Reviewerly/exHarmony huggingface dataset:
Image Captioning using Deep Learning: Text Augmentation by Paraphrasing via Backtranslation

Image captioning, an exciting but challenging area of research at the intersection of natural language processing and computer vision, has recently seen dramatic progress. Much of the progress has been due to advances in machine learning and the availability of suitable data sets consisting of images, each with multiple captions. However, there has been little research on the impact of varying the number of captions on the quality of image captioning results. In particular, does it improve image captioning performance if we increase the number of captions in a dataset by augmenting the existing captions with paraphrases created using backtranslation? In this paper, we address this question and make the following three main contributions. First, we present a novel method of adding captions to a dataset by means of paraphrasing via backtranslation. Backtranslation amounts, in our case, to translating an English caption to a French counterpart which is then translated back to English. Typically, this results in the backtranslated English caption being different from the original English caption. Such backtranslated English captions can thus be used to augment the original dataset. Second, using our method, we generate paraphrases for the seminal MS COCO image captioning dataset. Our paraphrase dataset consists of around 490 000 paraphrased captions that are different from the original captions in MS COCO. Third, in image captioning experiments with deep learning models, we use datasets augmented with backtranslated paraphrases. We study the extent to which augmenting image captioning training datasets with paraphrases improves image captioning models' performance, and report promising results. Code and data are publicly available.
Digital Contact Tracing and COVID-19: Design, Deployment, and Current Use in Italy

The technological innovation of digital contact tracing (DCT) has certainly characterized the COVID-19 pandemic, as compared to the previous ones. Based on the first studies, considerable support was expected from smartphone applications ("apps") for DCT. This commentary focuses on digital contact tracing. Its contributions are threefold: (a) Recall the initial expectations of these technologies and the state of diffusion. (b) Deal with the introduction of the app "Immuni" in Italy, while also highlighting the initiatives undertaken at the government level. (c) Report the state of diffusion and use of this App. The commentary ends by proposing some reflections on the continuation of this investigation in Italy.
Towards Malicious address identification in Bitcoin

The temporal aspect of blockchain transactions enables us to study the address's behavior and detect if it is involved in any illicit activity. However, due to the concept of change addresses (used to thwart replay attacks), temporal aspects are not directly applicable in the Bitcoin blockchain. Several pre-processing steps should be performed before such temporal aspects are utilized. We are motivated to study the Bitcoin transaction network and use the temporal features such as burst, attractiveness, and inter-event time along with several graph-based properties such as the degree of node and clustering coefficient to validate the applicability of already existing approaches known for other cryptocurrency blockchains on the Bitcoin blockchain. We generate the temporal and non-temporal feature set and train the Machine Learning (ML) algorithm over different temporal granularities to validate the state-of-the-art methods. We study the behavior of the addresses over different time granularities of the dataset. We identify that after applying change-address clustering, in Bitcoin, existing temporal features can be extracted and ML approaches can be applied. A comparative analysis of results show that the behavior of addresses in Ethereum and Bitcoin is similar with respect to in-degree, out-degree and inter-event time. Further, we identify 3 suspects that showed malicious behavior across different temporal granularities. These suspects are not marked as malicious in Bitcoin.
Dynamic Voltage and Frequency Scaling to Improve Energy-Efficiency of Hardware Accelerators

Neural networks (NNs) have been used in a wide variety of artificial intelligence (AI) applications, including speech recognition, image recognition, automatic robotics, and games. State-of-the-art NNs provide high prediction accuracy at the expense of massive computation that involves large model parameters which consume substantial energy. Though sparse NN s have emerged to reduce the computation and storage overhead, existing specialized DNN accelerators cannot maximize the energy savings when exploiting both dynamic and static sparsity, especially for irregular NNs. In this paper, we propose a dynamic voltage and frequency scaling (DVFS) based hardware accelerator that effectively exploits the dynamic and static sparsity of NNs with dynamic voltage/frequency (V/F) scaling and power gating techniques to reduce both static and dynamic power. To explore the efficiency of DVFS implementation at different granularities, we evaluate both coarse-grained and fine-grained DVFS implementation with different design trade-offs. Further, our proposed DVFS model predicts the dynamic computation workloads as well as V/ F pairs to be supplied to processing elements (PEs) in the hardware intelligently through pre-trained weight vectors. The machine learning based prediction algorithm is deployed to improve the DVFS mode selection accuracy. Our simulation results on AlextNet, VGG16, and ResNet50 show that we can achieve an average dynamic energy savings of 59–66 % and an average static power reduction of 69–80 % compared to the baseline.
RL-CSL: A Combinatorial Optimization Method Using Reinforcement Learning and Contrastive Self-Supervised Learning

