Reviewerly/exHarmony huggingface dataset:
A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

The majority of medical workers are eager to obtain realistic and real-time CT 3D reconstruction results. However, autonomous or involuntary motion of patients can cause blurring of CT images. For the 3D reconstruction scene of motion-blurred CT image, this paper consists of two parts: firstly, a GAN image translation network deblurring algorithm is proposed to remove blurred results. This algorithm adopts the clear image to supervise the training process of the blurred image, which creates solutions that are close to the clear image. Secondly, this paper proposes a Marching Cubes (MC) algorithm based on the fusion of golden section and isosurface direction smooth (GI-MC) for 3D reconstruction of CT images. The golden section algorithm is used to calculate the equivalent points and normal vectors, which reduces the calculation numbers from four to one. The isosurface direction smooth algorithm computes the mean value of the normal vector, so as to smooth the direction of all triangular patches in spatial arrangement. The experimental results show that for different blurred angle and blurred amplitude, comparing the results of the Shannon entropy ratio and peak signal-to-noise ratio, our GAN image translation network deblurring algorithm has better restoration than other algorithms. Furthermore, for different types of liver patients, the reconstruction accuracy of our GI-MC algorithm is 9.9%, 7.7%, and 3.9% higher than that of the traditional MC algorithm, Li’s algorithm, and Pratomo’s algorithm, respectively.
Combinatorial Subset Difference—IoT-Friendly Subset Representation and Broadcast Encryption

In the Internet of Things (IoT) systems, it is often required to deliver a secure message to a group of devices. The public key broadcast encryption is an efficient primitive to handle IoT broadcasts, by allowing a user (or a device) to broadcast encrypted messages to a group of legitimate devices. This paper proposes an IoT-friendly subset representation called Combinatorial Subset Difference (CSD), which generalizes the existing subset difference (SD) method by allowing wildcards (*) in any position of the bitstring. Based on the CSD representation, we first propose an algorithm to construct the CSD subset, and a CSD-based public key broadcast encryption scheme. By providing the most general subset representation, the proposed CSD-based construction achieves a minimal header size among the existing broadcast encryption. The experimental result shows that our CSD saves the header size by 17% on average and more than 1000 times when assuming a specific IoT example of IP address with 20 wildcards and 2 20 total users, compared to the SD-based broadcast encryption. We prove the semantic security of CSD-based broadcast encryption under the standard l-BDHE assumption, and extend the construction to a chosen-ciphertext-attack (CCA)-secure version.
BatikGAN: A Generative Adversarial Network for Batik Creation

We propose a texture synthesis method based on generative adversarial networks, focusing on a cultural emblem, called Batik, of southeastern Asian countries. We propose a two-stage training approach to construct the network, first generating patches and then combining patches to generate the entire Batik image. Regular repetition and synthesis artifact removal are jointly considered to guide model training. In the evaluation, we show that the proposed generator fuses two Batik styles, removes blocking artifacts, and generates harmonious Batik images. Qualitative and quantitative evaluations are provided to show promising performance from several perspectives.
Legal Case Summarization: An Application for Text Summarization

Today in the realm of developing advances, there is a gigantic development in technologies. These days every single theme identified with research is accessible on the web. Consequently to profit the clients, it is required to condense the substance as short as possible. Different research networks are proposing enormous number of methods to abridge the substance. So in this exploration an extractive methodology is executed so as to save Indian lawyer's time in dealing with the big cases. This research uses web scrapping technique that helps in direct extraction of data from online resources hence results in accurate results. This is done using python on ANACONDA to summarize the IT LAW cases which are extricated using web scrapping strategy. After the extraction, different techniques such as LUHN, LSA, LEXRANK and SUMBASIC are used to summarize the content and calculate the ROUGE scores. The exploration just compares these four techniques on the basis of their performance metrics of ROUGE-1 and ROUGE-2 scores and according to the precision, recall and f-measure scores finally concludes that LUHN and LSA are the two best algorithms according to the ROUGE-1 and ROUGE-2 scores.
Constructing five qutrit absolutely maximally entangled state via recurrence relation

Recently, Che et al presented a recurrence relation of multi-qubit states. Regarding this inspiration, we give and extend the recurrence relation of multi-qubit states to the recurrence relation of maximally qutrit entangled states. We also establish absolutely maximally entangled five qutrits states via maximally entangled four qutrits states. We also demonstrate that such a state is the 2-uniform state of five qutrits states.
Parallel Cascade R-CNN for object detection in remote sensing imagery

