Reviewerly/exHarmony huggingface dataset:
An effective human action recognition system based on Zernike moment features

Recognizing human actions from video has attracted a lot of attention from computer vision research community due to its many prospective applications. Feature extraction is a key step for achieving an efficient human recognition system. Among other feature extraction methods, complex Zernike moment (ZM) considered as one of the most simple and successful approach. Commonly, Zernike moments are employed for feature extraction using only magnitude value of the complex ZM. In this paper, we study the performance of combining various complex ZM parts such as: magnitude, real, imaginary and phase information to improve the recognition accuracy. The proposed methodology consists of five stages: motion energy image (MEI), Zernike moments (ZM), whitening transform, bag-of-features and support vector machine (SVM) algorithm. The proposed method achieve comparable results with other state-of-the-art methods using the publicly available Weizmann action recognition dataset.
Where can my career take me?

Career goals represent a special case for recommender systems and require considering both short and long term goals. Recommendations must represent a trade off between relevance to the user, achievability and aspirational goals to move the user forward in their career. Users may have different motivations and concerns when looking for a new long term goal, so involving the user in the recommender process becomes all the more important than in other domains. Additionally, the cost to the user of making a bad decision is much higher than investing two hours in watching a movie they don't like or listening to an unappealing song. As a result, we feel career recommendations is a unique opportunity to truly engage the user in an interactive recommender as we believe they will invest the cognitive load. In this paper, we present an interactive career goal recommender framework that leverages the power of dialogue to allow the user interactively improve the recommendations and bring their own preferences to the system. The underlying recommendation algorithm is a novel solution that suggests both short and long term goals through utilizing the sequential patterns extracted from career trajectories that are enhanced with features of the supporting user profiles. The effectiveness of the proposed solution is demonstrated with extensive experiments on two real world data sets.
EdgeDroid

Many emerging mobile applications, including augmented reality (AR) and wearable cognitive assistance (WCA), aim to provide seamless user interaction. However, the complexity of benchmarking these human-in-the-loop applications limits reproducibility and makes performance evaluation difficult. In this paper, we present EdgeDroid, a benchmarking suite designed to reproducibly evaluate these applications. Our core idea rests on recording traces of user interaction, which are then replayed at benchmarking time in a controlled fashion based on an underlying model of human behavior. This allows for an automated system that greatly simplifies benchmarking large scale scenarios and stress testing the application. Our results show the benefits of EdgeDroid as a tool for both system designers and application developers.
High-speed laser interferometric distance sensor with reference mirror oscillating at ultrasonic frequencies

Abstract Optical measurement systems are an important part of the portfolio of 3D topography sensors. By precise, contactless and rapid measurements these sensors constitute an alternative to tactile instruments. In this contribution the principle of a laser interferometric distance sensor is presented, which in combination with lateral scan axes acts as a topography sensor and also as distance sensor for the compensation of vibrations in a coherence scanning Linnik interferometer. An advantage of this distance sensor is its high acquisition rate of height values, which in case of working as a topography sensor enables high scan velocities as it is demonstrated at a chirp standard measured with a scan velocity of 80 mm/s. This is much higher than the scan velocity of tactile instruments, which are typically limited up to 1 mm/s. In addition, the compensation of vibration disturbances demonstrates the capability of the fast distance measurement. In contrast to other existing high-speed point sensors the relevant components are mass products. This keeps the costs of the sensor setup in a limited range. Furthermore, the sensor shows potential of much higher measurement rates than 116 kHz provided by the sensor used here.
Improved outlier detection using sparse coding-based methods

Outlier detection is an active area of research in data mining and a large number of algorithms exist. Our goal is to come up with a guideline on how to choose the most appropriate outlier detection algorithm for a given dataset without exploiting any domain- or application-specific information. Extensive experimentations with a number of state-of-the-art algorithms on thousands of benchmark datasets revealed a clear trend. For datasets with low dimensionality and low difficulty level, traditional methods outperform sparse coding-based outlier detection (SCOD) algorithms. But the trend reverses as the dimensionality or difficulty level increases. A threshold emerges as the point of intersection of the trends for SCOD and traditional algorithms, which is 250 and 21 for dimensionality and difficulty level respectively.
An Unsupervised Approach for Reputation Generation

