Reviewerly/exHarmony huggingface dataset:
Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning

The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, which is proposed to achieve efficient model training among multiple data providers without direct data access and aggregation. To encourage more parties owning high-quality data to participate in the federated learning, it is important to evaluate and reward the participant contribution in a reasonable, robust, and efficient manner. To achieve this goal, we propose a novel contribution estimation method: Intrinsic Performance Influence-based Contribution Estimation (IPICE). In particular, the class-level intrinsic performance influence is adopted as the contribution estimation criteria in IPICE, and a neural network is employed to exploit the non-linear relationship between the performance change and estimated contribution. Extensive experiments are conducted on various datasets, and the results demonstrate that IPICE is more accurate and stable than the counterpart in various data distribution settings. The computational complexity is significantly reduced in our IPICE, especially when a new party joins the federation. IPICE assigns small contributions to bad/garbage data and thus prevent them from participating and deteriorating the learning ecosystem.
Cryptomarkets’ Phenomenon: A Conceptualization Approach

In the last two decades, the Dark Web has become the perfect resort for various illicit activities. Cryptomarkets are widespread platforms in the cyberspace of the Dark Web where participants trade for illegal gaining. In this article, we present a conceptualization approach of cryptomarkets based on several recent studies on the phenomenon. We attempt to conceptualize cryptomarkets in technical and social aspects to introduce an understanding of their operating mechanisms and the robustness of their communities. We demonstrate the concept of the Satisfaction Factor that attracts individuals to cryptomarkets. Furthermore, we discuss the importance of forums to members of cryptomarkets as technical and social supportive platforms. The article also includes a comparison of cryptomarkets and conventional trading networks and discusses the efforts of law enforcement agencies and their effects in disrupting cryptomarkets. Moreover, we discuss the need for further researches.
Supporting Visual Exploration of Iterative Job Scheduling

We consider the general problem known as job shop scheduling, in which multiple jobs consist of sequential operations that need to be executed or served by appropriate machines having limited capacities. For example, train journeys (jobs) consist of moves and stops (operations) to be served by rail tracks and stations (machines). A schedule is an assignment of the job operations to machines and times where and when they will be executed. The developers of computational methods for job scheduling need tools enabling them to explore how their methods work. At a high level of generality, we define the system of pertinent exploration tasks and a combination of visualizations capable of supporting the tasks. We provide general descriptions of the purposes, contents, visual encoding, properties, and interactive facilities of the visualizations and illustrate them with images from an example implementation in air traffic management. We justify the design of the visualizations based on the tasks, principles of creating visualizations for pattern discovery, and scalability requirements. The outcomes of our research are sufficiently general to be of use in a variety of applications.
The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data

Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned hypothesis was evaluated by training three well-established convolutional neural networks using nine time–frequency representations. The results were compared to the base model, which was trained on the raw waveform data. The signals that were used in the experiment are three-component seismogram instances from the Local Earthquakes and Noise DataBase (LEN-DB). The results demonstrate that Pseudo Wigner–Ville and Wigner–Ville time–frequency representations yield significantly better results than the base model, while spectrogram and Margenau–Hill perform significantly worse (p &lt; 0.01). Interestingly, the spectrogram, which is often used in signal analysis, had inferior performance when compared to the base model. The findings presented in this research could have notable impacts in the fields of geophysics and seismology as the phenomena that were previously hidden in the seismic noise are now more easily identified. Furthermore, the results indicate that applying Pseudo Wigner–Ville or Wigner–Ville time–frequency representations could result in a large increase in earthquakes in the catalogs and lessen the need to add new stations with an overall reduction in the costs. Finally, the proposed approach of extracting valuable information through time–frequency representations could be applied in other domains as well, such as electroencephalogram and electrocardiogram signal analysis, speech recognition, gravitational waves investigation, and so on.
GiantVM: A Novel Distributed Hypervisor for Resource Aggregation with DSM-aware Optimizations

