Reviewerly/exHarmony huggingface dataset:
VU: Edge Computing-Enabled Video Usefulness Detection and its Application in Large-Scale Video Surveillance Systems

In the era of smart and connected communities, video surveillance systems, which typically involve tens to thousands of cameras, have increasingly become prominent components for public safety. In current practice, when a failure occurs in a video surveillance system, the operation and maintenance teams usually spend a substantial amount of time locating and identifying the failure; hence, the fast online response cannot be guaranteed in a large-scale video surveillance system. Meanwhile, the video data that contains potential failures consumes bandwidth that could be used for useful video data. The useless video will waste the scarce bandwidth in the network and storage usage in the cloud. The emergence of edge computing is highly promising in video preprocessing with an edge camera. A video surveillance system is a killer application for edge computing. In this article, we propose an edge computing-enabled video usefulness (i.e., VU) model for large-scale video surveillance systems. We also explore its application, e.g., early failure detection and bandwidth improvement. According to the usefulness of the video data, the VU model can locate a failure and send it to end-users on the fly. In this article, our goals are threefold: 1) proposing a comprehensive VU model. To the best of our knowledge, this is the first work to explore the feasibility of the VU model and to determine VU values in a real application; 2) reducing the mean time to detection (i.e., MTTD) efficiently via edge computing-enabled fast online failure detection approaches; and 3) relieving the network bandwidth for large-scale video surveillance systems. Our experimental results demonstrate the approaches in VU model accurately detect failures that were collected from a video surveillance system with approximately 4000 cameras. The MTTD is substantially shortened by the fast online detection approaches. The video data with the worst VU values is mostly discarded to lessen overload of the network.
Efficient and robust large-scale structure-from-motion via track selection and camera prioritization

Incremental Structure-from-Motion (SfM) techniques have exhibited superior practicability in many recent studies; however, efficiency and robustness remain key challenges for these techniques. In this work, we propose a new incremental SfM method that overcomes these problems in a united framework that contains two iteration loops. The inner loop is a track selection loop, where a well-conditioned subset of the feature tracks is iteratively selected to accelerate the time-consuming bundle adjustment. The outer loop is a camera registration loop, where the a priori camera rotations are estimated via rotation averaging on multiple orthogonal maximum spanning trees (OMSTs) of the view-graph and used as weak supervision for the registration. The calibrated camera poses that agree with the a priori camera rotations are preferentially registered, and after all the consistent cameras have been calibrated, the remaining cameras are incrementally registered. The results of extensive experiments demonstrate that our system can reconstruct both general and ambiguous image datasets, and our system outperforms many state-of-the-art SfM systems in terms of efficiency and robustness.
Fuzzy contextual inference system for medical image enhancement

In order to facilitate early detection and further diagnosis, enhancing contrast and preserving natural characteristics are the fundamental requirement. In MR imaging, the poor quality image specifically the low contrast image may deliver inadequate data for the visual interpretation of affected portions. Fuzzy Contextual Dissimilarity Adaptive Histogram Equalization (FCDAHE) is employed to enhance the contrast of medical images. The proposed method consists of two modules. In the first module, fuzzy dissimilarity histogram is formulated by finding fuzzy neighbourhood dissimilarity for every pixel using fuzzy rules. Then, the fuzzy dissimilarity clip limit is computed based on a fuzzy inference system. Contrast (C) and discrete entropy (E) are the two parameters used by the Fuzzy Inference System (FIS) to achieve fuzzy dissimilarity clip limit. Then, the enhanced output is obtained by equalizing the fuzzy dissimilarity adaptive histogram. In the second module, Contextual Intensity Transformation (CIT) is applied to fuzzy dissimilarity adaptive histogram equalized output to get final enhanced images. Experiments are conducted and tested on a wide variety of MR images extracted from BRATS 2015 database and Magnetic Resonance – Technology Information Portal to evaluate the performance of the proposed method both qualitatively and quantitatively. Extensive quantitative measures show that the proposed technique yields better performance in terms of PSNR, entropy, contrast ratio and EME when compared to state of art enhancement methods. The proposed FCDAHE algorithm not only improves contrast but also preserves natural characteristics. The proposed method offers a better scope for disease analysis and diagnosis.
An asymmetric watermarking scheme based on random decomposition in hybrid multi-resolution wavelet domain using 3D Lorenz chaotic system

