Reviewerly/exHarmony huggingface dataset:
Utility-efficient differentially private K-means clustering based on cluster merging

Differential privacy is widely used in data analysis. State-of-the-art k-means clustering algorithms with differential privacy typically add an equal amount of noise to centroids for each iterative computation. In this paper, we propose a novel differentially private k-means clustering algorithm, DP-KCCM, that significantly improves the utility of clustering by adding adaptive noise and merging clusters. Specifically, to obtain k clusters with differential privacy, the algorithm first generates n×k initial centroids, adds adaptive noise for each iteration to get n×k clusters, and finally merges these clusters into k ones. We theoretically prove the differential privacy of the proposed algorithm. Surprisingly, extensive experimental results show that: 1) cluster merging with equal amounts of noise improves the utility somewhat; 2) while adding adaptive noise only does not improve the utility, combining both cluster merging and adaptive noise further improves the utility significantly.
KGEL: A novel end-to-end embedding learning framework for knowledge graph completion

Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.
Virtual reality advertising with brand experiences: the effects of media devices, virtual representation of the self, and self-presence

(2021). Virtual reality advertising with brand experiences: the effects of media devices, virtual representation of the self, and self-presence. International Journal of Advertising: Vol. 40, No. 7, pp. 1096-1114.
Route Hijacking and DoS in Off-Chain Networks

Off-chain transaction networks can mitigate the scalability issues of today's trustless blockchain systems such as Bitcoin. However, these peer-to-peer networks also introduce a new attack surface which is not yet fully understood. This paper identifies and analyzes a novel type of Denial-of-Service attack which is based on attracting routes, i.e., which exploits the way transactions are routed and executed along the channels of the network in order to attract nodes to route through the attacker. This attack is conceptually interesting as it highlights a fundamental design tradeoff for the defender (who determines its own routes): to become less susceptible to hijacking, a rational node has to pay higher fees to nodes that forward its payments.
Exploration of entropic uncertainty bound in a symmetric multi-qubit system under noisy channels

Considering one of the particles as a quantum memory in a bipartite system can remarkably improve the measurement accuracy for two incompatible observables. Herein, we study quantum-memory-assisted entropic uncertainty bound for an arbitrary two-qubit X-state, and then we get a clear formula as the entropic uncertainty bound. In the following, we examine analytically and numerically the dynamics of entropic uncertainty bound in a symmetric multi-qubit system under four types of noisy channels, i.e. phase-flip, amplitude damping, phase-damping, and depolarizing channels. Our results show that the entropic uncertainty bound dynamics is related to the number of particles and especially the noise channel used. Noteworthy, our remarks reveal that under the amplitude damping channel, the entropic uncertainty bound can be suppressed during the time. It turns out that under an amplitude damping channel, these results can be important in practical goals where the minimum uncertainty is required such as quantum computation.
Optimized Quantum Circuit Partitioning

The main objective of this paper is to improve the communication cost in distributed quantum circuits. To this end, we present a method for generating distributed quantum circuits from monolithic quantum circuits in such a way that communication between partitions of a distributed quantum circuit is minimized. Thus, the communication between distributed components is performed at a lower cost. Compared to the existing works, our approach can effectively map a quantum circuit into an appropriate number of distributed components. Since teleportation is usually the protocol used to connect components in a distributed quantum circuit, our approach ultimately reduces the number of teleportations. The results of applying our approach to the benchmark quantum circuits determine its effectiveness and show that partitioning is a necessary step in constructing distributed quantum circuit.
Speculative Probing

To defeat ASLR or more advanced fine-grained and leakage-resistant code randomization schemes, modern software exploits rely on information disclosure to locate gadgets inside the victim's code. In the absence of such info-leak vulnerabilities, attackers can still hack blind and derandomize the address space by repeatedly probing the victim's memory while observing crash side effects, but doing so is only feasible for crash-resistant programs. However, high-value targets such as the Linux kernel are not crash-resistant. Moreover, the anomalously large number of crashes is often easily detectable. In this paper, we show that the Spectre era enables an attacker armed with a single memory corruption vulnerability to hack blind without triggering any crashes. Using speculative execution for crash suppression allows the elevation of basic memory write vulnerabilities into powerful speculative probing primitives that leak through microarchitectural side effects. Such primitives can repeatedly probe victim memory and break strong randomization schemes without crashes and bypass all deployed mitigations against Spectre-like attacks. The key idea behind speculative probing is to break Spectre mitigations using memory corruption and resurrect Spectre-style disclosure primitives to mount practical blind software exploits. To showcase speculative probing, we target the Linux kernel, a crash-sensitive victim that has so far been out of reach of blind attacks, mount end-to-end exploits that compromise the system with just-in-time code reuse and data-only attacks from a single memory write vulnerability, and bypass strong Spectre and strong randomization defenses. Our results show that it is crucial to consider synergies between different (Spectre vs. code reuse) threat models to fully comprehend the attack surface of modern systems.
Malicious URL Detection Using Machine Learning

