Reviewerly/exHarmony huggingface dataset:
Multi-source Ensemble Transfer Approach for Medical Text Auxiliary Diagnosis

In medical text auxiliary diagnosis systems, there exists some problems including few labeled samples, imbalanced classes and domains are related but different. Taking advantages of transfer learning, we propose the multi-source transfer learning approach based on ensemble learning to address the above problems. Source data sampling method is designed to ensure the transfer ability of source samples. Then, three classifiers are ensembled to guarantee the robustness. Finally, classifiers from multiple domains are reasonably combined using mutual information to further improve performance. Our approach has been evaluated on the benchmark medical text datasets, and the results show that our approach is superior to the existing algorithms and can meet the requirement of an auxiliary diagnosis in certain extent.
ANOVUL: Detection of logic vulnerabilities in annotated programs via data and control flow analysis

Logic vulnerabilities are largely dependent on the expected functions of web applications. Their appearance depends on both application logic and related security policy which may change based on modifications in business requirements. Accordingly, there are no specific and common patterns for logic vulnerabilities moreover, a security policy is required for their detection. In this study, a vulnerability detection method is proposed to detect logic vulnerabilities via analysing the program source code. Security checks enforce some constraints in the application so that the application behaves according to the logic intended by the programmer. The main goal is to find the vulnerabilities caused by bypassing some security checks. In this method, known as annotation-based vulnerability detection approach (ANOVUL), control and data flows are analysed to detect the application logic vulnerabilities. To analyse the flows of the program, access control and authenticity labelling are used. To evaluate ANOVUL, the authors have collected a data set. This comprises of PHP applications with reported logic vulnerabilities that have common vulnerabilities and exposures (CVE) identifiers. Based on the results, a 73% detection rate was achieved in the data set. The proposed method can detect logic vulnerabilities that are not detectable using conventional methods.
Detection of Sensitive Textual Information in User Photo Albums on Mobile Devices

The need to identify and delete sensitive textual information in images arises in many areas. In this paper, we propose a two-step method in order to detect sensitive textual information in images or video. This method consists of text detection and recognition in an input image with the subsequent identification of sensitive words. We propose to include lexical information in the final decision to provide more stable performance in unconstrained environments. The method is implemented in a mobile application. An experimental study of several text recognition libraries is provided for well-known datasets (ICDAR 2003, KAIST, SVT, CBDAR 2011, DIQA and MIDV-500).
Security Model for Preserving Privacy of Image in Cloud

Cloud can be referred to as a public or semi-public interface between network nodes, based on certain contractual agreement. Data transmits across networks within different connected nodes. Cloud provides on-demand self-service. It also provides broad network access means from any standard device such as PC, laptop, and so on, where connection can be made with the network cloud. Cloud provides broad network access and uses resource pooling. As many companies are facing insecurities while dealing with data within the cloud, security models plays a major role in the security of cloud. This paper will offer a comparative analysis of some of the widely accepted algorithms that are used, considering data reliability, scalability, and integrity it provides on the cloud from unauthorized access based on image encryption/decryption method.
An Efficient, Secure and Reliable Search Scheme for Dynamic Updates with Blockchain

Blockchain is a newly emerged technology to maintain data consistency through distributed network. Due to the nature of tamper-resistance, blockchain can well be used to achieve secure and reliable search without the participation of any third party. For data storage system with frequent updates, unfortunately, the existing blockchain-based search methods do not work. In this paper, we try to solve this problem by proposing a highly efficient search scheme for dynamic updates while still using blockchain to ensure security and reliability. Instead of modifying the original index directly, our updates are implemented by appending new indices when adding new data and maintaining a revocation list when deleting or changing data. Note that, the index tables and revocation list are preserved through dynamic searchable encryption, achieving high efficiency as well as security. Thus, attacker cannot learn if the newly added file contains a keyword that was searched before or not, so it can resist file-rejection attacks. Finally, we set up a blockchain-based search framework and validate the effectiveness and practicability of our method.
Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering

In recent years, Community Question Answering (CQA) has emerged as a popular platform for knowledge curation and archival. An interesting aspect of question answering is that it combines aspects from natural language processing, information retrieval, and machine learning. In this paper, we have explored how the depth of the neural network influences the accuracy of prediction of deleted questions in question-answering forums. We have used different shallow and deep models for prediction and analyzed the relationships between number of hidden layers, accuracy, and computational time. The results suggest that while deep networks perform better than shallow networks in modeling complex non-linear functions, increasing the depth may not always produce desired results. We observe that the performance of the deep neural network suffers significantly due to vanishing gradients when large number of hidden layers are present. Constantly increasing the depth of the model increases accuracy initially, after which the accuracy plateaus, and finally drops. Adding each layer is also expensive in terms of the time required to train the model. This research is situated in the domain of neural information retrieval and contributes towards building a theory on how deep neural networks can be efficiently and accurately used for predicting question deletion. We predict deleted questions with more than 90% accuracy using two to ten hidden layers, with less accurate results for shallower and deeper architectures.
Efficient Constraint Handling based on the Adaptive Penalty Method with Balancing the Objective Function Value and the Constraint Violation

Real world problems are often formularized as constrained optimization problems (COPs). Constraint handling techniques are important for efficient search, and various approaches such as penalty methods or feasibility rules have been studied. The penalty methods deal with a single fitness function by combining the objective function value and the constraint violation with a penalty factor. Moreover, the penalty factor can be flexibly adapted by feeding back information on search process in adaptive penalty methods. However, keeping the good balance between the objective function value and the constraint violation is very difficult. In this paper, we propose a new adaptive penalty method with balancing the objective function value and the constraint violation and examine its effectiveness. L-SHADE is adopted as a base algorithm to evaluate search performance, and the optimization results of 28 benchmark functions provided by the CEC 2017 competition on constrained single-objective numerical optimizations are compared with other methods. In addition, we also examine the behavioral difference between proposed method and the conventional adaptive penalty method.
A Study on Secured Authentication and Authorization in Internet of Things: Potential of Blockchain Technology

With the proliferation of Internet of Things (IoT) and its influence in various use case scenarios, it can be expected that IoT services will create a global reach. Smart cities, smart grids, smart industries, smart wearables etc. are some examples of IoT services today. Besides all the benefits that IoT provide, security issues of these services and data generated by IoT are of major concern. Traditional security practices of authentication and authorization have been initially designed for security needs of centralized client/server models which are good to deal with human-machine interaction over the Internet. In centralized systems, normally devices and users are trusted for being in the same application domain. Moreover, such systems can become a bottleneck for a number of queries at the same time; or may become a single point of failure causing unavailability of connected devices that are totally relying on a single trusted party. This paper explores the IoT security issues and concerns. Moreover, it provides a review of centralized and decentralized IoT security solutions in terms of authentication and authorization. Additionally, it discusses how Blockchain technology can be leveraged to provide IoT security.
An efficient USM sharpening detection method for small-size JPEG image

USM (Unsharp masking) sharpening is a popular digital image processing operation utilized to pursue better image quality. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. Some effective methods have been proposed to detect the sharpening forgery in large size image. It is, however, still a challenging task to detect the sharpened images with small size. To address this challenge, an efficient USM sharpening detection algorithm is proposed for small-size JPEG image. Firstly, the RGB image was converted into the YCrCb image and got the Y channel component. Secondly, the Y channel image was converted into the frequency domain by block DCT transform. Then, the image feature was gotten by difference matrix computation. Finally, the feature set was fed into an SVM classifier for training and testing. The experimental results show the superiority of the proposed method over the existing method in sharpening detection in small-size images.
GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks

Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.
Recognizing fingerspelling in SIBI (sistem isyarat bahasa Indonesia) using OpenPose and elliptical fourier descriptor

Humans are social beings; they communicate with other humans to coexist. In general, humans use verbal methods to communicate. However, the limited of verbal communication in people with hearing loss causes them to use non-verbal communication through sign language. It is challenging for nondisabled to grasp sign language quickly because it takes a long time to learn and understand. Therefore, a system that can recognize SIBI is needed. This research focused on fingerspelling gesture in SIBI, concentrating on finger and hand movements. The accuracy results by using OpenPose as feature extraction achieved a 61.49% accuracy. To improve accuracy, by using OpenPose as hand tracking, image masking as pre-processing, and Elliptical Fourier Descriptor as feature extraction, this method achieved a 64.05% accuracy. We investigated whether feature extraction with EFD and OpenPose for hand tracking can improve accuracy; however, some labels still could not be detected correctly. To improve accuracy, image masking and delete similarity frames are used as preprocessing; OpenPose uses as hand tracking, and EFD uses as feature extraction. This method achieved 67.23% accuracy.
Combined influences of iron-oxides and micropores on reddish coloration of alkali feldspars in granitic rocks

The international color parameters (L*, a*, and b*) of four granite alkali feldspars, determined by a new method using a scanner and personal computor system show a successive variation of reddish colors. Small reddish particles and whitish micropores were observed using a binocular microscope. Raman spectroscopy showed that the common reddish particles are hematite in all four feldspar samples, and showed maghemite in the G175 feldspar. Distributions of Fe-oxide particles and micropores were assessed by electron microprobe mapping, and their area percentages in the feldspars were obtained using image processing. The results suggest that the size distribution and number density of both hematite and micropores control the reddish color variation in the alkali feldspars, with some additional influencing factors such as the presence of maghemite.
Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks

In automotive digital development, 3D shape morphing techniques are used to create new designs in order to match design targets, such as aerodynamic or stylistic requirements. Control-point based shape morphing alters existing geometries either through human user interactions or through computational optimization algorithms that optimize for product performance targets. Shape morphing is typically continuous and results in potentially large data sets of time-series recordings of control point movements. In the present paper, we utilize recurrent neural networks to model such time-series recordings in order to predict future design steps based on the history of currently performed design modifications. To build a data set sufficiently large for the training of neural networks, we use target shape matching optimization as digital analogy for a human user interactive shape modification and to build data sets of control point movements in an automated fashion. Experiments show the potential of recurrent neural networks to successfully learn time-series data representing design changes and to perform single-and multi-step prediction of potential next design steps. We thus demonstrate the feasibility of recurrent neural networks for learning successful design sequences in order to predict promising next design steps in future design tasks.
Step-by-Step Guidelines for Making Smart Contract Smarter

Different from traditional programs, which run on local machines or in the clouds, smart contracts run mostly on miners' devices; moreover, users of smart contracts have to pay fees (i.e., gases in Ethereum) to miners for executing transactions on the blockchain. Lacking of enough experiences in developing smart contracts makes writing low cost and high efficiency smart contracts becoming a very challenging task. In this work, by collating existing approaches and developing new methods for reducing gas consumption, a set of systematical step-by-step guidelines for developing low cost smart contracts is proposed. To prove its usefulness, ZoKrates verifier, a very complicated and costly contract, is used to demonstrate how our scheme can effectively cut the gas consumption down.
Cross Media Feature Retrieval and Optimization: A Contemporary Review of Research Scope, Challenges and Objectives

Predictive analytics that learns from cross-media is one among the significant research objectives of the contemporary data science strategies. The cross-media information retrieval that often denotes as cross-media feature retrieval and optimization is the crucial and at its infant stage. The traditional approaches of predicative analytics are portrayed in the context of unidimensional media such as text, image, or signal. In addition, the ensemble learning strategies are the alternative, if the given learning corpus is of the multidimensional media (which is the combination of two or more of test, image, video, and signal). However, the contributions those correlates the information of divergent dimensions of the given learning corpus is still remaining in the nascent stage, where it is termed as cross media feature retrieval and optimization. This manuscript is intended to brief the recent escalations and future research scope in regard to cross-media feature retrieval and optimization. In regard to this, a contemporary review of the recent contributions has been portrayed in this manuscript.
Effects of Gradient Optimizer on Model Pruning

Abstract Deep convolutional neural networks deploy in applications are hindered by their large parameters and high computational cost. In this paper, we test different gradient optimizer’s effect on YOLOv3 model pruning. This is achieved by enforcing channel-level sparsity in YOLOv3 network. The model trained with Adam optimizer get 5× reduction in model size only after training 60 epochs.
Visual Analytics-Based Computational Tool for Electrical Distribution Systems of Vehicles

The present work provides a solid understanding on the challenges and opportunities of visual analytics (VA) use for the development of electrical distribution systems (EDS) of vehicles. First, the need for a VA-based software platform is justified to address the associated information overload and problem complexity. Later, a detailed background on the preceding use of VA in electrical systems as well in the automotive industry is provided. Based on the previous context and agile software implementation, specific guidelines for the development of a novel and functional VA-based computational tool are then provided to appropriately visualize, analyze and simulate in-car EDS.
An <i>in silico</i> pipeline for subject-specific hemodynamics analysis in liver surgery planning

