Reviewerly/exHarmony huggingface dataset:
Constructing a Virtual Space for Enhancing the Classification Performance of Fuzzy Clustering

Clustering offers a general methodology and comes with a remarkably rich conceptual and algorithmic framework for data analysis and data interpretation. As one of the most representative algorithms of fuzzy clustering, fuzzy C-means (FCM) is a widely used objective function-based clustering method exploited in various applications. In this study, a virtual-based fuzzy clustering algorithm is proposed to improve the classification performance coming as a result of using fuzzy clustering. This improvement is achieved by forming a virtual space based on the original data space. First, we construct a piecewise linear transformation function to modify the similarity matrix of the original data and build the so-called virtual similarity matrix (VSM). Considering the VSM, the effect of closeness becomes amplified; in other words, high similarity values (say, larger than α which is a cutoff value of the large and small similarity in this paper) present in the original similarity matrix are made higher, whereas lower similarity levels (say, smaller than α) are further reduced. In addition, data with high similarity (say, larger than a certain threshold value) observed in the original space will overlap (the attributes of the samples are exactly the same) significantly in the virtual space; the overlapping samples can be treated as one sample. This modification makes possible easier to identify clusters. Second, we build a relationship matrix between the original dataset and the determined similarity values and present two closed-form solutions to the problem of building the relationship matrix. Subsequently, a virtual space of the original data space is derived through the modified similarity matrix and the introduced relationship matrix. We offer a thorough analysis behind the developed clustering algorithm. The experimental results are in agreement with the underlying conceptual basis. Furthermore, the resulting classification performance is significantly improved compared with the results produced by the FCM and the kernel-based fuzzy C-means.
Unsupervised Video Action Clustering via Motion-Scene Interaction Constraint

In the past few years, scene contextual information has been increasingly used for action understanding with promising results. However, unsupervised video action clustering using context has been less explored, and existing clustering methods cannot achieve satisfactory performances. In this paper, we propose a novel unsupervised video action clustering method by using the motion-scene interaction constraint (MSIC). The proposed method takes the unique static scene and dynamic motion characteristics of video action into account, and develops a contextual interaction constraint model under a self-representation subspace clustering framework. First, the complementarity of multi-view subspace representation in each context is explored by single-view and multi-view constraints. Afterward, the context-constrained affinity matrix is calculated and the MSIC is introduced to mutually regularize the disagreement of subspace representation in scene and motion. Finally, by jointly constraining the complementarity of multi-views and the consistency of multi-contexts, an overall objective function is constructed to guarantee the video action clustering result. The experiments on four video benchmark datasets (Weizmann, KTH, UCFsports, and Olympic) demonstrate that the proposed method outperforms the state-of-the-art methods.
Equilibrium analysis of bitcoin block withholding attack: A generalized model

Block withholding attack is an action where a miner who has found a legal block chooses not to submit it but rather directly abandons it. This attack makes the mining pool lose all bitcoin rewards contained within the block. In this paper, we construct a generalized model where two participants can choose to either cooperate with each other or employ a block withholding attack in the mining pool. To make the model more realistic, we consider both the cost of partial proof of work and the cost of cooperation. We also calculate the reward-per-time instead of profit-per-time to better measure the payoffs of each party. Further, we discuss the case where the payoff function and cost function are directly related to the computational power. The pure strategy and mixed strategy are analyzed respectively and the segmentations of the equilibrium are shown. We demonstrate that increasing the information asymmetry by utilizing information conceal mechanisms could lower the occurrence of the BWH attack.
Multipath-DenseNet: A Supervised ensemble architecture of densely connected convolutional networks

Deep networks with skip-connections such as ResNets have achieved great results in recent years. DenseNet exploits the ResNet skip-connections by connecting each layer in convolution neural network to all preceding layers and achieves state-of-the-art accuracy. It is well-known that deeper networks are more efficient and easier to train than shallow or wider networks. Despite the high performance of very deep networks, they are limited in terms of vanishing gradient, diminishing forward flow, and slower training time. In this paper, we propose to combine the benefits of the depth and width of networks. We train supervised independent shallow networks on the same input in a block fashion. We use a state-of-the-art DenseNet block to increase the number of paths for gradient flow. Our proposed architecture has several advantages over other deeper networks including DenseNet; our architecture which we call Multipath-DenseNet is deeper as well as wider, reduces training time, and uses a smaller number of parameters. We evaluate our proposed architecture on the following four object recognition datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The evaluation results show that Multipath-DenseNet achieves significant improvement in performance over DenseNet on the benchmark datasets.
Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items

