Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks

Unsupervised Domain Adaptation (UDA) attempts to transfer knowledge from a labeled source domain to an unlabeled target domain. Recently, domain-adversarial learning has become an increasingly popular method to tackle this task, which bridges source domain and target domain by adversarially learning domain-invariant representations that cannot be discriminated by a domain discriminator. In spite of the great success achieved by domain-adversarial learning, most of existing methods still suffer two major limitations: (1) due to focusing only on learning domain-invariant representations, they ignore the individual characteristics of each domain and fail to extract domain-specific information that is beneficial for final classification; (2) by focusing only on performing domain-level distribution alignment to learn domain–invariant representations, they fail to achieve the invariance of representations at a class level, which may lead to incorrect distribution alignment. To address the above issues, we propose in this paper a novel model called Joint Domain-Adversarial Reconstruction Network (JDARN), which integrates domain-adversarial learning with data reconstruction to learn both domain–invariant and domain-specific representations. Meanwhile, we propose to employ two novel discriminators called joint domain-class discriminators to achieve the joint alignment and adopt a novel joint adversarial loss to train them. With both domain and class information of two domains, the two discriminators can be used to promote domain-invariant representation learning towards the class level, not only the domain level. Extensive experimental results reveal that the proposed JDARN exceeds the state-of-the-art performance on two standard UDA datasets.
Measurement and Analysis of Automated Certificate Reissuance

The Transport Layer Security (TLS) Public Key Infrastructure (PKI) is essential to the security and privacy of users on the Internet. Despite its importance, prior work from the mid-2010s has shown that mismanagement of the TLS PKI often led to weakened security guarantees, such as compromised certificates going unrevoked and many internet devices generating self-signed certificates. Many of these problems can be traced to manual processes that were the only option at the time. However, in the intervening years, the TLS PKI has undergone several changes: once-expensive TLS certificates are now freely available, and they can be obtained and reissued via automated programs. In this paper, we examine whether these changes to the TLS PKI have led to improvements in the PKI's management. We collect data on all certificates issued by Let's Encrypt (now the largest certificate authority by far) over the past four years. Our analysis focuses on two key questions: First, are administrators making proper use of the automation that modern CAs provide for certificate reissuance? We find that for certificates with a sufficiently long history of being reissued, 80% of them did reissue their certificates on a predictable schedule, suggesting that the remaining 20% may use manual processes to reissue, despite numerous automated tools for doing so. Second, do administrators that use automated CAs react to large-scale compromises more responsibly? To answer this, we use a recent Let's Encrypt misissuance bug as a natural experiment, and find that a significantly larger fraction of administrators reissued their certificates in a timely fashion compared to previous bugs.
A study on the uncertainty of convolutional layers in deep neural networks

This paper shows a Min–Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min–Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min–Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min–Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.
Decomposing Textures using Exponential Analysis

Decomposition is integral to most image processing algorithms and often required in texture analysis. We present a new approach using a recent 2-dimensional exponential analysis technique. Exponential analysis offers the advantage of sparsity in the model and continuity in the parameters. This results in a much more compact representation of textures when compared to traditional Fourier or wavelet transform techniques. Our experiments include synthetic as well as real texture images from standard benchmark datasets. The results outperform FFT in representing texture patterns with significantly fewer terms while retaining RMSE values after reconstruction. The underlying periodic complex exponential model works best for texture patterns that are homogeneous. We demonstrate the usefulness of the method in two common vision processing application examples, namely texture classification and defect detection.
Application of data mining techniques using the K-Means Method on Unmet Need of Health Services by Province in Indonesia

Abstract The research objective is to cluster the Unmet Need of health services in Indonesia by region by utilizing data mining techniques. The data source was obtained from the Central Statistics Agency (abbreviated BPS) in the health category using Unmet Need data on health services by province (2015-2018) consisting of 34 data records. The variable used is the Unmet Need of health services by province (percent). The data mining technique used is k-means which is part of clustering. Data processing is performed using RapidMiner 5.3 software. The cluster labels used in the study are high cluster (C1) and low cluster (C2). The results state that the application of k-means can be carried out where 9 provinces are in cluster C1 consisting of Riau, West Nusa Tenggara, East Nusa Tenggara, Central Kalimantan, South Borneo, Central Sulawesi, Southeast Sulawesi, Gorontalo and West Sulawesi with final centroids (C ) = 6.96 and 25 provinces are in cluster C2 with final centroid (C) = 4.36. The results of mapping in the form of regional clusters can provide information for the government and should Unmet Need health services in Indonesia must be worth 0% for each region.
Authentication of IoT Device and IoT Server Using Security Key

