Reviewerly/exHarmony huggingface dataset:
An Application of Image Recognition Technology Based on Deep Learning in Safety Review of Reservoir Dam Metal Structure

Abstract The safety review calculation of the gate is an important job in the safety evaluation of metal structures. In accordance with the requirements of the specification, this paper has developed an intelligent software for metal structures safety review calculation, which can automate the calculation process and generate a review evaluation report. In order to simplify the labor of the manual input of a large number of gate review calculation parameters, this paper used a deep-learning-based image recognition technology to partially realize the collection of the information relevant to the calculation from the drawings of metal structures, laying the foundation for later realization of the fully intelligent gate safety review.
Application of Full Connection Network in Submarine Formation Recognition

Recognizing enemy formation automatically is difficult for submarine's current software. Once implemented, the combat parameters can be calculated automatically and quickly. Full connection network in deep learning is introduced to recognize enemy's formation. Experiment with simulation data is carried out by Google's Tensorflow. The experimental results show that the double layer full connection network can realize the recognition function. Three kinds of formation are recognized. Experimental process and data are recorded in detail. The scale of training set suitable for research is about 200. When the nodes number in the first full connection layer is set to close to 64, the recognition rate is high.
Algebraic Decision Diagram-Based CP-ABE with Constant Secret and Fast Decryption

Ciphertext-policy attribute-based encryption (CP-ABE) is applied to many data service platforms to provides secure and fine-grained access control. In this paper, a new CP-ABE system based on the algebraic decision diagram (ADD) is presented. The new system makes full use of both the powerful description ability and the high calculating efficiency of ADD to improves the performance and efficiency of algorithms contained in CP-ABE. First, the new system supports both positive and negative attributes in the description of access polices. Second, the size of the secret key is constant and is not affected by the number of attributes. Third, time complexity of the key generation and decryption algorithms are O(1). Finally, this scheme allows visitors to have different access permissions to access shared data or file. At the same time, PV operation is introduced into CP-ABE framework for the first time to prevent resource conflicts caused by read and write operations on shared files. Compared with other schemes, the new scheme proposed in this paper performs better in function and efficiency.
Manager Asks: Which Vulnerability Must be Eliminated First?

Nowadays, the number of discovered vulnerabilities increases rapidly. In 2018, the 17, 308 vulnerabilities were discovered and during the 2019 even more, so up to 20, 362. The serious problem is that a substantial part of them is rated as critical or at least labeled as high according the CVSS (Common Vulnerability Scoring System). This fact causes a problem, the designers and/or developers do not know which vulnerability should be eliminated at the first place. Time for removal of the vulnerability is crucial from the practical point of cyber security. The main contribution of the article is a proposal of a new method that is used for prioritizing vulnerabilities. The aim of the proposed method is to eliminate the disadvantages of approaches commonly used today. Our method improves the prioritization of vulnerabilities utilizing the parameters: the possibility of exploitation, availability of information about them and knowledge obtained by Threat Intelligence. These three parameters are highly important, especially for newly discovered vulnerabilities, where a priority can differ from day to day. We evaluate the functionality of the proposed method utilizing the production environment of a medium-sized company and we copare results with CVSS method (30 servers, 200 end-stations).
Dynamic Searchable Symmetric Encryption with Full Forward Privacy

Many approaches address the problem of Searchable Symmetric Encryption (SSE), and in the last few years scholars introduced Dynamic SSE (DSSE) schemes in which a client is able to add, delete, and update documents along with performing searches over encrypted documents. The concept of forward privacy was introduced to guarantee a higher level of data privacy and to prevent crucial information leakage. In a forward private scheme, the cloud/attacker cannot link a newly added document to previous searches. However, forward private schemes still leak search pattern which can be employed to collapse the whole security system and an adaptive attacker can reveal plaintext data. To address this challenge, in this paper, we introduce the notion of Full Forward Privacy (FFP). We also propose a parallelizable DSSE scheme that achieves FFP by employing non-deterministic and one-time use search tokens to obfuscate the search pattern. Our cost-efficient scheme supports both updates and searches. Provided security proof and performance analysis demonstrate practicality, efficiency, and security of our approach.
GraphEBM: Energy-Based Graph Construction for Semi-Supervised Learning

