Reviewerly/exHarmony huggingface dataset:
DifFuzz: Differential Fuzzing for Side-Channel Analysis

Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or response size. Side-channel vulnerabilities are difficult to reason about as they involve analyzing the correlations between resource usage over multiple program paths. We present DifFuzz, a fuzzing-based approach for detecting side-channel vulnerabilities related to time and space. DifFuzz automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secret-dependent paths. The methodology of DifFuzz is general and can be applied to programs written in any language. For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers. We evaluate DifFuzz on a large number of Java programs and demonstrate that it can reveal unknown side-channel vulnerabilities in popular applications. We also show that DifFuzz compares favorably against Blazer and Themis, two state-of-the-art analysis tools for finding side-channels in Java programs.
AirVis: Visual Analytics of Air Pollution Propagation

Air pollution has become a serious public health problem for many cities around the world. To find the causes of air pollution, the propagation processes of air pollutants must be studied at a large spatial scale. However, the complex and dynamic wind fields lead to highly uncertain pollutant transportation. The state-of-the-art data mining approaches cannot fully support the extensive analysis of such uncertain spatiotemporal propagation processes across multiple districts without the integration of domain knowledge. The limitation of these automated approaches motivates us to design and develop AirVis, a novel visual analytics system that assists domain experts in efficiently capturing and interpreting the uncertain propagation patterns of air pollution based on graph visualizations. Designing such a system poses three challenges: a) the extraction of propagation patterns; b) the scalability of pattern presentations; and c) the analysis of propagation processes. To address these challenges, we develop a novel pattern mining framework to model pollutant transportation and extract frequent propagation patterns efficiently from large-scale atmospheric data. Furthermore, we organize the extracted patterns hierarchically based on the minimum description length (MDL) principle and empower expert users to explore and analyze these patterns effectively on the basis of pattern topologies. We demonstrated the effectiveness of our approach through two case studies conducted with a real-world dataset and positive feedback from domain experts.
A Key Protection Scheme Based on Secret Sharing for Blockchain-Based Construction Supply Chain System

Traditional construction supply chains suffer from extra delays, costs and information wastages due to information intermediaries.Blockchain, a decentralized infrastructure, can provide irreversibility, undeniableness, uniqueness and anonymity for trades.Hence, we first propose a blockchain-based construction supply chain framework to reduce limitations in traditional ones.However, payment security by blockchain must be guaranteed and token assets in accounts must be protected.Although the loss of private keys will not result in the exposure or the modification of records in blockchain due to merkle root and blockheader hash, fake payments can be generated and all tokens in the accounts controlled by the private keys may be stolen by attackers.Existing approaches towards private-key protections include biometric-basic signature schemes, index-hidden private key designs and post-quantum blockchain schemes.Nevertheless, none of them can recover lost private keys.Therefore, we design a private-key distribution protocol in blockchains to preserve security of private keys with key recovery.Specifically, our scheme not only uses secret sharing to improve possibilities of recovering lost keys but also introduces network protocols to guarantee security of secret share transmission.The proposed scheme is then proven secure and feasible both in theoretical and experimental analysis.
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation

Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories by comparing the feature embeddings of query images with those from a few labeled images (support examples) using a learned metric function. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains. In this work, we address the problem of few-shot classification under domain shifts for metric-based methods. Our core idea is to use feature-wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage. To capture variations of the feature distributions under different domains, we further apply a learning-to-learn approach to search for the hyper-parameters of the feature-wise transformation layers. We conduct extensive experiments and ablation study under the domain generalization setting using five few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, and Plantae. Experimental results demonstrate that the proposed feature-wise transformation layer is applicable to various metric-based models, and provides consistent improvements on the few-shot classification performance under domain shift.
A Survey of Recent Advances in Transfer Learning

The integration of transfer learning methods and other machine learning branches can bring a good improvement in speed and performance, it has become a good research topic in the recent years and it is necessary for researchers to understand the integration. This paper focuses on sorting and classifying the integrated results of the transfer learning and the other non-transfer machine learning, such as reinforcement leaning, lifelong learning, adversarial networks. Besides, this paper also organizes the latest work on the transitive transfer learning and categorizes the relevant applications for transfer learning.
Minimal linear codes arising from blocking sets

