Reviewerly/exHarmony huggingface dataset:
Heterogeneity in trajectories of cybercriminals: A longitudinal analyses of web defacements

Longitudinal criminological studies greatly improved our understanding of the longitudinal patterns of criminality. These studies, however, focused almost exclusively on traditional types of offending and it is therefore unclear whether results are generalizable to online types of offending. This study attempted to identify the developmental trajectories of active hackers who perform web defacements. The data for this study consisted of 2,745,311 attacks performed by 66,553 hackers and reported to Zone-H between January 2010 and March 2017. Semi-parametric group-based trajectory models were used to distinguish six different groups of hackers based on the timing and frequency of their defacements. The results demonstrated some common relationships to traditional types of crime, as a small population of defacers accounted for the majority of defacements against websites. Additionally, the methods and targeting practices of defacers differed based on the frequency with which they performed defacements generally.
Platooning LEGOs: An Open Physical Exemplar for Engineering Self-Adaptive Cyber-Physical Systems-of-Systems

Many modern systems interact with both cyber and physical environments. They are complex systems in which multiple constituent systems work together to achieve higher-level goals. These systems are called cyber-physical systems of systems (CPSoS). As the interest in CPSoS, such as platooning vehicles and robot-based smart factories, increases, engineering for adaptive goal achievement of CPSoS is needed. Common exemplars of a research community can facilitate research; however, existing exemplars of CPSoS are mostly based on virtual simulations. Although this allows researchers to share experimental scenarios and environments, it has the limitation that it is difficult to conduct experiments that reflect actual physical environments. To overcome this limitation, we propose a physical exemplar of an industrial CPSoS, called Platooning LEGOs, which employs platooning technology that is actively being developed by the autonomous driving industry. A platoon, in which independent vehicles drive together, achieves SoS-level goals through adaptive behavioral decisions of the vehicles. This exemplar provides a physical experimental environment that can be implemented with LEGOs. A simple LEGO assembly allows the use of real data from sensors and actuators, facilitating a focus on software engineering without considerable mechanical knowledge. Moreover, as this is an open exemplar, researchers can implement the same physical experimental environment with a limited budget and expand its physical or software elements. We provide system descriptions, physical and software implementation manuals, and sample experimental results of Platooning LEGOs.
A Cybersecurity Educated Community

The need for cybersecurity education has grown significantly for all citizens. However, cybersecurity education has not been adequately addressed in fields outside of this domain. Cybersecurity concerns increase every time a new device connects to a network. As such, we need to increase involvement and advancement of cybersecurity education at all levels and in every discipline. Education, audit, and enforcement regarding cybersecurity are critical aspects and are the responsibility of all system stakeholders. It is no longer enough to rely on the security team to be responsible for combating cyber threats. Involvement and commitment from all stakeholders are essential and universal and begins with a cross-disciplinary reform in education. In this paper, the need to deliver cybersecurity education across all disciplines and at all levels is discussed. Specifically, the focus is at the following four types of users: K-12, college, technical professionals, and all other citizens. A curriculum roadmap that integrates cybersecurity throughout technical and nontechnical curricula is proposed as a foundation for future cyber education planning. The goal is to introduce students and citizens to the concept of cybersecurity, to ensure they can apply cybersecurity concepts, and to expose them to multiple approaches to solving cybersecurity related problems.
DAT: Training Deep Networks Robust to Label-Noise by Matching the Feature Distributions

In real application scenarios, the performance of deep networks may be degraded when the dataset contains noisy labels. Existing methods for learning with noisy labels are limited by two aspects. Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels. Secondly, others based on the memorization effect outperform in synthetic noise but get weak promotion in real-world noisy datasets. To solve these problems, this paper proposes a novel label-noise robust method named Discrepant Adversarial Training (DAT). The DAT method has ability of enforcing prominent feature extraction by matching feature distribution between clean and noisy data. Therefore, under the noise-free feature representation, the deep network can simply output the correct result. To better capture the divergence between the noisy and clean distribution, a new metric is designed to change the distribution divergence into computable. By minimizing the proposed metric with a min-max training of discrepancy on classifiers and generators, DAT can match noisy data to clean data in the feature space. To the best of our knowledge, DAT is the first to address the noisy label problem from the perspective of the feature distribution. Experiments on synthetic and real-world noisy datasets demonstrate that DAT can consistently outperform other state-of-the-art methods. Codes are available at https://github.com/Tyqnn0323/DAT.
Uncertainty-aware robust adaptive video streaming with bayesian neural network and model predictive control