With the rapid improvement of various techniques in graph-based semi-supervised learning, the call for higher-quality graphs becomes more intensive. However, such affinity graphs are not naturally existing in most semi-supervised learning tasks. In this paper, we propose a learning-based approach, GraphEBM, for the graph construction problem. GraphEBM is designed to address three main requirements in graph construction: 1) supporting dynamic update; 2) providing interpretable metrics; 3) tailoring to tasks. Specifically, in GraphEBM, we adopt a probabilistic view, Edge Probability Space, to model a graph construction process as constituted of events from the space. Our objective is thus to learn, by our Energy-Based Model (EBM), the latent sampling distribution. Experimental results show that our proposed GraphEBM outperforms the existing graph construction methods in improving the semi-supervised learning tasks on various datasets and it can learn global properties of a target graph only with direct local guidance.
Android Applications Enrich Satcom Terminals Human Interfaces: Three Use Cases

An explosive growth in the use of mobile phones and the enormous capabilities of their graphics interfaces makes them a natural candidate for leveraging them for satellite-based applications and for inter-networking of technologies in the Internet of Things (IoT) scenario. The work presented in this paper combines satellite communication based terminals, networking technologies and mobile software application development on an Android OS environment. We report the implementation of three key Android based applications that have been developed for mobile satellite communication terminals, reception of data from sensors in low power wireless networks through satellite link and GIS-based tracking of mobile assets through satellite link for monitoring and surveillance. The development eliminates the need for an in-built GUI in these devices or a resident processor, leading to significant miniaturization in the terminal size while enhancing convenience in usage.
Detecting Crimes Related Twitter Posts using SVM based Two Stages Filtering

Crime is a major problem faced today by society. Crimes have affected the quality of life and economic growth badly. We can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. We can use social media like Twitter to detect crimes related activities. Because Twitter users sometimes convey messages related to his or her surrounding environment via Twitter. In this paper, we proposed a machine learning approach to detect the crimes and the location of the crimes. As the first step, we fetch the Twitter posts using predefined keywords relating to the crimes. Then, after the preprocessing, we applied a support vector machine-based filtering approach to eliminate the noise. Then in the final stage, we get the geolocation and crime type. The empirical study of our prototyping system has proved the effectiveness of our proposed crime detection approach.
Smart Community Model using TOGAF to Help Higher Education Students on Internship Program

Higher education students follow the internship program at the end of the year before writing the thesis. Physical and psychological support is needed for higher education students in conducting the internship period so that internships can be maximized. Students in higher education need not only a coach from a business coach, but also a higher education coach and alumni coach. The proposed design of a smart community model in governance to assist higher education students during the internship program. Using a qualitative research approach, researchers use TOGAF in designing a smart community model. TOGAF has 8 phases, but in this study, only the first 4 phases: architecture vision, business architecture, information system architecture, and technology architecture are used as an architectural model in forming a smart community model for interns. The results of this architecture become a proposed model that can be used for business or application owners to create a smart community internship application for higher education students while intern at companies or organizations.
Bayesian Optimization for Reverse Stress Testing

Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution to a black-box optimization problem in which the function to be optimized has particular properties that result in difficulties. It can only be expressed in terms of a complicated and lengthy stochastic algorithm, with the added complication that the value returned is only required to be sufficiently near to a pre-determined ‘target’. We consider the context of financial stress testing, for which the data used has a significant noise component. Commonly-used Bayesian Optimization acquisition functions cannot analyze the ‘target’ condition in a satisfactory way, but a simple modification of the ‘Lower Confidence Bound’ acquisition function improves results markedly. A proof that the modified acquisition function is superior to the unmodified version is given.
Deep Embedded Clustering with Asymmetric Residual Autoencoder

Deep clustering methods have obtained excellent performance on clustering tasks with the benefit of feature representations learned with deep neural networks. Even though promising performance of deep clustering has been shown in different applications, the efficiency of the features achieved is limited by the symmetric structure of the autoencoders employed. Deeper autoencoder will lead to less reliable features extracted from the encoder due to the strong decoding capability of the symmetric deep decoder. To address this issue, a novel Asymmetric Deep Residual Embedded Clustering algorithm is proposed in this paper. Specifically, an asymmetric residual deep autoencoder is constructed to learn the features embedded in high dimensional data. The asymmetric residual autoencoder uses residual connection to enhance the feature extraction ability of the encoder with deeper network, while a shallow CNN is adopted as the decoder. This arrangement could make the feature representation ability of the encoder stronger than decoder's reconstruction ability, which ensures the reliability of the extracted features. In addition, a clustering layer has been incorporated to form an end to end solution. Experiments on benchmark datasets have shown the effectiveness of the proposed method.
Predicting Cyber-Infections Via Web-Browsing Patterns

