Reviewerly/exHarmony huggingface dataset:
Reflection and Foresight on Personal Information Protection and Optimization in Public Health Emergencies in China—From the Perspective of Personal Information Collection during the Period of China’s Dynamic-Zero COVID-19 Prevention and Control Policy

Public health emergencies threaten the overall public health security of the country. Based on the need to control the ways of infection, the collection and processing of personal information by the government have become an important part of epidemic prevention and control. However, personal information related to the epidemic is highly sensitive, which contains other personal information and even private information in addition to information on personal health. In the early days of China's response to the public health emergency of COVID-19, a great deal of non-desensitized information was transmitted in an unaccredited manner. With the implementation of epidemic prevention and control measures, the collection and processing of personal information in China have gradually transited from the initial disorder and chaos to the current orderly, legal, and effective situation, continuously optimizing the processing paths of personal information. Serious summary and reflection on the optimization path of China's epidemic-related information collection and processing methods by looking for a border at which the way and scope of personal information disclosure in future major public health emergencies are compatible with its purpose and role may help to improve the development of China's personal information protection legal system from a long-term perspective.
A Fuzzy Variant of the Multi-verse Optimizer for Optimal Design of Fuzzy Controllers

In this work we propose a dynamic parameter adaptation approach using Fuzzy logic over the Multi-verse Optimizer algorithm (FMVO), which is tested with benchmark functions and control problems. For this study, we use some control problems, one is cruise control, which focuses on achieving a desired speed on a vehicle with a certain weight; from here, we use the temperature control in a shower, which controls the temperature of the water by adjusting the water valves, the last control problem is the inverted pendulum, which is one of the most common test control problems used for optimization in fuzzy logic controllers; all of these problems focus on optimizing membership functions for the fuzzy inference system using Mamdani and Sugeno models in the tests. The objective of this study is to determine if the Multi-verse Optimizer is improved by dynamically adjusting some of its parameters using fuzzy logic, making it more competitive over other metaheuristics.
Evolutionary Approach to the Optimal Design of Fuzzy Controllers for Trajectory Tracking

This paper presents three configurations for evolving the membership functions’ parameters for a path-following fuzzy controller. Designers of fuzzy controllers have many options when selecting which parameters and how the optimization algorithm adjusts them. The experiments obtained excellent results with a relatively simple controller using three membership for each of the variables. We tested the proposed controller by a robot simulation that follows a path using a rear-wheel tracking technique. We optimized the controller’s parameters using a genetic algorithm that consisted of a population of chromosomes containing the parameters of a controller. Each individual’s fitness in the populations was determined by calculating the root-mean-square error resulting from each simulation. We have observed, changes in the selection of parameters that parameters to optimize, we tested four, eight, and 18, affect the results obtained and are essential to minimize the total time of evaluations and a minimum possible error. We also tested two ranges for the random values used for initializing the population and verify that this selection also impacts the algorithms’ performance.
Efficient Extraction of Technical Requirements Applying Data Augmentation

Requirements for complex technical systems are documented in natural language sources. Manually extracting requirements from these documents – e.g., to transfer them to a requirements management tool – is time-consuming and error-prone. Today, machine learning approaches are used to classify natural language requirements and thus enable extraction of these requirements. However, in practice there is often not enough labeled domain-specific data available to train such models. For this reason, this work investigates the performance in artificially generating requirements through data augmentation. First, success criteria for a method for extracting and augmenting requirements are elicited in cooperation with industry experts. Second, the performance in the augmentation of requirements data is investigated. The results show that GPT-J is suitable for generating artificial requirements: weighted average F1-score: 62.74 %. Third, a method is developed to extract requirements from specifications, augment requirements data, and then classify the requirements. As a final step, the method is evaluated with requirements data from three industry case examples of the engineering service provider EDAG Engineering GmbH: assembly latch hood, adjustable stopper hood and trunk curtain roller blind. Evaluation shows that especially the transferability of models is improved when they are trained with augmented data. The developed method facilitates eliciting complete requirements sets. Performance of artificial intelligence models in requirements extraction is improved applying augmented data and therefore the method leads to efficient product development.
COVID-19 Health Passes: Practical and Ethical Issues

