Reviewerly/exHarmony huggingface dataset:
Four Problems with Extracting Human Semantics from Large Text Corpora

We present four problems that will have to be overcome by text co-occurrence programs in order for them to be able to capture human-like semantics. These problems are: the intrinsic deformability of semantic space, the inability to detect co-occurrences of (esp. distal) abstract structures, their lack of essential world knowledge, which humans acquire through learning or direct experience with the world and their assumption of the atomic nature of words. By looking at a number of very simple questions, based in part on how humans do analogy-making, we show just how far one of the best of these programs is from being able to capture real semantics.
Sorting of Fully Homomorphic Encrypted Cloud Data: Can Partitioning be Effective?

The challenge of maintaining confidentiality of stored data in cloud is of utmost importance to realize the potential of cloud computing as an emerging storage solution service. Storing data in encrypted form may solve the problem, but exposes data to an adversary for each required computation. This repeated encryption decryption also diminishes the essence of cloud for storing encrypted database and huge computation power of cloud remains unused. Fully homomorphic encryption (FHE) is an effective scheme to support arbitrary operations directly on encrypted data, but has serious performance issues. In this paper, we have considered sorting on encrypted data, which is a frequently required database operation. We have investigated the feasibility of performing comparison as well as partition based sort on CPA resistant FHE data and highlight an important observation that time requirement of partition based sort on FHE data is no better than comparison based sort owing to the underlying security of the cryptosystem. We identify the recrypt operation, which is the denoising step of FHE as the main reason of costly timing requirement of such operations. We propose a FHE specific two stage sorting technique termed as Lazysort with reduced recrypt operation, which proves to be better in terms of performance on FHE data in comparison to partition as well as comparison sort. Finally, we provide some multi-core implementation results to show that with proper implementation tricks performance of FHE computations can be improved further.
UP-CNN: Un-pooling augmented convolutional neural network

Convolutional neural network (CNN) has shown remarkable performance in various visual recognition tasks. Most of existing CNN is a purely bottom-up and feed-forward architecture, we argue that it fails to consider the interaction between low-level fine details and high-level semantic information. In this paper, a novel “Un-Pooling augmented Convolutional Neural Network” (UP-CNN) is proposed to boost the discriminative capability of the CNN with the following three distinctive properties: (1) UP-CNN is a deeper network, which is comprised of a bottom-up, a top-down and then a bottom-up sub-networks, associating to different level information that jointly improves its discriminative capability. (2) With the mixture of pooling and un-pooling layers, UP-CNN easily allows the interaction across convolutional layers with the same size from different sub-networks. This architecture effectively depresses the attenuation of important information including both activations in the forward process and gradient information in the back-propagation process. (3) UP-CNN employs the ratio un-pooling operation to reconstruct activations of the original size in the top-down sub-network, where the spatial information that is lost during pooling can be preserved within a receptive field. The experiments on four benchmark datasets (including the CIFAR-10, CIFAR-100, MNIST and SVHN datasets) well demonstrate that the proposed UP-CNN architecture considerably outperforms other state-of-the-art methods.
Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms

We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing algorithms rely heavily on low-level image features that are based mainly on local gradient information, we consider that gradient quantity can determine the proper exposure level, allowing a camera to capture the important image features in a manner robust to illumination conditions. We then extend this concept to a multi-camera system and present a new control algorithm to achieve both brightness consistency between adjacent cameras and a proper exposure level for each camera. We implement our prototype system with off-the-shelf machine-vision cameras and demonstrate the effectiveness of the proposed algorithms on practical applications, including pedestrian detection, visual odometry, surround-view imaging, panoramic imaging and stereo matching.
Decentralized Server-aided Encryption for Secure Deduplication in Cloud Storage

Cloud storage provides scalable and low cost resources featuring economies of scale based on multi-tenant architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. However, deduplication leads to problems with data confidentiality, thereby necessitating secure deduplication solutions. Server-aided encryption schemes have been proposed to achieve the strongest confidentiality but with the cost of managing a key server (KS). Previous schemes, however, are based on a centralized KS that uses only a single secret key assuming a single KS in the system. In cloud storage where multi-tenancy and scalability are crucial, such schemes degrade not only the effectiveness of deduplication but also the scalability with increasing users. In this paper, we extend server-aided encryption to a decentralized setting that consists of multiple KSs. The key idea of our proposed scheme is to construct an inter-KS deduplication algorithm, by which a cloud storage service provider can perform deduplication over ciphertexts from different KSs within a tenant or across tenants. This way, our scheme simultaneously offers flexibility of KS management and cross-tenant deduplication over encrypted data. The novelty of the approach is using a decentralized architecture that does not require any centralized entities for the coordination or pre-sharing of secrets among KSs. Therefore, it allows cloud storage services to offer high deduplication efficiency and scalability while preserving strong data confidentiality. We show the result of performance analysis on the proposed scheme by conducting extensive experiments. In addition, our security analysis demonstrate that the proposed scheme satisfies all desired security properties.
An anonymization protocol for continuous and dynamic privacy-preserving data collection

