Reviewerly/exHarmony huggingface dataset:
Minimizing Staleness and Communication Overhead in Distributed SGD for Collaborative Filtering

Distributed asynchronous stochastic gradient descent (ASGD) algorithms that approximate low-rank matrix factorizations for collaborative filtering perform one or more synchronizations per epoch where staleness is reduced with more synchronizations. However, high number of synchronizations would prohibit the scalability of the algorithm. We propose a parallel ASGD algorithm, <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -PASGD, for efficiently handling <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> synchronizations per epoch in a scalable fashion. The proposed algorithm puts an upper limit of <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> on <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> , for a <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -processor system, such that performing <inline-formula><tex-math notation="LaTeX">$\eta =K$</tex-math></inline-formula> synchronizations per epoch would eliminate the staleness completely. The rating data used in collaborative filtering are usually represented as sparse matrices. The sparsity allows for reduction in the staleness and communication overhead combinatorially via intelligently distributing the data to processors. We analyze the staleness and the total volume incurred during an epoch of <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math></inline-formula> -PASGD. Following this analysis, we propose a hypergraph partitioning model to encapsulate reducing staleness and volume while minimizing the maximum number of synchronizations required for a stale-free SGD. This encapsulation is achieved with a novel cutsize metric that is realized via a new recursive-bipartitioning-based algorithm. Experiments on up to 512 processors show the importance of the proposed partitioning method in improving staleness, volume, RMSE and parallel runtime.
A Repeated Unknown Game: Decentralized Task Offloading in Vehicular Fog Computing

Offloading computation to nearby edge/fog computing nodes, including the ones carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a promising approach for enabling low-latency and compute-intensive mobility applications, such as cooperative and autonomous driving. This work considers vehicular fog computing scenarios where the clients of computation offloading services try to minimize their own costs while deciding which VFNs to offload their tasks. We focus on decentralized multi-agent decision-making in a repeated unknown game where each agent, e.g., service client, can observe only its own action and realized cost. In other words, each agent is unaware of the game composition or even the existence of opponents. We apply a completely uncoupled learning rule to generalize the decentralized decision-making algorithm presented in [7] for the multi-agent case. The multi-agent solution proposed in this work can capture the unknown offloading cost variations susceptive to resource congestion under an adversarial framework where each agent may take implicit cost estimation and suitable resource choice adapting to the dynamics associated with volatile supply and demand. According to the evaluation via simulation, this work reveals that such individual perturbations for robustness to uncertainty and adaptation to dynamicity ensure a certain level of optimality in terms of social welfare, e.g., converging the actual sequence of play with unknown and asymmetric attributes and lowering the correspondent cost in social welfare due to the self-interested behaviors of agents.
Non-malleable Codes from Authenticated Encryption in Split-State Model

The secret key of any encryption scheme that are stored in secure memory of the hardwired devices can be tampered using fault attacks. The usefulness of tampering attack is to recover the key by altering some regions of the memory. Such attack may also appear when the device is stolen or viruses has been introduced. Non-malleable codes are used to protect the secret information from tampering attacks. The secret key can be encoded using non-malleable codes rather than storing it in plain form. An adversary can apply some arbitrary tampering function on the encoded message but it guarantees that output is either completely unrelated or original message. In this work, we propose a computationally secure non-malleable code from leakage resilient authenticated encryption along with 1-more extractable hash function in split-state model with no common reference string (CRS) based trusted setup. Earlier constructions of non-malleable code cannot handle the situation when an adversary has access to some arbitrary decryption leakage (i.e., during decoding of the codeword) function to get partial information about the codeword. In this scenario, the proposed construction is capable of handling such decryption leakages along with tampering attacks.
Reinforcement Technique for Classifying Quasi and Non-quasi Attributes for Privacy Preservation and Data Protection

