Reviewerly/exHarmony huggingface dataset:
Towards Achieving Keyword Search over Dynamic Encrypted Cloud Data with Symmetric-Key Based Verification

Verifiable Searchable Symmetric Encryption, as an important cloud security technique, allows users to retrieve the encrypted data from the cloud through keywords and verify the validity of the returned results. Dynamic update for cloud data is one of the most common and fundamental requirements for data owners in such schemes. To the best of our knowledge, the existing verifiable SSE schemes supporting data dynamic update are all based on asymmetric-key cryptography verification, which involves time-consuming operations. The overhead of verification may become a significant burden due to the sheer amount of cloud data. Therefore, how to achieve keyword search over dynamic encrypted cloud data with efficient verification is a critical unsolved problem. To address this problem, we explore achieving keyword search over dynamic encrypted cloud data with symmetric-key based verification and propose a practical scheme in this paper. In order to support the efficient verification of dynamic data, we design a novel Accumulative Authentication Tag (AAT) based on the symmetric-key cryptography to generate an authentication tag for each keyword. Benefiting from the accumulation property of our designed AAT, the authentication tag can be conveniently updated when dynamic operations on cloud data occur. In order to achieve efficient data update, we design a new secure index composed by a search table ST based on the orthogonal list and a verification list VL containing AATs. Owing to the connectivity and the flexibility of ST, the update efficiency can be significantly improved. The security analysis and the performance evaluation results show that the proposed scheme is secure and efficient.
Fuzzy Support Vector Machine With Relative Density Information for Classifying Imbalanced Data

Fuzzy support vector machine (FSVM) has been combined with class imbalance learning (CIL) strategies to address the problem of classifying skewed data. However, the existing approaches hold several inherent drawbacks, causing the inaccurate prior data distribution estimation, further decreasing the quality of the classification model. To solve this problem, we present a more robust prior data distribution information extraction method named relative density, and two novel FSVM-CIL algorithms based on the relative density information in this paper. In our proposed algorithms, a K-nearest neighbors-based probability density estimation (KNN-PDE) alike strategy is utilized to calculate the relative density of each training instance. In particular, the relative density is irrelevant with the dimensionality of data distribution in feature space, but only reflects the significance of each instance within its class; hence, it is more robust than the absolute distance information. In addition, the relative density can better seize the prior data distribution information, no matter the data distribution is easy or complex. Even for the data with small injunctions or a large class overlap, the relative density information can reflect its details well. We evaluated the proposed algorithms on an amount of synthetic and real-world imbalanced datasets. The results show that our proposed algorithms obviously outperform to some previous work, especially on those datasets with sophisticated distributions.
Movie genome: alleviating new item cold start in movie recommendation

As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions—a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual metadata. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et al. (in: International Conference on Electronic Commerce and Web Technologies. Springer, Berlin, pp 34–45, 2016b; Proceedings of the Twelfth ACM Conference on Recommender Systems. ACM, 2018b), to better exploit complementary information between different modalities; (iii) proposing a two-step hybrid approach which trains a CF model on warm items (items with interactions) and leverages the learned model on the movie genome to recommend cold items (items without interactions). Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric online experiment measuring different subjective aspects, such as satisfaction and diversity. Results show the benefits of this approach compared to existing approaches.
Application of Blockchain in Carbon Trading

This paper introduces the similarity between the mechanism of carbon trading and blockchain, then it elaborates on the application of blockchain in carbon trading. In corporate carbon trading, blockchain technology can record and transfer information flow reliably, realize point-to-point transactions between suppliers and demanders to achieve “decentralization”, help to reduce the entry threshold for the carbon trading market. At the same time, an analysis of social environment for blockchain-based carbon trading on person is made. Finally, the paper confirms the value of "blockchain + carbon trading" and looks forward to the future.
Guidance in Cinematic Virtual Reality-Taxonomy, Research Status and Challenges

In Cinematic Virtual Reality (CVR), the viewer of an omnidirectional movie can freely choose the viewing direction when watching a movie. Therefore, traditional techniques in filmmaking for guiding the viewers’ attention cannot be adapted directly to CVR. Practices such as panning or changing the frame are no longer defined by the filmmaker; rather it is the viewer who decides where to look. In some stories, it is necessary to show certain details to the viewer, which should not be missed. At the same time, the freedom of the viewer to look around in the scene should not be destroyed. Therefore, techniques are needed which guide the attention of the spectator to visual information in the scene. Attention guiding also has the potential to improve the general viewing experience, since viewers will be less afraid to miss something when watching an omnidirectional movie where attention-guiding techniques have been applied. In recent years, there has been a lot of research about attention guiding in images, movies, virtual reality, augmented reality and also in CVR. We classify these methods and offer a taxonomy for attention-guiding methods. Discussing the different characteristics, we elaborate the advantages and disadvantages, give recommendations for use cases and apply the taxonomy to several examples of guiding methods.
A visitors’ experience model for mixed reality in the museum