Recommendation in the modern world is not only about capturing the interaction between users and items, but also about understanding the relationship between items. Besides improving the quality of recommendation, it enables the generation of candidate items that can serve as substitutes and supplements of another item. For example, when recommending Xbox, PS4 could be a logical substitute and the supplements could be items such as game controllers, surround system, and travel case. Therefore, given a network of items, our objective is to learn their content features such that they explain the relationship between items in terms of substitutes and supplements. To achieve this, we propose a generative deep learning model that links two variational autoencoders using a connector neural network to create Linked Variational Autoencoder (LVA). LVA learns the latent features of items by conditioning on the observed relationship between items. Using a rigorous series of experiments, we show that LVA significantly outperforms other representative and state-of-the-art baseline methods in terms of prediction accuracy. We then extend LVA by incorporating collaborative filtering (CF) to create CLVA that captures the implicit relationship between users and items. By comparing CLVA with LVA we show that inducing CF-based features greatly improve the recommendation quality of substitutable and supplementary items on a user level.
The 9 Lives of Bleichenbacher's CAT: New Cache ATtacks on TLS Implementations

At CRYPTO'98, Bleichenbacher published his seminal paper which described a padding oracle attack against RSA implementations that follow the PKCS #1 v1.5 standard.Over the last twenty years researchers and implementors had spent a huge amount of effort in developing and deploying numerous mitigation techniques which were supposed to plug all the possible sources of Bleichenbacher-like leakages.However, as we show in this paper, most implementations are still vulnerable to several novel types of attack based on leakage from various microarchitectural side channels: Out of nine popular implementations of TLS that we tested, we were able to break the security of seven implementations with practical proof-of-concept attacks.We demonstrate the feasibility of using those Cache-like ATacks (CATs) to perform a downgrade attack against any TLS connection to a vulnerable server, using a BEAST-like Man in the Browser attack.The main difficulty we face is how to perform the thousands of oracle queries required before the browser's imposed timeout (which is 30 seconds for almost all browsers, with the exception of Firefox which can be tricked into extending this period).Due to its use of adaptive chosen ciphertext queries, the attack seems to be inherently sequential, but we describe a new way to parallelize Bleichenbacher-like padding attacks by exploiting any available number of TLS servers that share the same public key certificate.With this improvement, we can demonstrate the feasibility of a downgrade attack which could recover all the 2048 bits of the RSA plaintext (including the premaster secret value, which suffices to establish a secure connection) from five available TLS servers in under 30 seconds.This sequential-to-parallel transformation of such attacks can be of independent interest, speeding up and facilitating other side channel attacks on RSA implementations.
Hierarchical Facial Age Estimation Using Gaussian Process Regression

Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications.However, due to uncontrollable environments, insufficient and incomplete training data, strong person-specific and large within-age span variations, age estimation has become a challenging problem.Among published age estimation, hierarchical age estimation methods have achieved comparable performance improvement than single level approaches.Most of the published hierarchical approaches have mainly used support vector machines to classify age groups followed by support vector regression for withina-age group age estimation.In this paper, we present a novel hierarchical Gaussian process framework for automatic age estimation.It consists of multi-class Gaussian process classifier to classify the input images into different age groups followed by a warped Gaussian process regression to model group specific aging patterns.In this paper, we separately tune the hyper-parameters for each age group at the regression stage.Compared with existing single level Gaussian process approaches for age estimation, our approach is computationally efficient at both the levels of hierarchy.Partitioning data into different age groups and learning group-wise hyper-parameters is computationally more efficient than learning complete training data.Misclassifications at the group boundaries are compensated at the regression stage by overlapping the neighboring age ranges.Finally, through extensive experiments on two popular aging datasets, the FG-NET and the Morph-II, we demonstrate the effectiveness of our algorithm in improving age estimation performance.
SLA-Based Profit Optimization Resource Scheduling for Big Data Analytics-as-a-Service Platforms in Cloud Computing Environments