A quasi attribute refers to a distinct subset of unique attributes that can adequately recognize tuples in a table. Hasty distribution of the quasi attributes will prompt privacy leakage. Choosing private data from a list of attributes is decided by the publisher, and it undoubtedly changes from dataset to dataset. The need for dynamically choosing and informing systems about a quasi and a non-quasi attribute remains a challenging task. Presently, there is no particular automation model for the classification of quasi and non-quasi. It could be a burden when a massive dataset has to be classified, or aggregation of datasets has to be performed. This research paper considers the need to categorize quasi attributes for a non-expert through a direct attack and proposes a solution through the game theory approach and reinforcement machine learning model. For demonstration, a $$2 \times 2$$ state matrix is considered. The results include case-wise time consumption and comparison among all necessary steps for accurate navigation, between various attributes. Among all the notable cases, the matrix arrangement with a quasi attribute in $$00^{th}$$ and $$11^{th}$$ position, non-quasi in $$01^{th}$$ and $$10^{th}$$ position obtained better performance. This reinforcement-based solution helps the automation of the classification of quasi and non-quasi attributes.
Leveraging Emotional and Artificial Intelligences for Employees and Customers

On the basis of discussion in the preceding chapters, the current one draws on service profit chain theory and propose an integrated model to examine the influence of artificial and emotional intelligences on employees' and customers' satisfaction, engagement and loyalty. Employees are referred to as internal customers in this chapter. Two studies were undertaken with employees and customers in Australian-based hotels to examine these relationships. The results show that AI service quality is significantly related to internal and external customer satisfaction and engagement. Both internal and external customer engagement have significant effects on customer loyalty and play a significant mediation role in the service quality—customer loyalty relationship. EI has a significant moderation effect on the relationship between AI service quality and customer engagement for internal and external customers. Discussion and implications of the research findings are provided for researchers and practitioners.
Statistical Tests for Symmetric Primitives

In this work, we show the results of the NIST statistical tests performed on different datasets generated from the output of all possible reduced-round versions of the finalists of the NIST Lightweight standardization process and some of the most popular symmetric ciphers. The objective of the experiment is to provide a metric that compares how conservative or aggressive the choice of the number of rounds is for each candidate. This comparison can add up to the other comparison studies being carried out before the closing of the last round of the NIST Lightweight standardization process, which is supposed to end in late 2022. Note that a similar analysis was also performed during the Advanced Encryption Standard selection in 1999 and 2000 and later in 2011 for the SHA-3 candidates.
Towards Verifying Physical Assumption in Card-Based Cryptography

Card-based cryptography realizes cryptographic tasks, such as secure computation, with a deck of physical cards. The primary research subjects for card-based cryptography are theoretical studies that, for example, propose efficient protocols regarding the number of required cards and procedures. However, almost all prior studies are based on the ideal physical assumption that the backs of all cards are indistinguishable without verification. This study addresses this assumption from a physical perspective to improve the security of card-based cryptography. In the first attempt, we assume a strong attacker who uses ink and a high-performance camera to distinguish the backs of the cards. We experimented with them and confirmed that such an attacker could identify the inked area of the back by analyzing an image captured by the camera. Based on our study, one can address another approach, such as using invisible oil and smartphone cameras to verify the physical assumption. This study is a seminal work that addresses this physical assumption. In addition to the verification, we study secret information that such a strong attacker can obtain during the execution of card-based protocols.
On Single-Server Delegation of RSA

In delegated computation research, the main problem asks how a computationally weaker client device can obtain help from one or more computationally stronger servers to perform some computation. Desirable solution requirements include correctness of the computation, privacy of the inputs, high probability detection of any malicious behavior of a server, low client online runtime, low communication complexity, low client storage complexity, and minimal server trust. In this paper we investigate the problem of single-server delegated computation of the encryption and decryption algorithms in the ubiquitously applied RSA public-key cryptosystem. Our contribution includes state-of-the-art summaries, the first delegated computation protocol for small-exponent RSA encryption, a delegated computation protocol for RSA decryption with improved server runtime and client storage, and an upper bound on the impact of communication on client device energy, which may be of independent interest.
Serverless Computing: New Trends and Research Directions

