Reviewerly/exHarmony huggingface dataset:
Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News

Generating hyper-local news at scale is challenging because publicly available data is not provided at the desired spatial and temporal granularity. Besides, there is a lack of automated analytical and publishing tools. Crosstown Foundry, which is being actively developed and used by engineers and journalists, is a novel data-driven system that leverages a massive multi-modal dataset to generate personalized newsletters for Los Angeles County readers.
CPIQ - A Privacy Impact Quantification for Digital Medical Consent

Increasing digitization in healthcare promises easier exchange and more efficient use of medical information for patients, institutions and research. The number of sharing options for medical data increases, e.g., through personal health records, as well as the volume of data. To use this data in medical research patients’ consent is important. As more and more data access is regulated by consent forms and their complexity also increases. For the patient, it becomes less comprehensible which information could be gained from his or her disclosed data. This becomes more important when pressumably anonymized data has the risk of potential re-identification of an individual. In this paper we introduce a consent-privacy-impact-quantification (CPIQ) as a risk model of consent forms for the release of personal medical data for use within research projects. CPIQ evaluates how reasonable a consent decision is for the patient. It takes relevant factors such as the patient’s preferences into account, the circumstances and benefits of the research project, and the potential risk to the patient. The model can be parameterized so that different aspects such as the benefit of the research project, the risk of a data leak or the risk of a patient’s confidential data becoming known can be represented. We present the feasibility of this model by including it in an existing consent management system.
Multi-Style Artistic Portrait Drawing Generation

The software transforms face photos into artistic portrait drawings of multiple styles using a GAN-based model. The link to the software source code is https://github.com/yiranran/Unpaired-Portrait-Drawing. The software is the implementation for the CVPR 2020 paper “Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping” [1], which is also an improved version over our previous work on TPAMI [2] and CVPR 2019 [3]. The preliminary version of these code was integrated into a mini-program on WeChat, which is the most popular free messaging and calling app in China. Our mini-program became popular, receiving about 400K user clicks in only two weeks.
Multichannel controller synthesis for the plant with three input and two output channels using polynomial matrix decomposition

When applying polynomial methods for the synthesis of multichannel controllers, there is a need for polynomial matrix calculus. However, when using this method, plants with the number of output channels equal to the number of input channels are mainly considered. This is necessary for the convenience of solving a system of linear algebraic equations in a matrix polynomial calculation. A fairly large number of real technical systems have an unequal number of input and output channels. At the same time, the issue of the synthesis of controllers by the polynomial method for multi-channel plants with an unequal number of input and output effects is not worked out in depth enough. In this paper, we consider an example of a linear model of an unstable plant consisting of three standard links with three channels for the input action and two channels for the output action. It is necessary to achieve certain quality indicators of the output vector value, while the control is carried out in the feedback of the system and is summed up with the input action. The plant feature is to limit the task to the second output, since it is essentially a derivative of the first output. The plant was represented as a left - hand polynomial matrix fractional description, and the controller was represented as a right-hand one. For the formation of the characteristic matrix of a closed system, with this variant of the plant and controller decomposition, some structural system transformations are demonstrated. The plant simplicity under consideration is related to the convenience of demonstrating a polynomial synthesis method for such a plants class.
Performance Analysis of Tor Website Fingerprinting over Time using Tree Ensemble Models

Tor (The Onion Router) ensures network anonymity by encrypting contents through multiple relay nodes. Recent studies on website fingerprinting (WF) showed that websites can be identified with high accuracy by analyzing traffic data. However, websites are changing over time by updating contents, which can significantly reduce the accuracy of WF attacks. This study analyzes the performance over time by using ensemble models with excellent WF attack performance. The experiment are conducted in two cases with the initial model. The not updated analyzes the accuracy of models made from initial data over time, whereas the updated adds data that has changed over time to update the model to analyzes the accuracy. The average accuracy of the initial ensemble models is over 90.0% and the Rotation Forest algorithm shows high performance of 93.5%. Comparing the models trained after 30 days with the initial model, the classification performance dropped in both cases; the not updated dropped by more than 30.0% and the updated dropped by about 10.0%. The experimental results suggest that WF using machine learning may require model learning on a regular basis.
3D Coverless Image Steganography Scheme Based on 3D Slice Technology

