Reviewerly/exHarmony huggingface dataset:
Experimental demonstration of Einstein-Podolsky-Rosen entanglement in rotating coordinate space

Einstein-Podolsky-Rosen (EPR) entanglement involving a pair of particles entangled in their positions and momenta is of special interest in the field of quantum information. Previously, EPR entanglement has been studied in different physical systems but in fixed coordinate spaces. Here, we demonstrate an experiment of ghost imaging and ghost interference in rotated position-momentum spaces by using position-momentum entangled photons generated from a hot atomic ensemble. By using different image objects, the measured position-momentum correlations exhibit intriguing dynamics, including gradual decrease and axis-independent EPR entanglement. The reported results on manipulating the EPR entanglement in rotating coordinate spaces hold promise in quantum communication and distant quantum image processing.
Repairing Reed-Solomon Codes Over $GF(2^\ell)$

We provide a detailed algorithm to repair a single node failure in an (n, k) Reed-Solomon code over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sup> ) with repair bandwidth ℓ/a (n - 1)(a-s), for any integers a, s such that a|ℓ, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> ≥ n + 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ≤ n-k. We present the constructions of necessary lookup tables for the repair. The storage overhead and the repair complexity of our algorithm are also analyzed. The algorithm can be applied to the (14, 10) Reed-Solomon codes over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ), which is a modification of the code in Facebook's f4 system, and reaches the lowest repair bandwidth among the existing schemes to the best of our knowledge. The algorithm can be generalized to other codes, including the ones based on Yahoo Object Store and Baidu's Atlas.
Multisource Labeled Data: an Opportunity for Training Deep Learning Networks

This paper addresses the opportunities and the challenges offered by multisource labeled data in the framework of deep learning techniques. After a review of the types of multisource labeled data, the focus is devoted to their use for the training of deep learning classification architectures. The need to generate training sets containing a very large number of labeled samples pushes toward the exploitation of all the possible available sources of labeled data. This crucial topic is addressed by categorizing the approaches to the collection of labeled data and presenting a framework for characterizing and modeling their information content and uncertainties to be used in the training of processing algorithms. The framework defines the main expected properties of large multisource training sets and relates them to both the characteristics of different data sources and the possible learning paradigms for the training of a deep architecture.
An Augmented Reality Application for Personalised Diamond Shopping

This paper presents an Augmented Reality (AR) Application (App) developed for bespoke jewellery solutions. The App allows users to experience a visual representation of diamond rings through their mobile device. Customers could design and review bespoke jewellery without having to visit the jeweller's shop and could communicate changes to the jeweller in real-time (saving time and money). The use of AR would allow customers to view their bespoke jewellery on their fingers to gain a better idea of how it would look once completed. Twenty seven participants evaluated the App by completing a questionnaire after using it. The application of Exploratory Factor Analysis resulted in four factors (compatibility, likeability, functionality and usability). Overall, the participants appreciated the AR App; regardless of their gender, age and experience.
Efficiency Issues and Solutions in Blockchain: A Survey

With the growing number of blockchain-based solutions in a few industries, we have moved to the next level of digital privacy. Meanwhile, several concerns related to transaction speed, data size, tracking information on public distributed ledgers faced to us. Time constraints and data overflow will lead to high financial expenses. In this paper, we address the efficiency issues of blockchain technology by reviewing recent significant work. First, we list the main challenges facing for efficiency. Then, we survey prior methods to handle efficiency issues studied by authors. Finally, we will discuss open challenges and conclude the work. This work can be helpful to determine future work directions.
Research on Traffic Sign Detection Based on Convolutional Neural Network

TSD (Traffic Sign Detection) is a hotspot in autonomous driving and assisted driving research. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detecting tasks. It shows better accuracy or faster execution speed than traditional method. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved CNN model based on YOLO model, darknet 53 construction. By introducing batch normalization and RPN networks and improving the network structure for traffic sign detection tasks, the YOLO neural network detection model is optimized. The accuracy of the model in the traffic sign detection task is greatly improved, and the detection speed becomes faster. The results show that the method in this paper is of great help to improve the accuracy and detection speed of traffic sign detection and reduce the hardware requirements of the detection system as well.
On the Universally Composable Security of OpenStack

