Reviewerly/exHarmony huggingface dataset:
Integrating Cyber-Attack Defense Techniques into Real-Time Cyber-Physical Systems

With the rapid deployment of Cyber-Physical Systems (CPS), security has become a more critical problem than ever before, as such devices are interconnected and have access to a broad range of critical data. A well-known attack is ReturnOriented Programming (ROP) which can diverge the control flow of a program by exploiting the buffer overflow vulnerability. To protect a program from ROP attacks, a useful method is to instrument code into the protected program to do runtime control flow checking (known as Control Flow Integrity, CFI). However, instrumented code brings extra execution time, which has to be properly handled, as most CPS systems need to behave in a real-time manner. In this paper, we present a technique to efficiently compute an execution plan, which maximizes the number of executions of instrumented code to achieve maximal defense effect, and at the same time guarantees real-time schedulability of the protected task system with a new response time analysis. Simulation-based experimental results show that the proposed method can yield good quality execution plans, but performs orders of magnitude faster than exhaustive search. We also built a prototype in which a small auto-drive car is defended against ROP attacks by the proposed method implemented in FreeRTOS. The prototype demonstrates the effectiveness of our method in real-life scenarios.
P-frames and rhetorical moves in applied linguistics conference abstracts

This chapter examines the use of formulaic phrase frames (p-frames) in the rhetorical construction of Applied Linguistics Conference Abstracts (CAs). We consider both the distribution of p-frames across rhetorical moves/steps and the strength of the association of p-frames with individual moves/steps in 625 accepted AAAL 2017 CAs, all freely available on the organization's website. We manually tagged the corpus for rhetorical moves/steps and identified p-frames of 5 and 6 words (29 per million threshold) using kfNgram (Fletcher 2012). Results highlight that p-frames play a prominent role in the realization of CA writers' rhetorical goals both in the prevalence of p-frame use (roughly 1.7 instances per text in our data) and in the close relationship that many p-frames exhibit with particular rhetorical functions.
Ant Collaborative Filtering Addressing Sparsity and Temporal Effects

Though collaborative filtering (CF) is a popular and successful recommendation technique, it still suffers from the data sparsity and users' evolving taste over time.This paper presents a new collaborative filtering scheme: the Ant Collaborative Filtering.With the mechanism of pheromone transmission between users and items, the proposed method can pinpoint most relative users and items even in the case of the sparsity situation.Also, by virtue of the evaporation of existing pheromone, the proposed method captures the evolution of user preferences over time.Experiments are performed on the standard, public datasets and two real corporate datasets, which cover both explicit and implicit rating data.The results illustrate that the proposed algorithm outperforms current approaches in terms of accuracy and changing data.
SANet: Smoothed Attention Network for Single Stage Face Detector

Recently, significant effort has been devoted to exploring the role of feature fusion and enriching contextual information on detecting multi-scale faces. However, simply integrating features of different levels could lead to introducing significant noise. Moreover, recently proposed approaches of enriching contextual information are not efficient or ignore the gridding artifacts produced by dilated convolution. To tackle these issues, we developed a smoothed attention network (dubbed SANet), which introduces an Attention-guided Feature Fusion Module (AFFM) and a Smoothed Context Enhancement Module (SCEM). In particular, the AFFM applies an attention module to high-level semantic features and fuses attention-focused features with low-level semantic features to reduce the noise of the fused feature map. The SCEM stacks dilated convolution and convolution layers alternately to re-learn the relationship among completely separate sets of units produced by dilated convolution to maintain consistency of local information. The SANet achieves promising results on the WIDER FACE validation and testing datasets and is state-of-the-art on the UFDD dataset.
Performance Analysis of Convolutional Neural Network Models

Deep Neural Networks (DNNs) have become a promising solution for numerous machine learning tasks, which include object detection, classification, weather forecasting, video surveillance and safety management. Convolutional Neural Network (CNN) is a class of DNN, which can achieve state-of-art accuracy and high performance for most of the computer vision tasks. Training of CNNs is computationally intensive and may take multiple days for completion. In this work we have performed the feed forward path implementation and training of AlexNet, VGG-16 and ResNet models on Nvidia GeForce GTX 1080 Ti GPU with 3584 CUDA Cores, 1582 MHz, 11GB GDDR5X, using Caffe framework. Inference and training of these models are also performed on Nvidia Jetson TX2 hardware platform. We also present a detailed analysis of graphics processing unit (GPU) and general purpose processor (GPP) implementations of AlexNet, VGG-16 and ResNet-50 network models. A hardware architecture for performing convolution operations in feed forward path of CNN has also been implemented in Virtex-7 XC7VX690T FPGA, with throughput of 184 GFLOPS for AlexNet, 172 GFLOPS for VGG-16 and 197 GFLOPS for ResNet-50 model.
Smart Grid Block-Chain (BC) Conceptual Framework: Bi-Directional Models for Renewable Energy District and Utility