Collecting personal data without privacy breaches is important to utilize distributed microdata. Privacy-preserving data collection is anonymizing personal data within the data transmission from data holders to a data collector without privacy breaches. A number of research studies aiming at facilitating the privacy-preserving data collection have been recently conducted. However, the existing studies only allow very particular methods to anonymize data and require too strict assumptions for the private channels between the data holders and the data collector. Thus, these studies suffer from limited data utility and cannot be applied in many environments that does not support the particular requirements. In this paper, we present a novel protocol for the privacy preserving data collection. Unlike existing works, our protocol does not restrict the type of anonymization method and does not require the private channel. Our method requires only the k-anonymity model to prevent privacy attacks, and hence equivalent groups of data holders function as a mechanism for the privacy protection. We further devise a greedy heuristic for dealing with dynamic data holders, and discuss possible attacks on our protocol and prevention of them. Through experiments, we show the performance of the proposed protocol.
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Recently, multiple (k, n) region-incrementing VCSs (RIVCSs) have been proposed that can gradually reconstruct secrets in a single image. In (k, n)-RIVCS, the secret image is subdivided into multiple regions in such a way that any t shadow images, where k ≤ t ≤ n, can be used to reveal the secret in the (t - k + 1)th region. This region-incrementing property provides progressive decoding. However, the regions in previous RIVCSs are disjointed: no two regions have overlapping areas. In this paper, we discuss a (k, n) region-in-region progressive VCS (RPVCS). Our (k, n)-RPVCS has (n - k + 1) secrecy-level regions, and each region can be located within any previous region. This new type of region allocation not only provides more areas in which to hide the secret compared with the non-overlapping regions in RIVCS but also provides different presentation methods in progressive decoding.
A Full Density Stereo Matching System Based on the Combination of CNNs and Slanted-Planes

Stereo matching methods consist of matching cost computation and several post processing steps. Deep learning methods have greatly raised the accuracy of matching cost and achieved the lowest error rate on several public datasets. However, their generality capabilities are not the best due to potential overfitting, which is the common problem of supervised learning approaches. This paper proposes a convolutional neural network (CNN) based cost estimation method for computing the similarity of image patches. In consideration of accuracy and generalization capability, small size convolution kernels are chosen in the convolution layer and dropout in the decision layer is used for preventing overfitting. After obtaining stereo matching cost from the output of the CNN, several post-processing operations are adopted for disparity optimization, which includes semi-global matching in 1-D from different directions, a left-right consistency check, and the slanted plane smoothing method. The method is evaluated on KITTI 2012, KITTI 2015, and Middlebury stereo datasets and the experimental results on the KITTI benchmark demonstrate the competitive accuracy performance of the approach. Additionally, to test the generalization of the method, a series of extended crossover experiments are conducted in which the training samples and testing samples come from different datasets. The results indicate superior generalization capability of our method than other supervised learning methods.
Data Mining: Accuracy and Error Measures for Classification and Prediction

A variety of measures exist to assess the accuracy of predictive models in data mining and several aspects should be considered when evaluating the performance of learning algorithms. In this article, the most common accuracy and error scores for classification and regression are reviewed and compared. Moreover, the standard approaches to model selection and assessment are presented, together with an introduction to ensemble methods for improving the accuracy of single classifiers.
Schur–Weyl Duality for the Clifford Group with Applications: Property Testing, a Robust Hudson Theorem, and de Finetti Representations

