Reviewerly/exHarmony huggingface dataset:
Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment in Mobile Edge Computing

The development of the Internet of Things (IoT) combined with the emergence of federated learning (FL) makes it possible for mobile edge computing (MEC) to gain insight from physically separated data without violating privacy or burdening communication and the server. Due to the distributed nature of MEC, researchers have uncovered that the FL is vulnerable to backdoor attacks, which aim at injecting a subtask into the FL without corrupting the performance of the main task. However, the single-shot backdoor attack in the early training stage is weak. In this paper, we strengthen the early-injected single-shot backdoor attack by using information leakage. We show that FL convergence can be expedited if the client's dataset mimics the distribution and gradients of the whole population. Based on this observation, we propose a two-phase backdoor attack, which includes a preliminary phase for the subsequent backdoor attack. Benefiting from the preliminary phase, the later injected backdoor achieves better effectiveness, as the backdoor effect is less likely to be diluted by normal model updates. Numerical experiments show that the proposed backdoor outperforms existing backdoor attacks in both success rate and longevity, even when defense mechanisms are in place.
Data Privacy Protection Sharing Strategy Based on Consortium Blockchain and Federated Learning

The blockchain is non-tamperable and has a strong certificate storage function, it can realize controllable traceability of data. Federated learning can realize the availability and invisibility of data. Therefore, they have a certain complementarity in function. If they can be combined with each other, it can not only effectively improve the efficiency of data sharing, but also protect data privacy. This paper studies the privacy protection strategy of enterprise information data based on consortium blockchain and federated learning. Starting from the actual needs of data sharing in the big data analysis scenario, using consortium blockchain to build a decentralized trusted network, combined with The federated learning framework realizes the joint sharing modeling of multiparty data. A RAFT efficient consensus mechanism based on credibility guarantees the traceability of the federated learning process is studied, and finally realizes a private and secure enterprise credit information data sharing system.
Job satisfaction as a mediator in improving employee performance through talent and knowledge management

This study aims to analyze Job Satisfaction as a Mediator in Improving Employee Performance through Talent and Knowledge Management. The data analysis used is verification analysis. The population is focused on 16 logistics service companies in the city of Bandung. The analytical tool used is the Structural Equation Model (SEM)-PLS, data obtained through questionnaires to 160 employees. The findings reveal that talent management and knowledge management make a positive contribution to job satisfaction, either partially or simultaneously, which has an impact on employee performance. This is because logistics services will always innovate in accordance with good knowledge management and talent management, therefore employees are required to express their thoughts, ideas and abilities to achieve all of this. This research adds to knowledge about how logistics service companies can manage talented employees and knowledge management that can lead to increased job satisfaction which has an impact on employee performance.
Using similar patients to predict complication in patients with diabetes, hypertension, and lipid disorder: a domain knowledge-infused convolutional neural network approach

Abstract Objective This study aims to develop a convolutional neural network-based learning framework called domain knowledge-infused convolutional neural network (DK-CNN) for retrieving clinically similar patient and to personalize the prediction of macrovascular complication using the retrieved patients. Materials and Methods We use the electronic health records of 169 434 patients with diabetes, hypertension, and/or lipid disorder. Patients are partitioned into 7 subcohorts based on their comorbidities. DK-CNN integrates both domain knowledge and disease trajectory of patients over multiple visits to retrieve similar patients. We use normalized discounted cumulative gain (nDCG) and macrovascular complication prediction performance to evaluate the effectiveness of DK-CNN compared to state-of-the-art models. Ablation studies are conducted to compare DK-CNN with reduced models that do not use domain knowledge as well as models that do not consider short-term, medium-term, and long-term trajectory over multiple visits. Results Key findings from this study are: (1) DK-CNN is able to retrieve clinically similar patients and achieves the highest nDCG values in all 7 subcohorts; (2) DK-CNN outperforms other state-of-the-art approaches in terms of complication prediction performance in all 7 subcohorts; and (3) the ablation studies show that the full model achieves the highest nDCG compared with other 2 reduced models. Discussion and Conclusions DK-CNN is a deep learning-based approach which incorporates domain knowledge and patient trajectory data to retrieve clinically similar patients. It can be used to assist physicians who may refer to the outcomes and past treatments of similar patients as a guide for choosing an effective treatment for patients.
Five-Direction Occlusion Filling with Five Layer Parallel Two-Stage Pipeline for Stereo Matching with Sub-Pixel Disparity Map Estimation

Binocular stereoscopic matching is an essential method in computer vision, imitating human binocular technology to obtain distance information. Among plentiful stereo matching algorithms, Semi-Global Matching (SGM) is recognized as one of the most popular vision algorithms due to its relatively low power consumption and high accuracy, resulting in many excellent SGM-based hardware accelerators. However, vision algorithms, including SGM, are still somewhat inaccurate in actual long-range applications. Therefore, this paper proposes a disparity improvement strategy based on subpixel interpolation and disparity optimization post-processing using an area optimization strategy, hardware-friendly divider, split look-up table, and the clock alignment multi-directional disparity occlusion filling, and depth acquisition based on floating-point operations. The hardware architecture based on optimization algorithms is on the Stratix-IV platform. It consumes about 5.6 K LUTs, 12.8 K registers, and 2.5 M bits of on-chip memory. Meanwhile, the non-occlusion error rate of only 4.61% is about 1% better than the state-of-the-art works in the KITTI2015 dataset. The maximum working frequency can reach up to 98.28 MHz for the 640 × 480 resolution video and 128 disparity range with the power dissipation of 1.459 W and 320 frames per second processing speed.
Efficient Decoder for Turbo Product Codes Based on Quadratic Residue Codes

