Reviewerly/exHarmony huggingface dataset:
A Robust-Facial Expressions Recognition System using Deep Learning Architectures

In recent times, Facial Expression Recognition strategies are viewed as the main field of examination in biometric innovation. In this exploration paper, we present a Facial Expression Recognition (FER) framework partitioned into three stages: The Viola-Jones face detection algorithm, facial image enhancement utilizing Adaptive Histogram Equalization calculation (AHE), and component learning for classification. For learning the highlights followed by classification we utilized VGG16, ResNet50 Convolutional Neural Networks (CNN) models for the proposed framework. Our trial work was performed on the JAFEE data set and CK+ information base. At last, the correlation with different strategies on the two data sets shows the heartiness and adequacy of the proposed approach. The ResNet50 architecture achieved high accuracy rates of 98.21% and 97.08 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for the respective evaluations.
Calibration-Aware Bayesian Learning

Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions. In order to improve the quality of the confidence levels, also known as calibration, of a model, common approaches entail the addition of either data-dependent or data-independent regularization terms to the training loss. Data-dependent regularizers have been recently introduced in the context of conventional frequentist learning to penalize deviations between confidence and accuracy. In contrast, data-independent regularizers are at the core of Bayesian learning, enforcing adherence of the variational distribution in the model parameter space to a prior density. The former approach is unable to quantify epistemic uncertainty, while the latter is severely affected by model misspecification. In light of the limitations of both methods, this paper proposes an integrated framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs), that applies both regularizers while optimizing over a variational distribution as in Bayesian learning. Numerical results validate the advantages of the proposed approach in terms of expected calibration error (ECE) and reliability diagrams.
Blockchain Adoption for a Sustainable Agricultural Supply Chain: Opportunities and Challenges for the Dairy Industry

Blockchain is a decentralized and tamper-resistant digital ledger technology that records and stores transactions or information in a secure and transparent manner across a network of computers. Each transaction, or “block,” is linked to the previous one, creating a continuous chain of information. This technology ensures data integrity, transparency, and trust among participants, as changes to the blockchain require consensus from the network, making it highly resistant to manipulation or unauthorized alterations. It is a technology that has the potential to dramatically transform the supply chains. This study investigates the opportunities and challenges of blockchain technology in transforming supply chains, and the legitimacy of its implementation into the dairy industry. By conducting a comprehensive review of recent literature at the intersection of blockchain, sustainable supply chain, and the dairy supply chain, this paper aims to determine the best blockchain-based solution for improving the efficiency and sustainability of dairy supply chains.
Distributionally Robust Domain Adaptation

Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples from the target domain, which calls for DA methods that guarantee the robustness and generalization of the learned models. In this paper, we propose DRDA, a distributionally robust domain adaptation method. DRDA leverages a distributionally robust optimization (DRO) framework to learn a robust decision function that minimizes the worst-case target domain risk by transferring knowledge from a given labeled source domain sample. We utilize the Maximum Mean Discrepancy (MMD) metric to construct an ambiguity set of distributions that provably contains the source and target domain distributions with high probability. Hence, the risk is shown to upper bound the out-of-sample target domain loss. Our experimental results demonstrate that our formulation outperforms existing robust learning approaches.
Robust Decentralized Online Learning against Malicious Data Generators and Dynamic Feedback Delays with Application to Traffic Classification

Motivated by the real-world application of traffic classification at the network edge, we study the problem of robust decentralized online learning against malicious data generators that can manipulate their data features with an aim to gain preferred classification outcomes. Multiple agents cooperatively learn classification models to make online decisions. They periodically exchange their models, e.g., traffic classification models, between neighbors in a decentralized network and update local model parameters on the fly based on the models they have access to and feedback on the observed local data samples that are dynamically delayed. In this work, we propose two decentralized online learning algorithms, RDOC-O and RDOC-C, respectively against ordinary malicious and clairvoyant malicious data generators. Our theoretical performance analysis shows that the two algorithms have provable sub-linear individual regret bounds under mild conditions. To validate our analysis, extensive performance evaluations are conducted in the application of network traffic classification using two real-world data traces. Our results show that the two proposed algorithms compare favorably with an optimal offline classification model in the presence of malicious data generators, and they can achieve a steady-state F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of around 0.85, which validates their effectiveness and makes them appealing in practice.
VagueGAN: A GAN-Based Data Poisoning Attack Against Federated Learning Systems

