Reviewerly/exHarmony huggingface dataset:
Ready-to-(ab)use: From fake account trafficking to coordinated inauthentic behavior on Twitter

Fake accounts are the primary means for misuse and abuse of social media platforms, giving rise to coordinated inauthentic behaviors. Despite ongoing efforts to limit their exploitation, ready-to-use fake accounts can be found for sale on several underground markets. For the present study, we devised an innovative approach to detect accounts for sale on an underground market. Between June 2019 and July 2021, we detected more than 60,000 fake accounts, which we continuously tracked for changes in profile information and timeline. Afterward, we focused on the 23,579 accounts that produced at least one tweet in 2020, identifying the main characteristics like the most used names and profile descriptions. Also, we analyzed more than five million interactions, including mentions, replies, retweets, and the use of hashtags and URLs in tweets. These analyses exposed behavioral patterns indicating coordination, like using similar profile names or retweeting the same user. In particular, we spotted four coordinated campaigns, whose behavior ranged from attempting to influence the political debate in Buenos Aires to aggressive spam activity aimed at scamming cryptocurrency users or advertising counterfeit goods.
Efficient Multi-Scale Stereo-Matching Network Using Adaptive Cost Volume Filtering

While recent deep learning-based stereo-matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation, which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Although there are some efficient networks, most of them still require a heavy computational cost to incorporate them to mobile computing devices in real-time. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. A few works deal with this problem by generating artificial cost distribution using only the ground truth disparity value that is insufficient to fully regularize the cost volume. To address these problems, we first propose an efficient multi-scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost-volume-filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network which achieves higher accuracy. Results of several experiments using representative datasets for stereo matching show that our proposed method is more efficient than previous methods. Our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models. Concretely, our network achieves 1.01 EPE with runtime of 42 ms, 2.92M parameters, and 97.96G FLOPs on the Scene Flow test set. Compared with PSMNet, our method is 89% faster and 7% more accurate with 45% fewer parameters.
A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have attracted extensive attention of many researchers. However, owing to its capability to balance between exploration and exploitation is weak, HHO suffers from low precision and premature convergence. To tackle these disadvantages, an improved HHO called VGHHO is proposed by embedding three modifications. Firstly, a novel modified position search equation in exploitation phase is designed by introducing velocity operator and inertia weight to guide the search process. Then, a nonlinear escaping energy parameter E based on cosine function is presented to achieve a good transition from exploration phase to exploitation phase. Thereafter, a refraction-opposition-based learning mechanism is introduced to generate the promising solutions and helps the swarm to flee from the local optimal solution. The performance of VGHHO is evaluated on 18 classic benchmarks, 30 latest benchmark tests from CEC2017, 21 benchmark feature selection problems, fault diagnosis problem of wind turbine and PV model parameter estimation problem, respectively. The simulation results indicate that VHHO has higher solution quality and faster convergence speed than basic HHO and some well-known algorithms in the literature on most of the benchmark and real-world problems.
The invention of the archived web: tracing the influence of library frameworks on web archiving infrastructure

Web archives are increasingly considered key infrastructure for web histories, yet the story of their initial development is often overlooked. Paying attention to the period between the emergence of the web and the first web archives, this article asks: how was the archived web conceived as an object of knowledge? And how have its conceptual origins shaped web archives as a window into the web's past? Drawing on ethnographic and historical research at one of the first web archiving institutions, the National Library of Australia, it details how systems, standards, and skills in use at the library were applied to a nascent web. It shows how archiving the web became both conceivable and actionable by library workers as they came to understand and act on various web-based materials as types of publications. Reflecting on this history, this article argues that present-day web archives have inherited both strengths and limitations from the older knowledge infrastructures from which they emerged. By detailing the messy, incremental path of infrastructural development, this article extends recent STS-inflected work on web archives as epistemic agents, adding a historical dimension to our understanding of how web archives enable and constrain particular ways of knowing the web's past.
Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

Multi-agent reinforcement learning is difficult to apply in practice, partially because of the gap between simulated and real-world scenarios. One reason for the gap is that simulated systems always assume that agents can work normally all the time, while in practice, one or more agents may unexpectedly “crash” during the coordination process due to inevitable hardware or software failures. Such crashes destroy the cooperation among agents and lead to performance degradation. In this work, we present a formal conceptualization of a cooperative multi-agent reinforcement learning system with unexpected crashes. To enhance the robustness of the system to crashes, we propose a coach-assisted multi-agent reinforcement learning framework that introduces a virtual coach agent to adjust the crash rate during training. We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crash rate) and a re-sampling strategy for our coach agent. To our knowledge, this work is the first to study unexpected crashes in a multi-agent system. Extensive experiments on grid-world and StarCraft II micromanagement tasks demonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning strategy. The ablation study further illustrates the effectiveness of our re-sampling strategy.
HyperJumping in Virtual Vancouver: Combating Motion Sickness by Merging Teleporting and Continuous VR Locomotion in an Embodied Hands-Free VR Flying Paradigm