Minimal linear codes are algebraic objects which gained interest in the last 20 years, due to their link with Massey’s secret sharing schemes. In this context, Ashikhmin and Barg provided a useful and a quite easy-to-handle sufficient condition for a linear code to be minimal, which has been applied in the construction of many minimal linear codes. In this paper, we generalize some recent constructions of minimal linear codes which are not based on Ashikhmin–Barg’s condition. More combinatorial and geometric methods are involved in our proofs. In particular, we present a family of codes arising from particular blocking sets, which are well-studied combinatorial objects. In this context, we will need to define cutting blocking sets and to prove some of their relations with other notions in blocking sets’ theory. At the end of the paper, we provide one explicit family of cutting blocking sets and related minimal linear codes, showing that infinitely many of its members do not satisfy the Ashikhmin–Barg’s condition.
Blockchain of Carbon Trading for UN Sustainable Development Goals

Carbon credits should reduce the environmental pollution and carbon emission of the Earth in the future. The market for carbon credits will become a critical issue from 2021, and carbon credits will be applied to systems where individuals can trade. In order for these carbon credits to be traded between individuals, however, a corresponding exchange of carbon credits is needed. Policies, strategies, and technologies are also necessary to measure the trading of carbon credits. This paper aims at making transactions more reliable by applying blockchain technology to measure carbon emission rights. It uses blockchain to verify carbon emissions rights among the UN-SDGs’ (United Nations Sustainable Development Goals’) 17 tasks. In addition, it introduces the necessary dApp. In fact, we can protect against carbon emissions anomalies by using big data and artificial intelligence in mobile cloud environments. Thus, this paper proposes a blockchain-based carbon emission rights verification system to learn proven data further by using the governance system analysis and blockchain mainnet engine to solve these problems.
Continuous-variable quantum key distribution with non-Gaussian operations

Continuous-variable quantum key distribution (CV-QKD) provides an effective way to obtain the high secret key rate, but is limited by practical techniques. To overcome this limitation, we investigate the performance of CV-QKD protocols when non-Gaussian operations are applied to both sides of the channels, including photon subtraction and addition, and photon catalysis. A comparison is also made between Gaussian modulation and non-Gaussian modulation. We show that the optimal transmission distance cannot be improved by non-Gaussian modulation at the sender's side, but it can be improved by single-photon catalysis at the receiver's side. For a given modulation variance that exceeds a certain threshold, the performance of the zero-photon catalysis at the sender's side is the best when compared with other non-Gaussian modulations (except single-photon catalysis at the receiver's side), and it can always reach the optimal distance. Our paper will provide a useful insight for the use of non-Gaussian modulation to other CV-QKD protocols.
When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.
DefectChecker: Automated Smart Contract Defect Detection by Analyzing EVM Bytecode

Smart contracts are Turing-complete programs running on the blockchain. They are immutable and cannot be modified, even when bugs are detected. Therefore, ensuring smart contracts are bug-free and well-designed before deploying them to the blockchain is extremely important. A contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. Detecting and removing contract defects can avoid potential bugs and make programs more robust. Our previous work defined 20 contract defects for smart contracts and divided them into five impact levels. According to our classification, contract defects with seriousness level between 1-3 can lead to unwanted behaviors, e.g., a contract being controlled by attackers. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefectChecker</i> , a symbolic execution-based approach and tool to detect eight contract defects that can cause unwanted behaviors of smart contracts on the Ethereum blockchain platform. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefectChecker</i> can detect contract defects from smart contracts’ bytecode. We verify the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefectChecker</i> by applying it to an open-source dataset. Our evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefectChecker</i> obtains a high F-score (88.8 percent in the whole dataset) and only requires 0.15s to analyze one smart contract on average. We also applied <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DefectChecker</i> to 165,621 distinct smart contracts on the Ethereum platform. We found that 25,815 of these smart contracts contain at least one of the contract defects that belongs to impact level 1-3, including some real-world attacks.
PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis

This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing the characteristics of evolutionary algorithms. PSO has a property of faster convergence rate, which can be exploited to account for the slower pace of convergence of the traditional BP (which is due to low values of gradients). In contrast, the integration with GA complements the drawback of convergence to local minima as GA, possesses the capability of efficient global search. So by this integration of these algorithms, we propose our new hybrid algorithm for training ANNs. We compare both the algorithms for the application of medical diagnosis. Results display that the proposed hybrid training algorithm, significantly outperforms the traditional training algorithm, by enhancing the accuracies of the ANNs with an increase of 20% in the average testing accuracy and 0.7% increase in the best testing accuracy.
Virtual Reality in the Assessment, Understanding and Treatment of Mental Health Disorders