Federated learning (FL) is a privacy-preserving distributed learning paradigm relying on but without directly accessing privately owned datasets. However, the ‘‘available but not visible’’ nature of training data in FL leads to security risks. In particular, ‘‘not visible’’ local data can easily become the best targets of poisoning attacks. Although existing data poisoning methods may successfully attack FL systems, they mostly lead to significant data statistical changes and thus can be not hard to detect. In this paper, we propose VagueGAN, a new data poisoning attack model that unconventionally leverages the power of generative adversarial network (GAN) to generate seemingly legitimate vague data with appropriate amounts of poisonous noise. The quality of such vague data can be controlled on demand to achieve a balanced trade-off between attack effectiveness and stealthiness. Extensive experiments show that data poisoning attacks enhanced by our VagueGAN not only better degrade FL outcomes with low efforts but also are generally much less detectable.
Spatial Audio Production with a New Volumetric Amplitude Panning and Diffusion Technique

The world of spatial audio production is expanding rapidly with multichannel and Dolby Atmos formats becoming increasingly popular in music, cinema, and gaming. To keep up with the pace, it’s crucial to have tools that accurately represent sound objects in 3D, are intuitive, and effectively translate ideas into perceptual reality. A 3D panner is an essential tool that accurately places a sound source or an object in any point of the 3D space. However, traditional techniques such as Vector Based Amplitude Panning (VBAP) require the listener to be positioned at a "sweet spot" surrounded by loudspeakers on either a 2D ring or a 3D sphere, which limits their effectiveness. Our research presents a novel algorithm for volumetric amplitude panning that does not assume any sweet spot and can work for any loudspeaker layout, both asymmetric and symmetric. Additionally, we’ve included geometric and distance-based diffusion techniques that guarantee smooth spread across all directions, distances, and diffusion amounts. With this algorithm, we can improve the accuracy and efficiency of sound object placement and enhance the overall spatial audio experience.
Detection for Illegal Right Turn Vehicles in the Korea Traffic System

In recent years, the number of traffic fatalities caused by illegal right turn vehicles in Korea has increased by approximately 2 times, from 0.34 to 0.64. However, there is currently no dedicated enforcement system in place for detecting illegal right turn vehicles, unlike the speed violation enforcement systems. To address this issue, this study aims to develop an algorithm for the detection of illegal right turn vehicles. You Only Look Once version 5 (YOLOv5) is used for the recognition of vehicles, pedestrian crossings, and pedestrians. AI Hub dataset and Common Objects in Context(COCO) dataset are utilized as the training dataset. Specifically, 36,808 samples from the AI Hub pedestrian crossing video dataset are used for training to detect the pedestrian crossing, while the COCO dataset is divided into a training set of 118,287 samples, a validation set of 5,000 samples and it is used for detecting pedestrians and vehicles. For testing the algorithm, news footage related to illegal right turn vehicles provided by the Korean news channel JTBC is used as the test dataset.
Mixed Reality Human(MRH) Teacher for Guide for Manufacturing Operation

Recently, manufacturing site are applying mobile mixed reality information visualization and interaction technology that considers situational awareness and ease of use for workers to reduce safety accidents and increase work efficiency for troubleshooting. In this paper, we intend to implement a mobile mixed reality-based guide based on field work information in the form of guide guidance by virtual humans. When implementing the guide, in explaining the manual-based scenario, facial expressions or gestures of virtual dormancy are reflected according to the difficulty or content of the task, and when converting voice to text in the guide creation process, meaning is discriminated in phoneme units and converted we want to present the form of By applying the virtual human implemented in this way to the guide, the operator will feel more real and trust the contents of the guide more when interacting with the virtual human.
Simultaneous Person, Face, and Hand Detector Based on Improved YOLOv5

Many person, face, and hand detectors have reached remarkable achievements in the last century. With the convolutional neural networks' emergence, applications based on these detectors were increasingly widely applied in practice. Following this trend, this paper develops a simultaneous person, face, and hand detector based on YOLOv5 network architecture. The research focuses on redesigning the backbone and neck with compact network architectures EfficientNet and CBAM attention technique. The proposed architecture is trained and evaluated on the fine-tuned Human-Parts dataset. As a result, this network reached 87.2% of mAP@O.5 and 58.6% of mAP@O.5:0.95. This experimet outperforms most of the original YOLOv5 network architectures and is comparable to large-scale YOLOv5 architectures and the latest YOLOv8 architecture.
A systematic approach to monitoring and assessing the quality of training of students of the college of physical culture