Motion sickness, unintuitive navigation, and limited agency are critical issues in VR/XR impeding wide-spread adoption and enjoyable user experiences. To tackle these challenges, we present HyperJump, a novel VR interface merging advantages of continuous locomotion and teleportation/dashing into one seamless, hands-free, and easily learnable user interface supporting both flying and ground-based navigation across multiple scales.
High-fidelity eye, head, body, and world tracking with a wearable device

We describe the design and performance of a high-fidelity wearable head-, body-, and eye-tracking system that offers significant improvement over previous such devices. This device's sensors include a binocular eye tracker, an RGB-D scene camera, a high-frame-rate scene camera, and two visual odometry sensors, for a total of ten cameras, which we synchronize and record from with a data rate of over 700 MB/s. The sensors are operated by a mini-PC optimized for fast data collection, and powered by a small battery pack. The device records a subject's eye, head, and body positions, simultaneously with RGB and depth data from the subject's visual environment, measured with high spatial and temporal resolution. The headset weighs only 1.4 kg, and the backpack with batteries 3.9 kg. The device can be comfortably worn by the subject, allowing a high degree of mobility. Together, this system overcomes many limitations of previous such systems, allowing high-fidelity characterization of the dynamics of natural vision.
Interoperability and Architecture Requirements Analysis and Metadata Standardization for a Research Data Infrastructure in Catalysis

The National Research Data Infrastructure for Catalysis-Related Sciences (NFDI4Cat) is one of the disciplinary consortia formed within the German national research data infrastructure (NFDI), an effort undertaken by the German federal and state governments to advance the digitalization of all scientific research data within the German academic system in accordance with the FAIR principles. This work reports on initial outcomes from the NFDI4Cat project. The data value chain in catalysis research is analysed, and architecture and interoperability requirements are identified by conducting user interviews, collecting competency questions, and exploring the landscape of semantic artefacts. Methods from agile software development are employed to collect, organize, and present the collected requirements; workflows are annotated on the basis of metadata standards for research data provenance, by which requirements for domain ontologies in catalysis are deduced.
PCFed: Privacy-Enhanced and Communication-Efficient Federated Learning for Industrial IoTs

Federated learning (FL) is capable of analyzing tremendous data from smart edge devices in Industrial Internet of Things (IIoTs), empowering numerous industrial applications. However, the increasing privacy concerns and deployment costs of IIoT environment have been posing new challenges for FL. This article proposes PCFed, a novel privacy-enhanced and communication-efficient FL framework to provide higher model accuracy with rigorous privacy guarantees and great communication efficiency. In particular, we develop a sampling-based intermittent communication strategy via a PID (proportional, integral, and derivative) controller on the cloud server to adaptively reduce the communication frequency. In addition, we design a budget allocation mechanism to balance the tradeoff between model accuracy and privacy loss. Then, we develop PCFed+, an enhanced variant for PCFed, with further consideration of infinite data streams on edge servers. Extensive experiments demonstrate that both PCFed and PCFed+ can significantly outperform existing schemes, in terms of communication efficiency, privacy protection, and model accuracy.
Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions

The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria, or ‘features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ‘mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible. However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested. (1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model. (2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison. (3) Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units. (4) The training dataset is composed of known economic mineralisation sites (deposits) as ‘positive’ examples, and exploration drilling data providing ‘negative’ sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery.
Reinforcement Learning Based Cross-Layer Congestion Control for Real-Time Communication

Congestion control is a crucial part of Real-Time Communication (RTC), because it adjusts the current video bitrate according to the variable network environment, which determines the final Quality of Experience (QoE). Conventional congestion control algorithms only take packet-level delay and throughput into consideration. While in the application of RTC, video frame can provide a lot of reference information, because it is the basic unit of video processing and evaluation. Inspired by this, we propose CLCC, a reinforcement learning based Cross-Layer Congestion Control for WebRTC. CLCC uses not only packet-level information, but also frame-level information to decide the bitrate of the encoder. We evaluate CLCC on both random traces and LTE traces. Results show that CLCC outperforms GCC with improvements in average frame psnr of 0.4-0.6 and decreasing in average frame delay of 17%-23%.
Predicting DRAM-Caused Node Unavailability in Hyper-Scale Clouds

DRAM faults are major hardware sources of cloud node unavailability. To enable early preventive actions and mitigate DRAM fault impacts, prior studies focus on predicting DRAM uncorrectable errors (UEs) that typically cause immediate node unavailability. In our cloud with over half a million nodes, we firstly observe that the correctable error storm (numerous CEs occur in a short period) dominates 56% DRAM-caused node unavailability (DCNU). Therefore, we propose to predict DCNU that takes account into both UEs and CE storms. Observing that DCNUs have strong relevance to temporal statistics and spatial patterns of CEs, we design novel spatio-temporal features to train the prediction model. Considering the model’s real effects cannot be evaluated by traditional metrics like F1-score, we propose a new metric NURR to quantify the node unavailability reduction and tune model hyperparameters with NURR. Our approach achieves over 40% better NURR than existing methods on historical data and runs stably in the production environment.
TimeDice: Schedulability-Preserving Priority Inversion for Mitigating Covert Timing Channels Between Real-time Partitions