Block-Chain (BC) based Smart Grid (SG) energy market is a very challenging domain of today's era. Various researchers and scientists worked successfully in BC technology, still, further investigations and analysis are required with respect to the SG framework. Considering the above, we present a new architecture integrating energy-generating prosumers with the utility through a BC network. Moreover, we describe a trustworthy agreement named Service Level Agreement (SLA) managing and controlling all energy transactions in the BC. The smart contract of a single prosumer with the utility is also explained in the context of the de-regulated energy market. Finally, promising features and applications of BC are presented, considering the SG paradigm.
A Four-Valued Dynamic Epistemic Logic

Abstract Epistemic logic is usually employed to model two aspects of a situation: the factual and the epistemic aspects. Truth, however, is not always attainable, and in many cases we are forced to reason only with whatever information is available to us. In this paper, we will explore a four-valued epistemic logic designed to deal with these situations, where agents have only knowledge about the available information (or evidence), which can be incomplete or conflicting, but not explicitly about facts. This layer of available information or evidence, which is the object of the agents’ knowledge, can be seen as a database. By adopting this sceptical posture in our semantics, we prepare the ground for logics where the notion of knowledge—or more appropriately, belief—is entirely based on evidence. The technical results include a set of reduction axioms for public announcements, correspondence proofs, and a complete tableau system. In summary, our contributions are twofold: on the one hand we present an intuition and possible application for many-valued modal logics, and on the other hand we develop a logic that models the dynamics of evidence in a simple and intuitively clear fashion.
Bayesian Deep Learning Based Exploration-Exploitation for Personalized Recommendations

Personalized Recommendation Systems require an effective method to balance exploration and exploitation. To learn effective strategies, user and item attributes are critical data sources to capture contextual information. In this paper, we first present an approach based on Bayesian Deep Learning to learn a compact representation of user and item attributes to guide exploitation. A key novelty of the approach lies in its ability to also capture the uncertainty associated with the model output to guide exploration. We then show how to further boost exploration by incorporating model uncertainty with that of data uncertainty. Experimental results demonstrate the benefits of our approach in terms of accuracy in recommendations as well as its performance in an online setting.
Applying block-chain technology for commodity authenticity

The development of online trading mechanisms has led to the new retail markets for luxury commodities. It also prompts the counterfeit market that caused disputes in the transaction. This study proposes to integrate the decentralization and incorruptible characteristics of the block-chain technology with service design to propose an innovative service model for the authenticity verification of high-end products. The results show a solution to improve the performance of commodity authenticity verification effectively and efficiently.
A Skin-Conformal, Stretchable, and Breathable Fiducial Marker Patch for Surgical Navigation Systems

Augmented reality (AR) surgical navigation systems have attracted considerable attention as they assist medical professionals in visualizing the location of ailments within the human body that are not readily seen with the naked eye. Taking medical imaging with a parallel C-shaped arm (C-arm) as an example, surgical sites are typically targeted using an optical tracking device and a fiducial marker in real-time. These markers then guide operators who are using a multifunctional endoscope apparatus by signaling the direction or distance needed to reach the affected parts of the body. In this way, fiducial markers are used to accurately protect the vessels and nerves exposed during the surgical process. Although these systems have already shown potential for precision implantation, delamination of the fiducial marker, which is a critical component of the system, from human skin remains a challenge due to a mechanical mismatch between the marker and skin, causing registration problems that lead to poor position alignments and surgical degradation. To overcome this challenge, the mechanical modulus and stiffness of the marker patch should be lowered to approximately 150 kPa, which is comparable to that of the epidermis, while improving functionality. Herein, we present a skin-conformal, stretchable yet breathable fiducial marker for the application in AR-based surgical navigation systems. By adopting pore patterns, we were able to create a fiducial marker with a skin-like low modulus and breathability. When attached to the skin, the fiducial marker was easily identified using optical recognition equipment and showed skin-conformal adhesion when stretched and shrunk repeatedly. As such, we believe the marker would be a good fiducial marker candidate for patients under surgical navigation systems.
GUI information-based interaction logging and visualization for asynchronous usability testing

