Reviewerly/exHarmony huggingface dataset:
Performance Analysis of CT Scan and X-Ray Medical Images Compression Using PCA and WDR techniques

Image compression which is a subset of data compression plays a crucial task in medical field. The medical images like CT, MRI, PET scan and X-Ray imagery which is a huge data, should be compressed to facilitate storage capacity without losing its details to diagnose the patient correctly. Now a days artificial neural network is being extensively researched in the domain of image processing. This paper examines the performance of two techniques namely Principle Component Analysis (PCA) and Wavelet difference reduction (WDR). Wavelet difference reduction method is a wavelet coding technique. The potential of the techniques to compress the medical image and achieving good quality is measured by MSE and PSNR quality metrics. The investigation is carried on CT scan of lower abdomen and X-ray scan of Rib Cage medical images.
Analysis and Countermeasures of College Students’ Sentimental Tendency Based on Network Behavior Data

Based on the characteristics of college students' network users and micro blogs, this paper studies the analysis of micro blog emotional tendency, and proposes a micro blog emotional tendency method integrating users’ behavior. Firstly, the LDA model is constructed to reflect the topic distribution of microblog. Considering the needs of follow-up research, a topic emotion joint model is proposed. Then, LSTM algorithm is used to profile users based on sentiment analysis and personality prediction, and the relationship between each attribute is analyzed. Finally, through the subjective and objective category prediction of the evaluation task data, the effectiveness of the proposed behavior analysis fusion algorithm and the generality of the classification model based on emotional tendency are verified.
Security Evaluation of LIGHTMAC: Second Preimage Attack using Existential Forgery

Message Authentication Code (MAC) is a cryptographic hash function that uses the cryptographic key(s) to generate a MAC value or a tag. MAC security property that must be satisfied is the second preimage resistance; given an input, it is difficult to find different inputs with the same tag. A lightweight MAC called LIGHTMAC was introduced in 2016 by Luykx et al. The scheme uses lightweight block cipher as its encryption function. This paper discusses the application of a second preimage attack on the LIGHTMAC scheme using existential forgery methods. The purpose of this attack is to determine the resistance of LIGHTMAC's second preimage property. From observation and experiments with instantiation using SIMECK32/64, it is found that the attack is more efficient than brute force attacks with birthday attack complexity. Furthermore, a modification is recommended to avoid the second preimage attack on LIGHTMAC.
Blind Quality Assessment of Tone-Mapped Images with Multi-scale Visual Feature Extraction Neural Network

To guarantee the quality of high dynamic range image (HDRI), various tone-mapped operators (TMOs) have been designed to display HDRI on traditional displays recently. Naturally, the image perceptual quality deteriorates seriously due to the inevitable distortions under different TMOs. In this paper, we propose a multi-scale visual feature extraction neural network for blind image quality assessment (BIQA) of TMIs. Specifically, hierarchical image decomposition is elaborately considered to mimic the hierarchical perception mechanism in the human visual system, expecting to better extract and fuse the multi scale features for quality prediction. Besides, under the proposed learning framework, the procedure of feature extraction, multi-scale feature fusion and quality prediction can be jointly optimized in an end-to-end manner. The experiments verify the stable performance of the proposed method on two public TMIs datasets.
Effective Graph Based Vertex Cover Algorithm Using Multi-document Node Summarizations

The extraction of multi-document summarization (MDS) has become the dominant technique to offer a compact form of documents that manages the opposite characteristic of the real documents. This technique finds the subset of similarity from the input documents (words/phrases/sentences) in the original text to form the summary in contrast. This paper has illustrated upon the similarity between the MDS problem with the vertex cover problem (VCP) and some important factors for the effective implementation of vertex cover algorithm (VCA) identified through a systematic review of the literature using SMART (Specific, Measurable, Assignable, Realistic, Time related) target. Further, they are categorized into five fields namely text summarization, MDS, Automatic text summarization, Sentence extraction, and Graph-based summarization and the lesson learned from these studies are discussed. The results of the research demonstrate the effectiveness of the proposed method in graph-based automatic multi-document summarization.
Fixed-size video summarization over streaming data via non-monotone submodular maximization