Due to the property that coverless image steganography can hide secret information without modifying the carrier, it has the ability to resist the existing steganalysis tools effectively. However, with the continuous development of new media and the increasing complexity of application scenarios, finding a more se-cure means of covert communication has become an urgent problem. In this paper, we propose a 3D coverless image steganography (3D-CIS) method. Based on the properties of 3D image and the relationship between 2D image and 3D image, the 2D-CIS method is applied to 3D image. Using 3D image as the carrier not only enriches the data types of steganography, but also provides a more secure means of steganography communication. Specifically, this paper combined 3D image slice technology with the existing 2D-CIS scheme to achieve 3D image steganography, which is beneficial to improve the capacity and security performance of CIS technology and promote the development of coverless image steganography.
Interacting Face Detection-based Access Control with Various Authentication Factors

Today, biometric authentication is used to identify people who want to gain access to a highly sensitive system. The reasons are the need for high level of security and ease of use. In addition, the COVID-19 crisis has escalated digital transformation, promoting contactless methods for hygiene purposes. Although biometric factors can include any parts of the body, their use may encounter privacy issues. Facial data is one of the most acceptable biometric factors that people are willing to give as opposed to others such as fingerprints, retina, iris, and vein. However, a biometric approach alone cannot reach an acceptable level of security and ease of use. Other authentication factor methods are needed to integrate into the existing face detection-based method. In this paper, we will present a review study on face detection-based access control along with the use of various factors found during the COVID-19 pandemic. The study presents a framework to implement the system while identifying additional constraints including health, safety, personal data protection and seamless process, which inform a new design of the system architecture.
Generic Image Restoration with Flow Based Priors

Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision with suitable image pairs to tackle a specific degradation. However, in a generic setting with unknown degradations this is not possible and a good prior remains crucial. Recently, neural network based approaches have been proposed to model such priors by leveraging either denoising autoencoders or the implicit regularization captured by the neural network structure itself. In contrast to this, we propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation. By expressing the MAP optimization process in the latent space through the learned bijective mapping, we are able to obtain solutions through gradient descent. To the best of our knowledge, this is the first work that explores normalizing flows as prior in generic image enhancement problems. Furthermore, we present experimental results for a number of different degradations on data sets varying in complexity and show competitive results when comparing with the deep image prior approach.
Tackling Cybersecurity Regulatory Challenges: A Proposed Research Framework

Although concerns about cybersecurity have been around for more than a decade with significant attention by governments and regulators, the problem has actually continued to increase. So, it is clear that whatever is being done is not working. The research question for this study is: To what extent does compliance help or hinder cybersecurity for the organization – and why/how. When trying to understand the interplay between compliance and cybersecurity, generally, two scenarios may occur: (1) compliance helps security or (2) compliance hinders security – or maybe both. This research attempts to provide a better understanding of the factors by evaluating compliance as a critical factor in the organization's cybersecurity strategy through a series of corporate and government interviews to affirm, refute, and refine our initial hypotheses.
Automatic Intermediate Generation With Deep Reinforcement Learning for Robust Two-Exposure Image Fusion

Fusing low dynamic range (LDR) for high dynamic range (HDR) images has gained a lot of attention, especially to achieve real-world application significance when the hardware resources are limited to capture images with different exposure times. However, existing HDR image generation by picking the best parts from each LDR image often yields unsatisfactory results due to either the lack of input images or well-exposed contents. To overcome this limitation, we model the HDR image generation process in two-exposure fusion as a deep reinforcement learning problem and learn an online compensating representation to fuse with LDR inputs for HDR image generation. Moreover, we build a two-exposure dataset with reference HDR images from a public multiexposure dataset that has not yet been normalized to train and evaluate the proposed model. By assessing the built dataset, we show that our reinforcement HDR image generation significantly outperforms other competing methods under different challenging scenarios, even with limited well-exposed contents. More experimental results on a no-reference multiexposure image dataset demonstrate the generality and effectiveness of the proposed model. To the best of our knowledge, this is the first work to use a reinforcement-learning-based framework for an online compensating representation in two-exposure image fusion.
Adaptive task selection in automated educational software: a comparative study

In this study, we consider the challenge of adapting the difficulty level of the tasks suggested to a student using an educational software system. We investigate the effectiveness of different learning algorithms for the challenge of adapting the difficulty of the tasks to a student’s level, and compare their efficiency by means of simulation with virtual students. Our results demonstrate that the methods based on Bayesian inference outperformed most of the other methods, while in dynamic improvement domains, the item response theory method reached the best results. Given the fact that correctly adapting the tasks to the individual learners’ abilities can help them increase their improvement and satisfaction, our study can assist the designers of intelligent tutoring systems in selecting an appropriate adaptation method, given the needs and goals of the educational system, and given the characteristics of the learners.
Multi-stage 3D Pose Estimation Method of Robot Arm Based on RGB Image