We present GiantVM, 1 an open-source distributed hypervisor that provides the many-to-one virtualization to aggregate resources from multiple physical machines. We propose techniques to enable distributed CPU and I/O virtualization and distributed shared memory (DSM) to achieve memory aggregation. GiantVM is implemented based on the state-of-the-art type-II hypervisor QEMU-KVM, and it can currently host conventional OSes such as Linux. (1) We identify the performance bottleneck of GiantVM to be DSM, through a top-down performance analysis. Although GiantVM offers great opportunities for CPU-intensive applications to enjoy the aggregated CPU resources, memory-intensive applications could suffer from cross-node page sharing, which requires frequent DSM involvement and leads to performance collapse. We design the guest-level thread scheduler, DaS (DSM-aware Scheduler), to overcome the bottleneck. When benchmarking with NAS Parallel Benchmarks, the DaS could achieve a performance boost of up to 3.5×, compared to the default Linux kernel scheduler. (2) While evaluating DaS, we observe the advantage of GiantVM as a resource reallocation facility. Thanks to the SSI abstraction of GiantVM, migration could be done by guest-level scheduling. DSM allows standby pages in the migration destination, which need not be transferred through the network. The saved network bandwidth is 68% on average, compared to VM live migration. Resource reallocation with GiantVM increases the overall CPU utilization by 14.3% in a co-location experiment.
Crowdsourcing Thumbnail Captions via Time-Constrained Methods

Speech interfaces, such as personal assistants and screen readers, employ captions to allow users to consume images; however, there is typically only one caption available per image, which may not be adequate for all settings (e.g., browsing large quantities of images). Longer captions require more time to consume, whereas shorter captions may hinder a user’s ability to fully understand the image’s content. We explore how to effectively collect both thumbnail captions—succinct image descriptions meant to be consumed quickly—and comprehensive captions, which allow individuals to understand visual content in greater detail. We consider text-based and time-constrained methods to collect descriptions at these two levels of detail, and find that a time-constrained method is most effective for collecting thumbnail captions while preserving caption accuracy. We evaluate our collected captions along three human-rated axes—correctness, fluency, and level of detail—and discuss the potential for model-based metrics to perform automatic evaluation.
HistoSegResT: A Weakly Supervised Learning Method for Histopathology Image Segmentation

The morphology of glands, such as size and contour, has been used routinely by pathologists to diagnose the malignant degree of several adenocarcinomas in the process of pathological diagnosis. To automatically segment the gland regions, fully supervised segmentation algorithms require labor-intensive and time- consuming labeling at the pixel level. In this paper, we propose a weakly supervised learning method HistoSegResT (HSRT), which only uses image-level labels (i.e., malignant and benign) to complete histopathology image segmentation. In HSRT, the structure of CNN is used to extract the underlying features of the image, and the self-attention mechanism of the transformer is used to encode the long-range dependencies in histopathology images. In addition, a reconstruction loss is designed to discover the most integrated region of the object. A series of experiments show that the proposed HSRT method outperformed existing state-of-the-art methods with the same level of supervision on the GlaS dataset and can effectively relieve under-activation and over-activation of generated CAMs.
Algorithmic Workspace Programming of the Collaborative Multi-Robots

In the present study, the Controllable Instantaneous Screw Axes (C-ISA) 1 and C-ISA 2 shape variable angles are modified independently to realize various rule-based work spaces and trajectories for multi collaborative robot control. The toolbox developed previously is used to obtain the algorithm of the workspaces for 2-RR collaborative multi-robots herein. Six collaborative multi-robots are exploited to design the intersecting workspaces with generated trajectories. The classifications of the workspaces are unveiling the boundaries of the collaborations for the six multi-robots of the 2-RR (Revolute Revolute). The recent developments are showing that the multi-robots are embedding into the automation systems to achieve the novel requirements of the challenging technology. Therefore, the workspace algorithms developed herein are prepared to be utilized by these automation systems.
Modeling of fiber-coupled confocal and interferometric confocal distance sensors