Several countries have implemented COVID-19 health passes or certificates to promote a safer return to in-person social activities. These passes have been proposed as a way to prove that someone has been vaccinated, has recovered from the disease, or has negative results on a diagnostic test. However, many people have questioned their ethical justification. This article presents some practical and ethical problems to consider in the event of wishing to implement these passes. Among the former, it is questioned how accurate diagnostic tests are as a means of ensuring that a person is not contagious, whether vaccination guarantees immunity, the fact that health passes can be forged, whether they encourage vaccination, and the problem that there is no universally recognized health pass. Among the ethical issues, it is discussed whether health passes promote discrimination and inequality and whether they violate rights to privacy and freedom. It is concluded that health passes have enough ethical justification to be implemented.
Generative hypergraph models and spectral embedding

Many complex systems involve interactions between more than two agents. Hypergraphs capture these higher-order interactions through hyperedges that may link more than two nodes. We consider the problem of embedding a hypergraph into low-dimensional Euclidean space so that most interactions are short-range. This embedding is relevant to many follow-on tasks, such as node reordering, clustering, and visualization. We focus on two spectral embedding algorithms customized to hypergraphs which recover linear and periodic structures respectively. In the periodic case, nodes are positioned on the unit circle. We show that the two spectral hypergraph embedding algorithms are associated with a new class of generative hypergraph models. These models generate hyperedges according to node positions in the embedded space and encourage short-range connections. They allow us to quantify the relative presence of periodic and linear structures in the data through maximum likelihood. They also improve the interpretability of node embedding and provide a metric for hyperedge prediction. We demonstrate the hypergraph embedding and follow-on tasks-including quantifying relative strength of structures, clustering and hyperedge prediction-on synthetic and real-world hypergraphs. We find that the hypergraph approach can outperform clustering algorithms that use only dyadic edges. We also compare several triadic edge prediction methods on high school and primary school contact hypergraphs where our algorithm improves upon benchmark methods when the amount of training data is limited.
Comprehensive Comparisons of Uniform Quantization in Deep Image Compression

In deep image compression, uniform quantization is applied to latent representations obtained by using an auto-encoder architecture for reducing bits and entropy coding. Quantization is a problem encountered in the end-to-end training of deep image compression. Quantization’s gradient is zero, and it cannot backpropagate meaningful gradients. Many methods have been proposed to address the approximations of quantization to obtain gradients. However, there have not been equitable comparisons among them. In this study, we comprehensively compare the existing approximations of uniform quantization. Furthermore, we evaluate possible combinations of quantizers for the decoder and the entropy model, as the approximated quantizers can be different for them. We conduct experiments using three network architectures on two test datasets. The experimental results reveal that the best approximated quantization differs by the network architectures, and the best approximations of the three are different from the original ones used for the architectures. We also show that the combination of quantizers that uses universal quantization for the entropy model and differentiable soft quantization for the decoder is a comparatively good choice for different architectures and datasets.
A holistic survey of multipath wireless video streaming

Demand for wireless video streaming services increases with users expecting to access high-quality video streaming experiences. Ensuring Quality of Experience (QoE) is quite challenging due to varying bandwidth and time constraints. Since most of today's mobile devices are equipped with multiple network interfaces, one promising approach is to benefit from multipath communications. Multipathing leads to higher aggregate bandwidth and distributing video traffic over multiple network paths improves stability, seamless connectivity, and QoE. However, most of current transport protocols do not match the requirements of video streaming applications or are not designed to address relevant issues, such as networks heterogeneity, head-of-line blocking, and delay constraints. In this comprehensive survey, we first review video streaming standards and technology developments. We then discuss the benefits and challenges of multipath video transmission over wireless. We provide a holistic literature review of multipath wireless video streaming, shedding light on the different alternatives from an end-to-end layered stack perspective, reviewing key multipath wireless scheduling functions, unveiling trade-offs of each approach, and presenting a suitable taxonomy to classify the state-of-the-art. Finally, we discuss open issues and avenues for future work.
PAGroup: Privacy-aware grouping framework for high-performance federated learning

