Reviewerly/exHarmony huggingface dataset:
System theory and orthogonal multi-wavelets

In this paper we provide a complete and unifying characterization of compactly supported univariate scalar orthogonal wavelets and vector-valued or matrix-valued orthogonal multi-wavelets. This characterization is based on classical results from system theory and basic linear algebra. In particular, we show that the corresponding wavelet and multi-wavelet masks are identified with a transfer function F(z)=A+Bz(I−Dz)−1C,z∈D={z∈C:|z|<1},of a conservative linear system. The complex matrices A,B,C,D define a block circulant unitary matrix. Our results show that there are no intrinsic differences between the elegant wavelet construction by Daubechies or any other construction of vector-valued or matrix-valued multi-wavelets. The structure of the unitary matrix defined by A,B,C,D allows us to parametrize in a systematic way all classes of possible wavelet and multi-wavelet masks together with the masks of the corresponding refinable functions.
Context-Driven Granular Disclosure Control for Internet of Things Applications

The Internet of Things (IoT) represents a technology revolution transforming the current environment into a ubiquitous world, whereby everything that benefits from being connected will be connected. Despite the benefits, the privacy of these things becomes a great concern and therefore it is imperative to apply privacy preservation techniques to IoT data collection. One such technique is called data obfuscation in which data is deliberately modified to blur the sensitive information, while preserving the data utility. The current obfuscation techniques, however, focus on the privacy of published datasets shared with untrusted parties. The high connectivity and distributed nature of IoT, opens up the possibility of privacy compromise before obfuscation can take effect, and therefore privacy enforcement should be deployed at earlier stages. Additionally, classical privacy treatments are too restrictive for IoT, where coarser/finer data details should be revealed for different applications. Motivated by these challenges, we propose a framework for privacy preservation in IoT environments that is capable of multi-granular obfuscation by enforcing context-driven disclosure policies. Then, we customize our framework for a smart vehicle system and make use of data stream watermarking techniques to protect privacy at different stages of the data lifecycle. To address possible concerns about additional performance overhead, we show the burden to be very lightweight, thus validating the suitability of ubiquitous use of our framework for IoT settings.
Convergence properties of quantum evolutionary algorithms on high dimension problems

We propose and investigate new rotation gates for two modified Quantum Inspired Evolutionary methods for solving high dimension optimisation problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to high dimension problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding, respectively, while keeping close to the original quantum computing metaphor. We introduce five performance metrics and use them to evaluate the proposed approaches against sets of multimodal mathematical test functions and real world problems of high dimensionality. We report issues found while implementing some of the published real QIEA techniques which were the motivation for developing our real algorithm modifications. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (particularly with high dimension problems), demonstrating faster convergence and accuracy.
Monogamy of correlations and entropy inequalities in the Bloch picture

Abstract We investigate monogamy of correlations and entropy inequalities in the Bloch representation. Here, both can be understood as direct relations between different correlation tensor elements and thus appear intimately related. To that end we introduce the split Bloch basis , that is particularly useful for representing quantum states with low dimensional support and thus amenable to purification arguments. Furthermore, we find dimension dependent entropy inequalities for the Tsallis 2-entropy. In particular, we present an analogue of the strong subadditivity and a quadratic entropy inequality. These relations are shown to be stronger than subadditivity for finite dimensional cases.
Trajectory-Pooled Spatial-Temporal Architecture of Deep Convolutional Neural Networks for Video Event Detection

Nowadays content-based video event detection faces great challenges due to complex scenes and blurred actions in surveillance videos. To alleviate these challenges, we propose a novel spatial-temporal architecture of deep convolutional neural networks for this task. By taking advantage of spatial-temporal information, we fine-tune two-stream networks, and then, fuse spatial and temporal features at convolution layers using a 2D pooling fusion method to enforce the consistence of spatial-temporal information. Based on the two-stream networks and spatial-temporal layer, a triple-channel model is obtained. Furthermore, we implement trajectory-constrained pooling to deep features and hand-crafted features to combine their merits. A fusion method on triple-channel yields the final detection result. The experiments on two benchmark surveillance video data sets including VIRAT 1.0 and VIRAT 2.0, which involve a suit of challenging events, such as person loading an object to a vehicle or person opening a vehicle trunk, manifest that the proposed method can achieve superior performance compared with the state-of-the-art methods on these event benchmarks.
Automatic labeling of large amounts of handwritten characters with gate-guided dynamic deep learning

