Reviewerly/exHarmony huggingface dataset:
Mitigating algorithmic errors in quantum optimization through energy extrapolation

Quantum optimization algorithms offer a promising route to finding the ground states of target Hamiltonians on near-term quantum devices. None the less, it remains necessary to limit the evolution time and circuit depth as much as possible, since otherwise decoherence will degrade the computation. And even where this is done, there always exists a non-negligible error in estimates of the ground state energy. Here we present a scalable extrapolation approach to mitigating this error, which significantly improves estimates obtained using three of the most popular optimization algorithms: quantum annealing (QA), the variational quantum eigensolver (VQE), and quantum imaginary time evolution (QITE), at fixed evolution time or circuit depth. The approach is based on extrapolating the annealing time to infinity, or the variance of estimates to zero. The method is reasonably robust against noise, and for Hamiltonians which only involve few-body interactions, the additional computational overhead is an increase in the number of measurements by a constant factor. Analytic derivations are provided for the quadratic convergence of estimates of energy as a function of time in QA, and the linear convergence of estimates as a function of variance in all three algorithms. We have verified the validity of these approaches through both numerical simulation and experiments on an IBM quantum computer. This work suggests a promising new way to enhance near-term quantum computing through classical post-processing.
Hierarchical optimal transport for unsupervised domain adaptation

In this paper, we propose a novel approach for unsupervised domain adaptation that relates notions of optimal transport, learning probability measures, and unsupervised learning. The proposed approach, HOT-DA, is based on a hierarchical formulation of optimal transport that leverages beyond the geometrical information captured by the ground metric, richer structural information in the source and target domains. The additional information in the labeled source domain is formed instinctively by grouping samples into structures according to their class labels. While exploring hidden structures in the unlabeled target domain is reduced to the problem of learning probability measures through Wasserstein barycenter, which we prove to be equivalent to spectral clustering. Experiments show the superiority of the proposed approach over state-of-the-art across a range of domain adaptation problems including inter-twinning moons dataset, Digits, Office-Caltech, and Office-Home. Experiments also show the robustness of our model against structure imbalance. We make our code publicly available.
Deep images enhancement for turbid underwater images based on unsupervised learning

In agriculture, aquaculture technologies such as precise feeding, fish identification and fishing based on underwater machine vision all rely on the analysis of underwater images. However, due to the scatting and attenuation of the illumination in the real-world underwater environment, turbid underwater images are inevitably degraded, limiting their applicability in many vision tasks. In this paper, we present an unsupervised deep learning framework, called Underwater Loop Enhancement Network (ULENet), to improve the quality of turbid underwater images. We first propose an underwater dataset construction scheme and construct the dataset on which the network proposed above is trained. The underwater dataset contains images of three different scenes: lake and reservoir scene data (no label), pool scene data (weakly correlated label), and laboratory scene data (strongly correlated label). Then we propose a loop enhancement structure that uses the approximate candidates as labels and improves the visual quality of the image through the iterative training process. We formulate a new underwater visual perception loss function that evaluates the perceptual image quality based on its color, contrast, saturation and clarity. During the training process, a more realistic, higher-contrast, and clearer underwater image is gradually generated. Qualitative and quantitative evaluations show that the proposed method can effectively enhance image clarity. Moreover, the enhanced images are applied to several vision tasks to achieve better results, such as edge detection, key point matching, fish target detection and saliency prediction etc. • Underwater Loop Enhancement Network (ULENet), to improve the quality of turbid underwater images. • Lake and reservoir scene data, pool scene data, and laboratory scene data. • Our method can effectively improve the visual perception on the above datasets.
Probabilistic design of optimal sequential decision-making algorithms in learning and control

This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a problem formulation and a set of resolution methods. The formulation consists of an infinite-dimensional optimization problem. The methods come from approaches to search optimal solutions in the space of probability functions. Through the lenses of this overarching framework we revisit popular learning and control algorithms, showing that these naturally arise from suitable variations on the formulation mixed with different resolution methods. A running example, for which we make the code available, complements the survey. Finally, a number of challenges arising from the survey are also outlined.
Selection bias mitigation in recommender system using uninteresting items based on temporal visibility