In this letter, we study turbo product codes with quadratic residue codes (called QR-TPCs) as the component codes. We propose an efficient decoder based on Chase-II algorithm with two convergence conditions for the iterative decoding of QR-TPCs. For each row and column, the Chase-II decoder will stop immediately when one of the conditions is met. The simulation results show that the proposed algorithm has a lower computational complexity compared with existing decoding methods. Moreover, a comparison with 5G low-density parity-check codes shows that the proposed turbo product codes have better performance for short code lengths.
Conceptual analysis of telenursing: an integrative review

Objective: to analyze the concept of telenursing. Methods: this is an integrative review based on the Concept Analysis model, conducted in the LILACS, CINAHL, SCOPUS, Web of Science and MEDLINE/PubMed databases, using the descriptors Telenursing, Remote Consultation and Nursing. Results: 37 studies were analyzed, identifying among the antecedents the scientization of nursing care, advances in health informatics and the dissemination of telehealth and telemedicine. Regarding attributes, the use of Information and Communication Technologies in nursing care, telemonitoring, low cost, reliability and availability stand out. The most common consequences point to better establishment of the link between patient and nurse, expansion of care coverage, health promotion, and better adherence to treatment. Conclusion: scientific evidence contributed to the formulation of the concept of telenursing, which consists of the incorporation of Information and Communication Technologies in nursing care through technological products and processes, in order to meet health needs. Contributions to practice: it contributes to the innovation of nursing care, since it elucidates the scientific subsidies necessary for understanding and discussing telenursing as an emerging phenomenon, as well as presenting the basic components of the concept.
Utilization of Vision Transformer for Classification and Ranking of Video Distortions

The perceptual quality of video surveillance footage has impacts on several tasks involved in the surveillance process, such as the detection of anomalous objects. The videos captured by a camera are prone to various distortions such as noise, smoke, haze, low or uneven illumination, blur, rain, and compression, which affect visual quality. Automatic identification of distortions is important when enhancing video quality. Video quality assessment involves two stages: (1) classification of distortions affecting the video frames and (2) ranking of these distortions. A novel video dataset was utilized for training, validating, and testing. Working with this dataset was challenging because it included nine categories of distortions and four levels of severity. The greatest challenge was the availability of multiple types of distortions in the same video. The work presented in this paper addresses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreover, the average accuracy of level classification was 62% with an average F1 score of 61%.
Multi-Tier Kernel for Disease Prediction using Texture Analysis with MR Images

Denoising magnetic resonance images are traditionally done individually, introducing undesired artefacts like blurring. To solve this issue, this paper offers a unique adaptive interpolation architecture that simultaneously allows for image data augmentation, noise removal, and detail augmentation. The multi-tier kernel (MTK) algorithm adjusts the extrapolation weights based on mathematical features in magnetic resonance (MR) data. The MTK weight matrix is then adaptively sharpened, and a Rician bias corrective is used to reduce Rician noise and improve small details. After processing, the noise eliminates the bias produced by the asymmetric sources. The adaptive MTK, in this way, extends the zero ordering estimating methodology to higher regression power facilitating edge maintenance and detail restoration. Investigation outcomes using genuine and MR images (with/without noise) evidenced that the algorithm delivered good restoration outcomes than conventional deep-learning-based approaches but with fewer requirements and calculation burden.
Demonstration of the Holonomically Controlled Non-Abelian Geometric Phase in a Three-Qubit System of a Nitrogen Vacancy Center

The holonomic approach to controlling (nitrogen-vacancy) NV-center qubits provides an elegant way of theoretically devising universal quantum gates that operate on qubits via calculable microwave pulses. There is, however, a lack of simulated results from the theory of holonomic control of quantum registers with more than two qubits describing the transition between the dark states. Considering this, we have been experimenting with the IBM Quantum Experience technology to determine the capabilities of simulating holonomic control of NV-centers for three qubits describing an eight-level system that produces a non-Abelian geometric phase. The tunability of the geometric phase via the detuning frequency is demonstrated through the high fidelity (~85%) of three-qubit off-resonant holonomic gates over the on-resonant ones. The transition between the dark states shows the alignment of the gate's dark state with the qubit's initial state hence decoherence of the multi-qubit system is well-controlled through a π/3 rotation.
Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Cloud computing brings convenience and cost efficiency to users, but multiplexing virtual machines (VMs) on a single physical machine (PM) results in various cybersecurity risks. For example, a co-resident attack could occur when malicious VMs use shared resources on the hosting PM to control or gain unauthorized access to other benign VMs. Most task schedulers do not contribute to both resource management and risk control. This article studies how to minimize the co-resident risks while optimizing the VM completion time through designing efficient VM allocation policies. A zero-trust threat model is defined with a set of co-resident risk mitigation parameters to support this argument and assume that all VMs are malicious. In order to reduce the chances of co-residency, deep reinforcement learning (DRL) is adopted to decide the VM allocation strategy. An effective cost function is developed to guide the reinforcement learning (RL) policy training. Compared with other traditional scheduling paradigms, the proposed system achieves plausible mitigation of co-resident attacks with a relatively small VM slowdown ratio.
Integrating Traditional Input Devices to Support Rapid Ideation in an Augmented-reality-based Brainstorming