Asynchronous usability testing is efficient and promising usability testing methodology. However, evaluators are required to dedicate a considerable amount of effort for identifying usability problems in asynchronous testing. Evaluators cannot observe user interactions directly; only the collected data is available to them for identifying the usability problems. To reduce the amount of effort required, this paper presents a framework and tool for interaction visualization. This framework tracing user interactions based on meaningful GUI changes. The collected interaction log is visualized in a way that is similar to the visualization of traversed path used in web domains. Using our approach, the evaluator shall be able to understand user behavior more easily. In addition, we designed a black-box-based interaction logging for minimizing the implementation costs. Our tool especially helpful when testing has a large number of interaction data to analyze. To evaluate the proposed framework and tool, user interaction data were collected, and the results were evaluated by mobile application experts. The evaluation results demonstrate that the user interaction data traced by the proposed framework accurately reflect the user interactions and that the visualization results are valid.
Small Sized File Storage Problems in Hadoop Distributed File System

Hadoop Distributed File System (HDFS) is widely used to store the files, which are having heavy size. HDFS is so-called as distributed file system, which intends to store and access the huge volume of data. It is highly fault tolerant system. HDFS opts for the characteristic of cloud repository base, which is having capacity of ready-to-scale, good in performance efficiency and low-cost storing ability. It offers parallel processing therefore, it has high throughput. HDFS is more appropriate for applications, which are having massive datasets. However, it is inefficient for accumulation of multiple small sized files and faces many problems while processing. In this paper, the number of existing techniques have compared in terms of their performance throughput and a comparative chart has been prepared to compare the solutions in a better way for small file problem in Hadoop. By analyzing the chart, a better and efficient solution will able, to propose to store the small sized files in faster way.
Identity-Based Cross-Domain Authentication by Blockchain via PKI Environment

To tackle the difficulty of immediate identity revocation in the identity-based cryptography (IBC) authentication system, this paper proposes an identity signature scheme by security mediator (MED), which can revoke entity. Besides, aiming at cross IBC domains authentication under large-scale network architecture, an efficient identity-based cross-domain authentication protocol via public key infrastructure (PKI) environment is presented, combining blockchain. It reduces effectively complexity by consortium blockchain, thus more available for authentication in large-scale network. Through security and performance analysis, the evaluation results show the proposed scheme introduces lower overhead in computation and communication by blockchain, with resisting various attacks and excellent security in the process of authentication, compared with other authentication protocols.
Partition Heuristic RRT Algorithm of Path Planning Based on Q-learning

To solve the problem of high randomness in traditional rapid exploration random tree (RRT) path planning, a partition heuristic RRT planning algorithm based on Q-learning (Q-PRRT) is proposed. Partition heuristic rules are established by designing a sampling strategy of target bias and obstacle-avoided guidance. Markov modeling is carried out for partition heuristic RRT algorithm based on Q-learning, and actions are constructed based on partition heuristic rules. Each node is evaluated by designing the global optimal path reward function with Q-learning method, retain path nodes and eliminate redundant nodes based on greedy strategies. The simulation results show that Q-PRRT algorithm guarantees the optimality of the global planning path, obtains a smoother planning path, also improves path search efficiency and obstacle avoidance ability. It has better adaptability in different obstacle environments.
Local Directional Weighted Threshold Patterns (LDWTP) for Facial Expression Recognition

Data Over the decade, automatic facial expression recognition system is commanding in various fields due to the instigation of biometric applications to overcome the security issues. Existing systems are suffering with some problems like illumination, texture, more calculation etc. which are used to extract the feature from the image. In this paper proposing local directional weighted threshold patterns for feature extraction to recognize the expression. Initially, calculated gradient magnitudes of every pixel in four directions later gradient image and original image is multiplied with weights to enhance the feature from the given image. Proposed method tested on CK+ database and performed well compared to other threshold methods like LBP, LTP etc.
Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance

A simple yet accurate photovoltaic (PV) performance curve as a function of satellite-based solar irradiation is necessary to develop a PV power forecasting model that can cover all of South Korea, where more than 35,000 PV power plants are currently in operation. In order to express the nonlinear power output of the PV module with respect to the hourly global horizontal irradiance derived from satellite images, this study employed the Gompertz model, which is composed of three parameters and the sigmoid equation. The nonphysical behavior of the Gompertz model within the low solar irradiation range was corrected by combining a linear equation with the same gradient at the conjoint point. The overall fitness of Linear-Gompertz regression to the 242 PV power plants representing the country was R2 = 0.85 and nRMSE = 0.09. The Gompertz model coefficients showed normal distributions and equivariance of standard deviations of less than 15% by year and by season. Therefore, it can be conjectured that the Linear-Gompertz model represents the whole country’s PV system performance curve. In addition, the Gompertz coefficient C, which controls the growth rate of the curve, showed a strong correlation with the capacity factor, such that the regression equation for the capacity factor could be derived as a function of the three Gompertz model coefficients with a fitness of R2 = 0.88.
A class of fractal-chain fractance approximation circuit