In this paper, we propose BayesMPC, an uncertainty-aware robust adaptive bitrate (ABR) algorithm on the basis of Bayesian neural network (BNN) and model predictive control (MPC). Specifically, to improve the capacity of learning transition probability of the network throughput, we adopt a BNN-based predictor that is able to predict the statistical distribution of future throughput from the past throughput by not only considering the aleatoric uncertainty (e.g., noise), but also capturing the epistemic uncertainty incurred by lack of adequate training samples. We further show that by using the negative log-likelihood loss function to train this BNN-based throughput predictor, the generalization error can be minimized with the guarantee of PAC-Bayesian theorem. Rather than a point estimate, the learnt uncertainty can contribute to a confidence region for the future throughput, the lower bound of which then leads to an uncertainty-aware robust MPC strategy to maximize the worst-case user quality-of-experience (QoE) w.r.t. this confidence region. Finally, experimental results on three real-world network trace datasets validate the efficiency of both the proposed BNN-based predictor and uncertainty-aware robust MPC strategy, and demonstrate the superior performance compared to other baselines, in terms of both the overall QoE performance and generalization across all ranges of heterogeneous network and user conditions.
Deep Feature Representation Based Imitation Learning for Autonomous Helicopter Aerobatics

The drastic changes in flight parameters during aerobatics and the high instability of the system make the control of autonomous aerobatics unusually difficult. In this paper, we propose a deep feature representation based imitation learning method for autonomous aerobatics, which leverages expert demonstrations to efficiently learn the mapping of high-dimensional flight observations onto continuous actions (pitch, tail, and thrust). Different from the existing methods, our proposed method requires neither trajectory specification and alignment nor any assumptions and processing of system uncertainties, so it can greatly simplify the controller solution steps and reduce the computational burden. Particularly, our method uses the proposed deep feature representation network (DFR-network) to directly map the experts’ demonstration trajectory to a deep representation space spanned by a set of learned subspaces which represent the motion patterns with the same statistical property among demonstration trajectories. Various aerobatic maneuvers can be encoded in the deep representation space through a simple combination of embedding features. Accordingly, the proposed method can perform arbitrary aerobatic maneuvers by observing a limit set of expert demonstrations. The effectiveness of the deep feature representation based imitation learning method is verified on the real-world flight data. Experiments show that compared with the existing methods, our proposed method has higher control accuracy, stronger robustness and anti-interference ability.
Defending CNN against privacy leakage in edge computing via binary neural networks

As the IoT has developed, edge computing has played an increasingly important role in the IoT ecosystem. The edge computing paradigm offers low latency and high computing performance, which is conducive to machine learning tasks such as object detection in autonomous driving. However, data privacy risks in edge computing still exist and the existing privacy-preserving methods are not satisfactory due to the large computational overhead and unbearable accuracy loss. We have designed a privacy-preserving machine learning framework for both user and cloud data. Users and the cloud provide data for inference and training respectively, and the privacy protection of these two aspects is both considered in this paper. Users provide test data and want to access the data-processing models in cloud for inference, and the cloud provides the training data used for training an eligible model. For user data, in order to maintain the overall performance of the machine learning framework while using homomorphic encryption, instead of providing encrypted data to all machine learning tasks, we divide the neural network into two parts, with one part kept on the trusted edge and provided with plaintext, and the other deployed on the untrusted cloud and provided with encrypted input. For cloud data, we apply the binary neural network, a network with the binarized value of weights. This method is practical for narrowing the confidence score gap (between the training and test sets) predicted by the model, which accounts most for a successful exploratory attack on training data. Experiments demonstrate that the results of the adversary's membership inference attack are close to random guessing, and the accuracy is only slightly affected. Compared with the unencrypted network on VGG19, when the network is split from conv4_1 to fc8, the efficiency of using HE is only 100 to 30 times slower.
LogLog Filter: Filtering Cold Items within a Large Range over High Speed Data Streams