Abstract Object detection in remote sensing image is a challenging task in computer vision. Remote sensing images have different characteristics compared with conventional images. Especially object detection in remote sensing image needs to focus on small targets with different ratio and orientation. In this paper, we propose a novel detection architecture for remote sensing imagery to solve the problem of scale diversity. On the basis of Cascade R-CNN, we have developed Parallel Cascade R-CNN. In the second stage, parallel detection heads are used for separate detection, and their RoIAlign modules have different output sizes. In addition, different preprocessing methods are applied according to the shape and quantity characteristics of different classes. We evaluated our algorithm on DOTA dataset. Experiments have shown that our algorithm can achieve performance improvement on a high baseline. And the detection performance of those categories with smaller objects has been improved. In the detection task of horizontal bounding box, we obtained the mAP of 78.96%, which reached the state of the art. Our algorithm is simple and has good performance, easy to be migrated to various network structures.
A Computationally Efficient sEMG based Silent Speech Interface using Channel Reduction and Decision Tree based Classification

Silent Speech Interface is one of the promising areas of Human-Computer Interaction research. The surface electromyography based silent speech interface is a technique where the electric activity of facial muscles are used to detect speech. The existing sEMG based SSI techniques use complex machine learning algorithms and too many number of electrodes on the subject's face. It creates inconvenience to the user who might have undergone laryngectomy. More number of electrodes becomes highly invasive to the user, while complex classification algorithms increase the computational cost and prevents real time implementation of sEMG based SSI. Thus the objective of this research work was to develop a less complex and computationally less expensive model to classify words. To achieve this goal channel reduction technique and the use of Decision Tree based classification algorithm was employed. Only the time domain features are used as input to the classification algorithm. The motive was to exploit the advantage of computational ease in extracting the time domain features as compared to the frequency domain features. The sEMG data of the words used in this work are obtained from the complete utterance of the sentences and not by individual utterances of the word. Our algorithm was able to achieve a word accuracy of 95.17% even after applying a channel reduction, thereby allowing us to use only the data of 5 channels, in place of a conventional seven channel setup.
Robust Negative Obstacle Detection in Off-Road Environments Using Multiple LiDARs

Autonomous vehicles must analyze and understand the surrounding terrain in real time when driving in off-road environments. Yet few UGVs can effectively detect pits and ditches in an off-road environment at high speeds autonomously. This paper presents an adaptive negative obstacle detection method for autonomous vehicles using multiple side-mounted LiDARs. The method begins by extracting range jump in radial direction from the raw sensor data to get potential negative obstacle feature point pairs, and then estimates the vector of the scanned ground surface around the potential negative obstacles each moment. Subsequently, the feature point pairs are filtered based on several geometrical features related to the range and the deviation between the feature point pairs and the ground vector. Then we fuse the feature point pairs from multiple LiDARs and multiple frames to improve robustness and the longest detection distance. A series of experiments have been conducted in several typical off-road scenarios. The experimental results show that the proposed method can accurately detect negative obstacles and could deal with complex terrain in off-road environments.
Improved multi-layer online sequential extreme learning machine and its application for hot metal silicon content

Hot metal silicon content is an important indicator for measuring the smooth operation of the blast furnace. However, the hot metal silicon content cannot be directly detected online. Hence, this paper proposes a prediction model of the hot metal silicon content based on the improved multi-layer online extreme learning machine (ML-OSELM). The improved ML-OSLEM algorithm is based on ML-OSELM, the variable forgetting factor (VFF) and the ensemble model. VFF is introduced to make the new coming data get more emphasis. The ensemble model can overcome the overfitting problem of ML-OSELM. This improved algorithm is named as EVFF-ML-OSELM. The real blast furnace production data are used to testify the established prediction model based on EVFF-ML-OSELM. Compared with the prediction models of the hot metal silicon content based on other algorithms, the simulation results demonstrate that the prediction model based on EVFF-ML-OSELM has better prediction accuracy and generalization performance.
Deep Learning Model for Sensor based Swimming Style Recognition

This paper aim is to present deep learning based approach for swimming style recognition performed on publicly available data collected with a smartwatch. The proposed method is a Bi-LSTM (Bidirectional Long-Short Term Memory) network, which was constructed using MATLAB neural network toolbox. Data for the system was prepared by segmenting it into fixed-size windows and extracting pure signal features such as mean, standard deviation, median absolute deviation (MAD), signal magnitude area (SMA), interquartile range (IQR) as well as features from normalized signal spectrum such as entropy, energy, kurtosis, skewness and index of spectrum maximum (ISM) from each window. The Bi-LSTM method was able to perform with average F1 score of 91.39%.
A Novel Approach for Mammogram Enhancement using Nonlinear Unsharp Masking and L0 Gradient Minimization