Relevance. This study reflects ways to improve the effectiveness of training a mid-level specialist in the field of physical culture and sports through the implementation of a systematic approach to monitoring and evaluating students' academic achievements. The purpose of the study is to show the experience of implementing methodological recommendations for the development of inter-disciplinary tasks in natural science disciplines at the Yekaterinburg College of Physical Culture "UralGUFC". Methods and organization of research. To analyze the results of the next stage of the FEPO, we used the results presented in the rating lists in accordance with the value of the indicator AP3 "The proportion of students who completed 70 % or more of diagnostic work tasks during the evaluation of students' achievement of learning outcomes according to the declared educational program". The Yekaterinburg College of Physical Culture conducted an analysis of the test results of students majoring in 49.02.01 Physical Culture and 49.02.02 Adaptive physical culture in natural sciences. To do this, we used data from rating lists, which indicate the "Percentage of points scored for the performance of PIM" for each student. The analysis allows us to conclude that it is necessary to increase the requirements for the training of students in a number of disciplines. To this end, methodological recommendations were developed for the implementation of a systematic approach in the process of monitoring and assessing the level of readiness of students in natural sciences, taking into account the requirements of accreditation indicators. Conclusions. Thus, the leading vectors of the development of an educational organization are the development and implementation of an internal system for assessing the quality of education and, as the main component, the quality of educational activities. To achieve these goals, the Yekaterinburg College of Physical Culture has developed methodological recommendations for interdisciplinary control of student training. The work continues in the direction of the development of the bank of appraisal funds.
Optimized Provisioning Techniques for Geo-Distributed SDP-Enabled Next Generation Networks Security

What advancements might Next Generation Networks (NGN) unleash that existing ones cannot? NGNs are envisioned to empower the connection between billions of people and zillions of heterogeneous Internet of Things (IoT) devices while consolidating intelligence and autonomy. However, several security-related challenges will be introduced and apparently, the security solutions and architectures used in previous network generations will not be sufficient. The Cloud Security Alliance's (CSA) Software Defined Perimeter (SDP) is a potential candidate to provide the much-needed security framework for next-generation networks. However, the lack of a scalable SDP controller will be a considerable drawback for the wide adoption of the SDP framework. Therefore, this paper focuses on modeling a multi-SDP controller placement problem as a VNF-FGE in a Geo-distributed NFV-based environment as a potential solution to secure next-generation networks. Due to its NP-hard nature, this type of problem can be addressed by extending the NCO approach via Reinforcement Learning (RL) to optimize the reward policy in accordance with the constraints of the problem. The agent developed can learn the placement decisions of the SDP controllers by inference (i.e., policy strategy) through the RL process. The experiment's analysis reveals the RL approach's superiority over the well-known Gecode optimization solver.
Joint Communication and Learning Design of Differential Privacy for Federated Learning over Multi-cell Networks

In this paper, a joint communication and learning resource allocation design is investigated for differential privacy (DP) based federated learning (FL) over multi-cell networks. In the considered model, each cell serves multiple users and each user transmits its local model plus Gaussian noise to the corresponding serving base station for protecting privacy. Each base station aggregates the received local model and forwards the obtained result to the server for final aggregation. To consider the privacy of the whole system, the problem is formulated as a total DP minimization through optimizing user association, transmit power, DP noise power with considering the convergence, user association, and power control constraints. To solve this problem, an iterative algorithm is proposed through optimizing user association subproblem, power control subproblem, and DP noise power subproblem. Simulation results show the effectiveness of the proposed scheme.
PRRkNN: Efficient and Privacy-Preserving Range-Based Reverse kNN over Encrypted Data