Augmented reality(AR) has the potential to address the limitations of in-person brainstorming, such as digitization and remote collaboration while preserving the spatial relationship between participants and their environments. However, current AR input methods are not sufficient for supporting rapid ideation compared to non-digital tools used in brainstorming: pen, paper, sticky notes, and whiteboards. To help users create comprehensible notes rapidly for AR-based collaborative brainstorming, we developed IdeaSpace, a system that allows users to use traditional tools like pens and sticky notes. We evaluated this input method through a user study (N=22) to assess the efficiency, usability, and comprehensibility of the approach. Our evaluation indicates that IdeaSpace input method outperforms the baseline method in all metrics.
Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.
A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality.
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models

Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauthorized reproduction of DL models can lead to copyright infringement and cause huge economic losses to model owners. Existing copyright protection techniques are mostly based on watermarking, which embeds an owner-specified watermark into the model. While being able to provide exact ownership verification, these techniques are 1) invasive, as they need to tamper with the training process, which may affect the utility or introduce new security risks; 2) prone to adaptive attacks that attempt to remove the watermark; and 3) not robust to the emerging model extraction attacks. Latest fingerprinting work, though being non-invasive, also falls short when facing the diverse and ever-growing attack scenarios. In this paper, we propose a novel testing framework for DL copyright protection: DEEPJUDGE. DEEPJUDGE quantitatively tests the similarities between two DL models: a victim model and a suspect model. It leverages a diverse set of testing metrics and test case generation methods to produce a chain of supporting evidence to help determine whether a suspect model is a copy of the victim model. Advantages of DEEPJUDGE include: 1) non-invasive, as it works directly on the model and does not tamper with the training process; 2) efficient, as it only needs a small set of test cases and a quick scan of models; 3) flexible, as it can easily incorporate new metrics or generation methods to obtain more confident judgement; and 4) fairly robust to model extraction and adaptive attacks. We verify the effectiveness of DEEPJUDGE under typical copyright infringement scenarios, including model finetuning, pruning and extraction, via extensive experiments on both image and speech datasets with a variety of model architectures.
An Examination of Longitudinal Changes in Korean Middle School Students’ Psychological Health According to Their Body Mass Index and Weekly Exercise Hours

Background: It is necessary for adolescents to maintain proper body mass index (BMI) and regular exercise for proper growth, development, and psychological health.Existing studies have demonstrated the significant positive effect of regular physical activity or exercise on preventing socio-psychological problems and cognitive body image issues in adolescents.This study aimed to investigate the longitudinal changes in psychological health factors of mental health, self-concept, self-esteem, and self-efficacy according to BMI and weekly exercise hours among Korean middle school students.Methods: Cohort data were obtained from the Gyeonggi Institute of Education for three years (2015-2017), and a total of 16,027 data points were analyzed.We performed a repeated-measures analysis of variance (3 grades × 4 body mass index groups × 5 weekly exercise hours groups) using SPSS software (version 21.0,IBM Corp., Armonk, NY, USA) for data analysis.Results: The results obtained through the analysis are as follows.As school grades rose among Korean middle school students, psychological health factors such as mental health, self-concept, self-esteem, and self-efficacy decreased.And the deterioration of psychological health factors showed lower positive results for normal and underweight students than for overweight and obese students.Students with a lot of exercise time every week showed a lower decrease in psychological health factors. Conclusions:The results suggest that education for health promotion, considering body mass index and daytime exercise, is necessary to prevent the deterioration of psychological health factors in Korean middle school students.
Privacy-Preserving in FinTech using Deep Learning with Federated Learning in Cryptocurrency

Recent developments in deep learning techniques have produced significant improvements in long-standing AI jobs like drug discovery, gene analysis, and speech and image recognition. Although deep learning has numerous benefits, the identical training dataset that has did it so reliable also raises serious privacy concerns to address these privacy concerns, McMahan et al. created Federated Deep Learning, a together distributed deep learning paradigm for the mobile devices (FDL). Deep learning and distributed computation are essentially combined in FDL, where several parties get involved into the process of distributed training and parameter server records track of a deep learning model that needs to be built. The central parameter server first distributes a pre-trained model globally on a common set of data to each participant. Then, in each cycle, each party utilizes a local dataset to the train and improve the current global model. The gradients are gathered from each party by the central parameter server, which then utilises them to build a new global oriented model for upcoming iteration. At final stage the different parties and the central parameter of server repeat the aforementioned procedure till the global modelling attains a particular accuracy or ideal convergence.