Computer scientists usually describe virtual reality (VR) as a set of fancy hardware and software technologies. However, psychology and neuroscience are starting to consider VR as the most advanced form of human-computer interaction allowing individuals to act, communicate and become present in a computer-generated environment. In this view, the feeling of “being there” experienced during a VR experience can become a powerful tool for personal change: it offers a dynamic and social world where individuals can live and share a specific experience. For this reason, the use of VR in mental health shows promise: different researches support its clinical efficacy for conditions including anxiety disorders, stress-related disorders, obesity and eating disorders, pain management, addiction and schizophrenia. However, more research is needed to transform the promises of VR in a real clinical tool for mental health. This Special Issue aims to present the most recent advances in the mental health applications of VR, as well as their implications for future patient care.
Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.
Findings of the 2020 Conference on Machine Translation (WMT20)

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.
Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation

When implementing a pseudo-random number generator (PRNG) for neural network chaos-based systems on FPGAs, chaotic degradation caused by numerical accuracy constraints can have a dramatic impact on the performance of the PRNG. To suppress this degradation, a PRNG with a feedback controller based on a Hopfield neural network chaotic oscillator is proposed, in which a neuron is exposed to electromagnetic radiation. We choose the magnetic flux across the cell membrane of the neuron as a feedback condition of the feedback controller to disturb other neurons, thus avoiding periodicity. The proposed PRNG is modeled and simulated on Vivado 2018.3 software and implemented and synthesized by the FPGA device ZYNQ-XC7Z020 on Xilinx using Verilog HDL code. As the basic entropy source, the Hopfield neural network with one neuron exposed to electromagnetic radiation has been implemented on the FPGA using the high precision 32-bit Runge Kutta fourth-order method (RK4) algorithm from the IEEE 754-1985 floating point standard. The post-processing module consists of 32 registers and 15 XOR comparators. The binary data generated by the scheme was tested and analyzed using the NIST 800.22 statistical test suite. The results show that it has high security and randomness. Finally, an image encryption and decryption system based on PRNG is designed and implemented on FPGA. The feasibility of the system is proved by simulation and security analysis.
Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm

This paper presents a new swarm intelligence optimization algorithm: Flamingo Search Algorithm (FSA), which is inspired by the migratory and foraging behavior of flamingos. A mathematical model of flamingo behavior is built so that FSA has the global exploration and local exploitation capabilities required for an optimization algorithm. Three sets of experiments based on 68 test functions are designed to evaluate the convergence speed, optimization-seeking accuracy, stability, running time, and global search capability of FSA. The effect of different input parameters on the search results of FSA is then discussed, and the optimal parameter selection interval is summarized. In addition, nine test functions are selected to visualize the trajectory of the flamingo population during the search. The test results of the above designs all indicate that FSA is superior to other algorithms in solving optimization problems. Finally, three kinds of simulation experiments, which are push-pull circuit problem, path planning problem and network intrusion detection system, are designed to test the practicability of FSA. The code used for the main experiment in this article can be obtained from website https://github.com/18280426650/FSA.
A Conceptual Model and Taxonomy for Collaborative Augmented Reality

To support the nuances of collaborative work, many researchers have been exploring the field of Augmented Reality (AR), aiming to assist in co-located or remote scenarios. Solutions using AR allow taking advantage from seamless integration of virtual objects and real-world objects, thus providing collaborators with a shared understanding or common ground environment. However, most of the research efforts, so far, have been devoted to experiment with technology and mature methods to support its design and development. Therefore, it is now time to understand where the field stands and how well can it address collaborative work with AR, to better characterize and evaluate the collaboration process. In this article, we perform an analysis of the different dimensions that should be taken into account when analysing the contributions of AR to the collaborative work effort. Then, we bring these dimensions forward into a conceptual framework and propose an extended human-centered taxonomy for the categorization of the main features of Collaborative AR. Our goal is to foster harmonization of perspectives for the field, which may help create a common ground for systematization and discussion. We hope to influence and improve how research in this field is reported by providing a structured list of the defining characteristics. Finally, some examples of the use of the taxonomy are presented to show how it can serve to gather information for characterizing AR-supported collaborative work, and illustrate its potential as the grounds to elicit further studies.
Service Robots: Trends and Technology

The 2021 sales volume in the market of service robots is attractive. Expert reports from the International Federation of Robotics confirm 27 billion USD in total market share. Moreover, the number of new startups with the denomination of service robots nowadays constitutes 29% of the total amount of robotic companies recorded in the United States. Those data, among other similar figures, remark the need for formal development in the service robots area, including knowledge transfer and literature reviews. Furthermore, the COVID-19 spread accelerated business units and some research groups to invest time and effort into the field of service robotics. Therefore, this research work intends to contribute to the formalization of service robots as an area of robotics, presenting a systematic review of scientific literature. First, a definition of service robots according to fundamental ontology is provided, followed by a detailed review covering technological applications; state-of-the-art, commercial technology; and application cases indexed on the consulted databases.