Reviewerly/exHarmony huggingface dataset:
Nonparametric hierarchical mixture models based on asymmetric Gaussian distribution

Mixture models are broadly applied in image processing domains with hierarchical Bayesian approaches popularly considered for grouped data. The related inference process takes into consideration both the approximation of exact data shapes and estimation of adequate component numbers. In our work, we develop nonparametric hierarchical Bayesian models using the Dirichlet and Pitman-Yor processes with asymmetric Gaussian distribution. The parameters of these models are learned using variational inference methods. The effectiveness and merits of the proposed approaches are validated using the challenging real-life application of dynamic texture clustering.
Anonymous Lottery In The Proof-of-Stake Setting

When Proof-of-Stake (PoS) underlies a consensus protocol, parties who are eligible to participate in the protocol are selected via a public selection function that depends on the stake they own. Identity and stake of the selected parties must then be disclosed in order to allow verification of their eligibility, and this can raise privacy concerns. In this paper, we present a modular approach for addressing the identity leaks of selection functions, decoupling the problem of implementing an anonymous selection of the participants, from the problem of implementing others task, e.g. consensus. We present an ideal functionality for anonymous selection that can be more easily composed with other protocols. We then show an instantiation of our anonymous selection functionality based on the selection function of Algorand.
MultiLive: Adaptive Bitrate Control for Low-delay Multi-party Interactive Live Streaming

In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates to send; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we design MultiLive, an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We conduct extensive trace-driven simulations to evaluate the algorithm. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2-5× improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.
Logical foundations of knowledge-based recommender systems: A unifying spectrum of alternatives

The aim of this paper is to provide logical foundations for knowledge-based recommender systems, for which, unlike other problem solving tasks, a comprehensive formalization is not yet available. This goal is justified by the need to compare recommenders based on the way they use knowledge to generate recommendations and, consequently, on the underlying semantics of the recommendation process itself. Moreover, since the here adopted logical formalization has been borrowed from other tasks such as diagnosis, many interesting results and opportunities can be transposed from such tasks to recommendation. While we do not aim at proposing a new recommendation generation technique, we believe that our formalization will be the basis for unifying different approaches to knowledge-based recommendation, revealing their semantics and offering a conceptual framework to compare them. In fact, the framework covers different variations of knowledge-based recommendation, such as context-aware, constraints-based, package and group recommendation, as well as recommendation based on negative preferences.
Enforcing Determinism of Java Smart Contracts

Java is a high-level, well-known and powerful object-oriented language, with a large support library and a comfortable toolbelt. Hence, it has been proposed for writing smart contracts in blockchain. However, its support library is non-deterministic, which is a blocking issue for its application to smart contracts. This paper discusses the kind of (non-)determinism of the methods of the Java library and how a deterministic fragment of that library can be specified. It shows that some relevant parts are deterministic only under specific conditions on run-time values. It concludes with the description of an instrumentation, for the Takamaka blockchain, that enforces such conditions, statically or dynamically, reporting experiments with its implementation.
Mixed-noise robust face super-resolution through residual-learning based error suppressed nearest neighbor representation

The conventional face super-resolution (SR) methods are highly sensitive to noise because of signal conversion error, bit error in transmission, heat, and illumination effects in the camera sensor. This paper proposes a new position-patch based face SR method, namely RLENR, which effectively enhances the low-resolution (LR) facial images impaired with heavy mixed (Gaussian and impulse) noise. Firstly, it suppresses the impulse noise (IN) from the LR face image by utilizing the PCA oriented mate face and the matrix having pixel-wise noise details. Subsequently, it minimizes the effect of Gaussian noise (GN) by incorporating the residual-learning to update the LR training set. Meanwhile, it hallucinates the respective LR face using the updated LR training set. Here, the residual means reconstruction error occurred by the GN. It employs the sparse set of similar patches from the training samples to generate the optimal weights for LR patch representation. Thus, the method simultaneously achieves both the sparsity and locality to recover vital information of the face. The results of extensive experiments on standard facial image dataset, real-world face image dataset, and surveillance face images confirm that the RLENR method quantitatively and visually outperforms state-of-the-art face SR methods.
A Dutch book coherence condition for conditional completely alternating Choquet expectations

