Reviewerly/exHarmony huggingface dataset:
A Cluster based Intelligent Method to Manage Load of Controllers in SDN-IoT Networks for Smart Cities

Software Defined Network (SDN) is a programmable network which separates the control logic-plane and hardware data-plane. The SDN centrally manages different Internet of Things (IoT) enabled smart devices like, actuators and sensors connected in the networks. Smart city infrastructure is an application of IoT network which purpose is to manage the city network without human interventions. To collect the real time data, such smart devices generate large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN needs to deploy the multi-controllers. But the communication performance reduces due to unbalance load distribution on controllers. To balance the traffic load of controller an intelligent cluster based Grey Wolf Optimization Affinity Propagation (GWOAP) Algorithm is proposed when deploying the multiple controllers in SDN-IoT enabled smart city networks. The proposed algorithm is simulated and the experimental results able to calculates the minimum overall communication cost in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Affinity Propagation (AP). The proposed GWOAP better balance the IoT enabled smart switches among clusters and node equalization is balanced for each controller in deployed topology. By using the proposed methodology, the traffic load of IoT enabled devices in smart city networks intelligently better balance among controllers.
BTC price volatility: Fundamentals versus information

This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
Higher-Order Masked Ciphertext Comparison for Lattice-Based Cryptography

Checking the equality of two arrays is a crucial building block of the Fujisaki-Okamoto transformation, and as such it is used in several post-quantum key encapsulation mechanisms including Kyber and Saber. While this comparison operation is easy to perform in a black box setting, it is hard to efficiently protect against side-channel attacks. For instance, the hash-based method by Oder et al. is limited to first-order masking, a higher-order method by Bache et al. was shown to be flawed, and a very recent higher-order technique by Bos et al. suffers in runtime. In this paper, we first demonstrate that the hash-based approach, and likely many similar first-order techniques, succumb to a relatively simple side-channel collision attack. We can successfully recover a Kyber512 key using just 6000 traces. While this does not break the security claims, it does show the need for efficient higher-order methods. We then present a new higher-order masked comparison algorithm based on the (insecure) higher-order method of Bache et al. Our new method is 4.2x, resp. 7.5x, faster than the method of Bos et al. for a 2nd, resp. 3rd, -order masking on the ARM Cortex-M4, and unlike the method of Bache et al., the new technique takes ciphertext compression into account. We prove correctness, security, and masking security in detail and provide performance numbers for 2nd and 3rd-order implementations. Finally, we verify our the side-channel security of our implementation using the test vector leakage assessment (TVLA) methodology.
Fuzzy Failure Modes and Effects Analysis Using Summative Defuzzification Methods

Nowadays, in order to achieve competitiveness in the manufacturing industry, quality and reliability in design and manufacturing processes are essential factors.These requirements can only be fulfilled by using effective estimation models, instead of the costand time-consuming measurements.The widely used engineering technique Fuzzy-based Failure Modes and Effects Analysis (F-FMEA) also serves this purpose.This method is able to identify and eliminate potential problems of a product from the process, system, design, and service based on the available data to avoid possible risk (i.e.repetition of damage and additional costs).Data can be analyzed properly assessing the Probability of Failure (PoF), Consequence of Failure (CoF), and Detectability of Failure (DoF) using a fuzzy-based model, which can work well in situations where there is not enough data for a statistical model.Authors investigate different defuzzification methods in F-FMEA, and propose a novel Summative defuzzification to combine the results of the different fuzzy subsystems.
Inconspicuous Adversarial Patches for Fooling Image-Recognition Systems on Mobile Devices

Deep learning based image recognition systems have been widely deployed on mobile devices in today's world.In recent studies, however, deep learning models are shown vulnerable to adversarial examples.One variant of adversarial examples, called adversarial patch, draws researchers' attention due to its strong attack abilities.Though adversarial patches achieve high attack success rates, they are easily being detected because of the visual inconsistency between the patches and the original images.Besides, it usually requires a large amount of data for adversarial patch generation in the literature, which is computationally expensive and time-consuming.To tackle these challenges, we propose an approach to generate inconspicuous adversarial patches with one single image.In our approach, we first decide the patch locations based on the perceptual sensitivity of victim models, then produce adversarial patches in a coarse-to-fine way by utilizing multiple-scale generators and discriminators.The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities.Our approach shows the strong attack abilities in white-box settings and the excellent transferability in black-box settings through extensive experiments on various models with different architectures and training methods.Compared to other adversarial patches, our adversarial patches hold the most negligible risks to be detected and can evade human observations, which is supported by the illustrations of saliency maps and results of user evaluations.Lastly, we show that our adversarial patches can be applied in the physical world.
Auxiliary Tasks Speed Up Learning PointGoal Navigation

PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment. Wijmans et al. showed that this task is solvable but their method is computationally prohibitive, requiring 2.5 billion frames and 180 GPU-days. In this work, we develop a method to significantly increase sample and time efficiency in learning PointNav using self-supervised auxiliary tasks (e.g. predicting the action taken between two egocentric observations, predicting the distance between two observations from a trajectory,etc.).We find that naively combining multiple auxiliary tasks improves sample efficiency,but only provides marginal gains beyond a point. To overcome this, we use attention to combine representations learnt from individual auxiliary tasks. Our best agent is 5.5x faster to reach the performance of the previous state-of-the-art, DD-PPO, at 40M frames, and improves on DD-PPO's performance at 40M frames by 0.16 SPL. Our code is publicly available at https://github.com/joel99/habitat-pointnav-aux.
Leaking Secrets through Modern Branch Predictor in the Speculative World

Transient execution attacks that exploit speculation have raised significant concerns in computer systems. Typically, branch predictors are leveraged to trigger mis-speculation in transient execution attacks. In this work, we demonstrate a new class of speculation-based attack that targets branch prediction unit (BPU). We find that speculative resolution of conditional branches (i.e., in nested speculation) alter the states of pattern history table (PHT) in modern processors, which are not restored after the corresponding branches are later squashed. Such characteristic allows attackers to exploit BPU as the secret transmitting medium in transient execution attacks. To evaluate the discovered vulnerability, we build a novel attack framework, BranchSpectre, that enables exfiltration of unintended secrets through observing speculative PHT updates (in the form of covert and side channels). We further investigate PHT collision mechanism in the history-based predictor as well as the branch prediction mode transitions in Intel processors. Built upon such knowledge, we implement an ultra high-speed covert channel (BranchSpectre-cc) as well as two side channels (i.e., BranchSpectre-v1 and BranchSpectre-v2) that merely rely on BPU for mis-speculation trigger and secret inference in the speculative domain. Notably, BranchSpectre side channels can take advantage of much simpler code patterns than the ones used in Spectre attacks. We present an extensive BranchSpectre code gadget analysis on a set of popular real-world application code bases followed by a demonstration of real-world side channel attack on OpenSSL. The evaluation results show substantial wider existence and higher exploitability of BranchSpectre code patterns in real-world software. Finally, we discuss several secure branch prediction mechanisms that can mitigate transient execution attacks exploiting modern branch predictors.
WhatTheWikiFact

The rise of Internet has made it a major source of information. Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic, to deal with the problem. Here, we present our contribution in this regard: WhatTheWikiFact, a system for automatic claim verification using Wikipedia. The system can predict the veracity of an input claim, and it further shows the evidence it has retrieved as part of the verification process. It shows confidence scores and a list of relevant Wikipedia articles, together with detailed information about each article, including the phrase used to retrieve it, the most relevant sentences extracted from it and their stance with respect to the input claim, as well as the associated probabilities. The system supports several languages: Bulgarian, English, and Russian.
Soundscaping Learning Spaces: Online Synchronicity and Composing Multiple Sonic Worlds

The Covid-19 pandemic has occasioned many epistemic shifts in our lives and work practices, not the least of which has changed the contours of the field of education and learning. While this special issue explores constructions of the postdigital in relation to learning spaces my focus will be exclusively on ‘soundscaping’ nonverbal sounds. Nonverbal sound may include any category of sound removed from speech or lyrics, such as the sound of a voice, instrumental music, or the beeps, buzzes, screams, and silences of human, animal, machine, object, or environmental sound. Soundscaping will refer to intentionally ‘planting,’ designing, or intervening within a landscape of sound. By returning to a framework for evaluating physical classroom spaces, I consider here how planting sound within synchronous video meetings for online learners can offer productive potentials. Furthermore, I argue that to overly rely on the ‘mute’ button or ignore those productive potentials is not only a missed opportunity, but could unintentionally do harm to learners. Finally, I offer some concrete suggestions for ‘planting’ nonverbal sound.
Forensic Analysis of Popular Social Media Applications on Android Smartphones