With the growing risks involved in surfing the web, the ability to protect users from malicious web content has become essential for combatting cybersecurity threats. Extensive studies have shown the correlation between certain personal-traits and user behavior in the context of cybersecurity. Moreover, recent studies showed that it is possible to predict long-term probability of cyber-attack victimization by analyzing a user's browsing and file-downloading history. However, such prediction models often rely on storing and analyzing personal information such as detailed browsing history, the topics and types of sites the user visited, and even physical location history, hence, potentially compromising user privacy. We developed a simple Markovian model that provides real-time predictions of exposure to malicious web pages by identifying behavioral patterns, as manifested in web-browsing dynamics. Utilizing the Markov chain framework, we extracted a small number of features that serve as proxies to the user's current risk level. Our model was tested on a dataset of web-browsing logs collected over three months, including more than 6 million browsing sessions. Using a logistic regression classifier to predict next-step exposure and exposure further into the session, our model allows for proactive intervention to protect from potential cyber-threats while maintaining user privacy.
Data-Driven Persona Retrospective Based on Persona Significance Index in B-to-B Software Development

Business-to-Business (B-to-B) software development companies develop services to satisfy their customers’ requirements. Developers should prioritize customer satisfaction because customers greatly influence agile software development. However, satisfying current customer’s requirements may not fulfill actual users or future customers’ requirements because customers’ requirements are not always derived from actual users. To reconcile these differences, developers should identify conflicts in their strategic plan. This plan should consider current commitments to end users and their intentions as well as employ a data-driven approach to adapt to rapid market changes. A persona models an end user representation in human-centered design. Although previous works have applied personas to software development and proposed data-driven software engineering frameworks with gap analysis between the effectiveness of commitments and expectations, the significance of developers’ commitment and quantitative decision-making are not considered. Developers often do not achieve their business goal due to conflicts. Hence, the target of commitments should be validated. To address these issues, we propose Data-Driven Persona Retrospective (DDR) to help developers plan future releases. DDR, which includes the Persona Significance Index (PerSI) to reflect developers’ commitments to end users’ personas, helps developers identify a gap between developers’ commitments to personas and expectations. In addition, DDR identifies release situations with conflicts based on PerSI. Specifically, we define four release cases, which include different situations and issues, and provide a method to determine the release case based on PerSI. Then we validate the release cases and their determinations through a case study involving a Japanese cloud application and discuss the effectiveness of DDR.
A Key Frame Extraction Method of HEVC Video Based on Clustering Algorithm for Electric Utilities Management

It is necessary to enhance the efficiency and convenience of key frame extraction for power lines and equipment inspection. As more and more Unmanned Aerial Vehicles (UAV) equipped with camera are applied for power asset management, High Efficiency Video Coding (HEVC) standard is one of the video compression standards adopted by UAV-based inspection. Video data acquired from sports camera can be automatically processed according to the preprocessing and key frame extraction algorithms to enhance the efficiency. The video encoding and decoding is tested aiming at the aspen_1080p video and customized electric utilities video according to HEVC standard. According to the decoded YUV420 video frames and the Peak Signal-to-noise Ratio of frames, perform frame association analysis with the ISODATA clustering algorithm. The nearest 5 frames compared with the cluster center are regarded as the key frames. The number of key frames can be customized in practice. The clustering and key extraction results validate the key frame extraction method and provide more convenience and higher efficiency for electric utilities inspection and management.
Credit Card Fraud Detection with NCA Dimensionality Reduction

Credit card transactions for online payments have increased dramatically and fraud attempts on these payments have become prevalent with more advanced attacks. Thus, conventional fraud detection mechanisms are inadequate to provide acceptable accuracy for fraud detections. Machine learning algorithms may provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In paper, we propose a new approach with machine learning for credit card fraud detections by increasing the performance of classification algorithms. We use the Neighborhood Component Analysis (NCA) dimensionality reduction to improve success rate for credit card fraud detections that use K-Nearest Neighbors (KNN) classification algorithm. We implemented the proposed approach and we tested it on a dataset. Particularly, we evaluated the results with the Area under the Receiver Operating Characteristic (AUROC) metric. The analyses results show that our approach provides better accuracy for credit card fraud detections.
Classification of Imbalanced Tumor Gene Expression Data Based on Weighted Gaussian Process

