Reviewerly/exHarmony huggingface dataset:
Non-isotropic angular Stockwell transform and the associated uncertainty principles

For an efficient and directional representation of signals in higher dimensions, we propose the non-isotropic angular Stockwell transform in the context of time-frequency analysis. The proposed transform is aimed at rectifying the conventional Stockwell transform by employing an angular and scalable localized window which offers directional flexibility and thereby results in the multi-scale and directional analysis of signals in higher dimensions. The basic properties of the proposed transform such as orthogonality relation, reconstruction formula, derivation of the admissibility condition and characterization of the range are discussed using the machinery of operator theory and Fourier transforms. In addition, we introduce the discrete version of the non-isotropic angular Stockwell transform and establish a sufficient condition for the corresponding discrete family to be a frame in L2(R2). Furthermore, some generalizations of the well-known Heisenberg-type inequalities are derived for the non-isotropic angular Stockwell transform in the Fourier domain.
Full Consideration of Big Data Characteristics in Sentiment Analysis Context

We live in a digital world where every interaction with Internet generates digital traces that reveal a lot of details about our thinking. The volume of these daily-generated traces increases exponentially creating massive loads of information, which represents a big part of a new world called Big Data. This big data is used by different type of projects to extract valuable information either to take marketing decisions, track specific behaviors or detect threat attacks. Sentiment analysis (SA) is one of the most active research areas relying on big data, even if their involvement would be of great added value. However, most of its applications, like other big data projects, consider only the volume, sometimes also the veracity, and completely ignore the rest of big data characteristics (Variety, Velocity, Value, Variability, and Visualization). In this paper, we focus on projecting big data characteristics in a sentiment context to get out the most of big data. Our main contribution consists of presenting and analyzing the most known sentiment analysis approaches and contributions that rely on big data by showing their main characteristics. Furthermore, we explain how SA applications should consider big data characteristics to be fully aligned with big data contexts.
Learning-Based Resource Allocation in Cloud Data Center using Advantage Actor-Critic

Due to the ever-changing system states and various user demands, resource allocation in cloud data center is faced with great challenges in dynamics and complexity. Although there are solutions that focus on addressing this problem, they cannot effectively respond to the dynamic changes of system states and user demands since they depend on the prior knowledge of the system. Therefore, it is still an open challenge to realize automatic and adaptive resource allocation in order to satisfy diverse system requirements in cloud data center. To cope with this challenge, we propose an advantage actor-critic based reinforcement learning (RL) framework for resource allocation in cloud data center. First, the actor parameterizes the policy (allocating resources) and chooses continuous actions (scheduling jobs) based on the scores (evaluating actions) from the critic. Next, the policy is updated by gradient ascent and the variance of policy gradient can be significantly reduced with the advantage function. Simulations using Google cluster-usage traces show the effectiveness of the proposed method in cloud resource allocation. Moreover, the proposed method outperforms classic resource allocation algorithms in terms of job latency and achieves faster convergence speed than the traditional policy gradient method.
Efficiently Answering Regular Simple Path Queries on Large Labeled Networks

A fundamental query in labeled graphs is to determine if there exists a path between a given source and target vertices, such that the path satisfies a given label constraint. One of the powerful forms of specifying label constraints is through regular expressions, and the resulting problem of reachability queries under regular simple paths (RSP) form the core of many practical graph query languages such as SPARQL from W3C, Cypher of Neo4J, Oracle's PGQL and LDBC's G-CORE. Despite its importance, since it is known that answering RSP queries is NP-Hard, there are no scalable and practical solutions for answering reachability with full-range of regular expressions as constraints. In this paper, we circumvent this computational bottleneck by designing a random-walk based sampling algorithm called ARRIVAL, which is backed by theoretical guarantees on its expected quality. Extensive experiments on billion-sized real graph datasets with thousands of labels show that ARRIVAL to be 100 times faster than baseline strategies with an average accuracy of 95%.
Is the Bitcoin price dynamics economically reasonable? Evidence from fundamental laws