Nowadays, watching a movie, buying a product, making hotel reservations and other e-commerce trades are strung to consulting other peoples reviews and recommendations on the target entity. Indeed, Amazon, IMDB (Internet Movie Database) as well as several websites provide a convenient platform where users share freely their opinions and their subjective attitudes towards the target entity with no restrictions. However, those opinions are too much to be examined one by one, this is why a general reputation value makes the task of choosing the right product much easier. In this paper, we propose a reputation generation approach based on opinion clustering and semantic analysis. In our approach, opinions are grouped into a number of clusters that contain opinions with the same attitude or preference. By aggregating the ratings attached to the clusters, we generate the reputation of an entity. Experimental results demonstrate the effectiveness of the proposed approach in generating reputation value.
Multi-Rate Prefix-Free Code Distribution Matching

We propose a multi-rate prefix-free code distribution matching (PCDM) algorithm that implements a fixed-length probabilistic constellation shaping. It uses various small codebooks in an adaptive manner, thereby improving the shaping performance of PCDM.
Two-stage transfer network for weakly supervised action localization

Action localization is a central yet challenging task for video analysis. Most existing methods rely heavily on the supervised learning where the action label for each frame should be given beforehand. Unfortunately, for many real applications, it is often costly and source-consuming to obtain frame-level action labels for untrimmed videos. In this paper, a novel two-stage paradigm where only the video-level action labels are required, is proposed for weakly supervised action localization. To this end, an Image-to-Video (I2V) network is firstly developed to transfer the knowledge learned from the image domain (e.g. ImageNet) to the specific video domain. Relying on the model learned from I2V network, a Video-to-Proposal (V2P) network is further designed to identify action proposals without the need of temporal annotations. Lastly, a proposal selection layer is devised on the top of the V2P network to choose the maximal proposal response along each class subject and thus obtain a video-level prediction score. By minimizing the difference between the prediction score and video-level label, we fine-tune our V2P network to learn enhanced discriminative ability on classifying proposal inputs. Extensive experimental results show that our method outperforms the state-of-the-art approaches on ActivityNet1.2 and the mAP is improved from 13.7% to 16.2% on THUMOS14. More importantly, even with weak supervision, our networks attain comparable accuracy to those employing strong supervision, thus demonstrating the effectiveness of our method.
Toward Budgeted Online Kernel Ridge Regression on Streaming Data

“Concept drift”makes learning from streaming data fundamentally different from traditional batch learning. Focusing on the regression task on streaming data, this paper presents an efficient online learning algorithm, i.e., budgeted online kernel ridge regression (BOKRR). It is a budget version kernel ridge regression algorithm coupled with minimum contribution criterion to maintain the budget of the active set. BOKRR employs low-rank correction technology and the Sherman-Morrison-Woodbury formula to update the dynamic KRR model with the computational complexity of only O(B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) with B learning samples (Budget size of an active set). Limited storage burden and efficient computational ability make the proposed BOKRR algorithm an ideal candidate to process streaming data. The experimental results on benchmark and real-world datasets further demonstrate the validity and efficiency of the proposed algorithms.
What it takes to boost Internet of Things privacy beyond encryption with unobservable communication: a survey and lessons learned from the first implementation of DC-net

Privacy requires more than just encryption of data before and during transmission. Privacy would actually demand hiding the sheer fact that communication takes place. This requires to protect meta-data from observation. We motivate the need for strong privacy protection in a smart home use case by highlighting the privacy issues that cannot be solved by confidentiality mechanisms like encryption alone. Our solution is a implementation of DC-net on Re-Mote sensor nodes running Contiki OS. From this, we conclude that the computational and network overheads imposed by these techniques do not make them impractical to use in the IoT. To the best knowledge of the authors, this is the first implementation of DC-net on sensors. Alongside, we provide a survey of the required strong cryptographic security mechanisms, like encryption of communication, to be in place. We describe how the current existing techniques can be facilitated to achieve unobservable communication for the IoT. This includes mechanisms for encrypted IoT communication like DTLS or message authentication like ECDSA signatures on IoT devices. For readers unfamiliar with the concepts of MIXing and DC-net, we explain and analyse how those techniques, formerly used to provide private communication in the Internet, can be applied to the IoT. We briefly survey what complementary features from the IoT architecture are helpful in providing strong protection in this particular use case. Finally, we state some recommendations hoping that following these will enable us to reduce the privacy invasiveness of the IoT on all levels. We think that this will be indispensable if IoT devices shall become a part of our daily lives without rendering us into an Orwellian society.
Openness and Security in Cloud Computing Services: Assessment Methods and Investment Strategies Analysis