Most successful methods based on convolutional neural networks (CNNs) rely on a large number of labeled samples to achieve a promising performance. However, manually labeling data is a very expensive and time-consuming task. To address this problem, we propose a simple yet very effective method called gate-guided dynamic learning (GDL), to automatically label large amounts of handwritten data based on a relatively tiny number of labeled data. The proposed method utilizes an optimized path signature method to represent online characters and employs gated control to guide the CNN in dynamically correcting wrong labels. With this method, the CNN can gradually label large quantities of unlabeled data with high accuracy which is comparable to human labeling. Experiments on CASIA-OLHWDB showed that our method achieved a labeling error of 0.46% when labeling 97% of unlabeled data. For a large dataset with 10,081 classes, the proposed method can use 100 labeled samples per class to automatically label the rest of the 1100 unlabeled samples with a labeling error of 0.31%. For an offline MNIST dataset, by using only 10 samples for each class, the proposed method achieved a labeling error of 0.34% for 57,288 unlabeled samples, which outperforms existing state-of-the-art results.
Kinesiology-inspired estimation of pedestrian walk direction for smart surveillance

The article proposes a novel approach for estimating the walk direction of a pedestrian conjointly exploiting the complementary nature of video-based and frame-based walk direction estimation methods. Video-based direction estimation classifies inter-frame features by HMM as underlying machine learning method. To exploit the intra-frame features for orientation estimation, kinesiology based study has been performed and lower body kinematics of sagittal plane is identified to be the most suitable choice for feature. This frame-based intra-frame feature is classified using Least Square Support Vector Machine (LS-SVM) for finding orientation per frame. The score-level fusion of estimated results from both the topologies using a rule based look-up table produces the ultimate estimate for walk direction. The proposed method is tested over three different databases and is found to perform better than state-of-the-art methods due to inheritance of both inter-frame and intra-frame feature.
Gray Computing: A Framework for Computing with Background JavaScript Tasks

Website visitors are performing increasingly complex computational work on the websites' behalf, such as validating forms, rendering animations, and producing data visualizations. In this article, we explore the possibility of increasing the work offloaded to web visitors' browsers. The idle computing cycles of web visitors can be turned into a large-scale distributed data processing engine, which we term gray computing. Past research has looked primarily at either volunteer computing with specialized clients or browser-based volunteer computing where the visitors keep their browsers open to a single web page for a long period of time. This article provides a comprehensive analysis of the architecture, performance, security, cost effectiveness, user experience, and other issues of gray computing distributed data processing engines with heterogeneous computing power, non-uniform page view times, and high computing pool volatility. Several real-world applications are examined and gray computing is shown to be cost effective for a number of complex tasks ranging from computer vision to bioinformatics to cryptology.
Decision support system with multi-criteria, stability, and uncertainty analyses for resolving the municipal infrastructure conflict in the City of Jeddah

The goal of this paper is resolve a dispute for North Jeddah Airport municipal infrastructure expansion using the Graph Model for conflict resolution. The study identifies the preferences and concerns of the main stakeholders by addressing the economical, environmental, social and public welfare goals. In this study, an informative Decision Support System (DSS) has been employed. This DSS incorporates multiple-criteria decision, stability, and uncertainty analyses. It provides decision through three stages (A, B and C). Stage A focuses on eliminating infeasible solution using multi-criteria method. Stage B identifies the most robust and stable solution while considering stakeholders preferences with possible actions and counteractions using stability analysis. Stage C is concerned with evaluating the strength of the solutions for an array of uncertainty using info-gap technique. Based on its simple modeling and powerful analytical features, the DSS proved to be useful in modeling and resolving municipal infrastructure disputes, determining most robust solution, and examining the effect of uncertainty.
Entanglement generation between distant parties via disordered spin chains

We study the emergence of bipartite entanglement between a pair of spins weakly connected to the ends of a linear disordered XY spin-1/2 channel. We analyze how their concurrence responds to structural and on-site fluctuations embodied by long-range spatially-correlated sequences. We show that the end-to-end entanglement is very robust against disorder and asymmetries in the channel provided that the degree of correlations are strong enough and both entangling parties are tuned accordingly. Our results offer further alternatives in the design of stable quantum communication protocols via imperfect spin channels.
Combining knowledge with data for efficient and generalizable visual learning