Abstract Schur–Weyl duality is a ubiquitous tool in quantum information. At its heart is the statement that the space of operators that commute with the t -fold tensor powers $$U^{\otimes t}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>U</mml:mi> <mml:mrow> <mml:mo>⊗</mml:mo> <mml:mi>t</mml:mi> </mml:mrow> </mml:msup> </mml:math> of all unitaries $$U\in U(d)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>U</mml:mi> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is spanned by the permutations of the t tensor factors. In this work, we describe a similar duality theory for tensor powers of Clifford unitaries . The Clifford group is a central object in many subfields of quantum information, most prominently in the theory of fault-tolerance. The duality theory has a simple and clean description in terms of finite geometries. We demonstrate its effectiveness in several applications: We resolve an open problem in quantum property testing by showing that “stabilizerness” is efficiently testable: There is a protocol that, given access to six copies of an unknown state, can determine whether it is a stabilizer state, or whether it is far away from the set of stabilizer states. We give a related membership test for the Clifford group. We find that tensor powers of stabilizer states have an increased symmetry group. Conversely, we provide corresponding de Finetti theorems , showing that the reductions of arbitrary states with this symmetry are well-approximated by mixtures of stabilizer tensor powers (in some cases, exponentially well). We show that the distance of a pure state to the set of stabilizers can be lower-bounded in terms of the sum-negativity of its Wigner function. This gives a new quantitative meaning to the sum-negativity (and the related mana ) – a measure relevant to fault-tolerant quantum computation. The result constitutes a robust generalization of the discrete Hudson theorem . We show that complex projective designs of arbitrary order can be obtained from a finite number (independent of the number of qudits) of Clifford orbits. To prove this result, we give explicit formulas for arbitrary moments of random stabilizer states.
VT-Revolution: Interactive Programming Video Tutorial Authoring and Watching System

Procedural knowledge describes actions and manipulations that are carried out to complete programming tasks. An effective way to document procedural knowledge is programming video tutorials. Unlike text-based software artifacts and tutorials that can be effectively searched and linked using information retrieval techniques, the streaming nature of programming videos limits the ways to explore the captured workflows and interact with files, code and program output in the videos. Existing solutions to adding interactive workflow and elements to programming videos have a dilemma between the level of desired interaction and the efforts required for authoring tutorials. In this work, we tackle this dilemma by designing and building a programming video tutorial authoring system that leverages operating system level instrumentation to log workflow history while tutorial authors are creating programming videos, and the corresponding tutorial watching system that enhances the learning experience of video tutorials by providing programming-specific workflow history and timeline-based browsing interactions. Our tutorial authoring system does not incur any additional burden on tutorial authors to make programming videos interactive. Given a programming video accompanied by synchronously-logged workflow history, our tutorial watching system allows tutorial watchers to freely explore the captured workflows and interact with files, code and program output in the tutorial. We conduct a user study of 135 developers to evaluate the design and effectiveness of our system in helping developers learn programming knowledge in video tutorials.
A Triangulation-Based Coverage Path Planning

In this paper, we present an approach to local coverage path (CP) planning for a circular mobile sensor. Our family of algorithms provides completed and overlapped coverage control, curvature and path length control, localization, choice of observer's placement based on sensing range, and first-order differentiable CP. Our family of algorithms begins by partitioning the target region (TR) into several regular triangulations (RTs). Based on the size of the RT and the sensing range, the number and location of observers are determined for all RT. All observers found are then used as waypoints (WPs) to generate baseline CP with the Traveling Salesman Problem's nearest neighbor algorithm. The proposed algorithm solved the problems of how to design a differentiable and collision-free CP for all sensing range, limited and sufficient, while providing completed coverage control, overlapped coverage control, curvature and path length control, localization, collision avoidance, and the choice of observer's placement based on sensing range and the environment. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the observation WPs and then compute the smooth and collision-free CP. Computer simulations are provided to validate the effectiveness and correctness of our algorithms.
Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based Approach

With more awareness and growth in the cloud market, demands for computational resources have increased in order to provide services to the cloud users. Sometimes it is difficult for an individual cloud service provider (CSP) to meet the level of promised quality of service (QoS) and to fulfill all types of resource requests dynamically. Cloud federation has become a consolidated paradigm in which group of cooperative CSPs share their unused resources with peers to gain some economic benefit. Hence, the cloud federation overcomes the limitation of each CSP for maintaining QoS during sudden spikes in resource demand. However, the presence of untrusted CSPs degrades the QoS of the services delivered through federation. Trusted CSPs are highly reputed in the federation as they can extend their resources and services to maintain the level of committed QoS by the member CSPs of the federation. Therefore, to guarantee delivery of committed QoS, it will be necessary to form a federation with trusted CSPs only. In this paper, we present a broker based cloud federation architecture. The cloud federation formation is modeled as a hedonic coalitional game. The main objective of this work is to find the most suitable and stable federation of trusted CSPs that will maximize the satisfaction level of each individual CSP on the basis of QoS and profit. The proposed coalitional game inspired cloud federation formation (CGCFF) algorithm has been extensively compared with selected existing techniques. Simulation results show that the set of federation formed by CGCFF is Nash-stable and performs better than these techniques in terms of satisfaction, quality and profit.
Optimal Combination of Image Denoisers