Dynamics of the Bitcoin price as the most prominent cryptocurrency is studied in the light of two fundamental economic laws — the Law of One Price and the Equation of Exchange. Utilizing the unprecedented data availability of Bitcoin statistics, we are able to construct a theoretical exchange rate base of the cryptocurrency with respect to the US dollar. Through the concept of statistical equilibrium verified by the cointegration relationship, we show several interesting points. First, the Bitcoin price dynamics is very well captured by the underlying transaction data which leads to a strong fundamental basis of the cryptocurrency. Second, several historical bubbles hit the confidence intervals around the fundamental price but then start their correction back to the equilibrium. Third, the fundamental price implied by the transaction data is not ever-increasing but reflects the ups and downs of Bitcoin utility in transactions. And fourth, our theory-implied fundamental price suggests that the current (December 2018) Bitcoin price is very close to its fundamental price estimated at around $3,500.
K-Nearest Neighbors Hashing

Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign(·) function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space. In this work, we revisit the sign(·) function from the perspective of space partitioning. In specific, we bridge the gap between k-nearest neighbors and binary hashing codes with Shannon entropy. We further propose a novel K-Nearest Neighbors Hashing (KNNH) method to learn binary representations from KNN within the subspaces generated by sign(·). Theoretical and experimental results show that the KNN relation is of central importance to neighbor preserving embeddings, and the proposed method outperforms the state-of-the-arts on benchmark datasets.
Enabling Ternary Hash Tree Based Integrity Verification for Secure Cloud Data Storage

Cloud Computing enables the remote users to access data, services, and applications in on-demand from the shared pool of configurable computing resources, without the consideration of storage, hardware, and software management. On the other hand, it is not easy for cloud users to identify whether Cloud Service Provider's (CSP) tag along with the data security legal expectations. So, cloud users could not rely on CSP's in terms of trust. So, it is significant to build a secure and efficient data auditing framework for increasing and maintaining cloud users trust with CSP. Researchers suggested introducing Third Party Auditor (TPA) on behalf of cloud user for verifying the outsourced data integrity, which may reduce the computation overhead of cloud users. In this work, we proposed a novel integrity verification framework for securing cloud storage based on Ternary Hash Tree (THT) and Replica based Ternary Hash Tree (R-THT), which will be used by TPA to perform data auditing. Differing from existing work, the proposed framework performs Block-level, File-level and Replica-level auditing with tree block ordering, storage block ordering for verifying the data integrity and ensuring data availability in the cloud. We further extend our framework to support error localization with data correctness, dynamic updates with block update, insert, and delete operations in the cloud. The structure of THT and R-THT will reduce the computation cost and provide efficiency in data updates compared to the existing schemes. The security analysis of the proposed public auditing framework indicates the achievement of desired properties and performance has been evaluated with the detailed experiment set. The results show that the proposed secure cloud auditing framework is highly secure and efficient in storage, communication, and computation costs.
Zig-Zag Network for Semantic Segmentation of RGB-D Images

Semantic segmentation of images requires an understanding of appearances of objects and their spatial relationships in scenes. The fully convolutional network (FCN) has been successfully applied to recognize objects' appearances, which are represented with RGB channels. Images augmented with depth channels provide more understanding of the geometric information of the scene in an image. In this paper, we present a multiple-branch neural network to utilize depth information to assist in the semantic segmentation of images. Our approach splits the image into layers according to the “scene-scale”. We introduce the context-aware receptive field (CARF), which provides better control of the relevant context information of learned features. Each branch of the network is equipped with CARF to adaptively aggregate the context information of image regions, leading to a more focused domain that is easier to learn. Furthermore, we propose a new zig-zag architecture to exchange information between the feature maps at different levels, augmented by the CARFs of the backbone network and decoder network. With the flexible information propagation allowed by our zig-zag network, we enrich the context information of feature maps for the segmentation. We show that the zig-zag network achieves state-of-the-art performances on several public datasets.
Mitigating Trust Issues in Electric Vehicle Charging using a Blockchain

