Reviewerly/exHarmony huggingface dataset:
Initial Public Offering (IPO) on Permissioned Blockchain Using Secure Multiparty Computation

In this work, we add secure multiparty computation capabilities to the permissioned blockchain architecture of Hyperledger Fabric, and use them to implement the clearing price mechanism for initial public offering (IPO). As with any blockchain, the core property in Fabric is that all peers must see the same ledger, so using confidential data on the ledger is a challenge. To address this challenge we use cryptographic secure multiparty computation (MPC), which requires that we integrate a few new mechanisms into the Fabric architecture. Specifically we need to let the peers access local information such as their respective secret keys, and also send messages to each other while executing smart contracts. We also had to add to Fabric a library that implements the required cryptographic tools, and to make that library accessible from the smart contracts. We demonstrated the effectiveness of this solution by using it to implement the clearing price mechanism for IPOs. We designed an efficient cryptographic protocol for the IPO clearing price mechanism, and used our integrated system to run it on Fabric. Although not fully optimized yet, the performance of the resulting implementation is more than fast enough for this particular application, ranging from 8 to 23 seconds to execute an IPO sale.
No-reference quality index of depth images based on statistics of edge profiles for view synthesis

Virtual view synthesis has been increasingly popular due to the wide applications of multi-view and free-viewpoint videos. In view synthesis, texture images are rendered to generate the new viewpoint with the guidance of the depth images. The quality of depth images is vital for generating high-quality synthesized views. While the impact of texture image and the rendering process on the quality of the synthesized view has been extensively studied, the quality evaluation of depth images remains largely unexplored. With this motivation, this paper presents a no-reference image quality index for depth maps by modeling the statistics of edge profiles (SEP) in a multi-scale framework. The Canny operator is first utilized to locate the edges in depth images. Then the edge profiles are constructed, based on which the first-order and second-order statistical features are extracted for portraying the distortions in depth images. Finally, the random forest is employed for building the quality assessment model for depth maps. Experiments are conducted on two annotated view synthesis image/video quality databases. The experimental results and comparisons demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics by a large margin. Furthermore, it has better generalization ability.
Big History Trends in Information Processes

The encoding, transmission, processing, and other aspects of information have changed throughout Big History and especially on the planet Earth. Beginning ~15 billion years ago, information was manifested and communicated via the fundamental forces and particles described by physics. When life began ~5 billion years ago, DNA-RNA and other associated molecules like ATP made the various aspects of information possible. The divergence of the human brain from the remainder of the great apes began about ~5 million years and resulted in true humans around 50000 years ago with an increase in self-awareness, theory of mind which allowed for complex social relationships, abstract thought, and a rich language to name a few of its higher mental functions. High civilization and writing began about 5 thousand years ago which allowed for even greater social complexity, and improved communication of information with increased longevity, a decrease in errors, and increased diffusion to name a few important qualities. These and other changes in information continue to the current day. A previous analysis by Richard Coren of the events in human information processing seemed to indicate a hyperbolic pattern leading to a singularity, rather than an exponential curve. Various current factors might dampen the proposed hyperbolic growth to become more logistic, i.e., limited growth at the identified singularity time. Candidates for slowing this progression include processes such as adoption time, stress of increasing complexity, and overcoming the barrier of a deeper understanding of the human brain and mind. Also, information processes have many interrelated aspects, including the unanticipated types of entropy or disorder; information generation, diffusion, storage; unanticipated or unaccounted complexities; levels of abstraction; error correction; and inherent uncertainties. These and other aspects of information, like energy flow and entropy, cannot likely be ignored when forecasting future trends, including technological ones.
Preserving Patient’s Privacy using Proxy Re-encryption in Permissioned Blockchain

Privacy is a fundamental right for every human, but in today's world, this is not ensured in every sector. The health sector is such an example where patients privacy is not respected, and sensitive data like prescription dosages, medical bill amounts, and their entire medical history are leaked online without any encryption. We designed, implemented and deployed an architecture in this research where we tried to use Public Key Infrastructure (PKI) and Hyperledger fabric and simulated workflow of healthcare sector while ensuring that patients' medical records are in complete control of patient only. Hyperledger fabric [1], which is a blockchain framework, provides integrity to the medical records, which can be verified at any later point of time. Proxy re-encryption [2] has been used to provide medical records access to others but only with permission from the patient. Besides patients and hospitals, this architecture also involves other institutions like insurance companies and pharmacies to provide various services to the patients. Lastly, we performed some performance experiments on the architecture to check the throughput and latencies.
3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture

Massive amount of training samples is a challenge for 3D face recognition using deep learning frame. This paper shows a method that uses deep twin neural network for 3D face recognition by blending face 3D depth and 2D texture. First, a depth map is generated. In order to repair holes in the 3D face model with low complexity, we map those 3D hole points into 2D plane, and then reverse them back to 3D space by the least square rule. Second, a convolution kernel model with two layer channels is used to fuse face image and depth image. Finally, after sample pairs are generated, 3D face recognition is performed by convolutional twin neural network. The experimental results on CASIA-3D dataset show that, compared with the classical CNN method, the recognition accuracy of our method increases about 2.85%. And in the case of using small training sets, the recognition rate of our method is about 4% higher.
Examining the Social Organization Practices of Cybercriminals in the Netherlands Online and Offline

This study focuses on the organization practices of networks of cybercriminals engaged in serious financial offenses, through a qualitative analysis of the Best and Luckenbill's sociological framework. The study utilized data collected regarding 18 separate criminals investigations from the Netherlands. The results demonstrate that the participants within these networks operated at various stages of deviant sophistication. Surprisingly, the majority of networks exhibit organizational sophistication based on their division of labor and extended duration over time. In fact, most of this sample could be classified as "teams" or "formal organizations." Furthermore, in contrast with prior studies, no loners were present and only a few networks could be classified as "colleagues" or "peers."
Discrete Spherical Image Representation for CNN-Based Inclination Estimation

How an image is represented as the input of a convolutional neural network (CNN) is important because this input directly influences the performance of the CNN.In this paper, we investigate the representation of spherical images by focusing on the inclination estimation of a spherical camera.Unlike other approaches to CNN-based inclination estimation, a spherical image is represented as a geodesicdivision-based discrete spherical image (DSI) that is obtained by sampling a sphere as uniformly as possible.The input of the CNN is a single image that consists of five parallelograms flattened from a regular icosahedron.To demonstrate the advantage of the proposed method, comparative experiments are conducted with two other spherical image representations, namely, equirectangular projection (ERP) and cubemap projection (CMP).The experimental results show that the proposed method using a geodesic-division-based discrete spherical image as the CNN input obtains the best performance-better than that of the cubemap and far superior to that of the equirectangular image.The effect of the image representations used becomes more significant as the relative inclination decreases.Moreover, comparative experiments are conducted using the state-of-the-art methods for spherical camera inclination compensation to further illustrate the superiority of the DSI representation.Consequently, the proposed method provides an important reference for the development of CNNs intended for spherical images.
Indonesian Sign Language Recognition Based on Shape of Hand Gesture

In this research the proposes a method of recognition of BISINDO letters based on hand-shape features that hint every shape of BISINDO Letters. In outline, this method is divided into two parts: the first is part of formation database shape features of BISINDO letters A-Z and the second is part of BISINDO letters recognition. In the first section consist of hand-shape image acquisition that hint every BISINDO letters, segmentation process, edge detection process, feature extraction process that is probability value of hand-shape chain code occurrence and process of database feature formation. In the second section is consist of hand-shape image acquisition process as BISINDO letters query followed by segmentation process, edge detection process, hand-shape feature extraction and recognition process by using calculation difference in distance between query shape feature to each shape feature in database feature. The image acquisition process in two parts above conducted directly (real time) via Webcam connected to the computer device. The method above has been implemented into prototype of Bisindo letters recognition software interface. The experiment results show the accuracy level of BISINDO letter recognition (26 BISINDO letters A to Z) which is reaching above 95%.
Conditional restricted Boltzmann machine for item recommendation

Recommender systems provide an excellent solution to the issue of information overload by generating item recommendation from a huge collection of items based on users’ preferences. In terms of modeling users’ rating data, existing methods are mainly neighborhood- and factorization-based methods, most of which are rating oriented. Among network-based methods, the restricted Boltzmann machine (RBM) model is also applied to rating prediction tasks. However, item recommendation tasks play a more important role in the real world, due to the large item space as well as users’ limited attention. In this paper, we treat users’ rating behaviors from a new perspective and study the effectiveness of conditional RBM (CRBM) in modeling users’ rating preferences for top-k recommendation. We conduct extensive empirical studies on four real-world datasets and find that our proposed CRBM-IR is very competitive in exploiting users’ explicit rating feedback in comparison with the closely related works.
Manifold learning for efficient gravitational search algorithm

