Reviewerly/exHarmony huggingface dataset:
High-Efficiency Progressive Transmission and Automatic Recognition of Wildlife Monitoring Images With WISNs

Wireless image sensor networks (WISNs) are widely applied in wildlife monitoring, as they present a better performance in remote, real-time monitoring.However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions and narrow transmission bandwidth.For the contradiction between the above limitations of WISNs and the wildlife monitoring images with high resolution and complex background, we propose a novel wildlife intelligent monitoring system.On the foundation of saliency object detection, the convolutional encoder-decoder network is utilized to realize the progressive compression transmission and restoration for wildlife monitoring images, which guarantees the transmission efficiency and quality of wildlife part.Moreover, to deal with the problems of high labor intensity, low efficiency and low recognition accuracy in classical manual sorting method, an improved Faster RCNN algorithm is proposed on the automatic recognition of wildlife images.The experimental results on our own wildlife dataset, show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are improved by 7.93%, 18.15% and 7.01%, 12.67% respectively on reconstruction image, when compared with the set partitioned in hierarchical tree (SPIHT) and embedded zerotree (EZW) algorithms.Compared with the traditional Faster RCNN algorithm, the recognition accuracy of six species wildlife is respectively improved by 1%, 18%, 5%, 17%, 2% and 19%, and the final mAP value reaches to 92.2% in test set increased by 10.9%, which demonstrates the proposed algorithm can ideally achieve the wildlife intelligent monitoring with WISNs.
Verification of Authenticated Firmware Loaders

An important primitive in ensuring security of modern systems-on-chip designs are protocols for authenticated firmware load. These loaders read a firmware binary image from an untrusted input device, authenticate the image using cryptography and load the image into memory for execution if authentication succeeds. While these protocols are an essential part of the hardware root of trust in almost all modern computing devices, verification techniques for reasoning about end-to-end security of these protocols do not exist.This paper takes a step toward addressing this gap by introducing a system model, adversary model and end-to-end security property that enable reasoning about the security of authenticated load protocols. We then present a decomposition of the security hyperproperty into two simpler 2-safety properties that enables more scalable verification. Experiments on a protocol model demonstrate viability of the methodology.
New linear codes with few weights derived from Kloosterman sums

Linear codes with few weights have applications in data storage systems, secret sharing schemes and authentication codes. In this paper, inspired by the works of Heng and Yue (2016) [14] and Tan, Zhou, Tang and Helleseth (2017) [25], we extend Tan, Zhou, Tang and Helleseth's work to obtain a class of optimal 1-weight binary linear codes, new classes of 2-weight and 3-weight p-ary linear codes and a class of 4-weight binary linear codes. The lengths and weight distributions of the t-weight linear codes, where t=1,2,3, are closed-form expressions of Kloosterman sums over finite prime fields, and are completely determined when p=2 and p=3.
The Role of Cloud Electronic Records Management System (ERMS) Technology in the Competency of Educational Institutions

Although the Electronic Records Management System (ERMS) is necessary for organizations to enhance their performance, many organizations are yet to embark on its implementation, while some others attempt at implementing it without model, thus leading to an ineffective and erroneous implementation. On the other side, researchers have already started applying cloud computing in ERMS implementations of education sector for its benefits. Therefore, this paper aims to propose a model for cloud ERMS implementation in an attempt to improve the Yemeni public sector educational institutions competency, using Technology Acceptance Model 3 (TAM3). The study used the quantitative approach through questionnaire that was used as instrument to gather data from 350 respondents working in the education sector of Yemen. Partial Least Square-Structural Equation Modelling (PLS-SEM) was used to predict the relationship between the constructs. The findings revealed that both dimensions of ERMS and Cloud are important in implementation of ERMS. The results also showed that ERMS has a significant relationship with the competency of the public sector educational institutions. This has arises implication whereby the public sector needs to pay attention to the factors found in the model in order to promote a successful implementation.
ChinaStyle: A Mask-Aware Generative Adversarial Network for Chinese Traditional Image Translation