We initiate an effort to provide a rigorous, holistic and modular security analysis of OpenStack. OpenStack is the prevalent open-source, non-proprietary package for managing cloud services and data centers. It is highly complex and consists of multiple inter-related components which are developed by separate, loosely coordinated groups. All of these properties make the security analysis of OpenStack both a worthy mission and a challenging one. We base our modeling and security analysis in the universally composable (UC) security framework. This allows specifying and proving security in a modular way – a crucial feature when analyzing systems of such magnitude. Our analysis has the following key features: 1) It is user-centric: It stresses the security guarantees given to users of the system in terms of privacy, correctness, and timeliness of the services. 2) It considers the security of OpenStack even when some of the components are compromised. This departs from the traditional design approach of OpenStack, which assumes that all services are fully trusted. 3) It is modular: It formulates security properties for individual components and uses them to prove security properties of the overall system. Specifically, this work concentrates on the high-level structure of OpenStack, leaving the further formalization and more detailed analysis of specific OpenStack services to future work. Specifically, we formulate ideal functionalities that correspond to some of the core OpenStack modules, and then proves security of the overall OpenStack protocol given the ideal components. As demonstrated within, the main challenge in the high-level design is to provide adequately fine-grained scoping of permissions to access dynamically changing system resources. We demonstrate security issues with current mechanisms in case of failure of some components, propose alternative mechanisms, and rigorously prove adequacy of then new mechanisms within our modeling.
Poster

In this study, we conduct the first study on the analysis of voice assistant (VA) apps. We first collect the metadata of VA apps from the VA app directory and analyze them. Next, we call VA apps by the corresponding voice commands and examine how they identify users by analyzing the responses from the apps. We found that roughly half of the VA apps performed user identification by some means. We also found that several apps aim to acquire personal information such as birth date, age, or the blood type through voice conversations. As such data will be stored in the cloud, we need to have a mechanism to ensure that an end-user can check/control the data in a usable way.
Optical Image Encryption Using a Nonlinear Joint Transform Correlator and the Collins Diffraction Transform

The Collins diffraction transform (CDT) describes the optical wave diffraction from the generic paraxial optical system. The CDT has as special cases the diffraction domains given by the Fourier, Fresnel and fractional Fourier transforms. In this paper, we propose to describe the optical double random phase encoding (DRPE) using a nonlinear joint transform correlator (JTC) and the CDT. This new description of the nonlinear JTC-based encryption system using the CDT covers several optical processing domains, such as Fourier, Fresnel, fractional Fourier, extended fractional Fourier and Gyrator domains, among others. The maximum number of independent design parameters or new security keys of the proposed encryption system using the CDT increases three times in comparison with the same encryption system that uses the Fourier transform. The proposed encryption system using the CDT preserves the shift-invariance property of the JTC-based encryption system in the Fourier domain, with respect to the lateral displacement of both the key random mask in the decryption process and the retrieval of the primary image. The viability of this encryption system is verified and analysed by numerical simulations.
Evaluating salient object detection in natural images with multiple objects having multi‐level saliency

Salient object detection is evaluated using binary ground truth with the labels being salient object class and background. In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. Our dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multi-level, we propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, we generate multi-level maps as ground truth corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. We then propose the use of mean absolute error, Kendall's rank correlation and average area under precision-recall curve to evaluate existing salient object detection methods on our multi-level saliency ground truth dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in our dataset.
Digitisation of Assets from the Oil &amp; Gas Industry: Challenges and Opportunities

Automated processing and analysis of legacies of printed documents across the Oil & Gas industry provide a unique opportunity and at the same time pose a significant challenge.One particular example is the case of Piping and Instrumentation Diagrams (P&IDs).These are complex engineering drawings that are extensively used in the Oil & Gas industry, which contain critical information for risk assessment, and require highly skilled people to provide an accurate interpretation and analysis of their contents.This paper provides an overview of the P&IDs digitisation problem.We outline the opportunities and key challenges, discuss recent relevant work and state of the art and outline possible future direction to solve the problem.During a two-years collaborative project with an industrial partner from the Oil & Gas sector, we have encountered three main challenges other than traditional inherent image and document related challenges.These are, documents quality, skewed distribution of data and topology.In this paper, we discuss these challenges in depth and survey the main state-of-the art methodologies that may solve them.
An Evaluation Of Helpdesk With Gamification Using Indeks Kepuasan Masyarakat (IKM)