Metaheuristic algorithms provide a practical tool for optimization in a high-dimensional search space. Some mimic phenomenons of nature such as swarms and flocks. Prominent one is the Gravitational Search Algorithm (GSA) inspired by Newton’s law of gravity to manipulate agents modeled as point masses in the search space. The law of gravity states that interaction forces are inversely proportional to the squared distance in the Euclidean space between two objects. In this paper we claim that when the set of solutions lies in a lower-dimensional manifold, the Euclidean distance would yield unfitted forces and bias in the results, thus causing suboptimal and slower convergence. We propose to modify the algorithm and utilize geodesic distances gained through manifold learning via diffusion maps. In addition, we incorporate elitism by storing exploration data. We show the high performance of this approach in terms of the final solution value and the rate of convergence compared to other meta-heuristic algorithms including the original GSA. In this paper we also provide a comparative analysis of the state-of-the-art optimization algorithms on a large set of standard benchmark functions.
Deterministic Sub-Wallet for Cryptocurrencies

A big challenge in cryptocurrency is securing a user key from potential hackers because nobody can rollback a transaction made by an attacker with a stolen key once the blockchain network confirms it. One solution to protect users is splitting the money between super-wallet and sub-wallet. The user stores a large amount of money on her super-wallet and keeps it safe; she refills the sub-wallet when she needs while using the sub-wallet for her daily purchases. In this paper, we propose a new scheme to create sub-wallet that we call deterministic sub-wallet. In this scheme, the seed of the sub-wallet keys is derived from the super-wallet master seed, and therefore the super-wallet can build many sub-wallet addresses and refill them in a single blockchain transaction. Compared to existing approaches, our mechanism is cheaper, real-time, more secure against man-in-the-middle attack and easier for backup and recovery. We implement a proof-of-concept on a hardware wallet and evaluate its performance. In addition, we analyze the attacks and defenses of this design to demonstrate that our proposed method has a higher level of security than existing models.
Real-Time Single-Stage Vehicle Detector Optimized by Multi-Stage Image-Based Online Hard Example Mining

Vehicle detection is a fundamental function required for advanced driver assistance systems. Extensive research has shown that good performance can be obtained on public datasets by various state-of-the-art approaches, especially the deep learning methods. However, those methods are mostly two-stage approaches which inevitably require extensive computing resources and are hard to be deployed on an embedded computing platform with real-time computing performance. We introduce a single-stage vehicle detector which can work in real-time on NVIDIA DrivePX2 platform. The main contributions of this paper are threefold. We propose a detection scheme which includes multi-scale features and multi-anchor boxes to improve the accuracy of a single-stage detector. Secondly, a new data augmentation strategy is proposed to systematically generate a lot of vehicle training images whose appearances are randomly truncated, so our detector is trained to detect partially-seen vehicles better. Thirdly, we present a multi-stage image-based online hard example mining (MSI-OHEM) framework specifically designed for single-stage detectors. MSI-OHEM performs fine-tuning on hard examples and the ones with slightly-insufficient IOU that are considered true positives. Compared to other classical object detectors, the proposed detector achieves very competitive result in terms of average precision (AP) and computational speed. For the newly-defined vehicle class (car+bus) on VOC2007 test, our detector, using MobileNetV2, GoogLeNet, Inception-v2 and ResNet-50 as basenets, achieves 85.35%/85.62%/86.49%/87.81% AP and runs at 64/58/48/28 FPS on NVIDIA DrivePX2, respectively.
Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder

Spatiotemporal irregularities (i.e., the uncommon appearance and motion patterns) in videos are difficult to detect, as they are usually not well defined and appear rarely in videos. We tackle this problem by learning normal patterns from regular videos, while treating irregularities as deviations from normal patterns. To this end, we introduce a 3D fully convolutional autoencoder (3D-FCAE) that is trainable in an end-to-end manner to detect both temporal and spatiotemporal irregularities in videos using limited training data. Subsequently, temporal irregularities can be detected as frames with high reconstruction errors, and irregular spatiotemporal patterns can be detected as blurry regions that are not well reconstructed. Our approach can accurately locate temporal and spatiotemporal irregularities thanks to the 3D fully convolutional autoencoder and the explored effective architecture. We evaluate the proposed autoencoder for detecting irregular patterns on benchmark video datasets with weak supervision. Comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach. Moreover, the learned autoencoder shows good generalizability across multiple datasets.
Efficient embedding and retrieval of information for high-resolution videos coded with HEVC