In recent years, mobile operating robots have been widely used in industry and service industries due to their flexibility and efficiency. However, the acquisition of position information of mobile robots in a collaborative environment is still challenging. In this paper, a multi-stage robotic arm 3D pose estimation method based on RGB images is proposed. This method aims to obtain the 3D pose of the robot arm of the mobile robot through an RGB image, thereby reducing the positioning accuracy requirements and communication construction of the mobile robot in the cooperation scene. Our method has three main contributions. First, we provide a method for constructing an automatically labeled dataset. Then we propose a key-point estimation network with two branches to extract key-point positions of the manipulator and their confidences. Finally, we obtain the optimized 3D pose of the manipulator by combining the robot and the camera model. The experimental results show that the error of the spatial pose estimated by the method in this paper is less than 20mm. Experimental demonstration video will be posted on https://youtu.be/1fohZRQzMc4.
Self-supervised 3D face reconstruction based on multi-view UV fusion

Image-based 3D face reconstruction has a huge application in the field of face analysis, such as face recognition, facial animation, and face editing. Recently, the popular methods based on 3dmm suffer from the ill-posed face pose16 and depth ambiguity issue. In order to address the issue, two multi-view geometric constraints are included in the reconstruction process. Note that before using these two constraints, a complete UV texture needs to be generated by texture fusion. Then, we can establish dense correspondences between different views leveraging a novel self-supervised pixel consistency constraint. We also use the facial landmark-based epi-polar constraint to constrain the pose between different views to obtain more accurate results. Extensive experiments demonstrate the superiority of the proposed method over other popular 3dmm methods with single-view input in accuracy and robustness, especially under large poses.
Machine Anomaly Detection under Changing Working Condition with Syncretic Self-Regression Auto-Encoder

Condition monitoring is one of the key tasks for the intelligent maintenance of high-end equipment. Facing the challenge of its changing working conditions, intelligent monitoring models that are built upon constant working conditions are not qualified for this task. To solve this problem, a syncretic self-regression variational auto-encoder (SSR-VAE) model is proposed to realize the parallel training of distribution learning and regression learning for machine anomaly detection. Among them, self-regression learning plays an auxiliary role in distribution learning. Furthermore, multi-sensor information fusion at the decision level is implemented to improve the robustness of the proposed model. The effectiveness of this model is evaluated on a gearbox test platform under changing working conditions.
Strategic Knowledge Acquisition

The article proposes a trimodal logical system that can express the strategic ability of coalitions to learn from their experience. The main technical result is the completeness of the proposed system.
Business process modeling with cloud computing support

Precise and clear Business Process Modeling (BPM) can be used to implement the all of processes according to the user’s needs. On the other hand, cloud computing has become a solution for data storage and more effective and efficient computing processes. State Polytechnic of Malang (Polinema) is one of the vocational higher education Institutions in Indonesia that requires business process modeling to facilitate an integrated understanding of process flow. This study aims to describe the business processes of the financial realization system and the structure of cloud computing that can be used. This research used Bizagi for designing Business Process Model and Notation (BPMN) in modeling business processes of financial system realization. This paper describes the business model of the financial realization system at Polinema. This study proposes the use of an Infrastructure as a Service IaaS-based cloud computing service model using a hybrid cloud approach to support the work of the system.
Automated Compliance of Mobile Wallet Payments for Cloud Services

Mobile payments are on the rise, and as their popularity is emerging, providers must adhere to security regulations to ensure consumer confidence. There is currently no single regulation specific to mobile wallets, so existing banking transactions are used to secure mobile payment transactions. These financial regulations are large textual documents and require significant manual effort to comprehend and ensure compliance adherence. Thus, it is difficult for both the consumers and providers to understand which specific rules in these regulations apply to their mobile wallet transactions. We have created an integrated knowledge representation of the four main banking regulations that apply to mobile payment Electronic Funds Transfer Act (EFTA), Truth in Lending Act (TILA), Gramm-Leach-Bliley Act (GLBA), and Payment Card Industry Data Security Standards (PCI-DSS). In this paper, we present our framework in detail along with the qualitative and quantitative measures that were used to validate the design against the policies of six major vendors that deal with mobile payments. Our integrated mobile payment knowledge graph, which is available in the public domain, can be used by practitioners to automate mobile wallet transaction compliance in their organization.
Mutation Fuzzy Detection of TOD Vulnerability in Smart Contract