Given a set of image denoisers, each having a different denoising capability, is there a provably optimal way of combining these denoisers to produce an overall better result? An answer to this question is fundamental to designing an ensemble of weak estimators for complex scenes. In this paper, we present an optimal combination scheme by leveraging the deep neural networks and the convex optimization. The proposed framework, called the Consensus Neural Network (CsNet), introduces three new concepts in image denoising: 1) a provably optimal procedure to combine the denoised outputs via convex optimization; 2) a deep neural network to estimate the mean squared error (MSE) of denoised images without needing the ground truths; and 3) an image boosting procedure using a deep neural network to improve the contrast and to recover the lost details of the combined images. Experimental results show that CsNet can consistently improve the denoising performance for both deterministic and neural network denoisers.
Adaptive Distributed RDF Graph Fragmentation and Allocation based on Query Workload

As massive volumes of Resource Description Framework (RDF) data are growing, designing a distributed RDF database system to manage them is necessary. In designing this system, it is very common to partition the RDF data into some parts, called fragments, which are then distributed. Thus, the distribution design comprises two steps: fragmentation and allocation. In this study, we explore the workload for fragmentation and allocation, which aims to reduce the communication cost during SPARQL query processing. Specifically, we adaptively maintain some frequent access patterns (FAPs) to reflect the characteristics of the workload while ensuring the data integrity and approximation ratio. Based on these frequent access patterns, we propose three fragmentation strategies, namely vertical, horizontal, and mixed fragmentation, to divide RDF graphs while meeting different types of query processing objectives. After fragmentation, we discuss how to allocate these fragments to various sites while balancing the fragments. Finally, we discuss how to process queries based on the results of fragmentation and allocation. Experiments over large RDF datasets confirm the superior performance of our proposed solutions.
A 3.77TOPS/W Convolutional Neural Network Processor With Priority-Driven Kernel Optimization

Convolutional neural network (CNN) has become very popular in image classification tasks. With the increasing demand on intelligent classification on battery-powered devices, energy-efficient ASICs for CNN are badly needed. While previous CNN ASIC processors support operations of different kernel sizes, they sacrifice efficiency to support flexible convolution operations. In fact, convolution operations with a certain kernel size are dominating in many real-case CNNs. This brief proposes a kernel-optimized architecture for 3 × 3 kernels (KOP3), which are dominating operations in mainstream image classification CNNs. Although KOP3 aims at 3 × 3 kernel operations, it also provides programmability to support arbitrary kernel sizes. KOP3 achieves average energy efficiency of 3.77TOPS/W, which is 4.01× better than the best state-of-the-art CNN ASIC processor.
A New Certificateless Signature Scheme Provably Secure in the Standard Model

Certificateless cryptography eliminates the need of certificates from public-key cryptography and solves the key escrow problem in identity-based cryptography. Since Al-Riyami and Paterson introduced the concept of certificateless cryptography, there have been several proposals for certificateless encryption schemes and signature schemes provably secure in the random oracle model. In the random oracle paradigm, the schemes make use of cryptographic hash functions that are modeled as random oracles in the security proof. However, it has been shown that when random oracles are instantiated with concrete hash functions, the schemes may not be secure. Afterward, several certificateless signature (CLS) schemes provably secure without random oracles have been proposed. However, it turned out that all the schemes are insecure against type I or II adversaries. In this paper, we propose a new CLS scheme secure in the standard model under the computational Diffie-Hellman assumption. We then investigate the practical feasibility of our scheme.
Joint Learning of Spectral Clustering Structure and Fuzzy Similarity Matrix of Data

When spectral clustering analysis is applied, a similarity matrix of data plays a vital role in both clustering performance and stability of clustering results. In order to enhance the clustering performance and maintain the stability of the clustering results, a new method to jointly learn the similarity matrix and the clustering structure, called the joint learning method (FSCM) of spectral clustering structure and fuzzy similarity matrix of data, is proposed in this paper. In FSCM, the capability of a double-index fuzzy C-means clustering algorithm is used to determine an appropriate fuzzy similarity between any pair of data points. A fuzzy similarity matrix of data is also determined by adaptively assigning fuzzy neighbors of data points so the spectral clustering structure of data can be found and the clustering stability of FSCM can be assured. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed method.