Serverless computing is an innovative method for the production and distribution of software since it does not rely on a centralised server management infrastructure. As a result of this, serverless computing is becoming more widespread. Instead, the cloud service provider must ensure that the code will execute as intended in the cloud environment. Because everything is taken care of automatically, developers are free to concentrate on creating code rather than establishing and maintaining the infrastructure that is necessary for their programmes to execute. This frees up more time for the developers to work on their projects. This chapter introduces serverless computing and associated technologies. Further, this work summarizes the work done in this book, recent developments and presents future directions.
A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks

Split learning (SL) is an emergent distributed learning framework which can mitigate the computation and wireless communication overhead of federated learning. It splits a machine learning model into a device-side model and a server-side model at a cut layer. Devices only train their allocated model and transmit the activations of the cut layer to the server. However, SL can lead to data leakage as the server can reconstruct the input data using the correlation between the input and intermediate activations. Although allocating more layers to a device-side model can reduce the possibility of data leakage, this will lead to more energy consumption for resource-constrained devices and more training time for the server. Moreover, non-iid datasets across devices will reduce the convergence rate leading to increased training time. In this paper, a new personalized SL framework is proposed. For this framework, a novel approach for choosing the cut layer that can optimize the tradeoff between the energy consumption for computation and wireless transmission, training time, and data privacy is developed. In the considered framework, each device personalizes its device-side model to mitigate non-iid datasets while sharing the same server-side model for generalization. To balance the energy consumption for computation and wireless transmission, training time, and data privacy, a multiplayer bargaining problem is formulated to find the optimal cut layer between devices and the server. To solve the problem, the Kalai-Smorodinsky bargaining solution (KSBS) is obtained using the bisection method with the feasibility test. Simulation results show that the proposed personalized SL framework with the cut layer from the KSBS can achieve the optimal sum utilities by balancing the energy consumption, training time, and data privacy, and it is also robust to non-iid datasets.
A Light-weight Online Learning Framework for Network Traffic Abnormality Detection

Network traffic monitoring plays a crucial role in maintaining the security and reliability of the communication networks. Although Machine Learning (ML) assisted abnormal traffic detection has been emerged as a promising paradigm, the existing data-driven learning-based approaches are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. To this end, we establish an online learning framework for abnormality traffic detection by embracing Gaussian Process (GP) and Sparse Representation (SR). The contributions of this paper are two-fold: 1). We utilize a special kernel, i.e., mixture of Gaussian, to better explore and exploit the evolving traffic characteristics, so as to more accurately model network traffic. 2). To combat noise and modeling error, we formulate a feature vector based on Kullback-Leibler (KL) divergence to measure the difference between normal and abnormal traffic, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.
Approximating the Expectation and Variance of the Square Root of the Quadratic Form in Normal Random Variables

Extensive research has been conducted on the properties of quadratic forms in normal random variables, represented by the symbol Q here. However, analytical results on the square root of the quadratic form, √Q, are limited. The square root of the quadratic form is, for example, used in finance to calculate a portfolio's tracking error. Here we combine analytical reasoning and numerical simulations to evaluate the accuracy of truncated Taylor approximations for the expected value and variance of the random variable √Q.We show that an adjusted three-term Taylor expansion of the expected value of √Q has a relative accuracy of about 4.2%. The three-term Taylor expansion is simple to calculate as it depends on the entries of the covariance matrix.The truncated Taylor approximations for the variance of √Q are more imprecise. An adjusted two-term Taylor expansion fares the best, and it is the simplest to calculate, but its relative accuracy is only about 20.4%.
Swapping-Based Entanglement Routing Design for Congestion Mitigation in Quantum Networks

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.
Synthetic Data Approach for Unconstrained Low-Resolution Face Recognition in Surveillance Applications✱