Reinforcement learning-based methods have shown great potential in solving combinatorial optimization problems. However, the related research has not been mature in terms of both models and training methods. This paper proposes a method based on reinforcement learning and contrastive self-supervised learning. To be specific, the proposed method uses an attention model to learn a policy for generating solutions and combines a contrastive self-supervised learning model to learn the attention encoder in the way of node-by-node. Correspondingly, a two-phase learning method, including node-wise learning and solution-wise learning, is adopted to train the attention model and the contrastive self-supervised model jointly and collaboratively. The performance of the proposed method has been verified by numerical experiments on various combinatorial optimization problems.
Perspective on SDSB Human Visual Knowledge and Intelligence for Happiness Campus

Suffering globally by COVID-19 since 2020 constrained learner and worker outdoors, of which campus and public area naturally met environmental protection issue. In such cases, a newly AI moveable application, naming Self-Driving Sweeper Bot (SDSB), is invented by intelligently coordinating between self-driving system and sweeper mechanism. In this paper, the perspective on SDSB in terms of human visual knowledge and intelligence between pedestrian security and sweeping efficiency in campus is reported. To reach such a goal, our investigation is shown that human visual knowledge and intelligence, played a critical role requiring routinely collecting and learning visual dataset, accompanied with optimizing procedure by exploring the object recognition methods e.g., CNN, R-CNN, Fast-RCNN and Yolo, for detecting campus objects (including, pedestrians, vehicles, common rubbishes, i.e. fallen leaves, waste papers, plastic bottles etc.), and image segmentation techniques e.g., U-net for constraining sweeping road. In the preliminarily experiments, observation is shown that the factors for object detection and road segmentation in terms of weather, sunshine direction and shadowing/non-shadowing by trees and facilities are highly influencing on SDSB visual intelligence.
Cryptanalysis of quantum digital signature for the access control of sensitive data

Quantum digital signature provides a new way to ensure both integrity and transferability of digital messages with information-theoretic security. In this work, we give a cryptanalysis of quantum digital signature protocol for the access control of sensitive data in the big data era, and propose two new attack strategies on this protocol. In the first attack, the receiver Bob can forge a valid message–signature pair by a new known-message attack in this protocol. In the second attack, the signer Alice can successfully deny her signature. Furthermore, we discuss how to deal with these security problems and give some possible ways to improve this protocol.
Auto Insurance Knowledge Graph Construction and Its Application to Fraud Detection

In recent years, feature engineering based machine learning models have made great progress in auto insurance fraud detection. However, their performance on single fraud case detection has never reached to a high level, and they cannot detect gang frauds. One of the main causes is that such machine learning models directly neglect the associations between auto insurance cases. To resolve this problem, we propose to leverage knowledge graph techniques to discover associations between cases for fraud detection. We first construct an auto insurance knowledge graph (AIKG) with ontology building and knowledge extraction from relational database. We then apply AIKG to both gang fraud detection and single fraud case detection. We finally conduct comprehensive experiments on fraud detection, and our methods significantly outperform state-of-the-art baselines in different evaluation metrics. Our built auto insurance ontology which is the core part of AIKG has been published on the Web and can be open access.
Evaluating mail‐based security for electoral processes using attack trees

Since the reports of Russian interference in the 2016 United States General Election, the security of voting processes has received increased attention from both state and federal authorities. The declaration by the US Department of Homeland Security in January 2017 that election systems be classified as the 17th component of critical infrastructure is just the beginning of a need for more secure voting processes. More recently, the COVID-19 pandemic and the 2020 US General Election have placed greater emphasis specifically on mail-based voting processes for electoral systems. The objective of this research is to provide greater insight into potential threats to mail-based voting processes. Upon identifying an attack tree as an initial structure for evaluation, new threats are postulated, and an updated tree is proposed that accounts for more recent activities. Then, using an established assessment framework, the relative likelihood of each mail-based voting process attack scenario is identified. The results facilitate providing election officials and policymakers with greater knowledge of how mail-based voting system vulnerabilities develop as well as specific security measures that may be most beneficial.
QoE Modeling Associated with QoS Impairment Parameters in 5G Networks Using AHP Decision Making Technique

This paper aims to propose the quality of experience (QoE) models based on the expectation and/or the perception of 5G users to evaluate for mean opinion score (MOS) for real-time or interactive services/applications with high reliability. Therefore, Based on the fundamental QoE concept, the analytic hierarchy process (AHP) decision making technique has been applied.
Secure Aggregation in Federated Learning via Multiparty Homomorphic Encryption