Video summarization which potentially fast browses a large amount of emerging video data as well as saves storage cost has attracted tremendous attentions in machine learning and information retrieval. Among existing efforts, determinantal point processes (DPPs) designed for selecting a subset of video frames to represent the whole video have shown great success in video summarization. However, existing methods have shown poor performance to generate fixed-size output summaries for video data, especially when video frames arrive in streaming manner. In this paper, we provide an efficient approach k-seqLS which summarizes streaming video data with a fixed-size k in vein of DPPs. Our k-seqLS approach can fully exploit the sequential nature of video frames by setting a time window and the frames outside the window have no influence on current video frame. Since the log-style of the DPP probability for each subset of frames is a non-monotone submodular function, local search as well as greedy techniques with cardinality constraints are adopted to make k-seqLS fixed-sized, efficient and with theoretical guarantee. Our experiments show that our proposed k-seqLS exhibits higher performance while maintaining practical running time.
Election prediction using twitter and GIS

The 2019 Indian general election is scheduled to be held in7 phases from 11 April 2019 to 19 May 2019, millions of voters will cast their votes during the elections, the largest democratic exercise in human history. Twitter is one of the most popular social media platforms that are used to express views regarding topics like sports, movies, politics, etc. Social Media has become one of the key platforms for politicians and political parties to promote themselves in the lead up to the elections. The aim of this paper is to do sentiment analysis of the Twitter network in the months leading up to the election and build a model that will be able to interpret the political subject matter. The analysis will be based on the politicians and the parties involved in the elections. This analysis can be used to acquire knowledge of the sentiments involved in the elections and predict the outcome of the upcoming elections. The models built are a Bidirectional Recurrent Neural Network model. The model is optimized using varying neural architectures and hyper parameter tuning. Kernel Regularization methods are used to address over fitting to get the best performance.
Enhanced User Interest and Expertise Modeling for Expert Recommendation

The rapid development of Community Question Answering (CQA) satisfies users' request for professional and personal knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, namely expert recommendation. However, most of existing methods for expert recommendation ignore some key information, such as time information and historical feedback information, degrading the performance. On the one hand, users' interest are changing over time. It is biased if we don't consider the dynamics. On the other hand, feedback information is critical to estimate users' expertise. To solve these problems, we propose a unified framework for expert recommendation to exploit user interest and expertise more precisely. Considering the inconsistency between them, we propose to learn their embeddings separately. We leverage Long Short-Term Memory (LSTM) to model user's short-term interest and combine it with long-term interest. The user expertise is learned by the designed user expertise network, which explicitly models feedback on users' historical behavior. The extensive experiments on a large-scale dataset from a realworld CQA site demonstrate the superior performance of our method than state-of-the-art solutions to the problem.
Multiple Object Association Incorporating Object Tracking, Depth, and Velocity Analysis on 2D Videos

In today's world of large-scale, rapid data creation, using analytic algorithms to generate knowledge from that data is of high value. While many datasets have robust proven analytics, generating knowledge from video has been a persistent challenge. Various methods exist to extract useful information from a scene in an image or video, such as object detection, object classifiers, and, recently, human-object interaction (HOI), which attempts to identify and describe interactions between humans and objects. Although the field of HOI has made excellent strides, there are still significant challenges associated with HOI tasks, such as availability of datasets and the type and diversity of videos on which it will work. In this paper, we describe a system that is able to solve a relaxed version of the HOI problem, where only the presence of an interaction between humans and objects is of interest, rather than the type of interaction itself. Our system combines general, off-the-shelf methods of object detection, object tracking, monocular depth estimation, and velocity calculations to analyze video data and report the presence of multiple object associations. We find that this information is of great value in providing search and discovery in different use cases such as surveillance, security, data labelling, and advanced analytics.
Research on Vehicle Appearance Damage Recognition Based on Deep Learning