Steganography is the art of hiding information within a file. This work focuses on embedding such information in videos. In this scenario, it is critical to comply with the latest video standard, namely: the High Efficiency Video Coding (HEVC), which allows reducing the size of the file to be transmitted. In this paper we propose an HEVC-compliant method to hide and retrieve information in high-resolution videos. The procedure is based on modifying the luminance of certain blocks, not the HEVC encoder. Nevertheless, this must be carefully done, as the HEVC standard is a powerful attack in itself, since it compresses 50% the size of the video on average and the embedded information may disappear. Results show that it is possible to retrieve all the information while maintaining the quality of the video when using intra frames. Furthermore, the proposed flow has shown to be resilient to state-of-the-art steganalysis attacks.
Comparative Web Search Questions

\beginabstract We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple "ten blue links'' and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task.
Pose Guided Attention for Multi-Label Fashion Image Classification

We propose a compact framework with guided attention for multi-label classification in the fashion domain. Our visual semantic attention model (VSAM) is supervised by automatic pose extraction creating a discriminative feature space. VSAM outperforms the state of the art for an in-house dataset and performs on pair with previous works on the DeepFashion dataset, even without using any landmark annotations. Additionally, we show that our semantic attention module brings robustness to large quantities of wrong annotations and provides more interpretable results.
M-learning technology in Arab Gulf countries: A systematic review of progress and recommendations

With the recent development in mobile devices and mobile services, mobile learning (m-learning) has become one of the most common research topics across Arab Gulf countries (AGC). The literature showed an inconsistent usage of m-learning in these countries. This paper sheds light on the progress of m-learning use in AGC, as well as offering recommendations to improve its adoption success. A systematic literature review was conducted to identify the current evidence on the use of m-learning in AGC across several groups of instructors and students. The results from reviewing 31 previous studies showed that students' and instructors' acceptance and use of m-learning were the main topics of concerned. In addition, the lack of research on leadership and policy practices in AGC may lead to technology failure. The key recommendations on best practices related to the use of m-learning in AGC were presented and discussed.
Small Police Agency Use of Social Media: Positive and Negative Outcomes Noted in a Case Study

Abstract Many studies on police use of social media focus on large police agencies. The current case study investigates how a rather small police agency has attracted broad attention on ‘Facebook’. By selecting a relatively small police agency that received a great many Facebook ‘likes’, the study investigates two social media-centered questions: (1) What are this police agency’s strategies of engaging so many citizens on Facebook? and (2) what are potential risks related to these strategies? Findings suggest that two major strategies were used to achieve such uncommon ‘subscriber success’ on Facebook: (1) maintaining the department’s Facebook page as a personal account page directed towards a Chief’s or Sheriff’s own philosophy of policing and (2) writing Facebook posts in a folksy vernacular featuring slang terms and relating humorous (often suspect deprecating) stories using such language. The current study proposes that law enforcement agencies may use informal communication to engage citizens, but they also need a formal team to give consistency and avoid dependency on a single person regarding managing their Facebook accounts. Burgoon’s (1978) expectancy violations theory is used to help explain both the positive and negative outcomes noted in the current study. The principal public policy implications regarding social media use by police agencies are discussed, along with implications for further research.
Dimension Invariant Model for Human Head Detection

Detecting heads with full variations in camera view points, human poses, appearances, and scales is a key problem for many computer vision applications. Region convolutional neural networks (RCNN) achieved considerable success in handling the variances in poses and appearances. However, RCNN are inefficient in handling human heads of diverse scales. In this work, we focus on detecting human heads in complex scenes. Starting with traditional RCNN model, we extend it by leveraging person-scene relations and propose a dimension invariant convolutional neural network (DCNN) that coarsely predicts locations and scales of heads directly from the full image. We evaluate and compare our method with famous methods by using two benchmarks datasets. Experimental results show that our method outperforms these methods.
Active Contours Driven by Local and Global Region-Based Information for Image Segmentation

Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises.