Reviewerly/exHarmony huggingface dataset:
Ventral-Dorsal Neural Networks: Object Detection Via Selective Attention

Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of significant improvements. In this paper, we propose a new framework called Ventral-Dorsal Networks (VDNets) which is inspired by the structure of the human visual system. Roughly, the visual input signal is analyzed along two separate neural streams, one in the temporal lobe and the other in the parietal lobe. The coarse functional distinction between these streams is between object recognition — the what of the signal - and extracting location related information — the where of the signal. The ventral pathway from primary visual cortex, entering the temporal lobe, is dominated by what information, while the dorsal pathway, into the parietal lobe, is dominated by where information. Inspired by this structure, we propose the integration of a Ventral Network and a Dorsal Network, which are complementary. Information about object identity can guide localization, and location information can guide attention to relevant image regions, improving object recognition. This new dual network framework sharpens the focus of object detection. Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches on PASCAL VOC 2007 by 8% (mAP) and PASCAL VOC 2012 by 3% (mAP). Moreover, a comparison of techniques on Yearbook images displays substantial qualitative and quantitative benefits of VDNet.
Robust <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"><mml:mrow><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>−</mml:mo></mml:mrow></mml:math>Hypergraph and its applications

Hypergraph, an important learning tool to modulate high-order data correlations, has a wide range of applications in machine learning and computer vision. The key issue of the hypergraph-based applications is to construct an informative hypergraph, in which the hyperedges effectively represent the high-order data correlations. In practice, the real-world data is usually sampled from a union of non-linear manifolds. Due to the issues of noise and data corruptions, many data samples deviate from the underlying data manifolds. To construct an informative hypergraph that represents real-world data distribution well, we propose a hypergraph model (ℓ2-Hypergraph). Our model generates each hyperedge by solving an affine subspace ridge regression problem, where the samples with non-zero representation coefficients are used for hyperege generation. Specifically, to be robust to sparse noise and corruptions, a sparse constraint is imposed on data errors. We have conducted image clustering and classification experiments on real-world datasets. The experimental results demonstrate that our hypergraph model is superior to the existing hypergraph construction methods in both accuracy and robustness to sparse noise.
Target Three-Dimensional Reconstruction From the Multi-View Radar Image Sequence

Target three-dimensional (3D) reconstruction is a hot topic and also a challenge in remote sensing applications. In this paper, a new reconstruction algorithm is proposed to reconstruct the 3D surface of the stable attitude target from its multi-view radar image sequence. Uniform explicit expression of the radar and optical imaging geometries is derived to bridge the 3D target structure and these two sorts of observation images. In this way, the visual hull of the target is reconstructed by exploiting the multi-view stereo techniques to the silhouette information extracted from the radar image sequence. Meanwhile, the target absolute attitude is also determined. Furthermore, we analyze the primary difficulty of the method induced from the limited radar observation view in a typical application, the 3D reconstruction of an in-orbit satellite. Then, an extended algorithm is proposed with the feature fusion of the radar and optical images to achieve dramatic performance enhancement of the reconstruction in this condition. The feasibility of the proposed algorithm is confirmed in the experiment part, and some conclusions are drawn to guide the future work about extended applications of the proposed algorithm as well.
Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Summary Cloud computing is an innovative computing paradigm designed to provide a flexible and low‐cost way to deliver information technology services on demand over the Internet. Proper scheduling and load balancing of the resources are required for the efficient operations in the distributed cloud environment. Since cloud computing is growing rapidly and customers are demanding better performance and more services, scheduling and load balancing of the cloud resources have become very interesting and important area of research. As more and more consumers assign their tasks to cloud, service‐level agreements (SLAs) between consumers and providers are emerging as an important aspect. The proposed prediction model is based on the past usage pattern and aims to provide optimal resource management without the violations of the agreed service‐level conditions in cloud data centers. It considers SLA in both the initial scheduling stage and in the load balancing stage, and it looks into different objectives to achieve the minimum makespan, the minimum degree of imbalance, and the minimum number of SLA violations. The experimental results show the effectiveness of the proposed system compared with other state‐of‐the‐art algorithms.
Enabling Authorized Encrypted Search for Multi-Authority Medical Databases