Cloud computing companies need to achieve a balance between service openness and security control to promote the growth of the cloud service business. This paper is a theoretical discussion of the complete service openness condition and the complete security control condition based on Nash equilibrium from the perspective of investment, including investment in service and investment in security by the cloud computing company. The quantitative assessment methods of two conditions are designed based on the investments. A quantitative analysis of the impact of security investments on security control and service openness is presented. Based on this analysis, the optimal security investment for service openness and the minimum complete service openness investment standard are derived. Finally, the relationship between them is analyzed to help cloud computing providers decide the optimal strategy for coordinating investment in both service and security.
Ground state and thermal entanglement between two two-level atoms interacting with a nondegenerate parametric amplifier: Different sub-spaces

In this paper, a Hamiltonian model that includes interaction of two coupled two-level atoms with a nondegenerate parametric amplifier in a cavity is introduced. By using the two-mode squeezing operator and under a certain condition, the introduced Hamiltonian is reduced to a generalized Jaynes–Cummings Hamiltonian. The constants of motion of system imply the existence of a decomposition of the system’s Hilbert space [Formula: see text] into a direct sum of three infinite dimensional sub-spaces, as [Formula: see text]. This decomposition enables us to study ground and thermally induced entanglement between the atoms in each of the sub-spaces as well as whole Hilbert space. The effect of atom–atom and atom–photon couplings on the degree of ground state and thermal entanglement are also investigated using the concurrence measure. It is found that in the subspaces [Formula: see text] and [Formula: see text] for all experimental values of parameters of the system, the atoms are disentangled. It is also observed that in the total space [Formula: see text] the ground state entanglement is always zero, while in the subspace [Formula: see text] it is at its maximum value. Moreover, it is found that in the subspace [Formula: see text] thermal entanglement between the atoms is robust against temperature.
Zircon U-Pb geochronology of the Chinese continental crust: a preliminary analysis of the Elsevier science database

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Simulation of Hazy Image and Validation of Haze Removal Technique

Haze hampers the performance of vision systems.So, removal of haze appearance in a scene should be the first-priority for clear vision.It finds wide spectrum of practical applications.A good number of dehazing techniques have already been developed.However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity.To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique.The simulated hazy image is developed using atmospheric model with and without Perlin noise.The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.
Cloud Learning Management System in Higher Education

The main objective of this chapter is to discuss the growth of cloud-based learning management system (LMS) in higher education. This will look at the benefits of LMS adoption and how it has grown in higher education institutions. The utilization of information and communication technology (ICT) has permeated in almost every aspect of life. This chapter will cover a brief introduction about cloud-based LMS, roles of LMS in education, drivers for adoption and advantages of cloud-based LMS, factors to consider in the adoption of cloud-based LMS, the need for LMS and end with conclusion and future directions of cloud-based LMS.
A regression-type support vector machine for k-class problem

Abstract In this paper, a new method for multiclass classification is proposed, named regression based support vector machine (RBSVM). Rest-versus-1-versus-1 strategy is adopted in RBSVM. When constructing the classification hyperplane, a mixed regression and classification formulation is used. The training set is divided into positive class, negative class and rest class, the regression hyperplane is required to pass through the rest class and it can classify the positive and negative class correctly as far as possible simultaneously. The proposed method can not only improve the classification accuracy but also reduce the computational complexity compared with some currently existed multiclass classification methods. The numerical results show the effectiveness of our methods.
Robust moving objects detection in long-distance imaging through turbulent medium

The tough challenges of detecting moving objects in long-distance imaging stems from the optical turbulence effects, which leads to blurred image, flickering pixel value and shifted object positions. To address this problem, we propose a coarse-to-fine method based on dynamic background subtraction and pipeline filter. First, we model the turbulent foregrounds with a specific mixture of Gaussian (MoG) distribution, which is regularized by online low-rank subspace factorization. Moreover, we embed a transformation operator into the model to ensure that the low-rank space is not damaged in practical camera jitters or rotation. Then, to avoid the interference of residual turbulence and marginal noise, a method of Variable Weighted Pipeline Filter (VWPF) is proposed to discriminate the true from false objects by taking full advantage of objects’ spatial-temporal property. Compared with other state-of-art methods, the proposed method shows better false alarm and miss detection rates.
Cognitive solutions for security and cryptography

