Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Strong Simulation Queries on Large Graphs

This paper studies privacy preserving query services for strong simulation queries in the database outsourcing paradigm. In such a paradigm, clients send their queries to a third-party service provider (SP), who has the outsourced large graph data, and the SP computes the query answers. However, as SP may not always be trusted, the sensitive information of the clients' queries, importantly, the query structures, should be protected. Moreover, graph pattern queries often have high complexities, whereas data graphs can be large. This paper adopts strong simulation as a practical query semantic for this paradigm. Under this semantic, queries are matched with a notion of balls, which are subgraphs related to the query diameter. We transform the core of the existing strong simulation algorithm using data-oblivious operations (ObSSA) and propose its secure version. We show that the algorithm may encounter an overflow problem even partially homomorphic encryption (PHE) has been used. We then propose an efficient inexact algorithm EncSSA, which is secure under chosen plaintext attack (CPA). The results of privacy analysis are presented. We have conducted experiments on Twitter and Citeseer datasets, and the results show that EncSSA is both efficient and effective.
Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.
Efficient Local Reductions to Basic Modal Logic

Abstract We present novel reductions of the propositional modal logics "Image missing"<!-- image only, no MathML or LaTex --> , "Image missing"<!-- image only, no MathML or LaTex --> , "Image missing"<!-- image only, no MathML or LaTex --> , "Image missing"<!-- image only, no MathML or LaTex --> and "Image missing"<!-- image only, no MathML or LaTex --> to Separated Normal Form with Sets of Modal Levels. The reductions result in smaller formulae than the well-known reductions by Kracht and allow us to use the local reasoning of the prover "Image missing"<!-- image only, no MathML or LaTex --> to determine the satisfiability of modal formulae in these logics. We show experimentally that the combination of our reductions with the prover "Image missing"<!-- image only, no MathML or LaTex --> performs well when compared with a specialised resolution calculus for these logics and with the b̆uilt-in reductions of the first-order prover SPASS.
BEAAS: Blockchain Enabled Attribute-Based Access Control as a Service

We propose BEAAS, a cloud service for Attribute-Based Access Control (ABAC) with security guarantee provided through the use of blockchain, specifically Ethereum. It enables user organizations to verify whether its access control data as well as access mediation decisions made by the cloud service were indeed done in an authorized manner. All the changes to the various ABAC components and access history are added to the Ethereum blockchain using efficiently written smart contracts in Solidity. We have developed a prototype system on the Rinkeby Ethereum test network and verified its functionality.
“Zero-Shot” Point Cloud Upsampling

Recent supervised point cloud upsampling methods are re-stricted by the size of training data and are limited in terms of covering all object shapes. Besides the challenges faced due to data acquisition, the networks also struggle to gener-alize on unseen records. In this paper, we present an internal point cloud upsampling approach at a holistic level referred to as “Zero-Shot” Point Cloud Upsampling (ZSPU). Our approach is data agnostic and relies solely on the internal infor-mation provided by a particular point cloud without patching in both self-training and testing phases. This single-stream design significantly reduces the training time by learning the relation between low resolution (LR) point clouds and their high (original) resolution (HR) counterparts. This association will then provide super resolution (SR) outputs when origi-nal point clouds are loaded as input. ZSPU achieves com-petitive/superior quantitative and qualitative performances on benchmark datasets when compared with other upsampling methods.
Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora

Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of events need to be identified and clustered throughout a collection of documents. CDCR aims to benefit downstream multidocument applications, but despite recent progress on corpora and system development, downstream improvements from applying CDCR have not been shown yet. We make the observation that every CDCR system to date was developed, trained, and tested only on a single respective corpus. This raises strong concerns on their generalizability—a must-have for downstream applications where the magnitude of domains or event mentions is likely to exceed those found in a curated corpus. To investigate this assumption, we define a uniform evaluation setup involving three CDCR corpora: ECB+, the Gun Violence Corpus, and the Football Coreference Corpus (which we reannotate on token level to make our analysis possible). We compare a corpus-independent, feature-based system against a recent neural system developed for ECB+. Although being inferior in absolute numbers, the feature-based system shows more consistent performance across all corpora whereas the neural system is hit-or-miss. Via model introspection, we find that the importance of event actions, event time, and so forth, for resolving coreference in practice varies greatly between the corpora. Additional analysis shows that several systems overfit on the structure of the ECB+ corpus. We conclude with recommendations on how to achieve generally applicable CDCR systems in the future—the most important being that evaluation on multiple CDCR corpora is strongly necessary. To facilitate future research, we release our dataset, annotation guidelines, and system implementation to the public.1
FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter

This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes. We specify and evaluate a new few-shot anchor-free part-based instance segmenter (FAPIS). Our key novelty is in explicit modeling of latent object parts shared across training object classes, which is expected to facilitate our few-shot learning on new classes in testing. We specify a new anchor-free object detector aimed at scoring and regressing locations of foreground bounding boxes, as well as estimating relative importance of latent parts within each box. Also, we specify a new network for delineating and weighting latent parts for the final instance segmentation within every detected bounding box. Our evaluation on the benchmark COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset demonstrates that we significantly outperform the state of the art.
deformTable: Crafting a Shape-changing Device for Creative Appropriations Over Time

In this pictorial, we present critical reflections on the design process of crafting the deformTable. The table is a shape-changing device that can go up and down based on the weight of objects placed on it. This pictorial focuses on key design decisions in the making of a shape-changing artifact like the deformTable that explores how deformability, temporality, and materiality can encourage creative appropriations over time. We believe this pictorial contributes to how to design research products that can investigate shape-changing artifacts in everyday settings over time.
Blockchain based healthcare system with Artificial Intelligence

Blockchain is not only for the financial domain anymore. It evolves to accommodate a broad range of domains and applications where trust and privacy are required for smooth transitions. As blockchain evolving, researchers are automating the blockchain tasks for better security and performance. The blockchain management system monitors specific tasks like transaction management, consensus, block security, and blockchain network security. Blockchain management system comprises of specialized engineers and blockchain software platform. This study explores the opportunity for Machine learning concepts to work with blockchain system management to automate tasks in the healthcare scenario. Reinforcement learning is used in this study to automate blockchain tasks with multiagents. Our study found that agents can be trained and perform tasks listed under the healthcare system’s blockchain management system. Our study also suggests that storing and accessing data is efficient with machine learning concepts.
Verifiable Badging System for scientific data reproducibility

Reproducibility can be considered as one of the basic requirements to ensure that a given research finding is accurate and acceptable. This paper presents a new layered approach that allows scientific researchers to provide a) data to fellow researchers to validate research and b) proofs of research quality to funding agencies, without revealing sensitive details associated with the same. We conclude that by integrating smart contracts, blockchain technology, and self-sovereign identity into an automated system, it is possible to assert the quality of scientific materials and validate the peer review process without the need of a central authority.
Weighted software metrics aggregation and its application to defect prediction

Abstract It is a well-known practice in software engineering to aggregate software metrics to assess software artifacts for various purposes, such as their maintainability or their proneness to contain bugs. For different purposes, different metrics might be relevant. However, weighting these software metrics according to their contribution to the respective purpose is a challenging task. Manual approaches based on experts do not scale with the number of metrics. Also, experts get confused if the metrics are not independent, which is rarely the case. Automated approaches based on supervised learning require reliable and generalizable training data, a ground truth, which is rarely available. We propose an automated approach to weighted metrics aggregation that is based on unsupervised learning. It sets metrics scores and their weights based on probability theory and aggregates them. To evaluate the effectiveness, we conducted two empirical studies on defect prediction, one on ca. 200 000 code changes, and another ca. 5 000 software classes. The results show that our approach can be used as an agnostic unsupervised predictor in the absence of a ground truth.
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that MultiLink favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Not just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN.
Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.
Secure mobile internet voting system using biometric authentication and wavelet based AES

The number of mobile phone users increases daily, and mobile devices are used for various applications like banking, e-commerce, social media, internet voting, e-mails, etc. This paper presents a secure mobile internet voting system in which a biometric method authenticates the voter. The biometric image can either be encrypted at the mobile device and send to the server or process the biometric image at the mobile device to generate the biometric template and send it to the server. The implementation of biometrics on mobile devices usually requires simplifying the algorithm to adapt to the relatively small CPU processing power and battery charge. This paper proposes a wavelet-based AES algorithm to speed up the encryption process and reduce the mobile device’s CPU utilization. The experimental analysis of three methods(AES encryption, wavelet-based AES encryption, and biometric template generation) exhibits that wavelet-based AES encryption is much better than AES encryption and template generation. The security analysis of three methods shows that AES and wavelet-based AES encryption provides better security than the biometric template’s protection. The study of the proposed internet voting system shows that biometric authentication defeats almost all the mobile-based threats.
Development of Digital Health Messages for Rural Populations in Tanzania: Multi- and Interdisciplinary Approach