E-medical records are sensitive and should be stored in a medical database in encrypted form. However, simply encrypting these records will eliminate data utility and interoperability of the existing medical database system because encrypted records are no longer searchable. Moreover, multiple authorities could be involved in controlling and sharing the private medical records of clients. However, authorizing different clients to search and access records originating from multiple authorities in a secure and scalable manner is a nontrivial matter. To address the above issues, we propose an authorized searchable encryption scheme under a multi-authority setting. Specifically, our proposed scheme leverages the RSA function to enable each authority to limit the search capability of different clients based on clients' privileges. To improve scalability, we utilize multi-authority attribute-based encryption to allow the authorization process to be performed only once even over policies from multiple authorities. We conduct rigorous security and cost analysis, and perform experimental evaluations to demonstrate that the proposed scheme introduces moderate overhead to existing searchable encryption schemes.
Electric Powered Wheelchair Command by Information Fusion from Eye Tracking and BCI

Since the first electric version, a lot of arrangements have been done in Electric Powered Wheelchair to attend a wide and increasing types of user's needs. Many applications have evolved from a single into multimodal input methods that allow one wheelchair to support various types of disability. In cases of severe impairments, Brain-Computer Interface and Eye Tracking are commonly applied. Both input interfaces have weaknesses overcame by combined operation instead of each one independently. This proposal allows the user to command an Electric Power Wheelchair by information fusion from commercial Brain-Computer Interface and Eye Tracker devices, surpassing challenges of non-intentional commands and high workload of the user. Thereby, it is expected this user-wheelchair interface to be simple, safe and afford a better user experience.
Variational generalized rotating-wave approximation in the two-qubit quantum Rabi model

We present an analytical method for the two-qubit quantum Rabi model. While still operating in the frame of the generalized rotating-wave approximation (GRWA), our method further embraces the idea of introducing variational parameters. The optimal value of the variational parameter is determined by minimizing the energy function of the ground state. Comparing with numerical exact results, we show that our method evidently improves the accuracy of the conventional GRWA in calculating fundamental physical quantities, such as energy spectra, mean photon number, and dynamics. Interestingly, the accuracy of our method allows us to reproduce the asymptotic behavior of mean photon number in large frequency ratio for the ground state and investigate the quasi-periodical structure of the time evolution, which are incapable of being predicted by the GRWA. The applicable parameter ranges cover the ultrastrong coupling regime, which will be helpful to recent experiments.
Effective real-time augmentation of training dataset for the neural networks learning

In this paper we study the real-time augmentation - method of increasing variability of training dataset during the learning process. We consider the most common label-preserving deformations, which can be useful in many practical tasks. Due to limitations of existing augmentation tools like increase in learning time or dependence on a specific platform, we developed own real-time augmentation system. Experiments on MNIST and SVHN datasets demonstrated the effectiveness of suggested approach - the quality of the trained models improves, and learning time remains the same as if augmentation was not used.
SMART: Joint Sampling and Regression for Visual Tracking

Most existing trackers are either sampling-based or regression-based methods. Sampling-based methods estimate the target state by sampling many target candidates. Although these methods achieve significant performance, they often suffer from a high computational burden. Regression-based methods often learn a computationally efficient regression function to directly predict the geometric distortion between frames. However, most of these methods require large-scale external training videos and are still not very impressive in terms of accuracy. To make both types of methods enhance and complement each other, in this paper, we propose a joint sampling and regression scheme for visual tracking, which leverages the region proposal network by a novel design. Specifically, our method can jointly exploit discriminative target proposal generation and structural target regression to predict target location in a simple feedforward propagation. We evaluate the proposed method on five challenging benchmarks, and extensive experimental results demonstrate that our method performs favorably compared with state-of-the-art trackers with respect to both accuracy and speed.
Multi-Manifold Positive and Unlabeled Learning for Visual Analysis