Recently, with the increase in Internet usage, cybersecurity has been a significant challenge for computer systems. Different malicious URLs emit different malicious software and try to capture user information. Signature-based approaches have often been used to detect such websites and detected malicious URLs have been attempted to restrict access by using various security components. This chapter proposes using host-based and lexical features of the associated URLs to better improve the performance of classifiers for detecting malicious web sites. Random forest models and gradient boosting classifier are applied to create a URL classifier using URL string attributes as features. The highest accuracy was achieved by random forest as 98.6%. The results show that being able to identify malicious websites based on URL alone and classify them as spam URLs without relying on page content will result in significant resource savings as well as safe browsing experience for the user.
Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation

3D hand pose estimation is an important task for a wide range of real-world applications. Existing works in this domain mainly focus on designing advanced algorithms to achieve high pose estimation accuracy. However, besides accuracy, the computation efficiency that affects the computation speed and power consumption is also crucial for real-world applications. In this paper, we investigate the problem of reducing the overall computation cost yet maintaining the high accuracy for 3D hand pose estimation from video sequences. A novel model, called Adaptive Computationally Efficient (ACE) network, is proposed, which takes advantage of a Gaussian kernel based Gate Module to dynamically switch the computation between a light model and a heavy network for feature extraction. Our model employs the light model to compute efficient features for most of the frames and invokes the heavy model only when necessary. Combined with the temporal context, the proposed model accurately estimates the 3D hand pose. We evaluate our model on two publicly available datasets, and achieve state-of-the-art performance at 22% of the computation cost compared to traditional temporal models.
Empirical Evaluation of Gaze-enhanced Menus in Virtual Reality

Many user interfaces involve attention shifts between primary and secondary tasks, e.g., when changing a mode in a menu, which detracts the user from their main task. In this work, we investigate how eye gaze input affords exploiting the attention shifts to enhance the interaction with handheld menus. We assess three techniques for menu selection: dwell time, gaze button, and cursor. Each represents a different multimodal balance between gaze and manual input. We present a user study that compares the techniques against two manual baselines (dunk brush, pointer) in a compound colour selection and line drawing task. We show that user performance with the gaze techniques is comparable to pointer-based menu selection, with less physical effort. Furthermore, we provide an analysis of the trade-off as each technique strives for a unique balance between temporal, manual, and visual interaction properties. Our research points to new opportunities for integrating multimodal gaze in menus and bimanual interfaces in 3D environments.
Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.
ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion. We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (<em>n</em> = 8689) and randomly selected from PubMed (<em>n</em> = 1500) we iteratively developed a set of derived keyword categories. ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code). To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.73 and specificity of 0.97. Open Data was detected for 11.5% (<em>n</em> = 91) of publications. Open Code was detected for 1.4% (<em>n</em> = 11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science. Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub.
Optimal Broadcast Encryption from LWE and Pairings in the Standard Model

Broadcast Encryption with optimal parameters was a long-standing problem, whose first solution was provided in an elegant work by Boneh, Waters and Zhandry [BWZ14]. However, this work relied on multilinear maps of logarithmic degree, which is not considered a standard assumption. Recently, Agrawal and Yamada [AY20] improved this state of affairs by providing the first construction of optimal broadcast encryption from Bilinear Maps and Learning With Errors (LWE). However, their proof of security was in the generic bilinear group model. In this work, we improve upon their result by providing a new construction and proof in the standard model. In more detail, we rely on the Learning With Errors (LWE) assumption and the Knowledge of OrthogonALity Assumption (KOALA) [BW19] on bilinear groups. Our construction combines three building blocks: a (computational) nearly linear secret sharing scheme with compact shares which we construct from LWE, an inner-product functional encryption scheme with special properties which is constructed from the bilinear Matrix Decision Diffie Hellman (MDDH) assumption, and a certain form of hyperplane obfuscation, which is constructed using the KOALA assumption. While similar to that of Agrawal and Yamada, our construction provides a new understanding of how to decompose the construction into simpler, modular building blocks with concrete and easy-to-understand security requirements for each one. We believe this sheds new light on the requirements for optimal broadcast encryption, which may lead to new constructions in the future.
Efficient Encryption System for Numerical Image Safe Transmission