Timing predictability is a precondition for successful communication over a covert timing channel. Real-time systems are particularly vulnerable to timing channels because real-time applications can easily have temporal locality due to limited uncertainty in schedules. In this paper, we show that real-time applications can create hidden information flow even when the temporal isolation among the time partitions is strictly enforced. We then introduce an online algorithm that randomizes time-partition schedules to reduce the temporal locality, while guaranteeing the schedulability of, and thus the temporal isolation among, time partitions. We also present an analysis of the cost of the randomization on the responsiveness of real-time tasks. From an implementation on a Linux-based real-time operating system, we validate the analysis and evaluate the scheduling overhead as well as the impact on an experimental real-time system.
PassFlow: Guessing Passwords with Generative Flows

Recent advances in generative machine learning models rekindled research interest in the area of password guessing. Data-driven password guessing approaches based on GANs, language models, and deep latent variable models have shown impressive generalization performance and offer compelling properties for the task of password guessing.This paper proposes PassFlow, a flow-based generative model approach to password guessing. Flow-based models allow for precise log-likelihood computation and optimization, which enables exact latent variable inference. Additionally, flow-based models provide meaningful latent space representation, which enables operations such as exploration of specific subspaces of the latent space and interpolation. We demonstrate the applicability of generative flows to the context of password guessing, departing from previous applications of flow-networks which are mainly limited to the continuous space of image generation. We show that PassFlow is able to outperform prior state-of-the-art GAN-based approaches in the password guessing task while using a training set that is orders of magnitudes smaller than that of prior art. Furthermore, a qualitative analysis of the generated samples shows that PassFlow can accurately model the distribution of the original passwords, with even non-matched samples closely resembling human-like passwords.
Pre-Trained Feature Fusion and Multidomain Identification Generative Adversarial Network for Face Frontalization

The study of face frontalization is essential for improving face recognition accuracy in extreme pose scenarios. Mainstream methods like TP-GAN, CAPG-GAN, etc., have made meaningful contributions. However, they still suffer from two problems: the lack of extracted feature diversity and the blurred details in generated images. This paper proposes a pre-trained feature fusion and multi-domain identification generative adversarial network (PM-GAN) for face frontalization: the features of the model pre-trained on large-scale datasets are fused with the original features of the encoder to enhance the diversity and robustness of features. In order to fuse features more effectively, we designed a novel feature fusion module (FFM). In addition, a group of global and local discriminators is introduced to reinforce the local details and realism of generated frontal faces. Experimental results show that our proposed method outperforms state-of-the-art methods on M<sup>2</sup>FPA and CAS-PEAL datasets.
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding

Large language models (LM) based on Transformers allow to generate plausible long texts.In this paper, we explore how this generation can be further controlled at decoding time to satisfy certain constraints (e.g.being nontoxic, conveying certain emotions, using a specific writing style, etc.) without fine-tuning the LM.Precisely, we formalize constrained generation as a tree exploration process guided by a discriminator that indicates how well the associated sequence respects the constraint.This approach, in addition to being easier and cheaper to train than fine-tuning the LM, allows to apply the constraint more finely and dynamically.We propose several original methods to search this generation tree, notably the Monte Carlo Tree Search (MCTS) which provides theoretical guarantees on the search efficiency, but also simpler methods based on reranking a pool of diverse sequences using the discriminator scores.These methods are evaluated, with automatic and human-based metrics, on two types of constraints and languages: review polarity and emotion control in French and English.We show that discriminatorguided MCTS decoding achieves state-of-theart results without having to tune the language model, in both tasks and languages.We also demonstrate that other proposed decoding methods based on re-ranking can be really effective when diversity among the generated propositions is encouraged.
Enhanced Sliding Window-Based Periodic Pattern Mining from Dynamic Streams

Discovering frequent patterns has been an important problem for knowledge discovery. The efficient discovery of interesting patterns—such as weighted periodic patterns—from big data has been crucial to the development in new domains. Due to their high velocity of data generation and collection, these big data can form dynamic streams, which can be unbounded. Traditional approaches to this problem consist of the reconstruction of the underlying structure, while recent advances have shown new methods for dynamically updating the underlying structure for each new window. In this paper, we present an enhanced sliding window-based algorithm for mining weighted periodic patterns from dynamic streams. Evaluation results show the effectiveness of this algorithm.
Structural models for fog computing based internet of things architectures with insurance and risk management applications

Cybersecurity risk modeling and pricing are becoming a spotlight in actuarial science and operational research. This paper pertains to the analysis of the cybersecurity risks involved in the fog computing technology which has been intensively deployed in assorted Internet of Things (IoT) applications. To this end, a class of structural models are established to study the inherent cyber risk propagation process. Under the smart home applications, we manage to compute the compromise probabilities of individual nodes explicitly. Applications of the proposed structural models in the context of cyber insurance pricing are thoroughly explored. Finally, we propose an interval method for estimating the compromise probabilities of fog network’s elements, which can be used to efficiently identify weak nodes for cybersecurity risk management.