Reviewerly/exHarmony huggingface dataset:
Highly Secure Privacy-Preserving Outsourced <i>k</i>-Means Clustering under Multiple Keys in Cloud Computing

Data clustering is the unsupervised classification of data records into groups. As one of the steps in data analysis, it has been widely researched and applied in practical life, such as pattern recognition, image processing, information retrieval, geography, and marketing. In addition, the rapid increase of data volume in recent years poses a huge challenge for resource-constrained data owners to perform computation on their data. This leads to a trend that users authorize the cloud to perform computation on stored data, such as keyword search, equality test, and outsourced data clustering. In outsourced data clustering, the cloud classifies users’ data into groups according to their similarities. Considering the sensitive information in outsourced data and multiple data owners in practical application, it is necessary to develop a privacy-preserving outsourced clustering scheme under multiple keys. Recently, Rong et al. proposed a privacy-preserving outsourced k -means clustering scheme under multiple keys. However, in their scheme, the assistant server (AS) is able to extract the ratio of two underlying data records, and key management server (KMS) can decrypt the ciphertexts of owners’ data records, which break the privacy security. AS can even reduce all data records if it knows one of the data records. To solve the aforementioned problem, we propose a highly secure privacy-preserving outsourced k -means clustering scheme under multiple keys in cloud computing. In this paper, noncolluded cloud computing service (CCS) and KMS jointly perform clustering over the encrypted data records without exposing data privacy. Specifically, we use BCP encryption which has additive homomorphic property and AES encryption to double encrypt data records, where the former cryptosystem prevents CCS from obtaining any useful information from received ciphertexts and the latter one protects data records from being decrypted by KMS. We first define five protocols to realize different functions and then present our scheme based on these protocols. Finally, we give the security and performance analyses which show that our scheme is comparable with the existing schemes on functionality and security.
Power Trading Blockchain using Hyperledger Fabric

Various technologies are mentioned in the 4th Industrial Revolution. One of the emerging issues in the power market is the blockchain-based smart grid network. We propose the architecture of the network by incorporating blockchain technology into the existing smart grid system. The Ethereum and Hyperledger Fabrics are widely used in the blockchain technology as public blockchains and private blockchains, respectively. We describe the network architecture in the smart grid blockchain and compare the performance of transactions processing. The performance analysis shows that Hyperledger Fabric is viable approach by providing pipelined concurrent transaction among peers processing and MVCC(multi-version concurrency control).
Use of Ontology Learning in Information System Integration: A Literature Survey

Ontology-based information integration is a useful method to integrate heterogeneous data at the semantic level. However, there are some bottlenecks of the traditional method for constructing ontology, i.e., time-consuming, error-prone, and semantic loss. Ontology learning is a kind of ontology construction approach based on machine learning, it provides a new opportunity to tackle the above bottlenecks. Especially, it could be employed to construct ontologies and integrate large-scale and heterogeneous data from various information systems. This paper surveys the latest developments of ontology learning and highlights how they could be adopted and play a vital role in the integration of information systems. The recent techniques and tools of ontology learning from text and relational database are reviewed, the possibility of using ontology learning in information integration were discussed based on the mapping results of the aforementioned bottlenecks and features of ontology learning. The potential directions for using ontology learning in information systems integration were given.
Rate-distortion model for grayscale-invariance reversible data hiding

Recently, a novel scheme called grayscale-invariance reversible data hiding (RDH) has been developed, in which the generated color marked image will have the same grayscale as that of the host color image. Due to the property of grayscale invariance, for many cases, the further applications and image processing of the marked image will not be interfered with. However, the performance of current grayscale-invariance RDH is unsatisfactory, and the corresponding theoretical model is also lacking. For grayscale-invariance RDH, the modifications of the three color channels are interrelated. By decomposing the joint modification and associating the modification distortions of the green scales and blue scales with those of the red scales, we first approximate the modification-independent rate-distortion model for grayscale-invariance RDH. Subsequently, the optimal transition probability matrix can be estimated, according to which we implement the theoretically proven optimal modification, i.e., recursive code construction (RCC), to finish message embedding. The experimental results show that the presented method extends the applications of RCC and can significantly improve the performance of grayscale-invariance RDH.
Privacy-Preserving Packet Header Checking Over in-the-Cloud Middleboxes