This paper describes the new security solutions based on cognitive approaches and new computing paradigm called cognitive cryptography. This new security area establish a new generation of computational methods and security systems, focused on creation intelligent cryptographic algorithms and security protocols using cognitive information processing approaches. Such systems are designed for semantic evaluation of encrypted data, and allow to select the most appropriate techniques of its encryption. This paper presents a possible application of such techniques for different security tasks like authentication, secret sharing, secure data management etc. Additionally, some cryptographic solutions inspired by biological models will be presented.
Improving Bayesian network local structure learning via data-driven symmetry correction methods

Learning the structure of a Bayesian network (BN) from data is NP-hard. To efficiently handle high-dimensional datasets, many BN local structure learning algorithms are proposed. These learning algorithms can be categorized into two types: constraint-based and score-based. These learning algorithms learn the local structures separately for each node. As a result, asymmetric pairs of neighbors and Markov blankets create conflicts between the local structures. To resolve the conflicts, symmetry correction is required. The commonly used AND-rule symmetry correction method, which simply drops nodes in asymmetric pairs from the neighbor sets and Markov blankets of both nodes, may result in loss of information in learning the BN. In this paper, we propose a hybrid framework that combines a local structure learning algorithm of a particular type (either constraint-based or score-based) with a data-driven symmetry correction method of the other type. The score-based symG method and the constraint-based symC method are proposed to be used in the hybrid framework. Empirical results show that performances of constraint-based learning algorithms are improved by using the proposed score-based symG method. Similarly, the performance of score-based local learning algorithm is better when symC is used, compared to using symG .
Symmetrical Residual Connections for Single Image Super-Resolution

Single-image super-resolution (SISR) methods based on convolutional neural networks (CNN) have shown great potential in the literature. However, most deep CNN models don’t have direct access to subsequent layers, seriously hindering the information flow. Furthermore, they fail to make full use of the hierarchical features from different low-level layers, thereby resulting in relatively low accuracy. In this article, we present a new SISR CNN, called SymSR, which incorporates symmetrical nested residual connections to improve both the accuracy and the execution speed. SymSR takes a larger image region for contextual spreading. It symmetrically combines multiple short paths for the forward propagation to improve the accuracy and for the backward propagation of gradient flow to accelerate the convergence speed. Extensive experiments based on open challenge datasets show the effectiveness of symmetrical residual connections. Compared with four other state-of-the-art super-resolution CNN methods, SymSR is superior in both accuracy and runtime.
Goshawk: A Novel Efficient, Robust and Flexible Blockchain Protocol

Proof of Work (PoW), a fundamental blockchain protocol, has been widely applied and thoroughly testified in various decentralized cryptocurrencies, due to its intriguing merits including trustworthy sustainability, robustness against Sybil attack, delicate incentive-compatibility, and openness to any participant. Meanwhile, PoW-powered blockchains still suffer from poor efficiency, potential selfish mining, to-be-optimized fairness and extreme inconvenience of protocol upgrading. Therefore, it is of great interest to design new PoW-driven blockchain protocol to address or relieve the above issues so as to make it more applicable and feasible. To do so, we present Goshawk, a novel hybrid consensus protocol, in which a two-layer chain structure with two-level PoW mining strategy and a ticket-voting mechanism are elaborately combined. We show that this newly-proposed protocol has the merits of high efficiency, strong robustness against “51%” attack of computation power, as well as good flexibility for future protocol updating. As far as we know, Goshawk is the first blockchain protocol with these three key properties. Last but not the least, this scheme has been implemented and deployed in the testnet of the public blockchain project Hcash( https://github.com/HcashOrg ) for months, and has demonstrated its stability and high efficiency with such real-world test.
Image retrieval and classication using shifted Legendre invariant moments and Radial Basis Functions Neural Networks

Since shape is one of the important low-level features of any Image Retrieval and any image classification system, we present in this paper a new set of orthogonal polynomials called shifted Legendre polynomials, this helps to build a set of orthogonal moments, which are invariant to translation, rotation and scale. We apply new image Retrieval and classification systems based on the proposed invariant moments and Radial Basis Functions Neural Networks. To show the effectiveness of our approaches we present some experimental results.