Abstract Stemming from de Finetti’s coherence for finitely additive (conditional) probabilities, the paradigm of coherence has been extended to other uncertainty calculi. We study the notion of coherence for conditional completely alternating Choquet expectations, providing an avoiding Dutch book like condition.
Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of attack as <i>a priori</i> knowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantage of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this article proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on <inline-formula><tex-math notation="LaTeX">$MNIST$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$CIFAR10$</tex-math></inline-formula> datasets. The results showed that our defending method achieved up to 96.3 percent classification accuracy on black-box adversarial examples, and detected up to 98.7 percent of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by 2.1 percent for the <inline-formula><tex-math notation="LaTeX">$CIFAR10$</tex-math></inline-formula> dataset.
Competition-Driven Dandelion Algorithms With Historical Information Feedback

A Dandelion algorithm (DA) inspired by the seed dispersal process of dandelions has been proposed as a newly intelligent optimization algorithm. For improving its exploration ability as well as reducing the probability of its falling into a local optimum, this work proposes to add a novel competition mechanism with historical information feedback to current DA. Specifically, the fitness value of each dandelion in the next generation, which is calculated by linear prediction, is compared with the current best dandelion, and the loser is replaced by a new offspring. Current DA generates new offsprings without considering historical information. This work improves its offspring generation process by exploiting historical information with an estimation-of-distribution algorithm. Three historical information models are designed. They are best, worst, and hybrid historical information feedback models. The experimental results show that the proposed algorithms outperform DA and its variants, and the proposed algorithms are superior or competitive to nine participating algorithms benchmarked on 28 functions from CEC2013. Finally, the proposed algorithms demonstrate the effectiveness on four real-world problems, and the results indicate that the proposed algorithms have better performance than its peers.
New horizons in quantitative compositional mapping – Analytical conditions and data reduction using XMapTools

Abstract Quantitative compositional mapping is a key technique for imaging solids and, therefore, for trying to understand compositional zoning systematics in a broad range of geological materials. Compositional mapping requires an adapted analytical method for efficient data collection, a post-processing calibration stage to obtain elemental concentration values, and specific computer tools for exploring and visualising the data. In this contribution, we describe optimal analytical conditions, possible physical limitations and data reduction strategies for compositional maps of major, minor and trace elements obtained by electron probe microanalysis (EPMA) and laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS). Data reduction is performed using the computer software XMapTools, which incorporates a large variety of options and tools for data processing, data calibration and data visualisation based on single and multi-channel maps or via binary, ternary and spider diagrams.
Lightning prediction using an ensemble learning approach for northeast of Iran

This paper examines some data mining techniques for lightning prediction. If we indicate by one the lightning event occurrence and by zero the non-occurrence of the event, then we will have a binary classification problem. In some cases, the dataset of lightning event is class imbalance. Thus, in the current research, the method of undersampling will be employed to generate several balanced datasets. Two binary classification algorithms, including neural networks and decision tree, were examined for lightning prediction. Furthermore, their performance was evaluated and compared. The proposed method was applied for some selected regions in Iran. Based on the evaluation results, decision tree outperforms feed-forward neural networks with one hidden layer for all datasets.
Motion Guided LiDAR-Camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles

This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling. The calibration parameters are estimated by optimizing an objective function that penalizes distances between 2D and re-projected 3D motion vectors obtained from time-synchronized image and point cloud sequences. For upsampling, a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving the LiDAR measurements is proposed. Validation is performed on recorded real data from urban environments and demonstrations that our two methods are effective and suitable to mobile robotics and autonomous vehicle applications imposing real-time requirements is shown.
Multi-Turn Video Question Generation via Reinforced Multi-Choice Attention Network