Although virtual and augmented reality are receiving increasing attention in tourism and cultural heritage, the effect of mixed reality on museum visitors’ experience has still not been fully answered, and research on this topic is still in its infancy. This paper aims to contribute to this debate proposing a novel model that measures the impact of mixed reality on museum visitors’ experience and satisfaction. ‘The Ara as It Was’, an innovative project enhancing the value of the Italian masterpiece, the Ara Pacis Museum, has been used to test this model. The discussion and conclusion open up new avenues of research.
A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.
Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network

High-resolution (HR) magnetic resonance (MR) imaging is an important diagnostic technique in clinical practice. However, hardware limitations and time constraints often result in the acquisition of anisotropic MR images. It is highly desirable but very challenging to enhance image spatial resolution in medical image analysis for disease diagnosis. Recently, studies have shown that deep convolutional neural networks (CNN) can significantly boost the performance of MR image super-resolution (SR) reconstruction. In this paper, we present a novel CNN-based anisotropic MR image reconstruction method based on residual learning with long and short skip connections. The proposed network can effectively alleviate the vanishing gradient problem of deep networks and learn to restore high-frequency details of MR images. To reduce computational complexity and memory usage, the proposed network utilizes cross-plane self-similarity of 3D T1-weighted (T1w) MR images. Based on experiments on simulated and clinical brain MR images, we demonstrate that the proposed network can significantly improve the spatial resolution of anisotropic MR images with high computational efficiency. The network trained on T1w MR images is able to effectively reconstruct both SR T1w and T2-weighted (T2w) images, exploiting image features for multi-modality reconstruction. Moreover, the experimental results show that the proposed method outperforms classical interpolation methods, non-local means method (NLM), and sparse coding based algorithm in terms of peak signal-to-noise-ratio, structural similarity image index, intensity profile, and small structures. The proposed method can be efficiently applied to SR reconstruction of thick-slice MR images in the out-of-plane views for radiological assessment and post-acquisition processing.
AI-Mediated Communication

We are entering an era of AI-Mediated Communication (AI-MC) where interpersonal communication is not only mediated by technology, but is optimized, augmented, or generated by artificial intelligence. Our study takes a first look at the potential impact of AI-MC on online self-presentation. In three experiments we test whether people find Airbnb hosts less trustworthy if they believe their profiles have been written by AI. We observe a new phenomenon that we term the Replicant Effect: Only when participants thought they saw a mixed set of AI- and human-written profiles, they mistrusted hosts whose profiles were labeled as or suspected to be written by AI. Our findings have implications for the design of systems that involve AI technologies in online self-presentation and chart a direction for future work that may upend or augment key aspects of Computer-Mediated Communication theory.
Color Image Compression-Encryption Algorithm Based on Fractional-Order Memristor Chaotic Circuit

In this paper, a fractional-order memristive chaotic circuit system is defined according to memristor circuit. The dynamic characteristics are analyzed through the phase diagram, bifurcation diagram, and Lyapunov exponent spectrum, and the randomness of the chaotic pseudo-random sequence is tested by NIST SP800-22. Based on this fractional-order memristive chaotic circuit, we propose a novel color image compression-encryption algorithm. In this algorithm, compression sensing (CS) algorithm is used for compression image, and then using Zigzag confusion, add modulus and BitCircShift diffuse encrypt the image. The theoretical analysis and simulation results indicate that the proposed compression and encryption scheme has good compression performance, reconstruction effect, and higher safety performance. Moreover, it also shows that the new algorithm facilitates encryption, storage, and transmission of image information in practical applications.
An Attribute-Based Controlled Collaborative Access Control Scheme for Public Cloud Storage

In public cloud storage services, data are outsourced to semi-trusted cloud servers which are outside of data owners' trusted domain. To prevent untrustworthy service providers from accessing data owners' sensitive data, outsourced data are often encrypted. In this scenario, conducting access control over these data becomes a challenging issue. Attribute-based encryption (ABE) has been proved to be a powerful cryptographic tool to express access policies over attributes, which can provide a fine-grained, flexible, and secure access control over outsourced data. However, the existing ABE-based access control schemes do not support users to gain access permission by collaboration. In this paper, we explore a special attribute-based access control scenario where multiple users having different attribute sets can collaborate to gain access permission if the data owner allows their collaboration in the access policy. Meanwhile, the collaboration that is not designated in the access policy should be regarded as a collusion and the access request will be denied. We propose an attribute-based controlled collaborative access control scheme through designating translation nodes in the access structure. Security analysis shows that our proposed scheme can guarantee data confidentiality and has many other critical security properties. Extensive performance analysis shows that our proposed scheme is efficient in terms of storage and computation overhead.
Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

By tracking, aggregating, and analyzing student profiles along with students’ digital and analog behaviors captured in information systems, universities are beginning to open the black box of education using learning analytics technologies. However, the increase in and usage of sensitive and personal student data present unique privacy concerns. I argue that privacy-as-control of personal information is autonomy promoting, and that students should be informed about these information flows and to what ends their institution is using them. Informed consent is one mechanism by which to accomplish these goals, but Big Data practices challenge the efficacy of this strategy. To ensure the usefulness of informed consent, I argue for the development of Platform for Privacy Preferences (P3P) technology and assert that privacy dashboards will enable student control and consent mechanisms, while providing an opportunity for institutions to justify their practices according to existing norms and values.
Unsupervised Alignment of Embeddings with Wasserstein Procrustes