Energy systems are rapidly becoming decentralized due to advances in distributed renewable generation, storage technologies, and electric vehicles (EVs). One consequence of decentralization is the loss of a central trusted party. There is, therefore, a need for a solution that enables interactions between mutually untrusting agents. In this paper, we present a general methodology for blockchain-oriented system design and demonstrate its use to design a system for EV charging in a decentralized network of charging stations. We also show how a blockchain can be integrated with minimal changes to a legacy back end. Our solution is scheduled for roll-out by the EV charging service provider SWTCH in the near future.
SPuManTE

We present SPuManTE, an efficient algorithm for mining significant patterns from a transactional dataset. SPuManTE controls the Family-wise Error Rate: it ensures that the probability of reporting one or more false discoveries is less than an user-specified threshold. A key ingredient of SPuManTE is UT, our novel unconditional statistical test for evaluating the significance of a pattern, that requires fewer assumptions on the data generation process and is more appropriate for a knowledge discovery setting than classical conditional tests, such as the widely used Fisher's exact test. Computational requirements have limited the use of unconditional tests in significant pattern discovery, but UT overcomes this issue by obtaining the required probabilities in a novel efficient way. SPuManTE combines UT with recent results on the supremum of the deviations of pattern frequencies from their expectations, founded in statistical learning theory. This combination allows SPuManTE to be very efficient, while also enjoying high statistical power. The results of our experimental evaluation show that SPuManTE allows the discovery of statistically significant patterns while properly accounting for uncertainties in patterns' frequencies due to the data generation process.
Optimizing MEC Networks for Healthcare Applications in 5G Communications With the Authenticity of Users’ Priorities

Mobile edge computing (MEC) is pervasive to support high-speed end-to-end data transmission and computation for 5th generation (5G) mobile communications by offloading computation tasks to the cloud at the edge of a cellular base station.In a cellular network for healthcare applications, we must consider the different priorities of data transmission and computation by referring to the emergency levels of patients.A critical issue of determining the allocation of wireless resources among users is the authenticity of their priorities (i.e., emergency levels).Without authenticating the users' priorities, a user who advocates a higher emergency level can allocate more wireless resources, leading to the insufficient wireless resources to the users who are actually in high priorities.In this paper, we investigate the optimization of end-to-end delay of wireless users in a cellular network with the authenticity of their priorities by blockchain consensus and propose an algorithm of allocating communication and computation resources to minimize the delay of data transmission and computation.The numerical results illustrate that our proposed algorithm is capable of dramatically reducing the end-to-end delay under the various scenarios of healthcare applications.
Efficient deep learning of image denoising using patch complexity local divide and deep conquer

Image denoising is a fundamental task in computer vision and image processing domain. In recent years, the task has been tackled with deep neural networks by learning the patterns of noises and image patches. However, because of the high diversity of natural image patches and noise distributions, a huge network with a large amount of training data is necessary to obtain a state-of-the-art performance. In this paper, we propose a novel ensemble strategy of exploiting multiple deep neural networks for efficient deep learning of image denoising. We divide the task of image denoising into several local subtasks according to the complexity of clean image patches and conquer each subtask using a network trained on its local space. Then, we combine the local subtasks at test time by applying the set of networks to each noisy patch as a weighted mixture, where the mixture weights are determined by the likelihood of each network for each noisy patch. Our methodology of using locally-learned networks based on patch complexity effectively decreases the diversity of image patches at each single network, and their adaptively-weighted mixture to the input combines the local subtasks efficiently. Extensive experimental results on Berkeley segmentation dataset and standard test images demonstrate that our strategy significantly boosts denoising performance in comparison to using a single network of the same total capacity. Furthermore, our method outperforms previous methods with much smaller training samples and trainable parameters, and so with much reduced time complexity both in training and running.
Privacy Preserving Big Data Publication On Cloud Using Mondrian Anonymization Techniques and Deep Neural Networks

In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.
ANFIS and Deep Learning based missing sensor data prediction in IoT

Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.
Highly Efficient Pattern Mining Based on Transaction Decomposition