Positive and Unlabeled (PU) learning has attracted intensive research interests in recent years, which is capable of training a binary classifier solely based on positive and unlabeled examples when the negative data are absent or too are diverse. However, the existing PU learning methods largely overlook the relationship between the examples when handling the unlabeled data, leading to insufficient exploitation of data structure which actually contains useful distribution information. Therefore, by following the multi-manifold assumption which is observed in many real-world vision problems, this paper proposes a novel algorithm termed “Multi-Manifold PU learning” (MMPU), which assumes that the data belonging to different classes lie on different underlying manifolds. As such, the structural information revealed by the dataset is deployed, which is helpful in deciding the labels of unlabeled examples. Our MMPU contains two main steps, namely, multi-manifold exploration and positive confidence training, where the former is accomplished by computing the local similarity, structural similarity, and semantic similarity of pairwise data, and the latter establishes a binary classifier in reproducing kernel Hilbert space based on the real-valued confidence level of each example to be positive. Experimentally, we not only test the proposed MMPU on five highly nonlinear synthetic datasets but also apply MMPU to various typical computer vision tasks, including handwritten digit recognition, violent behavior detection, and hyperspectral image classification. The results demonstrate that MMPU can obtain a superior performance compared to the state-of-the-art PU learning methodologies.
Defending <i>N</i>-Version Programming Service Components against Co-Resident Attacks in IoT Cloud Systems

The real innovation of Internet of Things (IoT) can be spurred only when being combined with cloud computing, a paradigm that allows numerous users to simultaneously access configurable resources and services. However, serious vulnerability concerns have arisen from the virtual machine co-resident architecture of the IoT cloud. Specifically, co-resident attacks can be launched, where an attacker can access and corrupt a user's sensitive data/software by co-locating their virtual machines on the same physical server. Various solutions have been suggested in literature to mitigate negative effects of the co-resident attacks in the cloud environment. However, to the best of our knowledge no work has been performed for studying co-resident attacks in cloud systems with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -version programming (NVP), a popular redundancy technique for enhancing survivability of critical cloud service components. This paper makes original contributions by modeling IoT cloud system services implementing the NVP component redundancy, and evaluating the corruption probability of the NVP service component. Further, users’ policies on choosing the optimal number of service component versions are investigated through formulating and solving a new set of optimization problems with the objective to minimize the expected cost of losses of a cloud service provider. As demonstrated through examples, these policies can effectively help defend the NVP service component against the co-resident attacks in the cloud system.
Image set classification using candidate sets selection and improved reverse training

Image set classification is recently a competitive technique, of which each gallery set and query set contains multiple images of a person or an object. However, strong noise and outlier could be generated from undesirable environmental conditions in real-world applications, which will result in deterioration of classification accuracy, robustness, and high time complexity of special learning algorithm. To address these problems, this paper presents a novel parameterless method called Candidate Sets Selection and Improved Reverse Training (CSSIRT). It aims at enhancing noise robustness and reducing time complexity while retaining the high classification accuracy via two-stage successive cascaded framework. Specifically, an image set is modeled as a robust reduced affine hull, which provides a better representation to account for the unseen appearances of each image set. The first stage selects the most promising top-N candidate sets and rejects the vast majority of the impossible gallery sets quickly by measuring dissimilarity between two hulls. Since few returned candidate sets from the first stage may intersect the given test one, the second stage utilizes the returned candidate sets to address the issue on sets overlapped by using a novel K-means clustering and normalized frequency histograms fusion based reverse training algorithm. The method we proposed is more robust, has a lower time complexity and higher accuracy. Three kinds of extensive comparisons with the other state-of-the-art methods have corroborated the superiority of our method on a number of challenging datasets.
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data

Cloud computing has become a popular approach to manage personal data for the economic savings and management flexibility in recent year. However, the sensitive data must be encrypted before outsourcing to cloud servers for the consideration of privacy, which makes some traditional data utilization functions, such as the plaintext keyword search, impossible. To solve this problem, we present a multi-keyword ranked search scheme over encrypted cloud data supporting dynamic operations efficiently. Our scheme utilizes the vector space model combined with TF $\times $ IDF rule and cosine similarity measure to achieve a multi-keyword ranked search. However, traditional solutions have to suffer high computational costs. In order to achieve the sub-linear search time, our scheme introduces Bloom filter to build a search index tree. What is more, our scheme can support dynamic operation properly and effectively on the account of the property of the Bloom filter, which means that the updating cost of our scheme is lower than other schemes. We present our basic scheme first, which is secure under the known ciphertext model. Then, the enhanced scheme is presented later to guarantee security even under the known background model. The experiments on the real-world data set show that the performances of our proposed schemes are satisfactory.
Still Image Action Recognition by Predicting Spatial-Temporal Pixel Evolution