Video question generation is a challenging task in visual information retrieval, which generates questions given a sequence of video frames. The existing methods mainly tackle the problem of single-turn video question generation, but single-turn conversation usually can't meet the needs of video information acquisition. In this paper, we propose a new framework for single-turn VQG, which introduces attention mechanism to process inference of dialog history. And we introduce selection mechanism to choose from the candidate questions generated by each round of dialog history. In the framework, we leverage a recent video question answering model to predict the answer to the generated question and adopt the answer quality as rewards to fine-tune our model based on a reinforced learning mechanism. We also introduce a new task of multi-turn video question generation (M-VQG), which is generating multiple questions based on dialog history and video information to build conversation step by step. Our method achieves the state-of-the-art performance of the single-turn VQG task on two large-scale datasets, YouTube-Clips and TACoS-MultiLevel, and provides a baseline approach for M-VQG task.
Porter Stemmer and Cosine Similarity for Automated Essay Assessment

Essay assignments are one of the effective ways to find out the understanding of students about the material that has been studied in both high school and university. Of course, the essay is different from multiple choice questions which are easily automated to assess, while the essay has a text narrative that needs to be known and maintained in its meaning. This article aims to make an automatic assessment of essay assignments by using text mining techniques to maintain the meaning in the text. One process in text mining is a stemming process that returns words to their basic word form. The stemming process is important because stemming words are verbs that store the meaning of the text. This article discusses the Porter and Cosine Similarity Algorithms in the stemming process. Based on the experimental results of 35 essay assignments, the automatic assessment of the essay has an accuracy rate of around 97% compared to the results of the expert assessment, with the difference in value between automatic assessment and expert judgment 2.62 points.
AFNet: Temporal Locality-Aware Network With Dual Structure for Accurate and Fast Action Detection

Inspired by Faster R-CNN, current state-of-the-art region-based action detection approaches like R-C3D and TAL-Net creatively proposed Temporal Region Proposal Network (TRPN) to generate proposals, which greatly improved action detection accuracy. However, since smooth L1 loss adopted in TRPN focuses on relative offset to pre-set anchor segments and is not sensitive enough to action boundaries and temporal regions, there is still room for improvement in temporal proposal generation. In this work, we elaborately design a Temporal Locality-Aware Network (TLAN) to learn a binary classifier using frame-level annotations. This allows our framework to effectively distinguish action instance (positive temporal regions) from background (negative temporal regions) by jointly optimizing temporal regions classification and temporal reference boxes regression, thus enabling precise localization. We further introduce a novel pooling method named Contextual Structured Spatial Temporal Pooling (CSSTP) to better exploit context and spatial-temporal information in an end-to-end fashion. Finally, TLAN and CSSTP are incorporated into a unified framework named AFNet. Extensive experiments have been conducted to evaluate the performance of our method. We achieve state-of-the-art performance on THUMOS’14 (20.6% higher than R-C3D, 6.7% higher than TAL-Net mAP @0.5) and competitive performance on Charades and ActivityNet. Besides, our inference speed reaches 1024 FPS, which is 250× faster than TAL-Net (3.5 FPS) and comparable to R-C3D (1030 FPS).
Image Cryptosystem for Visually Meaningful Encryption Based on Fractal Graph Generating

Image information security is an important research direction in the field of information security. In existed image encryption schemes, original image is transformed to meaningless random noise signal which would draw attention and thus attract attacks. In this work, aiming to encrypt image to visually meaningful encrypted image, a novel image cryptosystem based on fractal graph generating is proposed which is called Visually Meaningful Image Encryption (VMIE) scheme. The VMIE scheme is to encrypt plain images into fractal scenery or fractal plant images by using fractal graph generation parameters as keys. According to these keys, receiver can get the plain image from fractal image by a reverse process. Based on the pixel value distribution characteristics of different plain images, different fractal transformations can be selected to produce different fractal images, which can better camouflage the plain images and make the encrypted plain images safer. In this paper, a new image encryption method is proposed, which is different from VMIE based on information hiding with limited hiding capacity, and from VMIE based on image sharing, which takes up a large amount of storage space and image size. Simulation experiments show that the proposed new scheme has low computation complexity and high security and the encrypted plain images have well understandability, strong resistance to compression and small data increase.
R-OO-KASE: Revocable Online/Offline Key Aggregate Searchable Encryption