Gamification is a concept of game element that is applied to non-game contexts, Helpdesk is an IT (Information Technology) section. It is first contacted by a user when someone has questions or problems related to IT services. UIN Syarif Hidayatullah Jakarta as a tertiary institution also has a help desk facilities as the tools to solve problems related to IT service. Based on study, the problem that common occurs is, the user is aware of the helpdesk service facilities on campus, but they prefer to make complaints directly to the relevant division. The concept of gamification with elements of points, badges, levels, leaderboard, and rewards is applied to the helpdesk system through the RAD (Rapid Application Development) development method. The method of evaluating the helpdesk system is done in two stages, first pre-test and second post-test. It through two application which is game based helpdesk and non-game based help desk applications. Using Indeks Kepuasan Masyarakat (IKM) as the calculation method of gamification helpdesk and End User Computing Satisfaction (EUCS) as an indicator service of the IKM that will be tested.
Extended multiple feature-based classifications for adaptive loop filtering

Abstract Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algorithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifications for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Furthermore, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm.
An Adaptive Robust Semi-supervised Clustering Framework Using Weighted Consensus of Random k-Means Ensemble

Semi-supervised cluster ensemble usually introduces a small amount of supervision in the first stage of cluster ensemble, i.e., ensemble generation, by performing many runs of semi-supervised clustering algorithms. However, it is neither efficient in terms of computational complexity, nor flexible in a dynamic learning environment where limited supervision changes over time. In this article we propose a new framework which generates base partitions in an unsupervised manner and attributes different weights to each cluster of the base partitions. The weighting scheme considers both the internal validation measures of clustering and the degrees of satisfaction of pairwise constraints. A weighted co-association matrix based consensus approach is then applied to achieve a final partition. To handle high-dimensional data, we generate base partitions using k-means with both random sampling and random subspace techniques. The new framework retains a high accuracy, and is efficient since it avoids performing semi-supervised clustering in ensemble generation and the complexity of the weighting scheme is independent of the number of instances in a dynamic environment. It is more adaptive than the traditional approach because it does not require rerunning semi-supervised clustering algorithms when the limited supervision changes. Empirical results on 12 datasets demonstrate that it is also more robust to noisy constraints.
Distribution Structure Learning Loss (DSLL) Based on Deep Metric Learning for Image Retrieval

The massive number of images demands highly efficient image retrieval tools. Deep distance metric learning (DDML) is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, which has achieved encouraging results. The loss function is crucial in DDML frameworks. However, we found limitations to this model. When learning the similarity of positive and negative examples, the current methods aim to pull positive pairs as close as possible and separate negative pairs into equal distances in the embedding space. Consequently, the data distribution might be omitted. In this work, we focus on the distribution structure learning loss (DSLL) algorithm that aims to preserve the geometric information of images. To achieve this, we firstly propose a metric distance learning for highly matching figures to preserve the similarity structure inside it. Second, we introduce an entropy weight-based structural distribution to set the weight of the representative negative samples. Third, we incorporate their weights into the process of learning to rank. So, the negative samples can preserve the consistency of their structural distribution. Generally, we display comprehensive experimental results drawing on three popular landmark building datasets and demonstrate that our method achieves state-of-the-art performance.
A Framework for Automatic Morphological Feature Extraction and Analysis of Abdominal Organs in MRI Volumes