Both spatial and temporal patterns provide crucial information for recognizing human actions. However, lack of temporal information in still images is a major obstacle in single-image action recognition. In this paper, (i) We introduce a novel image representation domain, Ranked Saliency Map and Predicted Optical Flow or Rank_SM-POF for short. This domain captures both actor appearance and the future movement patterns of the actor. This is accomplished through capturing the temporal ordering of each pixel by training a linear ranking machine on the predicted tensor of spatial-temporal representation of images. (ii) We employ a transfer learning approach to propose a new spatial-temporal Convolutional Neural Network, named STCNN for the task of single image action classification, by fine-tuning a CNN which is pre-trained specifically for appearance based classification. (iii) Finally, extensive experiments on five benchmarks clearly demonstrate that appearance and motion are complementary sources of information and using both leads to significant performance improvement in single image action recognition, hence outperforming state-of-the-art methods.
The Path of Reform: The Consequences of Institutional Volatility

Summary The new institutional literature widely acknowledges the benefits of growth‐enhancing institutions but rarely discusses the path of institutional reform. While good institutions stabilize the structure of exchange and decrease uncertainty in market transactions, institutional reform may involve institutional volatility. If institutional volatility increases uncertainty, it can mitigate the benefits of reform. Using a sample of 89 countries from 2000‐2015, this paper empirically examines the effects of institutional volatility on economic growth. We find that institutional volatility decreases economic growth, particularly during liberalization for countries with low quality institutions and low income. In fact, a one standard deviation increase in volatility decreases growth by about 0.50 percentage points. This finding is robust to multiple estimation techniques and omitted variable bias. Evidence is provided suggesting that this effect is partially mediated through volatility's impact on private investment. These results support prior works that policy makers should pursue economic freedom, but our work indicates they should do so along a stable reform path to maximize economic growth.
Multi-Lingual Scene Text Detection Using One-Class Classifier

The main purpose of scene text recognition is to detect texts in a given image. The problem of text detection and recognition in such images has gained great attention in recent years due to rising demand of several applications like visual based applications, multimedia and content-based retrieval. Due to low accuracies of existing scene text detection methods, an improved pipeline is developed for text localizing task. First, candidate text regions are generated using Maximally Stable Extremal Region and Stroke Width Transform methods that capture true positives along with many false positives. A One Class Classifier is trained to label the candidate regions obtained, as text or non-text, which in this case is suitable as non-text class cannot be adequately represented to train a binary classifier. The one class classifier is trained with some popular feature descriptors like Histogram of Oriented Gradients, Grey Level Co-Occurrence Matrix, Discrete Cosine Transform and Gabor filter. Experimental results show high recall for text containing regions and reducing false positives.
A Study over NoSQL Performance

Large amounts of data (BigData) are nowadays stored using NoSQL databases and typically stored and accessed using a key-value format. However, depending on the NoSQL database type, different performance is offered. Thus, in this paper, NoSQL database performance is evaluated and compared in aspects relating with, query performance, based on reads and updates. In this paper, the five most popular NoSQL databases are tested and evaluated: MongoDB; Cassandra; HBase; OrientDB; Voldemort; Memcached and Redis. To assess the mentioned databases, are used, workloads represented by Yahoo! Cloud Serving Benchmark. Results allow users to choose the NoSQL database that better fits application needs.
Adaptive neuro-fuzzy behavioral learning strategy for effective decision making in the fuzzy-based cloud service negotiation framework

Future cloud computing creates a new trend of opting service over the internet through some intelligent third-party broker. In cloud market, both consumer and provider compete with each other against the conflicting requirements, and the competition among cloud providers to trade their services to potential consumers of cloud market. There is an increasing need for automated negotiation framework to quickly reach agreement in competitive cloud market which can provide maximum utility value and success rate among the negotiating parties. Researchers develop various behavioral learning negotiation strategies (such as market driven) in the existing negotiation frameworks for maximizing either the choice of utility value or success rate of parties. Moreover these strategies can be applicable to the environment, where the opponent’s behaviors are predictable or precisely known. It may be daunting to apply in the dynamically varying competitive cloud market. So, the proposed Adaptive Neuro-Fuzzy Behavioral Learning (ANFBL) strategy can be applicable, where the opponent’s behavior is partially and imprecisely known. Therefore, the proposed strategy can maximize both utility value and success rate without compromising either choice. An extensive simulation is conducted to evaluate the efficiency of strategies which shows that proposed strategy achieve higher utility and higher success rate than existing learning approach, without any negotiation conflict among the parties.