In this paper, an asymmetric watermarking mechanism based on the random decomposition in hybrid multi-resolution wavelet domain is proposed. To further enhance the security of the watermarking scheme, Lorenz’s chaotic system is employed for pixel swap over process. The hybrid multi-resolution wavelets are created by utilizing Walsh transform and the fractional Fourier transform of several orders. These fractional orders along with the Lorenz framework's parameters and initial conditions broadened the key-space. The proposed mechanism has non-linear characteristics and an enlarged key-space to oppose any brute-force attack. The proposed system is validated on gray-scale images. The principle behind the development of the proposed scheme has been elaborated by carrying out cryptanalysis on system based on the random decomposition. Personal computer based reproductions have been implemented to prove the validity and authenticity of the scheme. Results obtained authenticate that the proposed cryptosystem not only offers higher protection against brute force and noise attacks but is also unassailable to special attack.
Tradable mobility permit with Bitcoin and Ethereum – A Blockchain application in transportation

Blockchain and its underlying technology have intrigued researchers with its promising applications and implications as "the second internet". There is a wide range of Blockchain applications in transportation and logistics. In this paper, we present the basic principle of cryptocurrencies and Blockchain as well as Ethereum. This is followed by the potential applications of Blockchain in transportation, logistics, and supply chain industries in which the concept of a tradable mobility permit (TMP) to combat traffic congestion is formulated and numerically tested. We then discuss the deployment of the TMP scheme based on a Blockchain platform and its by-products such as dynamic toll pricing, priority for emergency vehicles, heavy truck platooning, as well as connected vehicles.
Exploring Transfer Learning for Low Resource Emotional TTS

During the last few years, spoken language technologies have known a big improvement thanks to Deep Learning. However Deep Learning-based algorithms require amounts of data that are often difficult and costly to gather. Particularly, modeling the variability in speech of different speakers, different styles or different emotions with few data remains challenging. In this paper, we investigate how to leverage fine-tuning on a pre-trained Deep Learning-based TTS model to synthesize speech with a small dataset of another speaker. Then we investigate the possibility to adapt this model to have emotional TTS by fine-tuning the neutral TTS model with a small emotional dataset.
Blockchain in internet‐of‐things: a necessity framework for security, reliability, transparency, immutability and liability

Blockchain is a distributed operation and information supervision technology programmed initially for Bitcoin cryptocurrency. The awareness in Blockchain technology is rapidly growing since the notion was invented in the year 2008. The motivation for the concentration in Blockchain is its significant characteristics that deliver security, privacy, and information reliability devoid of any additional system regulating the communications, and consequently it generates fascinating research domains, specifically from the viewpoint of methodological difficulties and restrictions. This study discovers the wide-ranging Blockchain technology and studies it's perspective with respect to ‘internet-of-things’ controlled nodes. A resilient prototype method has been programmed that reveals a basic system exhausting Blockchain. The outcome illustrates that the established method is functional in test-bed environment.
A machine learning approach to modelling solar irradiation of urban and terrain 3D models

• Solar irradiation predictive machine learning models are proposed. • Models use SVF and surface orientation as predicting features. • Parametric simulations using RADIANCE generate training sets. • Gradient boosting is used and tuned using cross-validation. • The models perform well on terrains but caution is advised for cities.
Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables users to (1) understand the semantic space of the model, (2) identify regions of potential conflicts and problems, and (3) readjust the semantic relation of concepts based on their understanding, directly influencing the topic modeling. These tasks are supported by an interactive visual analytics workspace that uses word-embedding projections to define concept regions which can then be refined. The user-refined concepts are independent of a particular document collection and can be transferred to related corpora. All user interactions within the concept space directly affect the semantic relations of the underlying vector space model, which, in turn, change the topic modeling. In addition to direct manipulation, our system guides the users' decision-making process through recommended interactions that point out potential improvements. This targeted refinement aims at minimizing the feedback required for an efficient human-in-the-loop process. We confirm the improvements achieved through our approach in two user studies that show topic model quality improvements through our visual knowledge externalization and learning process.
Legal framework for health cloud: A systematic review

The complicated nature of cloud computing encompassing internet-based technologies and service models for delivering IT applications, processing capability, storage, and memory space brings along challenging problems. Some issues such as information security, privacy, and legal aspects of cloud computing may become challenging while cross passing with another complex domain like healthcare.The present study was conducted to report the results of a systematic literature review on the legal aspects of health cloud.The original English papers published in Pub Med, Scopus, Web of Science, and IEEE Digital Library databases were extracted, among which1582 were related to the legal aspects of health cloud environment and were selected using predefined search strings.Through the review process, effective factors in relation to a health cloud legal framework were identified and accordingly, a proper design was developed for this domain. Next, the identified factors were confirmed and adjusted by mapping the contents of the selected papers to different categories and subcategories under the proposed framework. Five Main categories like the issues related to the compliance, data protection, Identity Credential Access Management (ICAM), ownership, and quality of service were selected as the basic pillars in the proposed framework. Finally, 22 papers were selected, among which 19 were mapped to the compliance issues, 18 the issues related to "Data protection" were addressed, and 14 "Identity Credential Access Management (ICAM)" was discussed. Fifteen Papers were mapped to "Data ownership" and "Quality of service" categories. Some papers were found to present some solutions in all the mentioned areas; however, most of them have addressed only a few issues.
Choosing a cloud DBMS

As analytic (OLAP) applications move to the cloud, DBMSs have shifted from employing a pure shared-nothing design with locally attached storage to a hybrid design that combines the use of shared-storage (e.g., AWS S3) with the use of shared-nothing query execution mechanisms. This paper sheds light on the resulting tradeoffs, which have not been properly identified in previous work. To this end, it evaluates the TPC-H benchmark across a variety of DBMS offerings running in a cloud environment (AWS) on fast 10Gb+ networks, specifically database-as-a-service offerings (Redshift, Athena), query engines (Presto, Hive), and a traditional cloud agnostic OLAP database (Vertica). While these comparisons cannot be apples-to-apples in all cases due to cloud configuration restrictions, we nonetheless identify patterns and design choices that are advantageous. These include prioritizing low-cost object stores like S3 for data storage, using system agnostic yet still performant columnar formats like ORC that allow easy switching to other systems for different workloads, and making features that benefit subsequent runs like query precompilation and caching remote data to faster storage optional rather than required because they disadvantage ad hoc queries.
Humanoid Robot Path Planning and Rerouting Using A-Star Search Algorithm

A humanoid robot as a home assistant which operates in an indoor environment needs to be equipped with the ability to move autonomously. The robot needs to fulfill each command which is given by the user to get the object in a specific location. To achieve this mission, the robot needs intermediate points called waypoints to move from its current coordinate to the next coordinate until the robot reaches the goal position. In this research, based on the current robot's and the targeted object coordinates, the A-star algorithm was being used to find the shortest path. The goal in this research is the robot may approach the targeted object and reroute if the robot missed the target or moving away from its waypoints. The A-star algorithm can be successfully applied in this system. With the addition of a rerouting feature, the robot was able to find a new route if the robot suddenly moved by the user or if the robot accidentally walked outside the waypoints.
A Recursive Constrained Framework for Unsupervised Video Action Clustering

Video action understanding is an active field of intelligent video analytics, and contextual information in the videos has gained lots of attention for better action understanding. However, most existing works focus on using contextual information for supervised or semi-supervised analysis, and how to effectively use contextual information to boost the unsupervised action clustering performance is still a challenging problem. In this article, we propose a recursive constrained framework for unsupervised video action clustering by utilizing the contextual information of the action and scene. Considering the unique contextual characteristics of video action, action context clustering solution and scene context clustering solution are obtained simultaneously. Based on these two solutions, a recursive priori propagation is proposed to exploit information gain of the priori clustering solutions, and then the information gain is fed back into the procedures of both subspace representation and spectral clustering. Specifically, to explore the unknown relationships in the priori clustering solutions, the constraint-guided subspace representation is introduced by fusing the recursive priori constraint into the self-representation model. Taking priori information and multiview features into consideration, the priori-inherited multiview spectral clustering is proposed to obtain more discriminative spectral embeddings for action clustering. Experiments on three video benchmark datasets demonstrate that the proposed method outperforms state-of-the-art methods.
Convolutional Neural Networks Can Be Deceived by Visual Illusions

Visual illusions teach us that what we see is not always what is represented in the physical world. Their special nature make them a fascinating tool to test and validate any new vision model proposed. In general, current vision models are based on the concatenation of linear and non-linear operations. The similarity of this structure with the operations present in Convolutional Neural Networks (CNNs) has motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions. In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size. These results suggest that in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions.
Circular Convolutional Auto-Encoder for Channel Coding