Federated Learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the cloud, Federated Learning coordinates a group of devices to train a shared model in a distributed manner with the training data located on the devices. However, unbalanced data distribution and heterogeneous hardware configurations across different devices badly hurts the performance of collaborative model and severely impacts the overall training progress. Thus, a framework that can well balance the model accuracy and the training progress is urgently required. In this paper, we propose PAGroup, a privacy-aware grouping framework for high-performance Federated Learning. PAGroup intelligently divides the participating clients into different groups through carefully analyzing the privacy requirement of the training data and the solid social relationship of the participating clients. After that, PAGroup conducts data shaping and capability-ware training in order to improve the model performance while accelerating the overall training process. We evaluate the performance of PAGroup with both simulation and hardware testbed. The evaluation results show that PAGroup improves model accuracy up to 21%. Meanwhile, it decreases 81% communication overhead, 29% computation cost and 84% wall-clock time at best comparing with the baselines.
Revolutionary Dentistry through Blockchain Technology

Multitudinous health data are continually being produced as our activities, including medicine, evolve into the digital age where data plays a decisive role. Challenges come along as well, concerning the collection, secure storage, verification and secure access to the continuously growing data at such a broad scale before valuable information can be extracted to contribute to medical advancement nowadays. With the decentralization feature, huge successes of blockchain technology in overcoming similar challenges in the finance and cryptocurrency sector brought us the confidence to investigate and reveal its immeasurable potential for the health sector, specifically in dentistry. Dentistry is an important area of healthcare, but there is relatively little research focusing on its interactions with blockchain technology. Given the limited amount of existing research on this specific subject, this paper focuses on blockchain in dentistry and aims to provide a conceptual framework for the possible applications of blockchain in dentistry. The framework is organised by different areas of dentistry operations so that dental professionals can easily refer to and identify areas of interest. This contributes to increasing the awareness of blockchain technology among dental professionals and promoting blockchain-empowered revolutions in dentistry. This paper also discusses how blockchain fits alongside other emerging technologies, the challenges that have to be overcome to maximise the functionality and efficiency of this technology, as well as future research directions concerning blockchain implementations in the dental industry.
Lightweight Multimodal Domain Generic Person Reidentification Metric for Person-Following Robots

Recently, person-following robots have been increasingly used in many real-world applications, and they require robust and accurate person identification for tracking. Recent works proposed to use re-identification metrics for identification of the target person; however, these metrics suffer due to poor generalization, and due to impostors in nonlinear multi-modal world. This work learns a domain generic person re-identification to resolve real-world challenges and to identify the target person undergoing appearance changes when moving across different indoor and outdoor environments or domains. Our generic metric takes advantage of novel attention mechanism to learn deep cross-representations to address pose, viewpoint, and illumination variations, as well as jointly tackling impostors and style variations the target person randomly undergoes in various indoor and outdoor domains; thus, our generic metric attains higher recognition accuracy of target person identification in complex multi-modal open-set world, and attains 80.73% and 64.44% Rank-1 identification in multi-modal close-set PRID and VIPeR domains, respectively.
IoT based medical image encryption using linear feedback shift register – Towards ensuring security for teleradiology applications

Telemedicine gains prominence in today’s scenario and image security plays a vital role in the healthcare sector for authenticating data transfer. Medical data, when transferred through a cloud network should be encrypted to ensure security, this research work proposes a medical image encryption scheme using a linear feedback shift register (LFSR). The LFSR generates pseudo-random numbers and shuffles the pixel position. The encrypted image was transferred through a cloud platform, receiver side node decrypts the data to recover the original image. The Digital Imaging and Communications in Medicine (DICOM) computed tomography images are utilized in this research work and for IoT implementation, a Raspberry Pi B+ processor was used. The IoT implementation facilitates data transfer through the cloud network. The performance validation was done by metrics and the results reveal the proficiency of the encryption/decryption model. The decrypted image quality was evaluated in terms of the Peak to Signal Noise Ratio (PSNR) and Means Square Error (MSE), low value of the correlation coefficient proves the robustness of the encryption. The outcome of the research work paves the way for the researchers in the secure transfer of medical data through the cloud platform.
NeCSTGen: An approach for realistic network traffic generation using Deep Learning