Most collaborative filtering recommendation algorithms rely too much on the user's historical rating data. However, selection bias is common in explicit feedback data, which makes the learning of user preferences face more challenges. We verify the influence of selection bias on topN recommendation, and propose a data filling strategy using uninteresting items based on temporal visibility to alleviate the selection bias in the data. Specifically, our method includes a weighted matrix factorization model to learn users' pre-use preferences for unrated items. According to the experience of items that users have seen but not interacted show negative preferences, we combine user activity, item popularity and temporal rating information to carry out non-uniform weighting to evaluate the confidence of unrated items as a negative example. Then the items with low pre-use preferences are taken as uninteresting items and filled in a low value to restore the user's real rating distribution. Experiments on two real world datasets show that our algorithm can effectively alleviate the selection bias and improve the recommendation accuracy.
Static test flakiness prediction: How Far Can We Go?

Test flakiness is a phenomenon occurring when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the problem has been closely investigated by researchers and practitioners, who all have shown its relevance in practice. The software engineering research community has been working toward defining approaches for detecting and addressing test flakiness. Despite being quite accurate, most of these approaches rely on expensive dynamic steps, e.g., the computation of code coverage information. Consequently, they might suffer from scalability issues that possibly preclude their practical use. This limitation has been recently targeted through machine learning solutions that could predict the flakiness of tests using various features, like source code vocabulary or a mixture of static and dynamic metrics computed on individual snapshots of the system. In this paper, we aim to perform a step forward and predict test flakiness only using static metrics. We propose a large-scale experiment on 70 Java projects coming from the iDFlakies and FlakeFlagger datasets. First, we statistically assess the differences between flaky and non-flaky tests in terms of 25 test and production code metrics and smells, analyzing both their individual and combined effects. Based on the results achieved, we experiment with a machine learning approach that predicts test flakiness solely based on static features, comparing it with two state-of-the-art approaches. The key results of the study show that the static approach has performance comparable to those of the baselines. In addition, we found that the characteristics of the production code might impact the performance of the flaky test prediction models.
An Approach to Improve the Robustness of Machine Learning based Intrusion Detection System Models Against the Carlini-Wagner Attack

Machine Learning (ML) techniques have been applied over the past two decades to improve the abilities of Intrusion Detection Systems (IDSs). Over time, several enhancements have been implemented to help the ML-based IDS models tackle the ever-evolving attack behaviors. However, recent works reveal that ML models are vulnerable to adversarial perturbations. With the increasing volumes of data passing through systems, defeating adversarial attacks has become a significant challenge. Recent research suggests that Generative Adversarial Networks (GANs) possess a good potential in creating adversarial samples and tackling them, playing well on both offense and defense teams. With a motive to improve the resistance of ML-based IDS models against a powerful white-box evasion attack technique, the Carlini-Wagner, we propose a GAN-based defensive approach and evaluate it with the CSE-CIC-IDS2018 dataset. The paper presents preliminary evaluation results and discusses the direction in which we want to continue the work.
Graph-regularized federated learning with shareable side information

This study focuses on specifying local models in federated learning (FL), which allows a large number of clients to improve their corresponding models by training a shared global model. However, current FL models often fail to consider the difference between the data distributions in various clients while enforcing all local models to be identical, thus leading to a considerable loss of local personalization. To this end, this study proposes a graph-regularized federated learning framework, GraphFL, by exploiting the available client features commonly shared with other clients in the real world. Specifically, GraphFL achieves the similarity matrix of all clients using the permitted shareable side information and subsequently updates local models by returning a specific model from the server instead of an identical model. The proposed model iteratively learns the neural network parameters for each client. Compared with state-of-the-art FL models, GraphFL can benefit from the employed similarity and achieve improved classification performance in clients on three publicly available image datasets. • Exploit the side information of clients for personalized federated learning. • Propose a client-similarity graph regularized federated learning framework. • Introduce to calculate the similarity between non-iid clients by data distributions. • Conduct the evaluations on the personalization of federation frameworks.
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of topology-imbalance from a global view of the supervision information distribution in terms of under-reaching and over-squashing, which motivates two quantitative metrics as measurements. In light of our analysis, we propose a novel position-aware graph structure learning framework named PASTEL, which directly optimizes the information propagation path and solves the topology-imbalance issue in essence. Our key insight is to enhance the connectivity of nodes within the same class for more supervision information, thereby relieving the under-reaching and over-squashing phenomena. Specifically, we design an anchor-based position encoding mechanism, which better incorporates relative topology position and enhances the intra-class inductive bias by maximizing the label influence. We further propose a class-wise conflict measure as the edge weights, which benefits the separation of different node classes. Extensive experiments demonstrate the superior potential and adaptability of PASTEL in enhancing GNNs' power in different data annotation scenarios.
Zoom Face: Self-Surveillance, Performance and Display