The value that can be extracted from big data greatly motivates users to explore data analytics technologies for better decision making and problem solving in various application domains. Analytical solutions can be expensive due to the demand for large-scale and high-performance computing resources. To provision online big data Analytics-as-a-Service (AaaS) to users in various domains, a general purpose AaaS platform is required to deliver on-demand services at low cost and in an easy to use manner. Our research focuses on proposing efficient and automatic admission control and resource scheduling algorithms for AaaS platforms in cloud environments. In this paper, we propose scalable and automatic admission control and profit optimization resource scheduling algorithms, which effectively admit data analytics requests, dynamically provision resources, and maximize profit for AaaS providers, while satisfying QoS requirements of queries with Service Level Agreement (SLA) guarantees. Moreover, the proposed algorithms enable users to trade-off accuracy for faster response times and less resource costs for query processing on large datasets. We evaluate the algorithm performance by adopting a data splitting method to process smaller data samples as representatives of the original big datasets. We conduct extensive experiments to evaluate the proposed admission control and profit optimization scheduling algorithms. Experimental evaluation shows the algorithms perform significantly better compared to the state-of-the-art algorithms in enhancing profits, reducing resource costs, increasing query admission rates, and decreasing query response times.
PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices

Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients' symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients' data to different network attacks. A homomorphic encryption cipher is used to protect users' data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties' data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Naïve Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals' datasets, frequency of attribute values, and diagnosed symptoms.
Muti-stage learning for gender and age prediction

Automatic gender and age prediction has become relevant to an increasing amount of applications, particularly under the rise of social platforms and social media. However, the performances of existing methods on real-world images are still not satisfactory as we expected, especially when compared to that of face recognition. The reason is that, facial images for gender and age prediction have inherent small inter-class and big intra-class differences, i.e., two images with different skin colors and same age category label have big intra-class difference. However, most existing methods have not constructed discriminative representations for digging out these inherent characteristics very well. In this paper, a method based on muti-stage learning is proposed: The first stage is marking the object regions with an encoder-decoder based segmentation network. Specifically, the segmentation network can classify each pixel into two classes, “people” and others, and only the “people” regions are used for the subsequent processing. The second stage is precisely predicting the gender and age information with the proposed prediction network, which encodes global information, local region information and the interactions among different local regions into the final representation, and then finalizes the prediction. Additionally, we evaluate our method on three public and challenging datasets, and the experimental results verify the effectiveness of our proposed method.
Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine

Coal classification is an indispensable task in coal mining and production. The traditional method of coal classification has the disadvantages of high cost, low speed and low accuracy. Therefore, a rapid coal classification method based on visible-infrared spectroscopy is proposed in this research. First, we collected samples of different coal types and used spectrometers to measure the spectral data of these samples. Then, we proposed an improved multilayer extreme learning machine algorithm using this algorithm to build a coal classification model. The simulation results showed that the model has good classification results. Compared with the traditional coal classification methods, this method has an unparalleled advantage in economy, speed and accuracy.
ELSA

Sentence-based summarization aims at extracting concise summaries of collections of textual documents. Summaries consist of a worthwhile subset of document sentences. The most effective multilingual strategies rely on Latent Semantic Analysis (LSA) and on frequent itemset mining, respectively. LSA-based summarizers pick the document sentences that cover the most important concepts. Concepts are modeled as combinations of single-document terms and are derived from a term-by-sentence matrix by exploiting Singular Value Decomposition (SVD). Itemset-based summarizers pick the sentences that contain the largest number of frequent itemsets, which represent combinations of frequently co-occurring terms. The main drawbacks of existing approaches are (i) the inability of LSA to consider the correlation between combinations of multiple-document terms and the underlying concepts, (ii) the inherent redundancy of frequent itemsets because similar itemsets may be related to the same concept, and (iii) the inability of itemset-based summarizers to correlate itemsets with the underlying document concepts. To overcome the issues of both of the abovementioned algorithms, we propose a new summarization approach that exploits frequent itemsets to describe all of the latent concepts covered by the documents under analysis and LSA to reduce the potentially redundant set of itemsets to a compact set of uncorrelated concepts. The summarizer selects the sentences that cover the latent concepts with minimal redundancy. We tested the summarization algorithm on both multilingual and English-language benchmark document collections. The proposed approach performed significantly better than both itemset- and LSA-based summarizers, and better than most of the other state-of-the-art approaches.
Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach

Fraud transactions are one of the major threats faced by online e-commerce platforms. Recently, deep learning based classifiers have been deployed to detect fraud transactions. Inspired by findings on adversarial examples, this paper is the first to analyze the vulnerability of deep fraud detector to slight perturbations on input transactions, which is very challenging since the sparsity and discretization of transaction data result in a non-convex discrete optimization. Inspired by the iterative Fast Gradient Sign Method (FGSM) for the L8 attack, we first propose the Iterative Fast Coordinate Method (IFCM) for discrete L1 and L2 attacks which is efficient to generate large amounts of instances with satisfactory effectiveness. We then provide two novel attack algorithms to solve the discrete optimization. The first one is the Augmented Iterative Search (AIS) algorithm, which repeatedly searches for effective “simple” perturbation. The second one is called the Rounded Relaxation with Reparameterization (R3), which rounds the solution obtained by solving a relaxed and unconstrained optimization problem with reparameterization tricks. Finally, we conduct extensive experimental evaluation on the deployed fraud detector in TaoBao, one of the largest e-commerce platforms in the world, with millions of real-world transactions. Results show that (i) The deployed detector is highly vulnerable to attacks as the average precision is decreased from nearly 90% to as low as 20% with little perturbations; (ii) Our proposed attacks significantly outperform the adaptions of the state-of-the-art attacks. (iii) The model trained with an adversarial training process is significantly robust against attacks and performs well on the unperturbed data.
Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

This letter develops the Kalman and unbiased finite impulse response filtering algorithms for linear discrete-time state-space models with Gauss-Markov colored process noise (CPN) employing state differencing. The approach avoids problems caused by matrix augmentation, but requires solving a nonsymmetric algebraic Riccati equation to specify the system matrix modified for CPN. Higher accuracy of the algorithms proposed is demonstrated by simulation. A comparative analysis of filtering estimates is provided based on navigation data of walking humans.
Bidirectional image-sentence retrieval by local and global deep matching

In this paper, we propose a novel local and global deep matching model to tackle bidirectional image-sentence retrieval. Our proposed matching model can simultaneously exploit the image representation, sentence representation, as well as their complicated matching relationships from both local and global perspectives. For images, two different convolutional neural networks (CNNs) are leveraged to encode the local and global contents, with selective attentions to the image sub-regions and the whole image. For sentences, a CNN based sentence model and Fisher vector are employed to capture the global and local semantic meanings, respectively. Relying on the local and global representations of the image and sentence, the proposed deep matching model learns the complicated image-sentence matching relationships from local and global perspectives by integrating cross-modality correlations with intra-modality similarities. Extensive experimental results demonstrate that the proposed local and global matching model outperforms the state-of-the-art bidirectional retrieval approaches on the Flickr8K, Flickr30K, and MSCOCO datasets. Moreover, the image and sentence representations exploited in local and global levels are demonstrated to play synergic and complementary roles for bidirectional image-sentence retrieval.
Using smartphones to extend interaction beyond the EFL classroom

Successful second language learning can be seen as occurring in two arenas; the exposure and learning which occurs, under the guidance of a formal education setting, under the guidance of an instructor and that which occurs informally, under the guidance of the learner or incidentally as the learner pursues other activities. As smartphones have become more available and affordable, educators and learners have looked at the potential these devices offer to extend language learning opportunities beyond the classroom. This study looked at how university English language learners engaged in student-led communicative tasks on an Multimedia Messaging Systems platform accessed through their smartphones. In designing the study, I took an interactionist perspective and used sociocultural theory as an analytical tool in my analysis. The results showed that learners successfully engaged in these tasks and that they found them useful as language learning activities. Although there was a low level of negotiation of meaning between peers, the participants were heavily involved in using affordances on their smartphones to help them engage in the tasks.