Reviewerly/exHarmony huggingface dataset:
Network defense decision-making based on a stochastic game system and a deep recurrent Q-network

Defense decision-making in cybersecurity has increasingly relied upon stochastic game processes that combine game theory with a Markov decision process (MDP). However, the MDP presumes that both attackers and defenders are perfectly rational and have complete information, which greatly limits the scope of application and guidance value of MDP to the defense decision-making process. The present study addresses this issue by applying a partially observable MDP to analyze attack-defense behaviors, and a deep Q-network (DQN) algorithm based on a recurrent neural network for solving game equilibria dynamically and intelligently under conditions of partial rationality and incomplete information. The proposed DQN method enables network defense strategies to leverage online learning to gradually approach an optimal defense strategy. The rationality and convergence of the proposed approach are demonstrated by conducting simulations and comparative analyses of both the attacking and defending parties engaged in distributed reflection denial of service attacks.
Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations

In this paper, we present a hybrid recommendation framework based on the combination of graph embeddings and contextual word representations. Our approach is based on the intuition that each of the above mentioned representation models heterogeneous (and equally important) information, that is worth to be taken into account to generate a recommendation. Accordingly, we propose a strategy to combine both the features, which is based on the following steps: first, we separately generate graph embeddings and contextual word representations by exploiting state-of-the-art techniques. Next, these embeddings are used to feed a deep architecture that learns a hybrid representation based on the combination of the single groups of features. Finally, we exploit the resulting embedding to identify suitable recommendations. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and results show that the use of a hybrid representation leads to an improvement of the predictive accuracy. Moreover, our approach overcomes several competitive baselines, thus confirming the validity of this work.
reCode : A Lightweight Find-and-Replace Interaction in the IDE for Transforming Code by Example

Software developers frequently confront a recurring challenge of making code transformations—similar but not entirely identical code changes in many places—in their integrated development environments. Through formative interviews (n = 7), we found that developers were aware of many tools intended to help with code transformations, but often made their changes manually because these tools required too much expertise or effort to be able to use effectively. To address these needs, we built an extension for Visual Studio Code, called reCode. reCode improves the familiar find-and-replace experience by allowing the developer to specify a straightforward search term to identify relevant locations, and then demonstrate their intended changes by simply typing a change directly in the editor. Using programming by example, reCode automatically learns a more general code transformation and displays these transformations as before-and-after differences inline, with clickable actions to interactively accept, reject, or refine the proposed changes. In our usability evaluation (n = 12), developers reported that this mixed-initiative, example-driven experience is intuitive, complements their existing workflow, and offers a unified approach to conveniently tackle a variety of common yet frustrating scenarios for code transformations.
Security analysis of distributed ledgers and blockchains through agent-based simulation

In this paper,1 we describe LUNES-Blockchain, an agent-based simulator of blockchains that relies on Parallel and Distributed Simulation (PADS) techniques to obtain high scalability. The software is organized as a multi-level simulator that permits to simulate a virtual environment, made of many nodes running the protocol of a specific Distributed Ledger Technology (DLT), such as the Bitcoin or the Ethereum blockchains. This virtual environment is executed on top of a lower-level Peer-to-Peer (P2P) network overlay, which can be structured based on different topologies and with a given number of nodes and edges. Functionalities at different levels of abstraction are managed separately, by different software modules and with different time granularity. This allows for accurate simulations, where (and when) it is needed, and enhances the simulation performance. Using LUNES-Blockchain, it is possible to simulate different types of attacks on the DLT. In this paper, we specifically focus on the P2P layer, considering the selfish mining, the 51% attack and the Sybil attack. For which concerns selfish mining and the 51% attack, our aim is to understand how much the hash-rate (i.e. a general measure of the processing power in the blockchain network) of the attacker can influence the outcome of the misbehavior. On the other hand, in the filtering denial of service (i.e. Sybil Attack), we investigate which dissemination protocol in the underlying P2P network makes the system more resilient to a varying number of nodes that drop the messages. The results confirm the viability of the simulation-based techniques for the investigation of security aspects of DLTs.
Critical agenda driving child–computer interaction research—Taking a stock of the past and envisioning the future

There is a revitalized interest in power and politics around design and technology in the Human–Computer Interaction (HCI) field. Child–Computer Interaction (CCI) research community has also shown arousing interest towards the topic. However, despite this emerging interest, the CCI research community has remained quite silent about the potential of a critical agenda for CCI. Few studies have explicitly addressed critical research or critical design. This study introduces the notion of a critical agenda for CCI research and identifies CCI studies that are linked with the critical agenda, revealing that there are CCI studies showing emerging interests and seeds for addressing the critical agenda. Overall, this study explores the state-of-the-art critical research tradition in CCI and explicates the potential of this tradition for making the world a better place through design and technology in collaboration with children.
Leveraging local and global descriptors in parallel to search correspondences for visual localization