The use of mobile devices as an evidence of crime has increased. Social network forensics is a branch of science that includes accessing and analyzing many personal data that may contain evidence on social networks. Considering the multiplicity of personal data artefact by social networking applications in mobile devices, the importance of examining social media applications in mobile devices emerges. In this study, the necessary processes for the forensic investigation of mobile devices are examined, the necessary procedures for data extraction from mobile devices are explained and it is revealed how the analysis of social media applications in terms of forensic information in mobile devices should be realized. In the application part of the study, the most widely used mobile operating systems on android devices with android operating prompts; Facebook, WhatsApp Messenger, Instagram, Twitter applications have been installed, basic user behavior analysis of devices that have been performed. During the investigation phase, Oxygen Forensic, Paraben E3: DS and Magnet Axiom were used as tools for manual data extraction and mobile forensic information. The obtained data by both methods are shown comparatively.
Survey of Smart Contract Technology and Application Based on Blockchain

With the vigorous development of blockchain technology represented by Bitcoin, blockchain technology has gradually surpassed the era of programmable currency and entered the era of smart contracts.Smart contracts are eventdriven and stateful.With the in-depth development of blockchain technology, smart contracts use protocols and user interfaces to complete all steps of the contract process, allowing users to implement personalized code logic on the blockchain.Contract technology has the characteristics of decentralization, autonomy, observability, verifiability, and information sharing.It can effectively build programmable finance and programmable society, and is widely used in digital payment, financial asset disposal, multi-signature contracts, cloud computing, Internet of Things, sharing economy and other fields.First, it explains the basic concepts, full life cycle, basic classification, basic structure, key technologies, development status and main technology platforms of smart contracts; then discusses the application scenarios and development issues of smart contract technology, aiming to provide smart contract technology.The research and development provides reference.
Robust Multi-Object Tracking Using Re-Identification Features and Graph Convolutional Networks

We propose a graph neural network-based framework for multi-object tracking that combines detection and association along with the use of a novel re-identification feature. We explore the combination of multiple appearance features within our framework to obtain a better representation and improve tracking accuracy. Data augmentations with random erase and random noise are utilized to improve robustness during tracking. We consider various types of losses during training, including a unique application of the triplet loss to improve overall network performance. Results are presented on the UAVDT benchmark dataset for aerial-based vehicle tracking under various conditions.
Collaborative Physical Layer Authentication in Internet of Things Based on Federated Learning

With the booming growth of Internet of Things (IoT), ensuring the secure access of the IoT terminals is paramount importance. Machine learning (ML) based physical layer authentication (PLA) has been proposed as a promising solution for preventing unauthorized access of terminals due to its robust security. However, suffered from limited battery resources, IoT terminals are difficult to bear the ML tasks, which restricts the generality of ML-based PLA. In this paper, we propose a collaborative PLA scheme based on horizontal federated learning (HFL) to release computational pressure on resource-constrained IoT terminals. Particularly, we model the ML-based PLA as a training issue of the classifier, in which the training task is to obtain the weight parameter of the neural network. Then, we design a distributed authentication framework to assign the ML task of authentication to the trusted collaborators. Finally, all the local parameters trained by collaborators are aggregated at the center IoT terminal to obtain a global classifier used for PLA. Simulations show that both miss detection rate and false alarm rate are less than 1%, confirming the effectiveness of the proposed PLA scheme.
Public Health and Private Life Under COVID-19 Vaccination Policies in China: A Legal Analysis

Vaccination against SARS-CoV-2 has been the most significant measure in the preventing and controlling the spread of COVID-19. The COVID-19 vaccination rate in China is relatively high worldwide. However, the vaccination policies behind China's effective containment of COVID-19 are not well known. Some local governments have mandated vaccination for citizens and limited the freedom of movement and other fundamental rights of the unvaccinated. This paper examines the legitimacy of mandatory vaccination policies and their institutional logic within the Chinese legal framework. China prioritizes collective rights such as health over individual rights such as privacy. However, the mandatory vaccination policies lack statutory authorization and will restrict Chinese citizens' autonomy and compromise their privacy.
Intelligence augmentation: rethinking the future of work by leveraging human performance and abilities