A key operation in federated learning is the aggregation of gradient vectors generated by individual client nodes. We develop a method based on multiparty homomorphic encryption (MPHE) that enables the central node to compute this aggregate, while receiving only encrypted version of each individual gradients. Towards this end, we extend classical MPHE methods so that the decryption of the aggregate vector can be successful even when only a subset of client nodes are available. This is accomplished by introducing a secret-sharing step during the setup phase of MPHE when the public encryption key is generated. We develop conditions on the parameters of the MPHE scheme that guarantee correctness of decryption and (computational) security. We explain how our method can be extended to accommodate client nodes that do not participate during the setup phase. We also propose a compression scheme for gradient vectors at each client node that can be readily combined with our MPHE scheme and perform the associated convergence analysis. We discuss the advantages of our proposed scheme with other approaches based on secure multi-party computation. Finally we discuss a practical implementation of our system and compare the performance of our system with baseline approaches that do not perform encryption.
Speeding up block propagation in Bitcoin network: Uncoded and coded designs

This paper designs and validates new block propagation protocols for the peer-to-peer (P2P) network of the Bitcoin blockchain. Despite its strong protection for security and privacy, the current Bitcoin blockchain can only support a low number of transactions per second (TPS). Besides the PoW consensus protocol, the other key aspect of Bitcoin that limits TPS is its networking protocol. In this work, we aim at redesigning the current Bitcoin's networking protocol to increase TPS without changing the vital components in its consensus-building protocol. In particular, this work improves the compact-block relaying protocol to enable the propagation of blocks containing a massive number of transactions without inducing extra propagation latencies. The improvements of this work consist of (i) replacing the existing store-and-forward compact-block relaying scheme with a cut-through compact-block relaying scheme; (ii) exploiting rateless erasure codes for P2P networks to increase the block-propagation efficiency. Since the protocols designed in this work only need to rework the current Bitcoin's networking protocol and do not modify the data structures and crypto-functional components, they can be seamlessly incorporated into the existing Bitcoin blockchain. To validate the designs, we perform analysis on the protocols and implement a Bitcoin network simulator on NS3 to run different block propagation protocols. Our analysis and experimental results confirm that the new block propagation protocols could increase the TPS of the Bitcoin blockchain by 100x without compromising security and consensus-building.
Block Auction: A General Blockchain Protocol for Privacy-Preserving and Verifiable Periodic Double Auctions

We use blockchain technology to tackle the problem of securing periodic double auctions for financial ‘dark pool’ trading, such that the privacy of pre-trade order information is preserved and the behaviour of the auction operator can be verified. A smart contract is used as a public audit trail without revealing the order intention of traders. Auction matching is performed off-chain, allowing alternative auction mechanisms to be used without altering the smart contract code. A full system implementation is deployed on the Harmony blockchain, and comparative evaluation of the protocol demonstrates some clear advantages over the closest published alternative.
Short communication: Evolution of secondary studies in software engineering

Other disciplines commonly employ secondary studies to address the needs of practitioners and policy-makers. Since being adopted by software engineering in 2004, many have been undertaken by researchers. To assess how the role of secondary studies in software engineering has evolved. We examined a sample of 131 secondary studies published in a set of five major software engineering journals for the years 2010, 2015 and 2020. These were categorised by their type (e.g. mapping study), their research focus (quantitative/qualitative and practice/methodological), as well as the experience of the first authors. Secondary studies are now a well-established research tool. They are predominantly qualitative and there is extensive use of mapping studies to profile research in particular areas. A significant number are clearly produced as part of postgraduate study, although experienced researchers also conduct many secondary studies. They are sometimes also used as part of a multi-method study. Existing guidelines largely focus upon quantitative systematic reviews. Based on our findings, we suggest that more guidance is needed on how to conduct, analyse, and report qualitative secondary studies.
Analysis and Patterns of Unknown Transactions in Bitcoin

Bitcoin (BTC) is probably the most transparent payment network in the world, thanks to the full history of transactions available to the public. Though, Bitcoin is not a fully anonymous environment, rather a pseudonymous one, accounting for a number of attempts to beat its anonymity using clustering techniques. There is, however, a recurring assumption in all the cited deanonymization techniques: that each transaction output has an address attached to it. That assumption is false. An evidence is that, as of block height 591,872, there are several millions transactions with at least one output for which the Bitcoin Core client cannot infer an address. In this paper, we present a novel approach based on sound graph theory for identifying transaction inputs and outputs. Our solution implements two simple yet innovative features: it does not rely on BTC addresses and explores all the transactions stored in the blockchain. All the other existing solutions fail with respect to one or both of the cited features. In detail, we first introduce the concept of Unknown Transaction and provide a new framework to parse the Bitcoin blockchain by taking them into account. Then, we introduce a theoretical model to detect, study, and classify, for the first time in the literature, unknown transaction patterns in the user network. Further, in an extensive experimental campaign, we apply our model to the Bitcoin network to uncover hidden transaction patterns within the Bitcoin user network. Results are striking: we discovered more than 30,000 unknown transaction DAGs representing money flows never observed before. To the best of our knowledge, the proposed framework is the only one that enables a complete study of the unknown transaction patterns, hence enabling further research in the field, for which we provide some directions.
Artificial intelligence techniques