The explosive growth of network traffic is pushing forward the paradigm of cloud-based middlebox services today. However, due to the increasing attacking surfaces, redirecting enterprises traffic to outsourced middleboxes inevitably raises new privacy concerns about packet content exposure and unauthorized rulesets access. To address these issues, recent efforts have been made toward enabling middlebox services through encrypted traffic and middlebox rules. Following this direction, in this article, we investigate the issue of privacy-preserving header checking, which is an indispensable service of middlebox applications. Specifically, we propose two new encrypted header-matching schemes that significantly improve security and efficiency. Our main idea is to formulate the problem of encrypted header checking as range-based pattern matching, and carefully craft security designs to enable efficient header inspection in the ciphertext domain. Our first design is carefully tailored to generic range-based functions, while our second design is highly customized for contiguous rulesets to further improve the checking efficiency. We formally analyze the security strengths and implement a fully functional system prototype. The extensive experiments over the real-world rulesets demonstrate the practicality of our designs.
A Generalized Construction of Multiple Complete Complementary Codes and Asymptotically Optimal Aperiodic Quasi-Complementary Sequence Sets

In recent years, complete complementary codes (CCCs) and quasi-complementary sequence sets (QCSSs) have found many important applications in multi-carrier code-division multiple-access (MC-CDMA) systems for their good correlation properties. In this paper, we propose a generic construction of multiple sets of CCCs over Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> , consisting of sequences of length N, where N ≥ 3 is an arbitrary odd integer. Interestingly, the maximum inter-set aperiodic cross-correlation magnitude of the proposed CCCs is upper bounded by N. It turns out that the combination of the generated CCCs results in a new set of sequences to obtain asymptotically optimal and near-optimal aperiodic QCSSs. The proposed construction includes a recent optimal construction of QCSSs with prime length as a special case and leads to asymptotically optimal QCSSs with new flexible parameters.
Universal Data Anomaly Detection via Inverse Generative Adversary Network

The problem of detecting data anomaly under unknown probability distributions is considered. Whereas the probability distribution of the anomaly-free data is unknown, anomaly-free training samples are assumed to be available. For anomaly data, neither the underlying probability distribution is known nor anomaly data samples are available. A deep learning approach coupled with a statistical test based on coincidence is proposed where an inverse generative adversary network is trained to transform data to the classical uniform vs. nonuniform hypothesis testing problem. The proposed approach is particularly effective to detect persistent anomalies whose distributions have an overlapping domain with that of the anomaly-free distribution.
On the Security of Pixel-Based Image Encryption for Privacy-Preserving Deep Neural Networks

This paper aims to evaluate the safety of a pixel-based image encryption method, which has been proposed to apply images with no visual information to deep neural networks (DNN), in terms of robustness against ciphertext-only attacks (COA). In addition, we propose a novel DNN-based COA that aims to reconstruct the visual information of encrypted images. The effectiveness of the proposed attack is evaluated under two encryption key conditions: same encryption key, and different encryption keys. The results show that the proposed attack can recover the visual information of the encrypted images if images are encrypted under same encryption key. Otherwise, the pixel-based image encryption method has robustness against COA.
Measuring evidence: a probabilistic approach to an extension of Belnap–Dunn logic