Unconstrained low-resolution (LR) face recognition is still a challenging problem in computer vision. In real-world scenarios, the gallery images are generally of high-resolution (HR), while the probe images may be of low resolution. In surveillance applications, the challenge of matching LR to HR is more common because the probe images are captured in low resolution while gallery images are of high resolution. LR to HR face matching is challenging because, in the embedding space, there is a need for a common subspace for mapping the LR and HR embeddings. In LR to LR face matching, where probe and gallery both belong to low-resolution, face identification is more difficult because very less visual information is present in the images. In LR to LR face matching, the challenge becomes very hard if faces are tiny in size and belong to low-resolution. In this paper, we implement a deep learning pipeline for matching the LR to HR and LR to LR faces. Due to the absence of LR and HR images of the same identity in the real-world datasets, we have also generated the LR images from HR images using the synthetic data approach. Extensive experimental analyses have been made to compare the performance to other state-of-the-art models.
Adaptive &amp; Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise

This paper proposes a novel adaptive multi-resolution framework for generating terrains. Our framework combines diffusion-based generative network and novel frequency separated terrain features for terrain patch generation. Additionally, we propose to leverage learnable terrain super-resolution for enhancing generated terrain patch followed by novel kernel-based blending of these patches using Perlin noise to generate infinite terrain with realistic terrain features. We provide a comprehensive quantitative and qualitative evaluation of the proposed framework.
Insta Net: Recurrent Residual Network for Instagram Filter Removal✱

Social media filters cause various manipulations in the original image. While these manipulations make the images visibly pleasant, they can affect their authenticity and the performance of further image-processing tasks. Therefore, removing these social media filters is a crucial pre-processing step for the data pipeline of any image-processing task. In this paper, a recurrent residual UNet-based model is proposed to remove the filters. The recurrent residual block allows the units of each layer to evolve over discrete time steps so that the activity of each unit is modulated by the activities of its neighboring units. Because of this characteristic, the networks learn contextual information more efficiently and effectively. Assuming that a filter can be noise, the proposed model learns the feature representation of the filter, and then the original, unfiltered image is retrieved by taking the residuals of the input image and the filter representation. The experimental results show that the proposed model outperforms both supervised and unsupervised methods by a considerable margin in the IFFI dataset.
Multi-Sensor-Based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression

In the fields of body-worn sensors and computer vision, current research is being done to track and detect falls and activities of daily living using the automatic recognition of human actions.In the area of human-machine communication, different combinations of sensors and communication technologies are often used to capture human action.Many researchers have also worked with artificial intelligent systems to detect actions, understand scenes, and implement systems that are more efficient in human action recognition.Although effective approaches are needed to detect outdoor activities with the combination of human actions, feature extraction can be quite a complicated task in a human activity recognition system development.Thus, this paper proposed a solution to detect human activities via hybrid descriptors based on robust features and accurate results.In this study, complex backgrounds, including multiple humans in video frames, were detected.First, inertial signal and video frames are pre-processed using denoising techniques, after which the frames are used to remove the background by detecting human motions and extracting the silhouettes.Then, these silhouettes are further used to extract the human body key points to make the human skeleton.Then the time and frequency domain features are extracted for inertial signals, and geometric features are extracted for the skeleton body points.Finally, multiple feature sets are combined and fed into a zero order optimization model, after which logistic regression is utilized to recognize each action.The proposed system has been evaluated on three benchmark datasets, including, the UP Fall dataset, the University of Rzeszow Fall dataset, and the SisFall dataset and proved its significance by achieving accuracy of 91.51%, 92.98%, and 90.23%, on the aforementioned datasets respectively. INDEX TERMS Fall detection, geometric characteristics
DDS: An Auction Based on a Variant of Data Shapley for Federated Learning

Federated learning (FL) has received great attention in recent years due to its good performance and privacy security. However, there are still some problems in FL, such as resource allocation, client selection and incentive mechanism. Auctions, as an incentive mechanism with many advantages, solve these problems well. In this paper, we propose DDS, a lightweight and effective method to evaluate the data value for the scenario of federated learning. Based on DDS, we can pick up clients with high training value with low calculation cost. Furthermore, our method is independent of data aggregation, which makes it possible to implement in federated learning. We also conduct the simulation experiments to demonstrate the effectiveness and robustness of our method.