Mammography is an effective modality for detecting and diagnosing breast cancer. Mammogram enhancement is used to improve the local contrast of mammograms, such that lesions appear more visible in the enhanced image. This paper introduces two novel approaches for mammogram enhancement based on a new unsharp masking scheme, called nonlinear UM and L0 Gradient Minimization, NLUML0GMIN for detecting breast cancer at an early stage. Three different techniques are unified in the proposed method; i) a Nonlinear filtering process to enhance the fine details within a 3x3 local neighborhood, ii) an L0 Gradient Minimization step globally preserving the high-contrast edges while suppressing low-contrast details seen as opaque fibrous tissues; and to obtain a detail mammogram after subtracting smoothed mammogram from the original mammogram, iii) Finally, an Unsharp masking technique combining detail mammogram with filtered mammogram passed through Non-linear filter, using PLIP operators which satisfy both Weber’s law and saturation characteristics of the human visual system.. An HVS-based decomposition scheme is used to analyse and visualize the malignant areas in the enhanced mammogram. The distinct arrangement of PLIP operators in the proposed framework has given rise to two methods NLUML0GMINAUTO and NLUML0GMIN, which outperforms NLUM method and other existing techniques for mammogram enhancement. The former is good at enhancing less-dense and medium-dense mammograms whereas the later enhances unseen masses within high-dense mammograms. Experimental results have stated the significance of the proposed method in effectively diagnosing breast cancer in comparison with the previous methods for mammogram enhancement. The mini-MIAS database has categorized mammograms as fatty, fatty-glandular and dense-glandular, which has helped us in focusing on enhancing images uniquely for each category. The proposed NLUML0GMIN scheme is robust and effective in identifying cancerous masses and calcifications within dense X-ray mammograms, aiding doctors in better cancer diagnosis by saving the life of countless patients with cancer.
Towards a verified range analysis for JavaScript JITs

We present VeRA, a system for verifying the range analysis pass in browser just-in-time (JIT) compilers. Browser developers write range analysis routines in a subset of C++, and verification developers write infrastructure to verify custom analysis properties. Then, VeRA automatically verifies the range analysis routines, which browser developers can integrate directly into the JIT. We use VeRA to translate and verify Firefox range analysis routines, and it detects a new, confirmed bug that has existed in the browser for six years.
Model-Based Recommender Systems

Recommender systems are machine learning based algorithms that found application in various business scenarios, e.g., video on demand or music streaming like Netflix and YouTube, products sales recommendation such as Amazon, or content recommendation such as Facebook or Twitter. Besides successful utilization by multinational companies, the recommender systems found application in small business like supermarket, cinema, restaurants, retail stores, etc. In this chapter detailed overview about the algorithms behind the recommender systems was described with focus of the application using the ML.NET open source framework for training, building, and evaluating machine learning models. Furthermore, the methodology and use case scenario for the restaurant recommendation using ML.NET were developed to provide full life-cycle management of the modern cloud based recommended system.
Detection of Malicious Domain Name Based on DNS Data Analysis

Abstract This paper describes the research background of malicious domain name detection based on DNS data analysis, obtains DNS data through active domain name data or passive domain name data, then obtains DNS data through knowledge-based methods, detection methods based on machine learning and hybrid methods, and finally puts forward research suggestions.
Effective Chroma Subsampling and Luma Modification for RGB Full-Color Images Using the Multiple Linear Regression Technique

Differing from the traditional chroma subsampling on the YUV image converted from a RGB full-color image, in this paper, we propose a novel and effective chroma subsampling and luma modification (CSLM) method.For each 2 × 2 YUV block, first, a newly reconstructed 2 × 2 RGB full-color blockdistortion model is proposed, and then we propose a multiple linear regression approach to tackle our CSLM method such that the reconstructed 2 × 2 RGB full-color block-distortion can be minimized, achieving significant quality improvement of the reconstructed RGB full-color image.Based on the Kodak and IMAX datasets, the comprehensive experimental results demonstrated that on the versatile video coding (VVC) platform VTM-8.0,our method achieves substantial quality and quality-bitrate tradeoff improvement of the reconstructed RGB full-color images relative to six traditional methods and the three state-of-the-art methods.
A Blockchain Model for Trustworthiness in the Internet of Things (IoT)-Based Smart-Cities