Traffic generation is an important tool for many data network activities such as simulation, planning and provi-sioning. Such a generation could be based on theoretical models when available; however, we believe that mimicking ground truth traffic previously collected is a much more generic solution. It provides a comprehensive tool to accommodate the various characteristics of current or future communication systems (IoT, 5G service models,…). The Deep Learning toolbox is now mature enough to provide the right tools to capture and reproduce the multi-scale factors of network traffic. In this paper, we propose a new architecture, called NeC-STGen, based on Deep learning models such as: Variational Autoencoders (VAE) and Recurrent Neural Network (RNN) for the generation of various network traffic. NeCSTGen can generate live traffic that accurately reproduces the original behaviour at the packet, flow and aggregate levels. Our approach allows an innovative fine-grained understanding and generation without the need to understand, in depth, how the protocol to be generated works. Our reproducible architecture generates data that can be exported in a. pcap file to be used for any purpose.
An Accurate &amp; Efficient Approach for Traffic Classification Inside Programmable Data Plane

In-network traffic classification is a class of in-network computing that brings significant benefits to the network, i.e., the first line of defence, classification at line rate and fast reaction time. However, it is still challenging to accurately and efficiently classify Internet traffic at an early stage due to a clear trade-off between flow identification time and classification accuracy - both are competing objectives. To this end, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> e introduce a framework that focuses on deploying an accurate network traffic classifier inside a programmable data plane that can classify the traffic at maximal speed while considering the underlying constraints of the device. Notably, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> e move from statistical feature-based traffic analysis and argue that traffic flow can be classified using a single feature called sequential packet size information as input. We evaluate our approach by identifying different types of IoT traffic inside a programmable data plane. Our findings demonstrate that accurate and early-stage network traffic classification is achievable with minor use of networking device resources.
Learning Generalized Wireless MAC Communication Protocols via Abstraction

To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatically learn innovative MAC protocols catering to extremely diverse services. This topic has received significant attention, and several reinforcement learning (RL) algorithms, in which BSs and UEs are cast as agents, are available with the aim of learning a communication policy based on agents' local observations. However, current approaches are typically overfitted to the environment they are trained in, and lack robustness against unseen conditions, failing to generalize in different environments. To overcome this problem, in this work, instead of learning a policy in the high dimensional and redundant observation space, we leverage the concept of observation abstraction (OA) rooted in extracting useful information from the environment. This in turn allows learning communication protocols that are more robust and with much better generalization capabilities than current baselines. To learn the abstracted information from observations, we propose an architecture based on autoencoder (AE) and imbue it into a multi-agent proximal policy optimization (MAPPO) framework. Simulation results corroborate the effectiveness of leveraging abstraction when learning protocols by generalizing across environments, in terms of number of UEs, number of data packets to transmit, and channel conditions.
Energy Harvesting Aware Client Selection for Over-the-Air Federated Learning

Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.
Dynamic Deep Neural Network Adversarial Attacks for Edge-based IoT Devices

Edge-based IoT devices have experienced phenomenal growth in recent years due to rapidly increasing demand in various emerging applications which typically utilize machine learning (ML) models such as Deep Neural Network (DNN) and demand low latency and low power consumption. To support the edge requirements, ML models have to support faster inference and less computation. Dynamic DNNs (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NN) have been proposed to support low-latency and power-saving on edge devices by enabling conditional computations and context dependant activation of the network model for inference; saving computational time and edge resources, hence they are becoming popular for edge applications. In this paper, we show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NN are vulnerable to our novel adversarial attack, Dynamic DNN Adversarial attacks (DDAS). Unlike conventional adversarial attacks that target classification accuracy, DDAS targets the IoT device resources such as the battery, latency, and so on. We show that our attack is effective under various attack scenarios with a high attack success rate. We also provide a retraining scheme as a countermeasure to DDAS and show its effectiveness.