IoT is an emerging topic in the field of IT that has attracted the interest of researchers from different parts of the world. Authentication of IoT includes the establishment of a model for controlling access to IoT devices through the internet and other unsecured network platforms. Strong authentication of IoT is necessary for ensuring that machines and devices could be trusted when it comes to data sharing. The whole idea of authentication further prevents cybercriminals from using loopholes in IoT devices to access data that they are not allowed to access. Various authentication techniques could be used to secure IoT servers and devices. Establishing mutual authentication between IoT servers and IoT devices has attracted a lot of research interests because it helps enhance the effectiveness and overall security of data sharing. Therefore, this research provides the basis for analyzing the whole idea of using security keys to encrypt both IoT servers and IoT devices.
New quality weight used for phase unwrapping in color fringe reflection method

The fringe -reflection technology has been widely applied in the fields of industrial inspection and measurements. In the present study, three-step phase-shifting is developed using the color fringe-reflection approach to inspect a work-piece surface with rotational symmetry characteristics, and an improved experiment is proposed. In addition, by combining Robert gradient with fringe modulation, a new quality weight, referred to as ‘Modulation-Robert gradient variance’ (MRGV), is proposed for quality-guiding the phase-unwrapping (PU) algorithm. In comparison to the traditional quality weights, MRGV presented higher noise immunity and lower PU error in the root mean square (RMS). In addition, in terms of defect detection, MRGV performed better than the other weights. Finally, after MRGV-guided PU conduction, the proposed experiment method is used in combination with an improved Southwell integration algorithm to realize the 3D reconstruction. The reconstruction error is approximately 3.09% lower than that presented by certain other methods. Furthermore, the results indicated that the combination of the proposed method and the new quality weight could effectively detect the defects and offer an accurate measurement of a rotationally symmetrical surface to a certain extent.
Adversarial Attacks For Multi Target Image Translation Networks

Although image translation algorithms such as StarGAN, STGAN, StarGAN-v2, etc. based on the generative adversarial networks, bring enormous convenience to people's work and life, they also have greater security risks in terms of privacy and security ethics. We tackle this problem of generating adversarial attacks against image translation models, which disrupt the resulting output image. The existing adversarial technology can only generate adversarial samples for a single image translation model, but in actual applications, it is unknown to the user which model an attacker will use. The paper draws on the idea of multi-task learning and proposes a multi-objective adversarial sample generation algorithm to generate images that can simultaneously defend against tampering with multiple state- of-the-art image translation models. The simulation result shows that compared with the original images, adversarial images have no heavy visual distortion with PSNR of 27.17dB and SSIM of 0.70 which means that normal usages of images, such as showing something interesting, amazing, funny, cool or etc., are not affected. The adversarial samples generated by our proposed algorithm reduce the risk of malicious tampering and abuse of images. For the mainstream image translation models StarGAN, STGAN and StarGAN-v2, the attack success rates of the adversarial images generated by the algorithm are 100%, 66.67%, and 73.33%, respectively. It can be seen that the algorithm in this paper has obvious effects on protecting the reasonable and safe use of multimedia resources on social platforms.
TGANs with Machine Learning Models in Automobile Insurance Fraud Detection and Comparative Study with Other Data Imbalance Techniques

A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of a given dataset, in which, handling class imbalance is a major challenge. Phase II is that of classification using Machine Learning models. It is important to comprehend if there is any influence of the technique used in Phase I on the efficiency of the model used for Phase II. A natural query that intrigues one is whether there is a golden combination of a technique in Phase I and a specific model in Phase II for assured best performance of a Fraud Detection Model.In this work, we study a few techniques for handling data imbalance issue namely, SMOTE, MWMOTE, ADASYN and TGAN in combination with various classifier models like Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), LightGBM, XGBoost and Gradient Boosting Machines (GBM). The study is conducted on a dataset for motor vehicle insurance fraud detection.We present a comparison of various combinations of data imbalance technique and classifier models. It is observed that the combination of TGAN in Phase I and GBM in Phase II gives the best performance. This combination performs best in terms of important metrics such as false positive rate, precision and specificity. We obtained the lowest false positive rate of 0.0011 and precision of 0.9988 which minimizes the most critical risk for the insurance company of falsely classifying a non-fraud claim as a fraud. Finally, the specificity of 0.9989 indicates that the model was also very good at predicting the non-fraudulent claim.
Preprocessing Method of Structured Big Data in Human Resource Archives Database