Our traditional Internet is extended into the Internet of Things (IoT) by connecting physical world devices and objects (things) to it. The diversity of applications of IoT range from mission-critical applications, such as smart-grid, smart-transportation system, smart-surveillance, and smart-healthcare to business applications, such as smart-industries, smart-logistics, smart-banking, smart-insurance, etc. In the IoT revelation, with the assistance of sensors and actuators, conventional devices become smart and autonomous. In coming years, for the predicted huge evolution of the IoT, it is important to provide confidence in this enormous collection of information. Today's advances in the technology are turning IoT revelation into a reality. However, there are still challenges to address, especially for data reliability and security. There is a necessity for inclusive support of privacy and security in IoT for its mission-critical as well as business applications. Numerous frameworks are proposed to enable trustworthiness in the IoT. Academic and industrial researchers are continuously working to propose frameworks for security and privacy in IoT. At the same time, another technology, blockchain has emerged as a key technology that transforms the way in which we share information. Since the time it emerged, this technology has shown auspicious application scenarios. Blockchain builds trust in a distributed atmosphere without the need of central authorities. From the preliminary cryptography to the current smart contracts, blockchain has been functional in many fields. Blockchain as a technological advance has the extreme potential to change many industries, and IoT can be one of them. A blockchain stores all the processed information (known as a transaction in the blockchain terminologies) in sequential order, in a set of memory blocks that are tamper-free to adversaries. All nodes share these processed transactions. The information to these transactions is published as a public ledger that cannot be modified, and every node in the network retains the same ledger. In this way, a blockchain creates a distributed trustworthiness in the network. There have been many studies on the privacy and security issues of blockchain and IoT fusion, however, there lacks a systematic framework on the fusion of blockchain and IoT for trustworthiness. In this chapter, we overview blockchain as a model for IoT trustworthiness. We propose a novel blockchain-based framework for trustworthiness in IoT-based smart-cities.
A Novel Approach for Software Requirement Prioritization Based Upon Non Functional Requirements

Software becomes an essential part of our lives because of the required automation in every field. A software requirement plays an important role in its development. In requirement engineering, requirement prioritization is the crucial activity to successfully deliver the software system. Numerous techniques exist for software requirement prioritization such as Cumulative Voting, Hieratical Cumulative Voting, Planning Games (PG), and MOSCOW etc. But it is analyzed that these techniques are insufficient to prioritize large number of requirements and in most of the cases requirement engineer just prioritizes the functional requirements and ignore non-functional requirements. It becomes a challenge to prioritize Non-Functional Requirements (NFRs) in order to justify which requirement has higher priority along with functional requirements in large software systems. In order to overcome this gap, we have proposed a novel approach ”PGAHP” for Requirements Prioritization by merging Planning Games (PG) and analytical hierarchy process (AHP) concepts. Proposed approach PGAHP is validated using Library Management System case study. Results shows that complexity to prioritize requirements is decreased by using ”PGAHP” by performing thirty one (31) pair of comparison as compare to AHP which has (105) pair wise comparison for the same number of functional and nonfunctional requirements.
iCap: Interactive Image Captioning with Predictive Text

In this paper we study a brand new topic of interactive image captioning with human in the loop. Different from automated image captioning where a given test image is the sole input in the inference stage, we have access to both the test image and a sequence of (incomplete) user-input sentences in the interactive scenario. We formulate the problem as Visually Conditioned Sentence Completion (VCSC). For VCSC, we propose ABD-Cap, asynchronous bidirectional decoding for image caption completion. With ABD-Cap as the core module, we build iCap, a web-based interactive image captioning system capable of predicting new text with respect to live input from a user. A number of experiments covering both automated evaluations and real user studies show the viability of our proposals.
On searching explanatory argumentation graphs

Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. The framework and computational considerations lead to an iterated local iterative deepening depth-first search to collect ‘interesting’ semi-abstract argumentation graphs consistent with a collection of statement labellings. Finally, the framework is illustrated and evaluated with a proof-of-concept implementation.
Digital Transformation of the Industrial Sector: Challenges and Prospects

Purpose: Digital technologies create a fundamentally different model of today's reality. It is a model that enables fairly effective fundamental developments, allowing us to reach a high level of knowledge management in the area of science and technological progress. Design/Methodology/Approach: Innovations are based on digital technologies and business models as well as effectively used information and communication technologies. Findings: In the Russian Federation, state programmes helped to form a strong reserve for providing incentives to business for the introduction of various innovations in the economy and social area, which is certainly one of the main factors in the development of any country. Originality/Value: This topic is of high relevance because the widespread introduction of information and communication technologies and the digital transformation of the industrial sector is one of the most important factors of sustainable economic growth, food and economic security.