Artificial Intelligence (AI) garners many front-page headlines every day as the technology enables machines to learn from experience and perform human-like tasks. Though the term “AI” was coined in the 1950s, the field is still evolving due to the invention of new high-speed computing devices. Therefore, many variations of AI techniques have been reported and applied in anomaly detection, pattern recognition, natural language processing, feature extraction, regression, data augmentation, and many other fields of study. This chapter presents a brief history of AI, along with its foundation and basic components. Then, it discusses the basics of the popular machine learning techniques (a subset of AI), including the artificial neural networks, support vector machines, extreme learning machines, fuzzy logic models, genetic programming, and deep learning techniques. Besides, this chapter also briefly sheds light on hybrid, ensemble, and other AI techniques. It is expected that the presented discussions of this chapter help the readers to understand the foundation of the popular machine learning techniques and select appropriate strategies to deal with their problems. Furthermore, many of the discussed AI techniques are employed in solving power system fault diagnosis in the subsequent chapters of this book.
Exploring Gated Graph Sequence Neural Networks for Predicting Next Process Activities

A current trend in predictive business process monitoring is to construct predictive models using deep neural networks (DNNs), especially long short-term memory neural networks, convolutional neural networks, or multi-layer perceptron neural networks. While these DNN types typically require data defined on the Euclidean space (e.g., grids), graph neural networks (GNNs), a relatively new type of DNNs, can compute data defined on the non-Euclidean space (e.g., graphs). Because GNNs can directly compute graph-oriented data inputs, generally structured into nodes and edges, they can explicitly model event relationships. This paper investigates gated graph sequence neural networks (GGNNs) for the next activity prediction. First results with two real-life event logs show that GGNNs can outperform traditional DNNs regarding predictive quality, especially if nodes of an input graph are assumed as events, and the graph's adjacency matrix only describes event relationships of a process instance prefix.
Second-Hand Car Trading Framework Based on Blockchain in Cloud Service Environment

At present, the world economy is in recession, especially under the impact of the Covid-19 epidemic, China's economy has also been greatly impacted. In this context, the disposable personal income of residents has also declined to varying degrees. More and more people choose economical life. If they can buy a used car in good condition at a good price, they are less likely to buy a brand new one. Under such a consumption concept, China's demand for second-hand cars is increasing. However, although China's second-hand car industry has developed for more than 30 years and the market scale has gradually expanded, there are still many problems behind the prosperity of the second-hand car market. These problems have existed for a long time, leading to a lot of disputes, unhappiness, disappointment, and even threats to the lives of consumers. These long-term problems also affect the virtuous circle of the second-hand car market, and hinder the healthy development of China's economy to a certain extent. In the past, research work mainly focused on the role of new policies, relevant laws and vehicle management and traffic management functions, this paper introduces the blockchain technology, which has the advantages of non tampering, transparency and traceability. This paper attempts to use blockchain technology as an auxiliary means to solve the long-standing problems in the used car market. This paper proposes a framework of used car trading based on blockchain in cloud service environment, and explains the working principle of the framework. Finally, the future research work is prospected.
Unknown radar waveform recognition system via triplet convolution network and support vector machine

We propose an unknown radar waveform recognition system for identifying unknown radar waveforms and classifying known radar waveforms simultaneously, which can be summarized into three stages: a training stage that a triplet convolution network is trained to capture features and a support vector machine is leveraged to classify signals with the assistance of known radar waveforms, a pseudo-testing stage that a proper threshold is chosen to set a boundary between known and unknown radar waveforms, and a testing stage that new signal types are distinguished from conventional types by loading models trained in the training stage and the threshold chosen in the pseudo-testing stage. Furthermore, we need to derive the corresponding central feature vector of each known class in the training stage so as to prepare for computing Pearson correlation coefficient in the pseudo-testing stage and the testing stage. Experiments demonstrate the effectiveness of the proposed system on classifying known signals and identifying unknown signals. In the context of adaptive electronic warfare, this research accomplishes identification of radar signal waveforms including unknown signals for the first time, laying a solid foundation for the progress of electronic reconnaissance system.