This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction databases using clustering techniques. The set of transactions are first clus-tered using the k-means algorithm, where highly correlated transactions are grouped together. Next, the relevant patterns are derived by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one approximate and one exact. We demonstrate the efficiency and effectiveness of CBPM through a thorough experimental evaluation.
Academic Accomplices

This workshop brings together folks currently or interested in becoming academic accomplices, or scholars committed to leveraging resources and power to support the justice work of their community collaborators. Academic accomplices are necessary for research justice-research that materially challenges inequity-and owe it to community partners to challenge underlying oppressive structure and practices as perpetuated through academic research. The goal of this workshop is to discuss concrete strategies for challenging oppression through research methodologies, physical or institutional resources, and/or pedagogy. This workshop will generate practical strategies for research justice for DIS and HCI scholars.
Average versus maximal coherence

Coherence is a fundamental feature of quantum mechanics and plays a crucial role in the quantum realm. The issue of quantification of coherence has received considerable interests in recent years, and a variety of coherence quantifiers have been introduced. With these measures, one can investigate coherence in a more quantitative way. Based on the Wigner-Yanase skew information, we evaluate average coherence of a state with respect to arbitrary mutually unbiased bases as well as with respect to all orthonormal bases, and demonstrate their equivalence. We further evaluate the maximum coherence, and reveal a remarkable fact that in high dimensions, the coherence of a generic state is nearly maximal with respect to almost all reference bases. A similar concentration phenomenon also manifests itself when the coherence is quantified by the relative entropy of coherence. This observation concerning the almost equivalence between the average coherence and the maximal coherence for generic states may have theoretical implications for quantum information and quantum thermodynamics.
Active Deep Learning for Activity Recognition with Context Aware Annotator Selection

Machine learning models are bounded by the credibility of ground truth data used for both training and testing. Regardless of the problem domain, this ground truth annotation is objectively manual and tedious as it needs considerable amount of human intervention. With the advent of Active Learning with multiple annotators, the burden can be somewhat mitigated by actively acquiring labels of most informative data instances. However, multiple annotators with varying degrees of expertise poses new set of challenges in terms of quality of the label received and availability of the annotator. Due to limited amount of ground truth information addressing the variabilities of Activity of Daily Living (ADLs), activity recognition models using wearable and mobile devices are still not robust enough for real-world deployment. In this paper, we first propose an active learning combined deep model which updates its network parameters based on the optimization of a joint loss function. We then propose a novel annotator selection model by exploiting the relationships among the users while considering their heterogeneity with respect to their expertise, physical and spatial context. Our proposed model leverages model-free deep reinforcement learning in a partially observable environment setting to capture the action-reward interaction among multiple annotators. Our experiments in real-world settings exhibit that our active deep model converges to optimal accuracy with fewer labeled instances and achieves ~8% improvement in accuracy in fewer iterations.
Are 2D-LSTM really dead for offline text recognition?

There is a recent trend in handwritten text recognition with deep neural networks to replace 2D recurrent layers with 1D and in some cases even completely remove the recurrent layers, relying on simple feed-forward convolutional-only architectures. The most used type of recurrent layer is the long short-term memory (LSTM). The motivations to do so are many: there are few open-source implementations of 2D-LSTM, even fewer supporting GPU implementations (currently cuDNN only implements 1D-LSTM); 2D recurrences reduce the amount of computations that can be parallelized and thus possibly increase the training/inference time; recurrences create global dependencies with respect to the input, and sometimes this may not be desirable. Many recent competitions were won by systems that employed networks that use 2D-LSTM layers. Most previous works that compared 1D or pure feed-forward architectures to 2D recurrent models have done so on simple datasets or did not fully optimize the "baseline" 2D model compared to the challenger model, which was dully optimized. In this work, we aim at a fair comparison between 2D and competing models and also extensively evaluate them on more complex datasets that are more representative of challenging "real-world" data, compared to "academic" datasets that are more restricted in their complexity. We aim at determining when and why the 1D and 2D recurrent models have different results. We also compare the results with a language model to assess if linguistic constraints do level the performance of the different networks. Our results show that for challenging datasets, 2D-LSTM networks still seem to provide the highest performances and we propose a visualization strategy to explain it.