This paper introduces the logic of evidence and truth $${ LET}_{F}$$ as an extension of the Belnap–Dunn four-valued logic $${ FDE}$$. $${ LET}_{F}$$ is a slightly modified version of the logic $${ LET}_{J}$$, presented in Carnielli and Rodrigues (Synthese 196:3789–3813, 2017). While $${ LET}_{J}$$ is equipped only with a classicality operator $${\circ }$$, $${ LET}_{F}$$ is equipped with a non-classicality operator $${\bullet }$$ as well, dual to $${\circ }$$. Both $${ LET}_{F}$$ and $${ LET}_{J}$$ are logics of formal inconsistency and undeterminedness in which the operator $$ \circ $$ recovers classical logic for propositions in its scope. Evidence is a notion weaker than truth in the sense that there may be evidence for a proposition $$ \alpha $$ even if $$ \alpha $$ is not true. As well as $${ LET}_{J}$$, $${ LET}_{F}$$ is able to express preservation of evidence and preservation of truth. The primary aim of this paper is to propose a probabilistic semantics for $${ LET}_{F}$$ where statements $$P(\alpha )$$ and $$P({\circ }\alpha )$$ express, respectively, the amount of evidence available for $$\alpha $$ and the degree to which the evidence for $$ \alpha $$ is expected to behave classically—or non-classically for $$ P({\bullet }\alpha ) $$. A probabilistic scenario is paracomplete when $$P(\alpha ) + P(\lnot \alpha ) < 1$$, and paraconsistent when $$P(\alpha ) + P(\lnot \alpha ) > 1$$, and in both cases, $$P(\circ \alpha ) < 1$$. If $$P(\circ \alpha ) = 1$$, or $$P({\bullet }\alpha ) = 0$$, classical probability is recovered for $$ \alpha $$. The proposition $$ {\circ }\alpha \vee {\bullet }\alpha $$, a theorem of $${ LET}_{F}$$, partitions what we call the information space, and thus allows us to obtain some new versions of known results of standard probability theory.
Weakly-Supervised Multi-Person Action Recognition in 360° Videos

The recent development of commodity 360° cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. However, research in omnidirectional video analysis has lagged behind the hardware advances. In this work, we address the important problem of action recognition in topview 360° videos. Due to the wide filed-of-view, 360° videos usually capture multiple people performing actions at the same time. Furthermore, the appearance of people are deformed. The proposed framework first transforms top-view omnidirectional videos into panoramic videos using a calibrationfree method. Then spatial-temporal features are extracted using region-based 3D CNNs for action recognition. We propose a weakly-supervised method based on multiinstance multi-label learning, which trains the model to recognize and localize multiple actions in a video using only video-level action labels as supervision. We perform experiments to quantitatively validate the efficacy of the proposed method over state-of-the-art baselines and variants of our model, and qualitatively demonstrate action localization results. To enable research in this direction, we introduce the 360Action dataset. It is the first omnidirectional video dataset for multi-person action recognition with a diverse set of scenes, actors and actions. The dataset is available at https://github.com/ryukenzen/360action.
Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.
Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review

Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include 'detection' and 'interpretation' errors. Studies to reduce these errors have shown the feasibility of using convolution neural networks (CNNs). This narrative review presents recent studies in diagnosing mammographic malignancy investigating the accuracy and reliability of these CNNs. Databases including ScienceDirect, PubMed, MEDLINE, British Medical Journal and Medscape were searched using the terms 'convolutional neural network or artificial intelligence', 'breast neoplasms [MeSH] or breast cancer or breast carcinoma' and 'mammography [MeSH Terms]'. Articles collected were screened under the inclusion and exclusion criteria, accounting for the publication date and exclusive use of mammography images, and included only literature in English. After extracting data, results were compared and discussed. This review included 33 studies and identified four recurring categories of studies: the differentiation of benign and malignant masses, the localisation of masses, cancer-containing and cancer-free breast tissue differentiation and breast classification based on breast density. CNN's application in detecting malignancy in mammography appears promising but requires further standardised investigations before potentially becoming an integral part of the diagnostic routine in mammography.
Haze pollution causality mining and prediction based on multi-dimensional time series with PS-FCM

Haze has become a frequent disastrous weather condition in China. Its formation consists of evolution process of several pollutants in certain meteorological conditions with complex relationships among these factors of haze formation. How to explore the complex relationships among these multi-dimensional factors as well as effectively predict them have become a key issue in research community. The research in this paper presents a method to quantitatively reveal causality in the formation of haze, which can be used to effectively predict haze pollution. In order to make the complicated relationship among different factors be interpretable, an PS-FCM (Primary Sub-Fuzzy Cognitive Maps) model is proposed and its multi-dimensional causality solution is demonstrated. By considering the formation of haze as an evolving process with time, we explore and discover the causality based on time series data of haze pollution with PS-FCM. Thus, a multi-dimensional time series data mining method based on the PS-FCM is developed to investigate the formation of haze. We validate our model by comparing with other machine learning method via experimental data and discuss the performance of PS-FCM under different transformation functions. The results explicitly show the quantitative causality among the different factors in haze formation.
A Blockchain-based File-sharing System for Academic Paper Review