Abstract Laser distance sensors are a widespread, fast and contactless approach for distance and surface topography measurements. Main characteristics of those sensors are given by resolution, measurement speed and sensor geometry. With decreasing sensor size, the alignment of the optical components in sensor setup becomes more challenging. The depth response of optical profilers is analyzed to obtain characteristic parameters and, thus, to value the alignment and the transfer behavior of those sensors. We present a novel miniaturized sensor setup comprising of confocal and interferometric confocal signals within one sensor in order to compare both principles simply by obscuring the reference arm by an absorber. Further, we introduce a theoretical signal modeling in order to analyze influences such as spatial coherence, Gaussian beam characteristics and tilted reflectors on depth response signals. In addition to this, we show that the coherent superposition significantly reduces the axial resolution due to the confocal effect in interferometric signals compared to simple confocal signals in measurement and simulation results. Finally, an appropriate fit function is presented, in order to figure out characteristic sensor parameters from the obtained depth response signal. In this context, a good agreement to simulated and measured signals is achieved.
Canonical Workflows to Make Data FAIR

Abstract The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices, yet the incentives for researchers to change their practices are presently weak. In addition, data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices. To overcome these challenges, the Canonical Workflow Frameworks for Research (CWFR) initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof. This standardised approach, with FAIR Digital Objects as anchors, will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it. This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so, highlights several projects that hold promise for the CWFR approaches, including Galaxy, Jupyter Notebook, and RO Crate, and concludes with an assessment of the state of the field and the challenges ahead.
COVID-19 Exceptionalism: Explaining South Korean Responses

COVID-19 has presented challenges across the globe that led to a number of shared lessons to be learnt. Yet, we are inundated with comparative accounts that characterize national pandemic responses as inherent and unique to certain nation states, which, we argue, led to COVID-exceptionalism. This article challenges "cultural" explanations of South Korea's "successful" responses to COVID-19 crisis. The popular narrative has been that Korea's cluster-based mitigation strategy was sustained by rigorous contact tracing and mass testing systems, and this was made possible by three distinctive elements of pandemic preparedness: 1) Korean "culture" of normalizing face-covering, 2) Korean citizens' consensus of prioritizing public health to privacy, and 3) Korea's IT infrastructure enabling efficient digital contact tracing. By debunking the three myths, we demonstrate why neither the Asian "authoritarian advantages" thesis nor the counter-argument of "Asian civility" adequately captures the reality of Korea's reaction to the COVID pandemic. The ways in which risks are conceptualized as manageable objects produce particular modes of allocating responsibilities for risk mitigation, when dealing with a relatively unknown virus. COVID-exceptionalism may cause not only the issue of reinforcing "(East) Asian"/"Western" stereotypes, but also other problems such as implicitly granting political impunity to those responsible for coordinating COVID-19 responses.
Evaluation of marker tracking using mono and stereo vision in Microsoft HoloLens for surgical navigation

Optical-based navigation systems are widely used in surgical interventions. However, despite their great utility and accuracy, they are expensive and require time and effort to setup for surgeries. Moreover, traditional navigation systems use 2D screens to display instrument positions causing the surgeons to look away from the operative field. Head mounted displays such as the Microsoft HoloLens may provide an attractive alternative for surgical navigation that also permits augmented reality visualization. The HoloLens is equipped with multiple sensors for tracking and scene understanding. Mono and stereo-vision in the HoloLens have been both reported to be used for marker tracking, but no extensive evaluation on accuracy has been done to compare the two approaches. The objective of our work is to investigate the tracking performance of various camera setups in the HoloLens, and to study the effect of the marker size, marker distance from camera, and camera resolution on marker locating accuracy. We also investigate the speed and stability of marker pose for each camera setup. The tracking approaches are evaluated using ArUco markers. Our results show that mono-vision is more accurate in marker locating than stereo-vision when high resolution is used. However, this comes at the expense of higher frame processing time. Alternatively, we propose a combined low-resolution mono-stereo tracking setup that outperforms each tracking approach individually and is comparable to high resolution mono tracking, with a mean translational error of 1.8±0.6<i>mm</i> for 10cm marker size at 50cm distance. We further discuss our findings and their implications for navigation in surgical interventions.
Evolutionary Algorithm With Self-Learning Strategy for Generation of Adversarial Samples