GANs make it effective to generate artworks using appropriate collections. However, most training dataset either contain paintings that were only from one artist or contain only one category. There is few training datasets for Chinese traditional figure paintings. This paper presents a new high-quality dataset named ChinaStyle Dataset including six categories, containing 1913 images totally. We further proposes Mask-Aware Generative Adversarial Networks (MA-GAN) to transfer realistic portraiture to different styles of Chinese paintings. Different from existing mothed, MA-GAN uses a single model only once with our unpaired dataset. Besides, Mask-aware strategy is used to generate free-hand style of Chinese paintings. In addition, a color preserved loss is proposed to alleviate the color free problem. Experimental results and user study demonstrate that MA-GAN achieves a natural and competitive performance compared with existing methods.
Modeling birefringence in SiO <sub>2</sub> glass fiber using surface stress relaxation

Abstract The retardance of silica glass fibers was evaluated using photoelastic techniques. Here, surface birefringence in glass fibers is shown to be a consequence of surface stress relaxation for as‐received fibers drawn from Suprasil II. The surface features of the birefringent fibers were compared to a model of the residual axial stress profile resulting from a diffusion‐controlled surface stress relaxation. Additionally, a uniform birefringence in the fiber equivalent to a constant tensile stress was recognized and attributed to structural anisotropy produced during fiber drawing. The contribution of structural anisotropy to the observed birefringence remained constant as the surface features were successively etched away. Surface compressive stress generation was also observed, as retardance corresponding to a surface compressive stress was found to increase with applied tensile stress during short heat treatments. Significant features of the retardance profile in as‐received silica glass fibers, with a thin surface compressive stress layer and compensating interior tensile stress, agreed with the residual stress profiles predicted by the surface stress relaxation model after correcting for this observed structural anisotropy.
Construction of blind restoration model for super-resolution image based on chaotic neural network

Traditional blind restoration model for super-resolution image based on neural network is easy to fall into the problem of local minimum, so a blind restoration model for super-resolution image based on chaotic neural network is constructed. Firstly, the noise of degraded image is removed by using wavelet transform to reduce the influence of the noise of degraded image on the result of blind restoration. Then a simplified chaotic neural network model is constructed by introducing transient chaos and time-varying increment into chaotic neural network. The gray value of image is taken as the input of the network. Two Toeplitz matrices are generated by point spread function and Laplace operator. The connection weights and bias inputs of chaotic neural network are calculated by generating matrix. Iterative degradation of image gray is updated continuously according to the connection weights and bias outputs. Degree value is used to judge whether the allowable error value of network convergence meets the requirement, and when it meets the requirement, the blind restoration of super-resolution image will be output. The experimental results show that the mean values of blind restoration time, image sharpness and energy consumption are 9.273 ms, 99.045% and 118.524 J, respectively. The restoration performance of the model is good, and the blind super-resolution image has the highest similarity with the original image.
Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts

In the era of big data, online doctor review platforms, which enable patients to give feedback to their doctors, have become one of the most important components in healthcare systems. On one hand, they help patients to choose their doctors based on the experience of others. On the other hand, they help doctors to improve the quality of their service. Moreover, they provide important sources for us to discover common concerns of patients and existing problems in clinics, which potentially improve current healthcare systems. In this paper, we systematically investigate the dataset from one of such review platform, namely, ratemds.com, where each review for a doctor comes with an overall rating and ratings of four different aspects. A comprehensive statistical analysis is conducted first for reviews, ratings, and doctors. Then, we explore the content of reviews by extracting latent topics related to different aspects with unsupervised topic modeling techniques. As the core component of this paper, we propose a multi-task learning framework for the document-level multi-aspect sentiment classification. This task helps us to not only recover missing aspect-level ratings and detect inconsistent rating scores but also identify aspect-keywords for a given review based on ratings. The proposed model takes both features of doctors and aspect-keywords into consideration. Extensive experiments have been conducted on two subsets of ratemds dataset to demonstrate the effectiveness of the proposed model.
Reducing Dynamic Power in Streaming CNN Hardware Accelerators by Exploiting Computational Redundancies