Abstract Economic development has promoted the booming of the auto industry. With the increase of the number of cars, car insurance has become the largest type of insurance in the insurance industry with more than half of the market share. After the emergence of traditional vehicles, professional loss assessment personnel need to go to the scene to investigate the accident and complete the loss assessment. In recent years, With the rapid development of science and technology, the insurance industry has been changing from artificial and information to automation and intelligence. This paper presents a vehicle appearance damage recognition algorithm based on deep learning and its model evaluation method, which can accurately judge the vehicle damage in the image. The research shows that the Mask R-CNN model based on KL-loss performs well in vehicle damage detection and has good robustness; at the same time, the accuracy of the evaluation model results is greatly improved by replacing the traditional IOU calculation accuracy method with the component position.
Determining Fall direction and severity using SVMs

Fall detection has been an important consideration in the field of human activity recognition and has garnered significant interest from researchers. A typical aim within fall detection systems is the determination of whether a fall has occurred or not. However, less attention has been provided to the problem of fall direction detection and severity. In this paper, we experiment with the detection of direction and severity in falls using the SisFall dataset. We perform this by using a combination of time and frequency domain features on inertial measurement sensor values along with a Support Vector Machine classifier. We are able to achieve promising results for the considered task.
Digital maps of the connections in school mathematics

Educational jurisdictions typically prescribe the content of mathematics curricula in print documents, often available as downloadable files. Digital maps provide another view of curriculum that can depart from the linear structure of these documents and more effectively convey the connections within school mathematics. In this chapter, we introduce three digital mapping projects: Math Mapper 6–8, the Dynamic Mathematics Curriculum Network and the Cambridge Mathematics Framework. Each project draws on distinct theoretical and methodological approaches, and the connections shown in each map are based on different sources. Despite these differences, each project seeks to enhance mathematics teaching and learning by visually representing connections, making the basis for those connections explicit, ensuring the map can be used in flexible ways and providing on-demand access to related instructional materials. Initial feedback from the intended audiences for each map reveals the ways each project can contribute to mathematics education. Some future directions for digital mapping of school mathematics that emerged through discussion of the challenges that arose across the projects are also offered.
Identify Encrypted Packets to Detect Stepping-Stone Intrusion

Most attackers exploit stepping-stone to launch their attacks to avoid being captured. An encrypted TCP session established by attackers using ssh makes stepping-stone intrusion detection harder than non-encrypted sessions. Even though the contents of an encrypted packet are not readable, its header fields in different layers are not encrypted. In this paper, we propose a novel algorithm to detect stepping-stone intrusion based on IP address, port number, TCP packet flags, and the length of an encrypted packet. A preliminary experimental result in a local area network shows that the proposed algorithm cannot only detect stepping-stone intrusion, but also resist intruders’ session manipulation.
On How to Add SALT: ‘PLAYGORA’—A Real-World Case Study and Experimentation (Out of the Lab)

This contribution presents a real-world case study and experimentation based on the Serendipity, Ambiguity, Laterality and Tangibility (SALT) conceptual architecture. We present early-stage design engineering processes (DEP) coupled with assisted human–machine creativity and directed towards research Out of the Lab in which we engaged public participation during a nine-day public international design event in The Netherlands. The ‘Playgora’ study and experiment(s) were executed and conducted (i.e. early-stage research) with hybrid design tools (HDTs) and environments (HDTEs) support systems imbued with SALT.
Parallelization of Levelset-based Text Baseline Detection in Document Images

In this paper, we propose a text baseline detection method. The proposed method is based on a strategy of object separation in a binary image that consists of three steps. The first step is making a binary image with sobel edge detection and mathematical morphology operation to take a approximated text area from the ordinary document image. In the second step, line segments which are candidates for text baselines, are extracted by parallel levelset method. The last step fits a line from each segment with parallel random sample consensus and selects appropriate lines automatically. For parallel computation, OpenMP that is standard API for shared memory parallel programming in C/C++ is used.
A Research on Stock Index Prediction Based on Multiple Linear Regression and ELM Neural Network

The closing price of the stock index has the characteristics of non-linearity, volatility and noise, and it is difficult to predict. In this paper, the prediction result of the multiple linear regression model is used as one of the ELM model's input layer vectors, and a stock index prediction model combines the multiple linear regression model and the ELM neural network model. Then, we use the model to predict and analyse the Shanghai and Shenzhen 300 Index. The prediction results show that the combination model has improved prediction and accuracy compared with a single linear model or ELM neural network model. The combination model is practical.
A Novel Data-Driven Approach for Generating Verified Loop Invariants

Inspired by the procedure that experts construct loop invariants, we propose a novel data-driven approach to generate verified loop invariants automatically. The approach consists of the prediction phase and the evaluation phase. The prediction phase aims to generate candidate loop invariants automatically by solving polynomial equations and synthesizing the extended loop conditions. The evaluation phase aims to generate verified loop invariants that can be utilized to prove the correctness of program postconditions. The spurious candidate invariants are first pruned out using the method of predicate abstraction. Then, the redundant relations are removed from the candidate invariants using an Satisfiability Modulo Theories (SMT) solver. The proposed approach is compared with state-of-the-art methods on 32 benchmarks collected by the recent papers. The experimental results demonstrate that the novel data-driven approach generates 29 verified loop invariants successfully. The proposed approach is efficient at discovering verified loop invariants automatically for executable loop programs. Meanwhile, compared with other methods of generating loop invariants, the proposed approach not only costs less time to generate invariants, but also generates invariants with better quality.
An Improved Ant Colony Optimization for QoS-Aware Web Service Composition

Web service composition (WSC) provides a flexible framework for integrating independent Web services to meet complex functional needs. The Web service selection (WSS) problem centers on selecting the best service from a set of candidate Web services based on quality of service (QoS) features. In this paper, we propose an adaptive chaotic ant colony optimization algorithm for multi-pheromone distribution based on the swap concept. The aim of the improvement to the ACO is to avoid local optimum traps and reduce the search time. Chaotic disturbances and integration of multiple solutions will increase the chances of the algorithm getting an optimal solution and avoid stagnation, while multiple pheromones of QoS are used to enhance the exploration of the solution space. Experimental analysis of the algorithm with ACO and FACO shows that it outperforms the latter two ant colony optimization algorithms in terms of quality of solution, standard deviation and execution time.
Identity authentication based on keystroke dynamics for mobile device users

User authentication of mobile devices is an important means to protect data security and user privacy. Low overhead, high accuracy and continuous authentication are challenging problems in user authentication. Aiming at the disadvantages of current authentication methods based on keystroke dynamics such as low accuracy and one-time authentication, this paper proposes a new authentication method named UIKI (User Identity authentication method based on clusters of Keystroke time Intervals). UIKI consists of the valid user modeling phase and the runtime user authentication phase. In the valid user modeling phase, UIKI uses a clustering algorithm to find the stable centroids and the fluctuation range of a valid user's centroid positions. In the runtime user authentication phase, UIKI clusters the data to be authenticated with the stable centroids as the initial centroids, and compares the results of clustering with the stable centroids and the centroid fluctuation range of the valid user to determine whether the input data are from the valid user. Experimental results prove that UIKI has the average FAR (False Accept Rate) of 0.082 and the average FRR (False Reject Rate) of 0.052, which can effectively authenticate a user's identity. UIKI has the advantages of high accuracy, low overhead and continuous authentication, which is suitable for the user authentication of mobile devices.
Modeling Extent-of-Texture Information for Ground Terrain Recognition

Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally. At first, the proposed method uses a CNN backbone feature extractor network to capture meaningful information of ground terrain images, and model the extent of texture and shape information locally. Then, it encodes order-less texture information and ordered shape information in a patch-wise manner, and utilizes an intra-domain message passing mechanism to make every patch aware of each other for rich feature learning. Next, the model combines the extent of texture information with the encoded texture information and the extent of shape information with the encoded shape information patch-wise and then exploit Extent of texture (EoT) Guided Inter-domain Message passing module for sharing knowledge about the opposite domain to balance out the order-less texture information with ordered shape information. Finally, Bilinear model outputs a pairwise correlation between the order-less texture information and ordered shape information, and classifier classifies the ground terrain image efficiently. The experimental results indicate the superior performance of the proposed model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The source code of the proposed system is publicly available at https://github.com/ShuvozitGhose/Ground-Terrain-EoT over existing state-of-the-art techniques on DTD, MINC and GTOS-mobile datasets.