We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recently shown that it is possible to infer a bilingual lexicon, without supervised data, by aligning word embeddings trained on monolingual data. These recent advances are based on adversarial training to learn the mapping between the two embeddings. In this paper, we propose to use an alternative formulation, based on the joint estimation of an orthogonal matrix and a permutation matrix. While this problem is not convex, we propose to initialize our optimization algorithm by using a convex relaxation, traditionally considered for the graph isomorphism problem. We propose a stochastic algorithm to minimize our cost function on large scale problems. Finally, we evaluate our method on the problem of unsupervised word translation, by aligning word embeddings trained on monolingual data. On this task, our method obtains state of the art results, while requiring less computational resources than competing approaches.
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

Channel modeling is a critical topic when considering accurately designing or evaluating the performance of a communications system. Most prior work in designing or learning new modulation schemes has focused on using simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fading channels or other similar compact parametric models. In this paper, we extend recent work training generative adversarial networks (GANs) to approximate wireless channel responses to more accurately reflect the probability distribution functions (PDFs) of stochastic channel behaviors. We introduce the use of variational GANs to provide appropriate architecture and loss functions which accurately capture these stochastic behaviors. Finally, we illustrate why prior GAN-based methods failed to accurately capture these behaviors and share results illustrating the performance of such as system over a range of complex realistic channel effects.
An Efficient and Compacted DAG-Based Blockchain Protocol for Industrial Internet of Things

Industrial Internet of Things (IIoT) has been widely used in many fields. Meanwhile, blockchain is considered promising to address the issues of the IIoT. However, the current blockchains have a limited throughput. In this article, we devise an efficient and secure blockchain protocol compacted directed acyclic graph (CoDAG) based on a compacted directed acyclic graph, where blocks are organized in levels and width. New-generated blocks in the CoDAG will be placed appropriately and point to those in the previous level, making it a well-connected channel. Transactions in the network will be confirmed in a deterministic period, and the CoDAG keeps a simple data structure at the same time. We also illustrate the attack strategies by adversary, and it is proved that our protocols are resistant to these attacks. Furthermore, we design a CoDAG-based IIoT architecture to improve the efficiency of the IIoT system. Experimental results show that the CoDAG achieves 164× Bitcoin's throughput and 77× Ethererum's throughput.
Hashing with Mutual Information

Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval. We study the problem of learning binary vector embeddings under a supervised setting, also known as hashing. We propose a novel supervised hashing method based on optimizing an information-theoretic quantity, mutual information. We show that optimizing mutual information can reduce ambiguity in the induced neighborhood structure in the learned Hamming space, which is essential in obtaining high retrieval performance. To this end, we optimize mutual information in deep neural networks with minibatch stochastic gradient descent, with a formulation that maximally and efficiently utilizes available supervision. Experiments on four image retrieval benchmarks, including ImageNet, confirm the effectiveness of our method in learning high-quality binary embeddings for nearest neighbor retrieval.
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging

Image retagging aims to improve the tag quality of social images by completing the missing tags, rectifying the noise-corrupted tags, and assigning new high-quality tags. Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by mining the tag-image-user associations. However, such methods will become computationally infeasible with the rapidly increasing number of images, tags and users. It has been proven that the anchor graph can significantly accelerate large-scale graph-based learning by exploring only a small number of anchor points. Inspired by this, we propose a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to efficiently refine the tags of social images, which is insensitive to the scale of data. First, we construct an anchor-unit graph across multiple domains (e.g., image and user domains) rather than traditional anchor graph in a single domain. Second, a tensor completion based on Social anchor-Unit GrAph Regularization (SUGAR) is implemented to refine the tags of the anchor images. Finally, we efficiently assign tags to non-anchor images by leveraging the relationship between the non-anchor units and the anchor units. Experimental results on a real-world social image database well demonstrate the effectiveness and efficiency of SUGAR-TC, outperforming the state-of-the-art methods.
The Emergence of Pathomics

Our goal is to provide an overview of machine learning methods and artificial intelligence in digital pathology image analysis. We also highlight novel visualization tools to interpret quantitative image-based pathomics data that is extracted from whole slide images to describe diverse phenotypic characteristics of cancer in a spectrum of tissues. Image analysis of tissues is based on the identification and classification of tissue, architectural elements, cells, nuclei, and other histologic features. We report emerging digital pathology image analysis applications to study several types and subtypes of cancer to complement traditional histopathologic evaluation. WSIs typically contain hundreds of thousands to millions of objects within a heterogeneous histologic landscape. Therefore, Pathomics represents an incredibly powerful emerging approach to classify cellular interactions and signaling by identifying relevant spatial relationships. The quantification of the intrinsic variability of different phenotypes and behavior in cancer is useful in analyzing and predicting clinical outcomes and treatment response.