Knowledge engineering algorithms such as deep learning models have exhibited tremendous success in solving complex problems. However, the linear nature of the neural network is the primary reason for vulnerability to the perturbed samples. Adversarial attacks pose a severe threat to applying deep models, especially while designing safety-critical applications. This work proposes security attacks against neural architectures. In particular, we introduce a novel method to create adversarial samples. First, we propose a differential evolution population resizing scheme, which enlarges the generation of adversarial samples by allowing adversaries to speed the convergence process. The proposed system is a novel self-adaptive population resizing-based adversarial mechanism. The result shows the success rate for targeted attack LeNet(60.07%), Network_in_Network(97%), Wide_ResNet50(99%), Pure CNN (97%), DenseNet (54.11%),ResNet50(51%) and LeNet(85.13%), Network_in_Network(33.37%), WideResnet(24.40%), Pure_CNN(19.96%),DenseNet (63.67%), ResNet (68.00%) for non targeted attacks respectively.
Evaluating the use of meteorological predictions in directly pumped irrigational operations using photovoltaic energy

Evaluating the use of meteorological predictions in directly pumped irrigational operations using photovoltaic energy.
<i>EarWalk</i>

Stress on the knee --- caused by various factors including injuries, aging, and overweight --- is a major contributor to orthopedic disorders such as knee osteoarthritis (OA), a severe illness that can even lead to decreased ability to walk. One possible treatment for this problem is to have patients conduct gait modification, getting them to intentionally walk toe-in or toe-out, thereby reducing the stress on their knees. In this paper, we propose EarWalk, a novel solution that utilizes commodity wireless earables to provide constant and real-time feedback on the patients' gait modification. EarWalk leverages the built-in accelerometer in earables to sense and ultimately differentiate normal, toe-in, and toe-out gait postures due to the minute differences in their vibrations. As a proof-of-concept, we evaluate EarWalk with real-world data by inviting participants to walk while wearing a pair of earables, and demonstrate an average accuracy of over 95% in identifying the gait postures.
PPFL

Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System developers, vendors, network operators, etc.). Traditional ML designs assume (user) data are collected and models are trained in a centralized location. However, this approach has privacy consequences related to data collection and processing. Such concerns have incentivized the scientific community to design and develop Privacy-preserving ML methods, including techniques like Federated Learning (FL) where the ML model is trained or personalized on user devices close to the data; Differential Privacy, where data are manipulated to limit the disclosure of private information; Trusted Execution Environments (TEE), where most of the computation is run under a secure/ private environment; and Multi-Party Computation, a cryptographic technique that allows various parties to run joint computations without revealing their private data to each other.
Stream Cipher Based on Game Theory and DNA Coding

Securing communication over public communication channels is one of the challenging issues in the field of cryptography and information security.A stream cipher is presented as an approach for securing exchanged data between different parties through encryption.The core of stream cipher relies on its keystream generator, that is responsible for generating random and secure keystream of encrypting streaming data.Thus, the security of the keystream is measured by its randomness and its resistance to statistical and cryptanalytic attacks.As there is always a trade-off between the security and performance while designing new cryptographic primitives, we introduce a game theory-based stream cipher that performs fast and utilises the unpredictable behaviour of prisoners' dilemma players to generate random sequences that serve as keystreams.To assure further security, the ciphertext is coded by DNA bases that are randomly scrambled at the beginning of every iteration of generating new keystream.The proposed stream cipher's performance and security are thoroughly examined in terms of randomness, performance, and resistance to cryptanalytic attacks.The experiments show that our stream cipher can encrypt around 1230 Mbit/s.Statistically, our proposed stream cipher passed the NIST statistical tests successfully.
Study of Variational Inference for Flexible Distributed Probabilistic Robotics

By combining stochastic variational inference with message passing algorithms, we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases, the robots share the operating space and need to cross each other’s paths multiple times without colliding.
A Monte Carlo Approach to Bitcoin Price Prediction with Fractional Ornstein–Uhlenbeck Lévy Process

Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.