that focus on human pleasure. One important aspect of creating innovative digital platforms is that their concrete requirements and potential opportunities are vague before they are designed. Thus, designing, prototyping and evaluating digital platforms iteratively is essential for refining or customizing them, as knowledge is gradually gained throughout these iterations. However, it is costly to develop prototype platforms and evaluate them with traditional methods. A better tool that can be used to reveal these platforms’ potential opportunities by conceiving them in a simple and rapid way is needed. In this paper, we present our journey to develop nine digital platforms that share collective human sight and hearing with the Human-Material-Pleasure (HMP) annotation method, which is a tool that we use to describe the visually structured annotations of multiple digital platforms based on the annotated portfolio method. The most significant part of the paper presents annotated portfolios based on the HMP annotation method for the nine digital platforms that we develop and shows how these annotated portfolios play an essential role in revealing and exploring the potential opportunities of our platforms during the refinement process. We also discuss how the HMP annotation method is used in the context of exploring the potential opportunities of wearable shape-changing robotic devices; these devices have significantly different characteristics from our digital platforms, which allows for showing insights more objectively by extracting diverse insights from an alternative angle.