In order to solve the problem of false report and concealed report, this paper studies the characteristics of human resource and wage information statistics data, proposes the preprocessing method of structured big data of human resource archives database, adopts the outlier detection technology of data mining, and designs an adaptive detection method of human resource file data based on Hybrid clustering. In depth analysis of the advantages and limitations of big data preprocessing method, combined with the advantages of the two algorithms, make up for the limitations of the human resource file database structure, so that the detection process can eliminate manual intervention, and achieve the ability of adaptive calculation of initial parameters. Through the preprocessing method of structured big data of human resource archives database, the abnormal data in human resource statistical data is found. In order to verify the pre-processing method of structured big data of human resource archives database proposed in this paper, the enterprise data of enterprise wage summary table collected by statistics bureau is used. The experimental results show that the method can achieve the effect of expert detection in the detection of abnormal data, which can provide reference for the statistical department.
BinSide : Static Analysis Framework for Defects Detection in Binary Code

Software developers make mistakes that can lead to failures of a software product. One approach to detect defects is static analysis: examine code without execution. Currently, various source code static analysis tools are widely used to detect defects. However, source code analysis is not enough. The reason for this is the use of third-party binary libraries, the unprovability of the correctness of all compiler optimizations. This paper introduces BinSide : binary static analysis framework for defects detection. It does interprocedural, context-sensitive and flow-sensitive analysis. The framework uses platform independent intermediate representation and provide opportunity to analyze various architectures binaries. The framework includes value analysis, reaching definition, taint analysis, freed memory analysis, constant folding, and constant propagation engines. It provides API (application programming interface) and can be used to develop new analyzers. Additionally, we used the API to develop checkers for classic buffer overflow, format string, command injection, double free and use after free defects detection.
Optimal Task Allocation for Multiple UAVs

Unmanned aerial vehicles (UAVs) are currently used in many civil and military tasks. In cases where the multiple tasks must be assigned to multiple UAVs, the task assignment of the relevant UAVs should be carried out in a way that the consumption of the flight time and flight distances to perform the tasks must be minimized. In this study, we propose an approach to efficiently perform the task assignment problem in cases where the number of targets and UAVs is large. The proposed method consists of a combination of three efficient processes: In the first stage, desired target locations are clustered by clustering algorithms according to the number of UAVs. Then, optimal drone-to-task set assignments are made by using the Hungarian algorithm. Finally, the optimal task route is planned for each subset using the ant colony algorithm to minimize the distance traveled by each UAV in the assigned task. The proposed method has been simulated under different scenarios and successful results have been obtained.
Fast automatic step size selection for zeroth-order nonconvex stochastic optimization

The efficacy and simplicity of using only function evaluations in zeroth-order stochastic optimization (ZOSO) makes it achieve great attention in solving large scale learning tasks. However, the question of how to choose an appropriate step size sequence timely for ZOSO has been less researched. To fill this defect, this paper provides a fast automatic step size selection approach by using an improved Barzilai-Borwein (IBB) technique with the zeroth-order (ZO) gradient estimator for ZOSO. In detail, this work shows the efficacy of the IBB technique by applying it into the advanced ZOSO method, i.e., zeroth-order stochastic variance reduced gradient (ZO-SVRG) method, which leads to a method: ZO-SVRG-IBB. We theoretically analyze the convergence of the ZO-SVRG-IBB method under the random gradient estimator and the coordinate-wise gradient estimator settings, respectively, and show that the convergence rate of ZO-SVRG-IBB matches the best-known convergence rate of advanced ZOSO methods on nonconvex functions. We further show that the query complexity of ZO-SVRG-IBB is comparable to advanced ZOSO methods. Extensive numerical experiments performed on different datasets show that the proposed method outperforms or matches state-of-the-art ZOSO methods.
VM-NSP

Negative sequential patterns (NSPs) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to the involvement of both occurring and nonoccurring behaviors and events, which can contribute to many relevant applications. However, NSP mining is nontrivial, as it involves fundamental challenges requiring distinct theoretical foundations and is not directly addressable by PSP mining. In the very limited research reported on NSP mining, a negative element constraint (NEC) is incorporated to only consider the NSPs composed of specific forms of elements (containing either positive or negative items), which results in many valuable NSPs being missed. Here, we loosen the NEC (called loose negative element constraint (LNEC)) to include partial negative elements containing both positive and negative items, which enables the discovery of more flexible patterns but incorporates significant new learning challenges, such as representing and mining complete NSPs. Accordingly, we formalize the LNEC-based NSP mining problem and propose a novel vertical NSP mining framework , VM-NSP, to efficiently mine the complete set of NSPs by a vertical representation (VR) of each sequence. An efficient bitmap-based vertical NSP mining algorithm , bM-NSP, introduces a bitmap hash table--based VR and a prefix-based negative sequential candidate generation strategy to optimize the discovery performance. VM-NSP and its implementation bM-NSP form the first VR-based approach for complete NSP mining with LNEC. Theoretical analyses and experiments confirm the performance superiority of bM-NSP on synthetic and real-life datasets w.r.t. diverse data factors, which substantially expands existing NSP mining methods toward flexible NSP discovery.
Explainability of AI-predictions based on psychological profiling

Using a local surrogate approach from explainable AI, a new prediction method for the performance of start-up companies based on psychological profiles is proposed. The method assumes the existence of an interpreted 'base model', the predictions of which are enhanced by an AI-model delivering corrections that improve the overall accuracy. The surrogate (proxi) models the difference between the original (labeled) data and the data with labels replaced by the AI-corrections. As this corresponds to comparing the AI-correction applied before the base model is used with the original utilisation of the base model, the approach is called Before and After prediction Parameter Comparison (BAPC). The change of the base model under application of the AI-correction yields an interpretation of it by means of 'effective' parameter changes. This is useful for the interpretation of 'subjective' psychological profiles (such as 'risk-affinity', 'open-mindedness', etc.) in terms of effective changes of 'objective' monetary firm data (such as 'revenue', 'price of product', or 'cost of development').
A Survey Blockchain and Smart Contract Technology in Government Agencies

Abstract The development of Blockchain and Smart Contract technology provides significant changes in the form of solutions to social technology problems, such as the problem of document forgery on e-government services. In its implementation of government services, there are several challenges faced, such as social technology problems, blockchain mechanisms, virtual machines, and smart contract code levels, and automation of disintermediation. This paper summarizes and discusses the methodology and research results from various recent papers on the state of the use of smart contracts in e-government services and the weaknesses and potential problems in the use of e-government services.
Art Digital Jewellery: Practitioners’ Perspectives

We introduce the term 'Art Digital Jewellery' as a label for craft-oriented, bespoke approaches to embedding electronics in jewellery. These unconventional digital-physical jewellery practices struggle for attention compared with higher profile, often more mass-production oriented wearables. This is partly because discourses articulating and critiquing these experimental practices are scarce and obscure to HCI researchers. To address this, we describe how these artistic practices arose from earlier fashion movements and we engaged six leading creative practitioners in a structured and iterative dialogue. Analysis of our adapted Delphi survey suggests that core to Art Digital Jewellery is very individualised design processes and creating artefacts which are highly personal in terms of their form, their materials, their narratives and their interactivity. An appreciation of these unique practices may enrich perspectives on designing wearables, marrying craft with technology, and personalisation of experiences.
Learning Object Attributes with Category-Free Grounded Language from Deep Featurization

While grounded language learning, or learning the meaning of language with respect to the physical world in which a robot operates, is a major area in human-robot interaction studies, most research occurs in closed worlds or domain-constrained settings. We present a system in which language is grounded in visual percepts without using categorical constraints by combining CNN-based visual featurization with natural language labels. We demonstrate results comparable to those achieved using handcrafted features for specific traits, a step towards moving language grounding into the space of fully open world recognition.