Visual localization to compute 6DoF camera pose from a given image has wide applications. Both local and global descriptors are crucial for visual localization. Most of the existing visual localization methods adopt a two-stage strategy: image retrieval first is performed by global descriptors, and then 2D-3D correspondences are made by local descriptors from 2D query image points and its nearest neighbor candidates which are the 3D points visible by these retrieved images. The above two stages are serially performed in these methods. However, due to the fact that 3D points obtained from the retrieval feedback are only rely on global descriptors, these methods cannot fully take the advantages of both local and global descriptors. In this paper, we propose a novel parallel search framework, which fully leverages advantages of both local and global descriptors to get nearest neighbor candidates of a 2D query image point. Specifically, besides using deep learning based global descriptors, we also utilize local descriptors to construct random tree structures for obtaining nearest neighbor candidates of the 2D query image point. We propose a new probability model and a new deep learning based local descriptor when constructing the random trees. In addition, a weighted Hamming regularization term to keep discriminativeness after binarization is given in loss function for the proposed local descriptor. The loss function co-trains both real and binary local descriptors of which the results are integrated into the random trees. Experiments on challenging benchmarks show that the proposed localization method can significantly improve the robustness and accuracy compared with the ones which get nearest neighbor candidates of a query local feature just based on either local or global descriptors.
Streamlined quantum computing with macronode cluster states

Continuous-variable cluster states allow for fault-tolerant measurement-based quantum computing when used in tandem with the Gottesman-Kitaev-Preskill (GKP) encoding of a qubit into a bosonic mode. For quad-rail-lattice macronode cluster states, whose construction is defined by a fixed, low-depth beam splitter network, we show that a Clifford gate and GKP error correction can be simultaneously implemented in a single teleportation step. We give explicit recipes to realize the Clifford generating set, and we calculate the logical gate-error rates given finite squeezing in the cluster-state and GKP resources. We find that logical error rates of $10^{-2}$-$10^{-3}$, compatible with the thresholds of topological codes, can be achieved with squeezing of 11.9-13.7 dB. The protocol presented eliminates noise present in prior schemes and puts the required squeezing for fault tolerance in the range of current state-of-the-art optical experiments. Finally, we show how to produce distillable GKP magic states directly within the cluster state.
A Comparative Analysis of Machine Learning Approaches in Personality Prediction Using MBTI

Nisha, Kulsum Akter Kulsum, Umme Rahman, Saifur Hossain, Md. Farhad Chakraborty, Partha Choudhury, TanupriyaA vast amount of data is generated every day on the Internet with the advent of web technology and the increasing number of Internet users. Social media websites such as Twitter and Facebook are increasingly gaining popularity because they help users around the globe to connect and share their opinions on different issues. Personality is how people behave, feel, or think on a specific subject. So using sentimental analysis on Twitter posting the personality of an individual can be easily identified. Twitter data sentiment analysis has been used frequently by lots of researchers for personality classification and prediction. The proposed work also relied on Twitter sentiment analysis to determine a person’s personality. In this study, not only the Naive Bayes (NB), Support Vector Machine (SVM), and XGBoost classifier were used to predict the personality of Twitter users but also their performance was compared. Finally, an average of 78% accuracy using NB, 80% accuracy using SVM, and 85% accuracy in XGBoost was found which performed better than others.
Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications

Recommender systems (RSs) have widely grown thanks to the outstanding capability of providing users with accurate and tailored recommendations. Recently, public awareness and new regulations forced RS researchers and practitioners to study solutions to user privacy endangerment. This tutorial will guide the attendees through the possible threats and the solutions towards private RSs.
Research Progress and Challenges on Application-Driven Adversarial Examples: A Survey

Great progress has been made in deep learning over the past few years, which drives the deployment of deep learning–based applications into cyber-physical systems. But the lack of interpretability for deep learning models has led to potential security holes. Recent research has found that deep neural networks are vulnerable to well-designed input examples, called adversarial examples . Such examples are often too small to detect, but they completely fool deep learning models. In practice, adversarial attacks pose a serious threat to the success of deep learning. With the continuous development of deep learning applications, adversarial examples for different fields have also received attention. In this article, we summarize the methods of generating adversarial examples in computer vision, speech recognition, and natural language processing and study the applications of adversarial examples. We also explore emerging research and open problems.
Statistical Certification of Acceptable Robustness for Neural Networks

Neural network robustness measurement is a critical step before deploying neural network applications. However, existing methods, such as neural network verification and validation, do not fully meet our criteria for robustness measurement. From the industrial point-of-view, this paper proposes to use statistical robustness certificates (SRC) for measuring the robustness of neural networks against random noises as well as semantic perturbations and tries to bridge between verification and validation methods through Hoeffding Inequality. Our experiments show that our method is accurate in comparing robustness of different neural networks and has polynomial time complexity which leads to 3x-30x boost in efficiency compared to related methods. Together with the intrinsic statistical guarantee, the issued certificates are considered practical in comparing the robustness of various commercial neural networks.
Net buying pressure and the information in bitcoin option trades

Bitcoin prices are driven by upward as well as downward jumps and so the bitcoin implied volatility surface behaves differently from those of established options markets. We analyze tick-level Deribit option price data, demonstrating increasing support for the limits-to-arbitrage hypothesis. Hence market makers are managing order imbalance and inventory more effectively as Deribit bitcoin options trading volumes increases. On the demand side, volatility traders drive both at-the-money and out-of-the-money option prices, the latter also being driven by directional traders. Directional effects were most pronounced during the price bubble of 2021. Further refinements of our tests assess time-to-maturity and time-of-day effects.
Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms

With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns are major obstacles in distributed and wireless networks. In addition, IoT has a limitation on system resources depending on the purpose of services. In addition, a blockchain technology enables secure transactions among participants through consensus algorithms and encryption without a centralized coordinator. In this paper, we first review the federated leaning (FL) and blockchain mechanisms, and then, present a survey on the integration of blockchain and FL for data sharing in industrial, vehicle, and healthcare applications.
Fault localization based on wide &amp; deep learning model by mining software behavior

Many learning based fault localization approaches haven been proposed to improve the effectiveness by fusing various dimension of fault diagnosis features. However, method calls behavior has been neglected, and the interaction between features has not been fully explored. To solve this problem, firstly, a fault localization method by mining software behavior graphs has been proposed to improve the effectiveness of localizing function call related faults. Then, a fault localization approach by wide & deep learning on multi-feature groups has been proposed. Not only the spectrum based and mutation based suspiciousness features have been analyzed, but also the behavior based and invariants based suspiciousness, the static metrics, as well as the combined features of crash stack trace with the invariants change features have been integrated. Wide & Deep model is adopted as the ranking model, to explore the relationships between these features, so as to improve the effectiveness of fault localization. Experiments on practical software defects benchmark Defects4J have shown that our model outperforms the traditional spectrum-based and mutation-based approaches, it also outperforms the state-art-of learning-based approaches on the capability of early fault detection.
ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
Evolution of Transaction Pattern in Ethereum: A Temporal Graph Perspective

Ethereum is one of the most popular blockchain systems that support more than half a million transactions every day and foster miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this article, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytic platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract, and user-contract graphs, are constructed according to trading relationships and are segmented with an appropriate time window. We observe a strong correlation between the size of the user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights, and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions are validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, “the rich is already very rich.”
Communicating Finite State Machines and an Extensible Toolchain for Multiparty Session Types

Multiparty session types (MPST) provide a typing discipline for message passing concurrency, ensuring deadlock freedom for distributed processes. This paper first summarises the relationship between MPST and communicating finite state machines (CFSMs), which offers not only theoretical justifications of MPST but also a guidance to implement MPST in practice. As one of the applications, we present \(\nu \text {S}\textsc {cr}\) (NuScr), an extensible toolchain for MPST-based multiparty protocols. The toolchain can convert multiparty protocols in the Scribble protocol description language into global types in the MPST theory; global types are projected into local types, and local types are converted to their corresponding CFSMs. The toolchain also generates APIs from CFSMs that implement endpoints in the protocol. Our design allows for language-independent code generation, and opens possibilities to generate APIs in various programming languages. We design our toolchain with modularity and extensibility in mind, so that extensions of core MPST can be easily integrated within our framework. As a case study, we show the implementation of the nested protocol extension in \(\nu \text {S}\textsc {cr}\), to showcase our extensibility.
Research advancement and foci of mobile technology-supported music education: a systematic review and social network analysis on 2008-2019 academic publications

(2021). Research advancement and foci of mobile technology-supported music education: a systematic review and social network analysis on 2008-2019 academic publications. Interactive Learning Environments. Ahead of Print.
Prior Probability Estimation in Dynamically Imbalanced Data Streams

Despite the fact that real-life data streams may often be characterized by the dynamic changes in the prior class probabilities, there is a scarcity of articles trying to clearly describe and classify this problem as well as suggest new methods dedicated to resolving this issue. The following paper aims to fill this gap by proposing a novel data stream taxonomy defined in the context of prior class probability and by introducing the Dynamic Statistical Concept Analysis (DSCA) - prior probability estimation algorithm. The proposed method was evaluated using computer experiments carried out on 100 synthetically generated data streams with various class imbalance characteristics. The obtained results, supported by statistical analysis, confirmed the usefulness of the proposed solution, especially in the case of discrete dynamically imbalanced data streams (DDIS).
A dense stereo matching method based on optimized direction-information images for the real underwater measurement environment

The refraction phenomenon of light at the interface of different media causes the epipolar to bend (the traditional camera model is invalid), and the stereo matching can’t be performed under the row matching. Therefore, we proposed a new model based on light field, in which an underwater image is expressed with the direction-information image and position image. The underwater stereo matching is conducted in the direction-information image instead of the original underwater image because the curve constraint of the correspondence becomes the row constraint in the direction-information image. In addition, the essence of stereo matching is similarity detection, but the phenomenon of absorption and scattering in water causes problems such as blurred outlines and loss of a large number of details in real underwater images, which will cause great interference in the similarity detection process. Therefore, we propose an image optimization method that fuses color correction and Dark channel Prior to improve the quality of the directional-information image. Finally, the underwater stereo matching process is divided into three steps. In order to verify the feasibility and effectiveness of the proposed method, we carry out more experiments. The experimental results show that our method and equipment can be used for underwater 3D measurement with high accuracy.