This article examines the concepts of 'zoom gaze' and 'zoom fatigue' as two recently theorised consequences of the Covid-19 pandemic. It considers how the dramatic shift in people's social and professional interactions, from face-to-face communication to virtual interaction via videoconferencing software such as Zoom, produces new ways of reading, understanding, surveilling, and performing through, and with the face. It argues that these myriad changes necessitate a return to prior theories of the close-up, the cinematic face, and even the face of literature and portraiture to better understand the long-term implications of videoconferencing at a personal and collective level. It considers the affective and cognitive processes that are engaged during moments of cinematic 'close-up' to ask whether existing theories of the facial close-up in artistic fields might shed light on the psychological and psychosocial effects of seeing oneself and others' faces for extended periods in the digital milieu. The article also attends to self-surveillance as a constitutive element of the 'zoom gaze' to consider how we might resist the extractive control imposed by videoconferencing software as a now ubiquitous and arguably essential component of life during and after the pandemic.
Bitcoin-Blockchain Technology: Security Perspective

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken.
HADOOP- An Open Source Framework for Big Data

In this paper we will discuss about an open source framework for storing and processing a huge amount of data, known as HADOOP (High Availability Distributed Object Oriented Platform). Originally HADOOP is written in Java Language. HADOOP work on the concept of Write Once Read as many as times as you want but don&#x2019;t change the content of the file (Stream Line Access Pattern). HADOOP consist a cluster containing heterogeneous computing devices with commodity hardware. A HADOOP cluster consist two things: HDFS (Hadoop Distributed File System) and MapReduce. HDFS used for data storage and MapReduce used for data process. HDFS is suitable for storing data from Tear byte to Petabyte on a cluster and it run on a commodity hardware.
EtherGIS: A Vulnerability Detection Framework for Ethereum Smart Contracts Based on Graph Learning Features

The financial property of Ethereum makes smart contract attacks frequently bring about tremendous economic loss. Method for effective detection of vulnerabilities in contracts imperative. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. There is still a lack of effort that considers combining expert-defined security patterns with deep learning. This paper proposes EtherGIS, a vulnerability detection framework that utilizes graph neural networks (GNN) and expert knowledge to extract the graph feature from smart contract control flow graphs (CFG). To gain multi-dimensional contract information and reinforce the attention of vulnerability-related graph features, sensitive EVM instruction corpora are constructed by analyzing EVM underlying logic and diverse vulnerability triggering mechanisms. The characteristic of nodes and edges in a CFG is initially confirmed according to the corpora, generating the corresponding attribute graph. GNN is adopted to aggregate the whole graph's attribute and structure information, bridging the semantic gap between low-level graph features and high-level contract features. The feature representation of the graph is finally input into the graph classification model for vulnerability detection. Furthermore, automated machine learning (AutoML) is adopted to automate the entire deep learning process. Data for this research was collected from Ethereum to build up a dataset of six vulnerabilities for evaluation. Experimental results demonstrate that EtherGIS can productively detect vulnerabilities in Ethereum smart contracts in terms of accuracy, precision, recall, and F1-score. All aspects outperform the existing work.
Stone Soup open source framework for tracking and state estimation: enhancements and applications