In this article, we investigate deep auto-encoders for channel coding to combat the curse of dimensionality commonly existing in learning based coding. Inspired by convolutional codes, we use hierarchical convolutional layers in both the encoder and the decoder. Codes with rate 1/2 and length 100 is learned automatically by the auto-encoder and have shown better performance than the convolutional codes. Our investigation indicates that the curse of dimensionality can be overcome by using the convolutional layers. We also show that the deep auto-encoder can learn to handle nonwhite noise automatically when the channel goes beyond additive white Gaussian noise (AWGN).
A Security Case Study for Blockchain Games

Blockchain gaming is an emerging entertainment paradigm. However, blockchain games are still suffering from security issues, due to the immature blockchain technologies and its unsophisticated developers. In this work, we analyzed the blockchain game architecture and reveal the possible penetration methods of cracking. We scanned more than 600 commercial blockchain games to summarize a security overview from the perspective of the web server and smart contract, respectively. We also conducted three case studies for blockchain games to show detailed vulnerability detection.
Improved Open Set Domain Adaptation with Backpropagation

Open set domain adaptation by back propagation (OSDA-BP) was recently proposed as a novel end-to-end training approach for tackling the open set scenario, where only a few categories of interest are shared between source and target data. This paper provides an insightful understanding of the binary cross entropy loss employed in OSDA-BP for picking up the potential unknown samples. With this new understanding, we propose to replace the binary cross entropy loss with a symmetrical Kullback Leibler(KL) distance based loss. This improved OSDA-BP method is extensively evaluated over Office-31 dataset and a considerable performance improvement is observed.
High Performance Data Encryption with AES Implementation on FPGA

Nowadays big data has getting more and more attention in both the academic and the industrial research. With the development of big data, people pay more attention to data security. A significant feature of big data is the large size of the data. In order to improve the encryption speed of the large size of data, this paper uses the deep pipeline and full expansion technology to implement the AES encryption algorithm on FPGA. Achieved throughput of 31.30 Gbps with a minimum latency of 0.134 us. This design can quickly encrypt large amounts of data and provide technical support for the development of big data.
Perceptual Quality Study on Deep Learning Based Image Compression

Recently deep learning based image compression has made rapid advances with promising results based on objective quality metrics. However, a rigorous subjective quality evaluation on such compression schemes have rarely been reported. This paper aims at perceptual quality studies on learned compression. First, we build a general learned compression approach, and optimize the model. In total six compression algorithms are considered for this study. Then, we perform subjective quality tests in a controlled environment using high-resolution images. Results demonstrate learned compression optimized by MS-SSIM yields competitive results that approach the efficiency of state-of-the-art compression. The results obtained can provide a useful benchmark for future developments in learned image compression.
Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification

This paper presents an improved opposition-based Particle Swarm Optimizer (PSO) with partial opposition-based learning. The partial opposition-based learning scheme is a new form of opposition-based learning and it is employed to improve the performance. Nowadays, the artificial neural network (ANN), an important machine learning tool, is used in medicine especially in medical disease diagnosis. ANN training is a complex task and a training algorithm has a significant role in ANN’s performance. Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network (FFNN) for classification task in medical data mining. The classification task is carried out on 15 well-known medical data sets. A comparative study has been made with Levenberg–Marquardt algorithm, Artificial Bee Colony, Trigonometric Mutation Differential Evolution, basic PSO and other nine opposition-based PSO algorithms. The experimental results with statistical analysis demonstrate that the proposed algorithm performs better than all other algorithms in classification.
Fast and robust template matching with majority neighbour similarity and annulus projection transformation

In the paper, a novel fast and robust template matching method named A-MNS based on Majority Neighbour Similarity (MNS) and the annulus projection transformation (APT) is proposed. Its essence is the MNS, a useful, rotation-invariant, low-computational-cost and robust similarity measurement. The proposed method is theoretically demonstrated and experimentally evaluated as being able to estimate the rotation angle of the target object, overcome challenges such as background clutter, occlusion, arbitrary rotation transformation, and non-rigid deformation, while performing fast matching. Empirical results evaluated on the up-to-date benchmark show that A-MNS is 4.419 times faster than DDIS (the state-of-the-art) and is also competitive in terms of its matching accuracy.