The order of transactions in the blockchain is easily manipulated by miners. The existing methods detecting TOD (Transaction-Ordering Dependent, TOD) vulnerability have problems including high false-positive rate. Aiming at the problems with the existing TOD vulnerability detection methods, we propose a novel mutation fuzzy to detect the TOD vulnerability of smart contracts. Firstly, we generate test data based on the ABI (Application Binary Interface, ABI) of the smart contracts and instruments the bytecode of the contract under test. Secondly, the TOD test oracle is determined to detect TOD vulnerability and the existing data are mutated to generate new test data. Finally, in order to report TOD vulnerability, the fuzzing execution process of the contract under test is controlled to log smart contacts runtime behaviors. To verify the effectiveness of the proposed method, 500 ERC20 token contracts are chosen as the tested objects in Etherscan. The experimental results show that the false-positive rate of the TOD vulnerability detection method that we proposed is reduced by 72.4%.
A simple method for artworks monitoring by simultaneous speckle interferometry (ESPI) and speckle photography

In this paper, a method for artworks monitoring is proposed, able to perform simultaneously speckle interferometry and speckle photography, thus the system can map both out-of-plane and in-plane displacements. The aim is to keep it as simple as possible to increase its possibilities to be routinely used in restoration laboratories. Though simple, the proposed method is tailored to some specific needs of artwork diagnostics.
Human Action Recognition Using a New Hybrid Descriptor

Human action recognition is a very promising field for security, surveillance, elder care, etc. We proposed a new amalgamation of local and global features to describe the human action in a video. A new local spatiotemporal feature has been proposed in terms of fuzzy lattices of the frame in a video. These fuzzy lattices are described by the kinetic energy of the lattices. The kinetic energy of the lattices is found out by Schrödinger Wave Equation. Any changes in the body due to action motion change the kinetic energy of the lattices. Further to maintain the structural and shape information, most widely used global features descriptor motion energy image (MEI) and motion history image (MHI) are used. To validate the proposed methodology, three datasets Weizmann, KTH and IXMAS are used. The proposed methodology proved its superiority over other state-of-the-art methods.
A Fuzzy Take on the Logical Issues of Statistical Hypothesis Testing

Statistical Hypothesis Testing (SHT) is a class of inference methods whereby one makes use of empirical data to test a hypothesis and often emit a judgment about whether to reject it or not. In this paper, we focus on the logical aspect of this strategy, which is largely independent of the adopted school of thought, at least within the various frequentist approaches. We identify SHT as taking the form of an unsound argument from Modus Tollens in classical logic, and, in order to rescue SHT from this difficulty, we propose that it can instead be grounded in t-norm based fuzzy logics. We reformulate the frequentists’ SHT logic by making use of a fuzzy extension of Modus Tollens to develop a model of truth valuation for its premises. Importantly, we show that it is possible to preserve the soundness of Modus Tollens by exploring the various conventions involved with constructing fuzzy negations and fuzzy implications (namely, the S and R conventions). We find that under the S convention, it is possible to conduct the Modus Tollens inference argument using Zadeh’s compositional extension and any possible t-norm. Under the R convention we find that this is not necessarily the case, but that by mixing R-implication with S-negation we can salvage the product t-norm, for example. In conclusion, we have shown that fuzzy logic is a legitimate framework to discuss and address the difficulties plaguing frequentist interpretations of SHT.
Item Genre-Based Users Similarity Measure for Recommender Systems

A technique employed by recommendation systems is collaborative filtering, which predicts the item ratings and recommends the items that may be interesting to the user. Naturally, users have diverse opinions, and only trusting user ratings of products may produce inaccurate recommendations. Therefore, it is essential to offer a new similarity measure that enhances recommendation accuracy, even for customers who only leave a few ratings. Thus, this article proposes an algorithm for user similarity measures that exploit item genre information to make more accurate recommendations. This algorithm measures the relationship between users using item genre information, discovers the active user’s nearest neighbors in each genre, and finds the final nearest neighbors list who can share with them the same preference in a genre. Finally, it predicts the active-user rating of items using a definite prediction procedure. To measure the accuracy, we propose new evaluation criteria: the rating level and reliability among users, according to rating level. We implement the proposed method on real datasets. The empirical results clarify that the proposed algorithm produces a predicted rating accuracy, rating level, and reliability between users, which are better than many existing collaborative filtering algorithms.