Cloud computing's ease of management and on-demand availability features have triggered the boom of outsourcing query services to the cloud. Considering the not-fully trustfulness of the cloud server, the outsourced query services should take the data privacy into consideration, especially when sensitive data are involved. Although many query types have been studied in the context of cloud computing, the range-based reverse k nearest neighbors (RkNN) query is still an unexplored area, which searches data records having any record in the query range as kNN and has wide applications in the taxi dispatching and advertisement placement. As a steppingstone, we propose the first efficient and privacy-preserving range-based RkNN query (PRRkNN) scheme in the cloud. We first design a modified R-tree (MR-tree) to organize the dataset and introduce an efficient MR-tree based RkNN query algorithm to handle RkNN queries with sublinear query efficiency. Then, we propose a sign-preserving matrix encryption (SPME) scheme to privately determine the sign of the scalar product between two records and deploy Paillier homomorphic encryption to introduce a two-party multiplication protocol for secure multiplication between a matrix and a vector. After that, we leverage the SPME scheme and the multiplication protocol to design our PRRkNN scheme. In addition, security analysis shows that our PRRkNN scheme achieves the desired security; and the performance evaluation verifies its efficiency.
GBMIA: Gradient-based Membership Inference Attack in Federated Learning

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.
Specific Emitter Identification for Integrated Sensing and Communication

In this paper, we investigate the specific emitter identification (SEI) problem for the integrated sensing and communication (ISAC) receiver. Here, we consider the wireless channel effect in the SEI problem, which makes a poor identification performance. Then, the sparse Bayesian learning (SBL) identifier is proposed. Specifically, by exploiting the communication training sequence as the sensing signal, the fingerprint extractor using the SBL is first designed to decouple the fingerprints and the wireless channel. Then, the classifier using the weighted Euclidean distance is proposed to identify the unknown transmitter. The proposed identifier realizes the dual functions of integrated identification and communication together with the communication receiver without the additional dedicated sensing signals. Numerical results show that the proposed SBL identifier achieves high identification performance even with the wireless channel effect.
Cross-Layer Device Fingerprinting and Its Applications to Network Security

In this paper, we describe a novel cross-layer fingerprinting approach (CL-FP) that aims at uniquely identifying wireless devices by extracting inherent cross-layer device features based on the requirements of the driving network application(s). We demonstrate how our system can be applied to MAC spoofing detection. Our contributions include: (1) We propose the CL-FP framework and pipeline for detecting MAC spoofing attacks, which, to our knowledge, is the first of its kind; (2) We experimentally confirm theoretical results showing that easier-to-extract FP features can reliably represent harder-to-extract intrinsic physical characteristics of devices; (3) We test and evaluate the performance of the proposed CL-FP approach through simulations in the context of the MAC spoofing detection use case. Our preliminary results show that the proposed CL-FP pipeline provides a lightweight, scalable and reliable end-to-end cross-layer device fingerprinting framework.
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks

Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy.However, the current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and model accuracy declines.To address the statistical imbalances in FL, we propose a clustered data sharing framework which spares the partial data from cluster heads to credible associates through device-to-device (D2D) communication.Moreover, aiming at diluting the data skew on nodes, we formulate the joint clustering and data sharing problem based on the privacy-preserving constrained graph.To tackle the serious coupling of decisions on the graph, we devise a distribution-based adaptive clustering algorithm (DACA) basing on three deductive cluster-forming conditions, which ensures the maximum yield of data sharing.The experiments show that the proposed framework facilitates FL on non-IID datasets with better convergence and model accuracy under a limited communication environment.
FLEC: Federated Learning for Cloud/Edge-Based Smart Industry via Batch Normalization

The rapid growth in the amount of data generated from connected devices in smart industry, opens up new opportunities for improving the quality of service through data sharing. However, the key barriers for data sharing by the users of smart industries are security and privacy concerns. In addition to financial losses, the disclosure of sensitive information might cause major problems such as reputation damage, loss of competitive advantage, legal and regulatory violations, and even harm to individuals or society as a whole. In this paper, we propose FLEC, a federated learning framework for Cloud based smart industries. We use blockchain technology as an underlying architecture for data management and sharing between edge and cloud devices. Federated learning solves the problem of privacy leakage by sharing the model updates instead of raw data while blockchain provides a secure decentralized platform, robustness against poisoning attacks, and incentives to the participants. Finally, to make our approach well-suited for real-world non-iid data scenarios, we use batch normalization to alleviate the data heterogeneity problem. Extensive evaluation and assessment results demonstrate the superior performance of FLEC in aspects of accuracy, efficiency, and privacy protection.