Abstract The existing Key Aggregate Searchable Encryption (KASE) schemes allow searches on the encrypted dataset using a single query trapdoor, with a feature to delegate the search rights of multiple files using a constant size key. However, the operations required to generate the ciphertext and decrypt it in these schemes incur higher computational costs, due to the computationally expensive pairing operations in encryption/decryption. This makes the use of such schemes in resource-constrained devices, such as Radio Frequency Identification Devices, Wireless Sensor Network nodes, Internet of Things nodes, infeasible. Motivated with the goal to reduce the computational cost, in this paper, we propose a Revocable Online/Offline KASE (R-OO-KASE) scheme, based on the idea of splitting the encryption/decryption operations into two distinct phases: online and offline. The offline phase computes the majority of costly operations when the device is on an electrical power source. The online phase generates final output with the minimal computational cost when the message (or ciphertext) and keywords become known. In addition, the proposed scheme R-OO-KASE also offers multi-keyword search capability and allows the data owners to revoke the delegated rights at any point in time, the two features are not supported in the existing schemes. The security analysis and empirical evaluations show that the proposed scheme is efficient to use in resource-constrained devices and provably secure as compared to the existing KASE schemes.
Early Disruptors: Examining the Determinants and Consequences of Blockchain Early Adoption

ABSTRACT This study examines factors that influence a public firm's decision to early-adopt blockchain technology. Blockchain technology has the potential to disrupt how firms collect, process, and maintain information about a wide range of firm activities including transactions and supply chain interactions. We examine several determinants of early blockchain adoption including patented technology, agency costs, complexity, and external monitoring. Our results suggest that blockchain early adoption involves opportunistic managerial behavior. Further, firms with greater technology innovations, proxied by number of patents, are more likely to disclose early adoption, possibly to overcome productivity concerns or attract inter-firm opportunities. We also examine the consequences of early adoption using a market-based approach. Our results suggest that blockchain adoption could be a lengthy and costly process. Our study provides evidence on why firms adopt this disruptive technology and informs regulators and policy makers on how managers can influence the blockchain early adoption decision.
WarpClothingOut: A Stepwise Framework for Clothes Translation From the Human Body to Tiled Images

With the increasing popularity of online shopping, searching for products with images for item retrieval has gradually become an effective approach. This trend is especially evident in the fashion industry. In common media, clothing items are usually worn on the human body. They can be straightforwardly segmented from the source media by utilizing detection or parsing algorithms. However, this may be deleterious to retrieval performance due to distortion, occlusion, and different backgrounds. In this article, a stepwise translation framework using generative adversarial network and thin plate spline is developed to transfer human body images to tiled clothing images, which can be directly used for clothing retrieval. Experimental results demonstrate the effectiveness of the resultant tiled images produced from our framework in comparison to other extant methods.
Cloud spot instance price prediction using kNN regression

Abstract Cloud computing can provide users with basic hardware resources, and there are three instance types: reserved instances, on-demand instances and spot instances. The price of spot instance is lower than others on average, but it fluctuates according to market demand and supply. When a user requests a spot instance, he/she needs to give a bid. Only if the bid is not lower than the spot price, user can obtain the right to use this instance. Thus, it is very important and challenging to predict the price of spot instance. To this end, we take the most popular and representative Amazon EC2 as a testbed, and use the price history of its spot instance to predict future price by building a k -Nearest Neighbors ( k NN) regression model, which is based on our mathematical description of spot instance price prediction problem. We compare our model with Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), Multi-layer Perception Regression (MLPR), gcForest, and the experiments show that our model outperforms the others.