The accurate 3D reconstruction of organs from radiological scans is an essential tool in computer-aided diagnosis (CADx) and plays a critical role in clinical, biomedical and forensic science research. The structure and shape of the organ, combined with morphological measurements such as volume and curvature, can provide significant guidance towards establishing progression or severity of a condition, and thus support improved diagnosis and therapy planning. Furthermore, the classification and stratification of organ abnormalities aim to explore and investigate organ deformations following injury, trauma and illness. This paper presents a framework for automatic morphological feature extraction in computer-aided 3D organ reconstructions following organ segmentation in 3D radiological scans. Two different magnetic resonance imaging (MRI) datasets are evaluated. Using the MRI scans of 85 adult volunteers, the overall mean volume for the pancreas organ is 69.30 ± 32.50cm3, and the 3D global curvature is (35.23 ± 6.83) × 10-3. Another experiment evaluates the MRI scans of 30 volunteers, and achieves mean liver volume of 1547.48 ± 204.19cm3 and 3D global curvature (19.87 ± 3.62) × 10- 3. Both experiments highlight a negative correlation between 3D curvature and volume with a statistical difference (p < 0.0001). Such a tool can support the investigation into organ related conditions such as obesity, type 2 diabetes mellitus and liver disease.
Fully Convolutional SVM for Car Detection In Uav Imagery

Semantic segmentation is understanding images at pixel level, which becomes increasingly vital in unmanned aerial vehicles (UAV) imagery classification tasks. With the powerful calculating ability of GPU, a growing number of deep convolutional neural networks (DCNNs) are designed to address semantic segmentation challenges. However, compared with CPU, GPU is much more costly, and GPU relies on powerful supplementary equipments to support it. Therefore, GPU-based methods are hard to carry out in practical applications. In this study, we propose a CPU-based method named fully convolutional support vector machine (FCSVM) to address the semantic segmentation challenge in UAV images. On the one hand, we adopt the SVM kernel from the original convolutional SVM networks (CSVM), which completes classification at image level. On the other hand, FCSVM consists of two processes which are a compressed process and a extensive process. In the compressed process, the FCSVM has convolutional layers and reduction layers. In the extensive process, the FCSVM has convolutional layers and upsampling layers. This structure allows FCSVM to classify images at pixel level using limited number of training data. The experiments are implemented on one UAV dataset with very little training data. The result shows our FCSVM achieves competitive performance compared to modern state-of-the-art semantic segmentation methods.
Learning Gestures Using A Passive Data-Glove With RFID Tags

Hand gesture recognition enables non-tactile interfaces for human-machine interactions. Cameras are currently powerful tools to recognize these gestures, however, use of cameras is constrained by privacy concerns and need for welllit, line of sight implementation. In this study, we propose an alternate method to recognize gestures using a passive data-glove augmented with passive RFID tags. We envision passive tags-based gesture recognition will have applications in improving operator safety around machines, activity monitoring in factories and sign to speech recognition, etc. Low-level reader information (RSSI, Phase and Doppler frequency) can be used to capture changes to the tags in the environment, therefore generating enough information to infer gestures. In this paper, we present a technique to enable fast feature recognition using low-level reader data by correcting for inconsistencies in phase data due to frequency hopping. We experimented with four different classifiers on the low-level reader data and our Fully-Connected Neural Network (FCCN) classifier is able to learn gestures from tag-data with 98% accuracy.
Objects and scenes classification with selective use of central and peripheral image content

The human visual recognition system is more efficient than any current robotic vision setting. One reason for this superiority is that humans utilize different fields of vision, depending on the recognition task. For instance, experiments on human subjects show that the peripheral vision is more useful than the central vision in recognizing scenes. We tested our recently-developed model, that is, the elastic net-regularized hierarchical MAX (En-HMAX), in recognizing objects and scenes. In various experimental conditions, images were occluded with windows and scotomas of varying sizes. With this model, classification accuracies of up to 90% for objects and scenes were possible. Modelling human experiments, window and scotoma analysis with the En-HMAX model revealed that object and scene recognition are sensitive to the availability of data in the centre and the periphery of the images, respectively. Similarly, results of deep learning models have shown that the classification accuracy diminishes dramatically in the absence of the peripheral vision. These differences led us to further analyse the performance of the En-HMAX model with the parafoveal versus peripheral areas of vision, in a second study. Results of the second study show that approximately 50% of the visual field would be sufficient to achieve 96% accuracy in the classification of unseen images. The En-HMAX model adopts a relative order of importance, similar to the human visual system, depending on the image category. We showed that utilizing the relevant regions of vision can significantly reduce the image processing time and size.