Substantial progress has been made in the past decades in computer vision, in particular as a result of the application of deep learning methods. Despite these rapid developments, there still exist a significant gap between computer vision and human vision. One factor contributing to this gap is the data-driven and purely bottom-up nature of the existing visual learning methods and their inability to use prior knowledge. The data-driven and bottom-up approaches often cannot generalize well beyond the data that is used to train them. Parallel to data, there is usually prior knowledge in many domains that governs the target object, its context, and the computer vision tasks. Such knowledge, if utilized properly, can not only improve visual recognition performance but also reduce our dependence on data. To this goal, we propose to identify the related prior knowledge from different sources and to systematically encode them into visual learning tasks though joint bottom-up and top-down inference. Specifically, we first identify four types of prior knowledge, including permanent theoretical knowledge, circumstantial knowledge, subjective experiential knowledge, and data knowledge. We then demonstrate how permanent theoretical knowledge and circumstantial knowledge can be identified for different vision tasks and introduce methods to systematically represent and integrate them with the image data. Experiments on benchmark datasets show that by employing related prior knowledge, we can produce vision algorithms that are more data efficient, robust and generalizable and that are less dependent on training data.
Online and Stable Learning of Analysis Operators

In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size. The fourth algorithm, singular value AOL, uses a similar strategy as Analysis K-SVD while avoiding its high computational cost. All algorithms are proven to decrease or preserve the target function in each step and a characterization of their stationary points is provided. Further they are tested on synthetic and image data, compared to Analysis SimCO and found to give better recovery rates and faster decay of the objective function, respectively. In a final denoising experiment the presented algorithms are again shown to perform similar to or better than the state-of-the-art algorithm ASimCO.
Copy-Move Forgery Detection Exploiting Statistical Image Features

Majority of image manipulation identification methods attempt to find variation or dissimilarities in statistical image features. But such schemes do not identify in case of copy-move forgery type of image manipulation, because the forged region comes from the same image. In this chapter, we present a statistical image features-based copy-move forgery detection method. The mean value gives the pixel intensity of the entire image, and variance is used to find how each pixel intensity differs from its neighbors within a block. We evaluate and compare the presented algorithm with other existing methods through a comprehensive set of experiments. Our experimental results show that this technique achieves considerably higher detection accuracy as compared to the existing methods.
Modeling Users' Collaborative Behavior With a Serious Game

User modeling is an important area in the field of artificial intelligence. Traditionally, user modeling approaches have focused on individual features of users, leaving aside aspects of users as part of a group. However, in enterprise, academic, or political contexts, it is common to find groups of people working together to solve a collaborative task using online collaborative platforms. In this context, counting with a knowledge based on describing the users' behavior when working in a group (i.e., the collaborative profile) is vital for a system aiming at assisting them to enhance their collaborative abilities that might impact the group dynamics. Traditionally, collaborative abilities have been measured by means of questionnaires or by an external human observer. In this work, we propose an approach to build collaborative profiles by the means of observation of users' behavior in a collaborative serious game. The main advantage of our approach is that it avoids external human observers of the group dynamics and the use of self-perception questionnaires. We obtained promising results when comparing the user profiles obtained with our approach with respect to the classical measurement instruments.
Metaquerying made practical for OWL2QL ontologies

Metamodeling and metaquerying are gaining momentum in the context of both conceptual modeling and semantic web. Indeed it has been largely recognized that metamodeling represents a very useful tool to formalize complex patterns involving elements of the domain of interest, that otherwise are forced to be excluded from the modeling process, and a number of languages equipped with a spectrum of metamodeling features have been studied. However, it has been recognized as well, that the benefit of using metamodeling is greatly limited if one cannot use metaquerying as tool for extracting knowledge deriving from such meta-level patterns. Unfortunately, at the moment, no system exists that correctly manages metamodeling and metaquerying. The goal of this work is precisely to fill this gap by introducing a system, called MQ-Mastro, that allows metaquerying over ontologies expressed in a language of the OWL 2 family equipped with a semantics appropriate for metamodeling.
CAFE: A Virtualization-Based Approach to Protecting Sensitive Cloud Application Logic Confidentiality