Convolutional neural networks (CNNs) have achieved tremendous successes in various application domains such as computer vision. However, current implementations are characterized by large memory requirements and accesses, which pose an impediment towards their deployment on low cost embedded devices with fast runtime requirements. Recently, FPGA based streaming CNN hardware accelerators have been reported for alleviating these memory bottlenecks. However, these implementations suffer from large number of convolution operations which incur high power consumption. In this paper, we investigate methods to exploit the redundancies in the activation layers in order to reduce the dynamic power. We propose a computationally efficient approximation method to reduce the overall convolution operations with marginal accuracy loss. Experimental results of our FPGA implementation based on image classification datasets show that the proposed method leads to considerable power savings.
Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: preliminary results

Segmentation convolutional neural networks (CNNs) are now popular for the semantic segmentation (i.e., dense pixel-wise labeling) of remote sensing imagery, such as color or hyperspectral satellite imagery. In recent years a large number of hand-labeled datasets of overhead imagery have emerged, leading to breakthrough performance for CNNs. However, these datasets are typically used in isolation of one another because they are either (i) annotated with heterogeneous object type labels, or (ii) they are collected over different geographic areas. This imposes a major bottleneck on the value of these datasets. In this work we present what we call a class-asymmetric loss function that makes it possible to train a single multi-class network using multiple datasets that are heterogeneously-labeled. We show, for example, that it is possible to train a segmentation algorithm for Buildings, roads, and background using two datasets: one annotated with buildings and one annotated with buildings. We propose a class asymmetric loss that under certain common conditions, allows for one to train models on datasets in which the target class is unlabeled.
A Role of Blockchain in IoT and Financial Applications

A blockchain, is a growing list of information, called blocks, which are linked using cryptography. A blockchain is a decentralized, distributed and a public digital ledger that is used to record transactions across many devices so that any involved record cannot be changed easily, without modifying all subsequent blocks, because of this, blockchain has found its use in wide-ranging fields. This review paper, therefore, describes the role of blockchain in IoT and financial applications, two fields which will be benefitted the most by it. Furthermore, this paper examines the privacy and security concerns related to it and shares some insights on how these problems can be tackled. In this paper, we have reviewed multiple such papers in which similar issues were addressed. Lastly, the public perception of blockchain technology is taken to addresses how blockchain is perceived in different segments of society.
Segmentation of abdominal organs in computed tomography using a generalized statistical shape model

Segmentation of anatomical structures in computed tomography images remains an important stage in computer-aided diagnostics and therapy. Due to the complexity of anatomical structures in the abdominal cavity, the occurrence of anatomical variants and pathological changes of organs in computed tomography images, segmentation is still treated as a current research problem. The paper presents the segmentation method based on the generalized statistical shape model. The method was tested in the application to segmentation based on 40 cases of computed tomography with contrast: 20 cases were included in training set and 20 in the testing set. For each case, expert outlines were made for the following organs: spleen, kidney, liver, pancreas, and duodenum. The following average results of the DICE coefficient were obtained: 0.96, 093, 0.88, 0.86, 0.81. The obtained results on the developed method can be treated as a step towards a universal method of segmentation in normalized scaled images, because the method does not require the selection of new parameter values when applied to the segmentation of a diverse group of parenchymal anatomical organs.
A Comprehensive Evaluation of HTTP Header Features for Detecting Malicious Websites

Security researchers have used website features including the URL, webpage content, HTTP headers, and others to detect malicious websites. In prior research, features derived from HTTP headers have shown promise for malicious website detection. This paper includes a comprehensive evaluation of HTTP header features to assess whether additional HTTP header features improve malicious website detection. We analyze HTTP headers from 6,021 malicious and 39,853 benign websites. We define malicious websites as those identified by Cisco Talos Threat Intelligence Group for association with phishing, drive-by downloads, and command and control infrastructure. Benign websites consist of popular websites from the Alexa Traffic Rank. We collect 672 HTTP header features from these websites and identify 22 for further analysis. Among these, 11 have been studied in prior research while the other 11 are new and identified in our research. From these 22 features, eight features, three identified by our study, consistently rank as the most important features and represent 80% of the total feature importance. We build eight models with supervised learning techniques and observe that the detection performance metrics for the 22 features are consistently better than for the 11 previously studied features. We also apply two feature transformation techniques and find that performing Principal Component Analysis on the features identified increases detection ability. From our results, we postulate that use of additional HTTP header features will lead to more accurate detection of malicious websites.
Multi-vehicle coordination based on hierarchical quadratic programming