Many real-world datasets are given in the format of data streams, and processing these data streams is fundamental for many applications such as anomaly detection. In this paper, we study the problem of computing item frequencies, finding topk hot items, and detecting heavy changes. However, the widelyused sketches cost large memory usage and their performance is easily affected by the unbalanced distribution of data streams. To solve this issue, a novel method Cold Filter (CF) is proposed to split cold items and hot items, and use a separate structure to record the frequencies of hot items. Typically, CF has a small filter range and is only effective for filtering cold items with small frequencies. For some real-world applications, however, the cold items' frequencies may also be greater than hundreds or even tens of thousands. To solve the above challenges, we exploit the “LogLog” structure and develop a memory-efficient method LogLog Filter (LLF) to accurately estimate the above three metrics. LLF builds a register array where each register approximately counts the sum of item frequencies hashed into it. Our method remarkably enlarges the filter range of CF with fewer bits and only requires 4 bits to filter cold items with frequencies up to 224. We conduct extensive experiments on real-world and synthetic datasets, and the experimental results demonstrate the efficiency and effectiveness of our method.
ZGridBC: Zero-Knowledge Proof based Scalable and Private Blockchain Platform for Smart Grid

The total number of photovoltaic power producing facilities whose FIT-based ten-year contract expires by 2023 is expected to reach approximately 1.65 million in Japan. If the number of renewable electricity-producing/consuming facilities reached two million, an enormous number of transactions would be invoked beyond blockchain's scalability.We propose mutually cooperative two novel methods to simultaneously solve scalability, data size, and privacy problems in blockchain-based trading platforms for renewable energy environmental value. One is a management scheme of electricity production resources (EPRs) using an extended UTXO token. The other is a data aggregation scheme that aggregates a significant number of smart meter records with evidentiality using zero-knowledge proof (ZKP).
Verifiable Observation of Permissioned Ledgers

Permissioned ledger technologies have gained significant traction over the last few years. For practical reasons, their applications have focused on transforming narrowly scoped use-cases in isolation. This has led to a proliferation of niche, isolated networks that are quickly becoming data and value silos. To increase value across the broader ecosystem, these networks must seamlessly integrate with existing systems and interoperate with one another. A fundamental requirement for enabling cross-chain communication is the ability to prove the validity of the internal state of a ledger to an external party. However, due to the closed nature of permissioned ledgers, their internal state is opaque to an external observer. This makes consuming and verifying states from these networks a non-trivial problem.This paper addresses this fundamental requirement for state sharing across permissioned ledgers. In particular, we address two key problems for external clients: (i) assurances on the validity of state in a permissioned ledger and (ii) the ability to reason about the currency of state, i.e., whether the state is currently valid. We assume an adversarial model where the members of the committee managing the permissioned ledger can be malicious in the absence of detectability and accountability. We present a formalization of the problem for state sharing and examine its security properties under different adversarial conditions. We propose the design of a protocol that uses a secure public ledger for providing guarantees on safety and the ability to reason about time, with at least one honest member in the committee. We then provide a formal security analysis of our design and a proof of concept implementation based on Hyperledger Fabric demonstrating the effectiveness of the proposed protocol.
Robust Optimization Model for Probabilistic Protection With Multiple Types of Resources

This paper proposes a robust optimization model for probabilistic protection with multiple types of resources to minimize the required backup capacity for each type of resource against multiple random failures of physical machines in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the preplanned backup physical machines, which are determined in advance. Probabilistic protection restricts the probability that the workload caused by failures exceeds the backup capacity by a given survivability parameter. We introduce three survivability parameters for central processing unit (CPU), memory, and the entire cloud provider considering both CPU and memory. By using the relationship between the three survivability parameters, the proposed model guarantees probabilistic protection for each resource, CPU and memory, and the entire cloud provider. By adopting the robust optimization technique, we formulate the proposed model as a multi-objective mixed integer linear programming problem. To deal with the multi-objective optimization problem, we apply the lexicographic weighted Tchebycheff method with which a Pareto optimal solution is obtained. We show that our proposed model reduces the average value between the backup capacity ratios of CPU and memory compared with the conventional model. A multi-objective simulated annealing (MOSA) and nondominated sorting genetic algorithm II (NSGA-II) are adopted to solve larger size problems. By using them, approximate solutions are obtained for larger size problems. In addition, we find that NSGA-II searches for solutions more effectively than MOSA, in our backup capacity allocation problem.
Structural pixel-wise target attention for robust object tracking