Abstract Nowadays, digitalization has an immense impact on the landscape of jobs. This technological revolution creates new industries and professions, promises greater efficiency and improves the quality of working life. However, emerging technologies such as robotics and artificial intelligence (AI) are reducing human intervention, thus advancing automation and eliminating thousands of jobs and whole occupational images. To prepare employees for the changing demands of work, adequate and timely training of the workforce and real-time support of workers in new positions is necessary. Therefore, it is investigated whether user-oriented technologies, such as augmented reality (AR) and virtual reality (VR) can be applied “on-the-job” for such training and support—also known as intelligence augmentation (IA). To address this problem, this work synthesizes results of a systematic literature review as well as a practically oriented search on augmented reality and virtual reality use cases within the IA context. A total of 150 papers and use cases are analyzed to identify suitable areas of application in which it is possible to enhance employees' capabilities. The results of both, theoretical and practical work, show that VR is primarily used to train employees without prior knowledge, whereas AR is used to expand the scope of competence of individuals in their field of expertise while on the job. Based on these results, a framework is derived which provides practitioners with guidelines as to how AR or VR can support workers at their job so that they can keep up with anticipated skill demands. Furthermore, it shows for which application areas AR or VR can provide workers with sufficient training to learn new job tasks. By that, this research provides practical recommendations in order to accompany the imminent distortions caused by AI and similar technologies and to alleviate associated negative effects on the German labor market.
Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs

Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these black-box models. However, comparisons across explanation methods for link prediction remains difficult, as there is neither a method nor dataset to compare explanations against. Furthermore, there exists no standard evaluation metric to identify when one explanation method is preferable to the other. In this paper, we leverage linked data to propose a method, including two datasets (Royalty-20k, and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. In particular, we rely on the Semantic Web to construct explanations, ensuring that each predictable triple has an associated set of triples providing a ground truth explanation. Additionally, we propose the use of a scoring metric for empirically evaluating explanation methods, allowing for a quantitative comparison. We benchmark these datasets on state-of-the-art link prediction explanation methods using the defined scoring metric, and quantify the different types of errors made with respect to both data and semantics.
Dequantizing the Quantum singular value transformation: hardness and applications to Quantum chemistry and the Quantum PCP conjecture

The Quantum Singular Value Transformation (QSVT) is a recent technique that gives a unified framework to describe most quantum algorithms discovered so far, and may lead to the development of novel quantum algorithms. In this paper we investigate the hardness of classically simulating the QSVT. A recent result by Chia, Gilyén, Li, Lin, Tang and Wang (STOC 2020) showed that the QSVT can be efficiently “dequantized” for low-rank matrices, and discussed its implication to quantum machine learning. In this work, motivated by establishing the superiority of quantum algorithms for quantum chemistry and making progress on the quantum PCP conjecture, we focus on the other main class of matrices considered in applications of the QSVT, sparse matrices. We first show how to efficiently “dequantize”, with arbitrarily small constant precision, the QSVT associated with a low-degree polynomial. We apply this technique to design classical algorithms that estimate, with constant precision, the singular values of a sparse matrix. We show in particular that a central computational problem considered by quantum algorithms for quantum chemistry (estimating the ground state energy of a local Hamiltonian when given, as an additional input, a state sufficiently close to the ground state) can be solved efficiently with constant precision on a classical computer. As a complementary result, we prove that with inverse-polynomial precision, the same problem becomes BQP-complete. This gives theoretical evidence for the superiority of quantum algorithms for chemistry, and strongly suggests that said superiority stems from the improved precision achievable in the quantum setting. We also discuss how this dequantization technique may help make progress on the central quantum PCP conjecture.
RealSWATT: Remote Software-based Attestation for Embedded Devices under Realtime Constraints

Smart factories, critical infrastructures, and medical devices largely rely on embedded systems that need to satisfy realtime constraints to complete crucial tasks. Recent studies and reports have revealed that many of these devices suffer from crucial vulnerabilities that can be exploited with fatal consequences. Despite the security and safety-critical role of these devices, they often do not feature state-of-the-art security mechanisms. Moreover, since realtime systems have strict timing requirements, integrating new security mechanisms is not a viable option as they often influence the device's runtime behavior. One solution is to offload security enhancements to a remote instance, the so-called remote attestation.
Blockchain and cryptocurrencies: economic and financial research

The motivation of proposing and editing the Special Issue “Blockchain and cryptocurrencies” came from the inspirational invited and contributed talks at the 43rd annual A.M.A.S.E.S. conference held in Perugia in September 2019. All the papers have gone through the journal regular refereeing process under the same standards set by the journal, and nine contributions were finally accepted for publication.