Cloud application marketplaces of modern cloud infrastructures offer a new software deployment model, integrated with the cloud environment in its configuration and policies. However, similar to traditional software distribution which has been suffering from software piracy and reverse engineering, cloud marketplaces face the same challenges that can deter the success of the evolving ecosystem of cloud software. We present a novel system named CAFE for cloud infrastructures where sensitive software logic can be executed with high secrecy protected from any piracy or reverse engineering attempts in a virtual machine even when its operating system kernel is compromised. The key mechanism is the end-to-end framework for the execution of applications, which consists of the secure encryption and distribution of confidential application binary files, and the runtime techniques to load, decrypt, and protect the program logic by isolating them from tenant virtual machines based on hypervisor-level techniques. We evaluate applications in several software categories which are commonly offered in cloud marketplaces showing that strong confidential execution can be provided with only marginal changes (around 100-220 lines of code) and minimal performance overhead. The results demonstrate the effectiveness and practicality of CAFE in cloud marketplaces.
Detecting Seam-Carved Image by Extreme Learning Machines Using Patch Analysis Method, Jury Voting, and Combinatorial Fusion

Seam carving is a content-aware image processing approach that has been successfully applied to resizing or removing objects from digital images. Seam-carved images are hard to identify from the original image, making this detection method an important and attractive research topic. Existing methods are based on steganography attacks or statistical features, and their trained and tested models are all constructed using support vector machines (SVMs). The trained models of these methods take a long time to build from a limited number of images. This paper presents a new patch-based sobel operator (PSO) method using SVM and extreme learning machines (ELMs) based on the patch analysis method with square-based features. The ELM adopts five types of neurons to obtain five prediction results, which are then combined using the jury voting scheme in order to improve the accuracy. In addition, combinatorial fusion is first used to choose two of five prediction results according to the diversity rank/score graph, and these two results are then combined to make the final decision. The PSO method in the ELM with jury voting and combinatorial fusion achieves accuracies of 70.60-98.80% and 95.58-99.36% for 10-50% seam-carved and seam-insertion images, respectively. Additionally, for the PSO method, the ELM required only 1.2% of the training time required by the SVM. In conclusion, the PSO method in the ELM is useful for constructing trained models from a large number of images.
Reconfigurable Architecture for Neural Approximation in Multimedia Computing

Due to inherent error resiliency, many high performance multimedia applications can be approximated by multilayer perceptrons (MLPs), with little quality loss. An MLP accelerator can be designed to improve the power efficiency of multimedia systems. However, previous MLP accelerators' fixed computational pattern lowers the performance when the MLP topology varies for different applications. In this paper, we propose a scheduling framework to guide mapping MLPs onto limited hardware resources. The scheduling framework adjusts the computational patterns for various MLP topologies, obtaining 30% higher performance than the conventional scheduling. We implement a reconfigurable neural architecture (RNA) to support different patterns in the framework and further improve the performance and efficiency. RNA achieves a speedup of 572× on the approximable part, whole application speedup of 7.9× and energy savings of 6.3×, with little quality loss on the benchmarks.
Towards Key-Dependent Integral and Impossible Differential Distinguishers on 5-Round AES

Reduced-round AES has been a popular underlying primitive to design new cryptographic schemes and thus its security including distinguishing properties deserves more attention. At Crypto'16, a key-dependent integral distinguisher on 5-round AES was put forward, which opened up a new direction to take more insights into the distinguishing properties of AES. After that, two key-dependent impossible differential (ID) distinguishers on 5-round AES were proposed at FSE'16 and CT-RSA'18, respectively. It is strange that the current key-dependent integral distinguisher requires significantly higher complexities than the key-dependent ID distinguishers, even though they are constructed with the same property of MixColumns ( $$2^{128} \gg 2^{98.2}$$ ). Proposers of the 5-round key-dependent distinguishers claimed that the corresponding integral and ID distinguishers can only work under chosen-ciphertext and chosen-plaintext settings, respectively, which is very different from the situations of traditional key-independent distinguishers. In this paper, we first construct a novel key-dependent integral distinguisher on 5-round AES with $$2^{96}$$ chosen plaintexts, which is much better than the previous key-dependent integral distinguisher that requires the full codebook proposed at Crypto'16. Secondly, We show that both distinguishers are valid under either chosen-plaintext setting or chosen-ciphertext setting, which is different from the claims of previous cryptanalysis. However, under different settings, complexities of key-dependent integral distinguishers are very different while those of the key-dependent ID distinguishers are almost the same. We analyze the reasons for it.
The limit of blockchains

Although smart contracts are Turing complete, it is a misconception that they can fulfill all routine contracts.