In this paper, we propose an efficient cryptosystem for digital image encryption and authentication. The cryptosystem is a hybrid scheme that uses symmetric and asymmetric approaches. The first one is used to encrypt the host image by utilizing a chaos-based key generator. The second one is used to encrypt the initial secret key and the owner’s signature that permit authentication. The algorithm is evaluated and validated by its application on several types of standard images and tools such as the statistical analysis, the key, and the performance analysis. The results indicate that the proposed cryptosystem provides high performance and enhanced security. The NIST 800-22 is used for testing the pseudorandom numbers generation (PRNG). The obtained simulation results are better than those cited in the recent works in terms of execution time and security level and low computational complexity.
Combining pretrained CNN feature extractors to enhance clustering of complex natural images

Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most research, the CNN architecture for feature extraction is chosen arbitrarily, without justification. This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC). First, extensive experiments are conducted and show that, for a given dataset, the choice of the CNN architecture for feature extraction has a huge impact on the final clustering. These experiments also demonstrate that proper extractor selection for a given IC task is difficult. To solve this issue, we propose to rephrase the IC problem as a multi-view clustering (MVC) problem that considers features extracted from different architectures as different "views" of the same data. This approach is based on the assumption that information contained in the different CNN may be complementary, even when pretrained on the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. This approach is tested on nine natural image datasets, and produces state-of-the-art results for IC.
Trust‐aware generative adversarial network with recurrent neural network for recommender systems

Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust-Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short-term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.
A scalable sub-graph regularization for efficient content based image retrieval with long-term relevance feedback enhancement

The goal of content-based image retrieval (CBIR) is to search relevant images through the analysis of image content. Manifold Ranking (MR) and Efficient Manifold Ranking (EMR) method has been successfully applied to content-based image retrieval due to its ability to discover underlying geometrical structure of dataset given the query data. But given the image database is scalable, the graph in MR and EMR cannot be extended or updated as their graph size is fixed. In this paper, to solve the above problem, we consider to formulate a sub-graph based on fixed anchors, instead of constructing the graph based on the while dataset, where the anchors are selected by utilizing conventional k-means method and the sub-graph weight matrix is defined by the similarity between any pair-wise anchors. Since the number of anchors is much smaller than the original dataset, updating the sub-graph is much easier than the original graph of whole dataset. Motivated by such sub-graph construction, we then develop an efficient graph regularization framework to predict the ranking scores for the whole data along the sub-graph, where the ranking score is first propagated from query image to the partial anchors, then from partial anchors to all anchors via the sub-graph and finally to the whole dataset. It can also utilize user relevance feedbacks to update the sub-graph so that the discriminative information can be involved to enhance the retrieval performance in a long term. Extensive simulations verify the effectiveness of the proposed method.
Beyond 3DMM Space: Towards Fine-Grained 3D Face Reconstruction

Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space. As a result, the reconstruction results lose the fine-grained geometry and look different from real faces. To alleviate this issue, we first propose a solution to construct large-scale fine-grained 3D data from RGB-D images, which are expected to be massively collected as the proceeding of hand-held depth camera. A new dataset Fine-Grained 3D face (FG3D) with 200k samples is constructed to provide sufficient data for neural network training. Secondly, we propose a Fine-Grained reconstruction Network (FGNet) that can concentrate on shape modification by warping the network input and output to the UV space. Through FG3D and FGNet, we successfully generate reconstruction results with fine-grained geometry. The experiments on several benchmarks validate the effectiveness of our method compared to several baselines and other state-of-the-art methods. The proposed method and code will be available at https://github.com/XiangyuZhu-open/Beyond3DMM .