This paper presents an optimization-based control scheme for generating online multi-vehicle coordination behaviors to accomplish missions in indoor environments. The proposed control scheme relies on the use of hierarchical task functions in terms of the multi-vehicle configuration variables. The task functions are related to individual and group obstacle avoidance, reaching fixed targets, group trajectory tracking, maintaining formations, enclosing the group within a geometric area, among others. The stack of hierarchical tasks automatically handles possible conflicts between them. Quadratic programs are formulated for explicitly solving inequality and equality task constraints at any hierarchy. In addition, a finite state machine is employed to build complex group behaviors for successfully fulfilling group missions. The proposed control scheme is demonstrated on two experiments with static and moving obstacles where a group composed by six vehicles tracks a predefined trajectory for the center of mass of the group. In the second experiment, the group is asked to clean the workspace by pushing movable objects.
ShearSheet: Low-Cost Shear Force Input with Elastic Feedback for Augmenting Touch Interaction

We propose ShearSheet, a low-cost, feasible, and simple DIY method that enables tangential (shear) force input on a touchscreen using a rubber-mounted slim transparent sheet. The sheet has tiny conductive material(s) attached to specific positions on the underside so that displacement of the sheet is recognized as touch input(s). This allows the system to distinguish between normal touch input and shear force input depending on whether the sheet moves or not, without requiring any external sensors or power. We first introduce ShearSheet's simple implementation for a smartphone and then discuss several promising interaction techniques that augment shear interaction with one- and two-finger operations. We demonstrate the effectiveness of ShearSheet's smooth transition between position- and rate-based control through a controlled user study using simple scrolling tasks. The results confirm that ShearSheet offers improved performance such as fewer operations and higher subjective preference, suggesting its substantial benefits and potential applications.
Optimal Trajectory Generation for Quadrotor with Suspended Load Under Swing Angle Constraint

In this paper, optimal trajectory generation for a quadrotor with a cable-suspended payload is suggested with consideration to a narrow space. This problem has great significance in practical UAV applications. The key point of this problem is that payload angle must be kept in a specific boundary while quadrotor is in the specified space. As the first step to solve the problem, a dynamic model of the quadrotor with a payload is obtained by applying the Euler-Lagrange equation of motion. Then the system model was designed, linearized and discretized to generate optimal trajectory based on dynamic programming method. The trajectories generated by the proposed method were verified in simulations with a PID control. From the simulation results, it is concluded that the quadrotor with a cable suspended payload can fly through the specific narrow space and arrive at a desired point with linearized system model.
Design of traceability system for medical devices based on blockchain

Aiming at the centralization problem of the traditional medical device traceability system. This paper proposes a medical device traceability system based on blockchain technology. By integrating the traditional tracing system with the blockchain technology, based on the decentralization and non-tamperability of the blockchain, the alliance chain and the smart contract construction system are adopted. Manufacturers, distributors, hospitals and consumers in the system can access product information and trace the entire circulation process of the product through RFID. In addition, consumers do not need to register an account number, but directly through the RFID query products related to the main information, such as the shelf life, production enterprises, production time, product price, etc. This system solves the problem that the traditional system is vulnerable to network attack due to centralization, which can improve the robustness of the system, enhance the reliability of data and realizes the traceability of the production, sales and use of medical devices. Therefore, it ensures that the source of medical devices can be traced, the flow can be tracked, the information can be inquired and the responsibility can be investigated to ensure the public consumption safety.
Super Resolution Image Reconstruction of Textile Based on SRGAN

For the problem of image distortion in textile flaw detection, a super-resolution image reconstruction technique based on GAN (Generative adversarial network) can reconstruct the obtained low-pixel image into a high-pixel image. The generative adversarial network consists of a discriminative network and a generative network. Generative network is responsible for generate high-resolution images, discriminative network is responsible for identifying the authenticity of the image. the generative loss and discriminative loss continuously optimize the network and guide the generation of high-quality images. The experimental results show that, the PNSR of SRGAN is 0.83 higher than that of the Bilinear, and the SSIM is higher than 0.0819. SRGAN can get a clearer image and reconstruct a richer texture, more high-frequency details, and easier to identify defects, which is important in the flaw detection of fabrics.