Tumor gene expression data is characterized with high dimension, small samples and imbalance. Gaussian Process Classification presents good applicability to high-dimensional and small sample data, but class-imbalanced problem will cause the low classification accuracy on the samples of minority classes. In this paper, a weighted likelihood function is introduced to balance the importance of positive and negative samples during the training of Gaussian Process Classifier, and further extended to multi-class classification tasks. The experimental results on real tumor gene expression data sets show that this method could effectively deal with the class-imbalanced problem caused by One-Against-Rest strategy in multi-class classification, meanwhile, achieve better classification accuracy on both the minority classes and overall data sets.
Correlation and Analysis of Overlapping Leukocytes in Blood Cell Images Using Intracellular Markers and Colocalization Operation

This chapter contributes to the study of uncertainty of signal dimensions within a microscopic image of blood sample. Appropriate colocalization indicator classifies the leukocytes in the region of interest having ragged boundaries. Signal transduction has been interpreted using correlation function determined fluorescence intensity in proposed work using just another colocalization plugin (JaCoP). Dependence between the channels in the colocalization region is being analysed in a linear fashion using Pearson correlation coefficient. Manders split, which gives intensity, is represented in a channel by co-localizing pixels. Overlap coefficients are also being analysed to analyse coefficient of each channel. Li's intensity correlation coefficient is being used in specific cases to interpret the impact of staining.
Reinforcement Learning with Evolutionary Computation to Policy Search for Autonomous Navigation

Reinforcement learning has good applications for autonomous navigation in unknown and complex environments. Traditional reinforcement learning methods with the actor-critic framework sometimes will fall into a local optimum because of the complexity of the loss function. Meanwhile, evolutionary computation(EC) is a type of black box optimization algorithm, which has good robustness in policy search but lower sampling efficiency. In order to address the challenge, we introduce an algorithm that combines evolutionary computation with reinforcement learning into navigation intuitively. The parameters of actor neural network are listed as individual characteristics. Each individual represents a policy network. At the end of each episode, individuals with higher fitness function value are selected to the next generation. Other individuals update a certain number of steps through the critic network with shared replay buffer and then move into the next generation. Simulation results demonstrate the effectiveness and feasibility of this algorithm on navigation.
Side Channel Analysis - A Demonstrative Approach on a 128-Bit AES Algorithm

In the recent years, with the advent of mass globalization, circuits that are being manufactured are not done end to end in the same roof. Some parts are manufactured by a company while others are being outsourced to others, sometimes even to different countries. Therefore, the security of the circuit is heavily compromised for quality and speed, thus forsaking the integrity. This is the reason why logic-locking was proposed and many methods and algorithms were tested and deployed to maintain the integrity of the circuit when it passes through IC integrator, foundry, assembly/test facilities, distributor before it reaches the end customer. While many of them have been successful, more and more attackers are finding a way to make them obsolete. One of the recent methods is by side channel analysis. This method is analysed and dealt with deeply. In this paper we demonstrate the design and implementation of a 128-bit Advanced Encryption Standard (AES) encryption algorithm and how an efficient side channel attack mollifies the strength of AES by deploying it on SASEBO-GIII FPGA board with the Xilinx Spartan and Kintex 7. The implementation has been tested successfully.
A Methodology to Automate the Security Patterns Selection

Security has become an important issue for software systems. The numbers of threats are advancing day by day making it more challengeable to cope with it. During the software development life cycle (especially security oriented applications) it is difficult for the novice users to adopt the security measures correctly. A lot of security patterns are available but the question is how to choose the right pattern from the set of given patterns. For this reason we proposed a three step methodology which will help in automating the security pattern selection process. We exploit the proposed methodology by leveraging the capabilities of Fuzzy c-mean and Text categorization approach. We used a sample of security patterns along with a set of 3 security problems to assess the efficacy of proposed methodology. The experimental results are promising in terms of organization and selection of security design patterns for given design problems at hand.