The tracking and state estimation community is broad, with diverse interests. These range from algorithmic research and development, applications to solve specific problems, to systems integration. Yet until recently, in contrast to similar communities, few tools for common development and testing were widespread. This was the motivation for the development of Stone Soup - the open source tracking and state estimation framework. The goal of Stone Soup is to conceive the solution of any tracking problem as a machine. This machine is built from components of varying degrees of sophistication for a particular purpose. The encapsulated nature and modularity of these components allow efficiency and reuse. Metrics give confidence in evaluation. The open nature of the code promotes collaboration. In April 2019, the Stone Soup initial beta version (v0.1b) was released, and though development continues apace, the framework is stable, versioned and subject to review. In this paper, we summarise the key features of and enhancements to Stone Soup - much advanced since the original beta release - and highlight several uses to which Stone Soup has been applied. These include a drone data fusion challenge, sensor management, target classification, and multi-object tracking in video using TensorFlow object detection. We also detail introductory and tutorial information of interest to a new user.
Regularizing autoencoders with wavelet transform for sequence anomaly detection

Nowadays, systems or entities are usually monitored by devices, generating large amounts of time series. Detecting anomalies in them help prevent potential losses, thus arousing much research interest. Existing studies have adopted autoencoders to detect anomalies, where reconstruction errors are used to indicate outliers. However, sometimes autoencoders may also reconstruct anomalies well due to the learned general features in latent spaces. To solve the above problem, we propose to regularize autoencoders to grasp specific features of normal sequences. Specifically, spectral unique patterns are captured by statistical analysis on discrete wavelet transform (DWT) coefficients of input sequences, restricting latent spaces to reflect unique patterns of normal sequences in both time and frequency domains. Furthermore, a Weight Controller calculating sample-adaptive regularization weights is designed to fully utilize the regularization effect. Extensive experiments on three public benchmarks demonstrate the effectiveness and superiority of the proposed model compared with state-of-the-art algorithms.
Blockchain-based Medical Image Sharing and Automated Critical-results Notification: A Novel Framework

In teleradiology, medical images are transmitted to offsite radiologist for interpretation and the dictation report is sent back to the original site to aid timely diagnosis and proper patient care. Although teleradiology offers great benefits including time and cost efficiency, after-hour coverages, and staffing shortage management, there are some technical and operational limitations to overcome in reaching its full potential. We analyzed the current teleradiology workflow to identify inefficiencies. Image unavailability and delayed critical result communication stemmed from lack of system integration between teleradiology practice and healthcare institutions are among the most substantial factors causing prolonged turnaround time. In this paper, we propose a blockchain-based medical image sharing and automated critical-results notification platform to address the current limitation. We believe the proposed platform will enhance efficiency in workflow by eliminating the need for intermediaries and will benefit patients by eliminating the need for storing medical images in hard copies. While considerable progress was achieved, further research on governance and HIPAA compliance is required to optimize the adoption of the new application. Towards an idea to a working paradigm, we will implement the prototype during the next phase of our study.
Practical Blockchain-Based Steganographic Communication Via Adversarial AI: A Case Study In Bitcoin

Abstract With the development of 5G, the wireless Internet of Things (IoT) has become possible; how to provide privacy protections for the communication of IoT devices in a more vulnerable wireless transmission environment is a huge challenge. Thus, steganography is introduced as a safe and effective technology. Blockchain systems have been widely used in the area of steganography. Several works attempted to embed covert data into transactions in public blockchain systems such as Bitcoin, Ethereum and Monero. However, most of them merely focus on putting covert data into certain fields in transactions based on cryptographic algorithms. In this paper, a Covert Transaction Recognition (CTR) model is proposed by the Text Convolutional Neural Networks and Back Propagation Neural Networks. When utilizing the covert data-embedded field for recognizing, our CTR model can attain 0.79 precision and 0.83 recall on average for seven covert transaction construction schemes. The precision and recall can increase by at most 43 and 47%, respectively, if other unembedded fields were additionally exploited for recognition. We further propose a Practical Covert Transaction Construction (PCTC) model. This model fixes the contents in the embedded fields of the constructed transactions, and generates the contents in other fields using Generative Adversarial Networks. Experimental results demonstrated that the precision and recall are greatly decreased when identifying the covert transactions generated by our PCTC model. The data underlying this article are available in ‘covert-transaction-model’, at https://github.com/1997mint/covert-transaction-model.