As a tool for human technological advancement, the peer-review system acts as a gateway for ensuring academic paper qualities. However, the system has proven to be slow and expensive. Also, biasedness remains an unsolved problem. Such issues could become a major bottleneck, which can adversely impact research progress and dissemination of knowledge. This paper aims to propose a double-blind paper review system to preserve the authors and reviewers anonymity. This system also addresses issues concerning the reviewers payment, inconsistent review metrics, and biased reviews. The proposed solution utilizes the Hyperledger Fabric blockchain with the InterPlanetary File System (IPFS). The blockchain smart contracts provide a base for financial transactions between paper publishers and the reviewers. Hence, we introduce AcadCoin, a novel cryptocurrency used for supporting said financial transactions. Also, the Hyperledger blockchain provides user access control to achieve double-blindness in reviews. Along with the Hyperledger blockchain, the IPFS is used to store the paper documents, review documents and open metrics documents to reduce the storage requirement of the blockchain. A broad system architecture is constructed to combine the blockchain and the file storage system. This system architecture distributes nodes of the system to related parties. Finally, the blockchain network is implemented and tested using the Hyperledger Composer Playground environment.
Disincentivizing Double Spend Attacks Across Interoperable Blockchains

Blockchain was originally developed to support decentralized cryptocurrency applications within a single network. However, the proliferation of blockchain technology has led to the need of supporting transactions across multiple networks requiring interoperability. Thus far, minimal analysis has been dedicated to the interoperability scenario and in particular the prevention of double-spending attacks across interoperable blockchain networks. In this paper, we propose the use of neutral observers to monitor transactions that span multiple blockchains and design a protocol that obviates the double-spending problem across interoperable blockchain networks. We show that the observers, can detect double spending, while remaining honest to the protocol as it is more profitable to them than colluding due to our proposed disincentivization scheme. Leveraging Ethereum's smart-contract functionality, we simulate our proposed disincentivization scheme and show its cost-effectiveness.
Erasing Scene Text with Weak Supervision

Scene text erasing is a task of removing text from natural scene images, which has been gaining attention in recent years. The main motivation is to conceal private information such as license plate numbers, and house nameplates that can appear in images. In this work, we propose a method for scene text erasing that approaches the problem as a general inpainting task. In contrast to previous methods, which require pairs of original images containing text and images from which the text has been removed, our method does not need corresponding image pairs for training. We use a separately trained scene text detector and an inpainting network. The scene text detector predicts segmentation maps of text instances which are then used as masks for the inpainting network. The network for inpainting, trained on a large-scale image dataset, fills in masked out regions in an input image and generates a final image in which the original text is no longer present. The results show that our method is able to successfully remove text and fill in the created holes to produce natural-looking images.
Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images

In e-commerce, product content, especially product images have a significant influence on a customer's journey from product discovery to evaluation and finally, purchase decision. Since many e-commerce retailers sell items from other third-party marketplace sellers besides their own, the content published by both internal and external content creators needs to be monitored and enriched, wherever possible. Despite guidelines and warnings, product listings that contain offensive and non-compliant images continue to enter catalogs. Offensive and non-compliant content can include a wide range of objects, logos, and banners conveying violent, sexually explicit, racist, or promotional messages. Such images can severely damage the customer experience, lead to legal issues, and erode the company brand. In this paper, we present a computer vision driven offensive and non-compliant image detection system for extremely large image datasets. This paper delves into the unique challenges of applying deep learning to real-world product image data from retail world. We demonstrate how we resolve a number of technical challenges such as lack of training data, severe class imbalance, fine-grained class definitions etc. using a number of practical yet unique technical strategies. Our system combines state-of-the-art image classification and object detection techniques with budgeted crowd-sourcing to develop a solution customized for a massive, diverse, and constantly evolving product catalog.