Some Siamese-based trackers use temporal context prior as structural constraint to suppress background distractors. However, due to the lack of contour recognition, it is difficult to obtain a better performance. In order to address this issue, we propose a structural pixel-wise target attention strategy for robust object tracking with memory model. Firstly, a pixel-wise target attention model is constructed for evaluating the probability that the pixel belongs to the target, which can effectively discriminate the target boundary so as to highlight the target area. Meanwhile, structural information is used to solve pixel-wise distractors, which is combined with complementary pixel-wise label constraints to obtain a structural pixel-wise target attention model. This attention mechanism can improve the confidence of the final response map and achieve more reliable target location. Secondly, a memory model is learned using the highly reliable memory pattern for providing high-quality training samples for updating pixel-wise target attention model. Benefiting from this method, our method realizes the pixel-wise target attention model to adapt to the variation of the target while preventing the background noise, thus improving the discriminability of the model. Finally, the structural pixel-wise target attention mechanism and memory model are integrated into the Siamese-based tracking framework, which shows better merit for robust object tracking. Extensive experiments on multiple tracking benchmarks show that our approach achieves excellent performance in various challenging target tracking tasks.
Online Synchronous Model of Interpretive Sustainable Guiding in Heritage Sites: The Avatar Tourist Visit

The health crisis caused by the coronavirus (COVID-19) pandemic created an unprecedented social situation. In the most critical moments, tourism activities were cancelled or reduced to their minimum operation, creating an extreme situation of separation of a country’s heritage from residents and tourists. The objective of this work is to propose new options and strategies to reconnect the public with heritage under any circumstance and, at the same time, offer tourism services in healthy, secure conditions. Thus, live-streaming experiences of online interpretative tourist guidance were developed in heritage spaces; those visits were led by real guide-interpreters in a real environment and transmitted for remote visitors. We have called this visiting model the “Avatar Tourist Visit”. It is based on tools associated with Heritage Interpretation, Interpersonal Communication, Filmmaking and Audio-Visual Language, and Information and Communication Technologies. The methodological development of this tool was based on Design Sciences Research as the creation of a new procedure was pursued. The result is a sustainable, immersive, interactive, inclusive, unique, and resilient tourism product designed not only for times of crisis but also to connect the public with heritage at all times and ensure universal access to it. In addition, this activity constitutes a marketable experience as a specialized and customizable tourist product.
Is Virtual Reality Cue Exposure a Promising Adjunctive Treatment for Alcohol Use Disorder?

This narrative review presents recent developments in virtual reality (VR)-based interventions for alcohol use disorders (AUDs). The latest advances in mental healthcare hail an imminent cyber revolution, ushering in novel treatment options, with immersive virtual technology at the very forefront of expected change. With an aim to (a) provide a background on VR use in mental healthcare of AUD patients, (b) summarize existing evidence on conventional approaches to the treatment of AUDs and a trending paradigm shift towards VR applications in their management, and (c) describe key issues and future directions in research on craving assessment and VR cue-induced therapy in AUDs, a search for experimental and meta-analytic evidence was performed in six databases: PubMed and EBSCO (Medline, ERIC, PsychINFO, Academic Search Ultimate, and Health Source: Nursing/Academic Edition). Pooled results were screened for eligibility, and relevant papers were selected for inclusion. The analysis revealed VR’s promising effects in the treatment of AUDs. Its remarkable potential to simulate cues underlying subsequent addictive behaviors makes its application in the assessment and treatment of AUDs an attractive alternative to researchers and clinicians alike. Nevertheless, more evidence is needed before virtual reality cue exposure therapy (VR-CET) can become a clinical standard of care.
An on-site and remote study during the COVID-19 pandemic on virtual hand appearance and tactile feedback

A virtual reality (VR) study was conducted both in our lab setting and remotely to investigate the effects of virtual hand appearance and tactile feedback on ownership, external appearance, and tactile sensation embodiment dimensions when participants were instructed to perform an assembly task in a virtual environment. Six experimental conditions that combine hand appearances (abstract, mannequin, and realistic) and tactile feedback (tactile and no tactile) levels were examined. The results of our study indicated that: (1) the more realistic hand had a stronger effect on tactile sensation and external appearance, while the mannequin hand was associated with a greater ownership effect; (2) tactile feedback was associated with a stronger effect on external appearance compared to no tactile feedback; (3) the realistic hand model in conjunction with tactile feedback significantly affected the tactile sensation of the participants; (4) the participants in the lab setting rated the external appearance of the realistic hand model higher than the remote participants; (5) the participants exposed for less than one hour per week to VR rated the tactile sensation in the presence of tactile feedback higher, while they similarly rated the external appearance of the virtual hand model in the presence of a realistic hand model higher when compared to those who were exposed for more than one hour per week to VR; and finally, (6) the younger participants rated the tactile sensation higher than the older participants in the presence of the realistic hand model. In this paper, we discuss our findings and provide design considerations for virtual reality applications that consider virtual hands and tactile feedback.