A class of fractal-chain fractance approximation circuit (FCFAC) has been discussed in this paper. The FCFAC type 1 (FCFAC-1) can achieve the rational approximation of negative-half-order fractances. Changing the series and parallel connections or the positions of resistors and capacitors in FCFAC-1 produces another three FCFACs of negative-half-order. Making scaling extensions to the four original FCFACs, the scaling FCFACs can be obtained and achieve the rational approximation of fractances of arbitrary-order. The operational order, which is determined by the progression ratio of resistance α and the progression ratio of capacitance β, can be expressed as μ=−lgα/lg(αβ). The approximation performance of the scaling FCFACs has been improved and the optimised circuits are really simple. The oscillation phenomena of the frequency-domain characteristics of the scaling FCFACs have been described quantitatively. In addition, a comparison of approximation performance shows the scaling FCFACs provide a good choice in available fractance approximation circuits (FACs).
Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

Designing fitness-based adaptive scaling factor (F) is an effective method to enhance the performance of differential evolution (DE) algorithms.This paper investigates the correlations between F and fitness values of target vectors, base vectors and difference vectors.The correlations are described by the notations of monotonicity and nonlinearity.Monotonicity is used to examine whether the optimization performance of DE and the fitness values of certain vectors have positive or negative correlation.Nonlinearity denotes the operation in which nonlinear mappings are used to redistribute the values of F in [0, 1] so as to boost the optimization performance.These two aspects of correlations are empirically tested on the Numerical Optimization Competition benchmark functions in IEEE Congress on Evolutionary Computation.Simulation results reveal different qualitative and quantitative correlations between F and fitness values of different vectors.Then, a new F that combines these relations is designed.Its strength is numerically verified by testing different CEC Benchmark functions.
Automated Repair of Cross-Site Scripting Vulnerabilities through Unit Testing

Many web applications are vulnerable to Cross Site Scripting (XSS) attacks enabling attackers to steal sensitive information and commit frauds. Much research in this area have focused on detecting vulnerable web pages using static and dynamic program analysis. The best practice to prevent XSS vulnerabilities is to encode untrusted dynamic content. However, a common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We propose a new approach that can automatically fix this common type of XSS vulnerability in many situations. This approach is integrated into the software maintenance life cycle through unit testing. Vulnerable codes are refactored to reflect the suggested encoder and then verified using an attack evaluating mechanism to find a proper repair. Evaluation of this approach has been conducted on an open source medical record application with over 200 web pages written in JSP.
Hand Gesture Recognition: A Survey

In this paper we present a literature survey on Hand Gesture Recognition (HGR). Having reached all the best possible ways for data acquisition like cameras, wrist sensors, hand gloves now these are of less concern. Now the higher emphasis is on feature extraction from the available data, algorithms used to improvise feature extraction. These processes have also been tested and in recent papers the drawback associated with one method of feature extraction is overcome by the fusion of another feature extraction method. The recent papers in HGR are about fusion of multi-modal like skeleton joint information, depth and RGB images are set of input entities and problem associated with one modal can be overcome by another.
All about uncertainties and traps: Statistical oracle-based attacks on a new CAPTCHA protection against oracle attacks

CAPTCHAs are security mechanisms that try to prevent automated abuse of computer services. Many CAPTCHAs have been proposed but most have known security flaws against advanced attacks. In order to avoid a kind of oracle attacks in which the attacker learns about ground truth labels via active interactions with the CAPTCHA service as an oracle, Kwon and Cha proposed a new CAPTCHA scheme that employ uncertainties and trap images to generate adaptive CAPTCHA challenges, which we call "Uncertainty and Trap Strengthened CAPTCHA" (UTS-CAPTCHA) in this paper. Adaptive CAPTCHA challenges are used widely (either explicitly or implicitly) but the role of such adaptive mechanisms in the security of CAPTCHAs has received little attention from researchers. In this paper we present a statistical fundamental design flaw of UTS-CAPTCHA. This flaw leaks information regarding ground truth labels of images used. Exploiting this flaw, an attacker can use the UTS-CAPTCHA service as an oracle, and perform several different statistical learning-based attacks against UTS-CAPTCHA, increasing any reasonable initial success rate up to 100% according to our theoretical estimation and experimental simulations. Based on our proposed attacks, we discuss how the fundamental idea behind our attacks may be generalized to attack other CAPTCHA schemes and propose a new principle and a number of concrete guidelines for designing new CAPTCHA schemes in the future.