Health workers have traditionally delivered health promotion and education to rural communities in the Global South in paper leaflet formats or orally. With the rise of digital technologies, health promotion and education can be provided in innovative and more effective formats, which are believed to have a higher impact on disease prevention and treatment.The aim of this tutorial is to illustrate how a multi- and interdisciplinary approach can be applied in the design process of digital health messages for use in the Global South.The multi- and interdisciplinary team of the Non-discriminating access for Digital Inclusion (DigI) project digitalized and customized available government-approved paper-based health promotion messages into a screen-suitable format. The team worked closely together and used its diverse expertise to develop digital health messages with disease-specific content in Tanzania's national language (Swahili) as well as English. The development process included the following phases: a local needs assessment; identification of government-approved health promotion materials in a nondigital format; identification of key health messages; creation of a practical and engaging story, easy to understand for the general public; drafting of a storyboard for an animated video with review, feedback, and revisions; forward and backward translation; audio recording of the story in both languages; finalization and presentation of the animations; development of relevant questions related to the health messages in each domain; and development of web and mobile apps to access the digital health messages.Between 2017 and 2019, we developed key health messages, quizzes, and animated health videos to address HIV/AIDS, tuberculosis, Taenia solium cysticercosis and taeniasis, and anthrax, all of which are of public health importance in Tanzania. Feedback from local stakeholders and test users was included in various phases of the process. The 4 videos and other content are available in local information spots on a digital health platform (DigI platform), established by the DigI project, in both Tanzanian Swahili and English.Our methodological multi- and interdisciplinary approach ensures that the digital health messages for the public are clear, high quality, and align with the government's objectives for health promotion. It also demonstrates the diversity of scientific disciplines required when collaborating on a digital health project. We recommend this approach to be applied to the development of other digital health messages for a wide range of diseases.RR2-10.2196/25128.
Reversible Data Hiding in Encrypted Image Based on (7, 4) Hamming Code and UnitSmooth Detection

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room for improvement in terms of hiding capacity, security and separability. Based on (7, 4) Hamming Code and our proposed prediction/ detection functions, this paper proposes a Hamming Code and UnitSmooth detection based RDHEI scheme, called HUD-RDHEI scheme for short. To prove our performance, two database sets—BOWS-2 and BOSSBase—have been used in the experiments, and peak signal to noise ratio (PSNR) and pure embedding rate (ER) are served as criteria to evaluate the performance on image quality and hiding capacity. Experimental results confirm that the average pure ER with our proposed scheme is up to 2.556 bpp and 2.530 bpp under BOSSBase and BOWS-2, respectively. At the same time, security and separability is guaranteed. Moreover, there are no incorrect extracted bits during data extraction phase and the visual quality of directly decrypted image is exactly the same as the cover image.
Enhancing the Surveillance Detection Range of Image Sensors Using HDR Techniques

Image sensor-based surveillance systems are used for scene analysis, image sequence creation, and object detection. Natural scenes contain areas of high and low illumination, and standard cameras can only transform a fraction of the available visual information. The wide variation in illumination conditions limits the detection capabilities of image sensors. Therefore, in this study, the feasibility of applying high dynamic range (HDR) techniques using image sensors for enhancing range performance was investigated. The HDR requirements were analyzed, and available HDR techniques along with tone mapping and image enhancement were optimized for real-time scenarios. To determine the HDR requirements for specific visibility conditions, a theoretical analysis was conducted to evaluate camera performance using the human visual system. Additionally, a HDR imaging framework was established, and experiments were conducted to validate the performance of the framework. Results demonstrate that there is an empirical relationship between the dynamic range and visual detection range of the surveillance system. Subjective and objective evaluations indicate that surveillance cameras employing HDR imaging for long range applications have increased target detection range.