The progresses in fast simulations of the hepatic flow in subject-specific vascular tree have created new toolkits for pre-surgical planning. The aim of this short communication is to introduce a computational pipeline that integrates several recently developed in silico liver models and algorithms. Firstly, a semi-automatic segmentation pipeline is used to digitise hepatic vessels. Then, a constructive constraint optimisation (CCO) algorithm is used to extend the digitised vascular tree, and also to compute the blood pressure and flow velocity in the tree. Couinaud segments are simulated from the diffusion zones of the portal venous tree. The constructed surgical planning model is then deployed cross-platform for use in various scenarios.
Persuasive Argumentation and Epistemic Attitudes

This paper studies the relation between persuasive argumentation and the speaker’s epistemic attitude. Dung-style abstract argumentation and dynamic epistemic logic provide the necessary tools to characterize the notion of persuasion. Within abstract argumentation, persuasive argumentation has been previously studied from a game-theoretic perspective. These approaches are blind to the fact that, in real-life situations, the epistemic attitude of the speaker determines which set of arguments will be disclosed by her in the context of a persuasive dialogue. This work is a first step to fill this gap. For this purpose we extend one of the logics of Schwarzentruber et al. with dynamic operators, designed to capture communicative phenomena. A complete axiomatization for the new logic via reduction axioms is provided. Within the new framework, a distinction between actual persuasion and persuasion from the speaker’s perspective is made. Finally, we explore the relationship between the two notions.
Predicting Eye Fixations Using Computer Vision Techniques

The goal of this work is to study mechanisms of visual attention to assist visual perception for patients suffering from age-related macular degeneration (AMD) or retinitis pigmentosa (RP) through artificial retina devices. We present a method to predict where humans look; we extend a visual saliency model by incorporating additional features and use this model to obtain saliency maps. These are thresholded at different scales to estimate the points of an image upon which the human eye fixates as well as the exact sequence of these fixations. The sequence of fixations extracted is further used to identify the part of the image that will mostly attract visual attention. Contrary to most existing approaches our method can indicate specific coordinates for the fixation points rather than generic areas that may attract visual attention and is thus more appropriate to imitate human fixations. Our method performs marginally better than the well-known method for saliency prediction we compare against (≈76% accuracy) and very satisfactorily in terms of estimating the sequence of fixations upon any given image (up to 98% accuracy).
CNN Architecture for Surgical Image Segmentation Systems with Recursive Network Structure to Mitigate Overfitting

Laparoscopic surgery, a minimally invasive camera-aided surgery, is performed commonly. However, it requires a camera assistant who holds and maneuvers a laparoscope. If the laparoscope can be controlled automatically using a robot, a surgeon can perform the operation without a camera assistant, which would be beneficial in the areas suffering from lack of surgeons. In this paper, a prototype image segmentation architecture, based on a convolutional neural network, is proposed to realize an automatic laparoscope control for cholecystectomy. Since the learning dataset is annotated manually by a few surgeons, its scale is currently quite limited. Therefore, we devised a recursive network structure, with some sub-networks which are used multiple times, to mitigate overfitting. Furthermore, instead of the common transposed convolution, the flip-based subpixel reconstruction is introduced into upsampling layers. Evaluation results reveal that these improvements bring better classification accuracy without increasing the number of parameters. The system shows a throughput sufficient for real-time laparoscope robot control with a single NVIDIA GTX 1080 GPU.
Real-Time 3D Hand Gesture Based Mobile Interaction Interface

Hand gesture recognition is a challenging problem for natural human-computer interaction(HCI). We address this problem by introducing a real-time human-mobile interaction interface with a depth sensor. Our interface consists of two components, 3D hand pose estimation and hand skeleton state based gesture description. Firstly, we propose a 3D hand pose estimation method that combines learning based pose initialization and physical based model fitting, which can estimate the per-frame's hand pose that appears in the depth camera's field of view. Afterwards, we map the estimated pose to gesture, e.g. open or close, through a hand skeleton state based method. With the tracked hand gesture, we can stably and smoothly implement common operations such as 'Touch', 'Grasp' and 'Hold' with mid-air interface. Our main contribution is combine 3D hand pose estimation and hand gesture tracking, and implementing an interaction application system with the details.