Reviewerly/exHarmony huggingface dataset:
A novel mathematical modeling in shift in emotion for gauging the social influential in big data streams with hybrid sarcasm detection

Summary Online social network is a platform that plays an essential role in identifying the emotional values of user‐generated content such as blogs, posts, and comments along with their influential factors. Especially on Twitter, network users are growing worldwide day by day and creating a massive amount of data that is not analyzed effectively in a quick way. Identifying the most influential persons on the social network is also a challenging task over the wide range of real‐time applications like recommendation systems. Now, to handle these situations, this article proposes a novel approach for prediction of information diffusion that includes emotion recognition with sarcasm detection based influence spreader identification (PID‐ERSDISI). The proposed method uses the user‐generated posts for emotion recognition in tandem with sarcasm detection both implicitly and explicitly. This approach helps to gauge the leverage that influences spreaders and also enhances the prediction accuracy of information diffusion in a better way. The implementation of the proposed work executed their task one after another in the following way, namely, sarcasm detection, emotional‐level computation, breakpoint computation, breakpoint validation, influential user generation, and information diffusion. After the successful implementation of this proposed PID‐ERSDIS, it produced prominent results against other state‐of‐art methods.
Cyberbullying Detection on Indonesian Twitter using Doc2Vec and Convolutional Neural Network

Cyberbullying is the act of threatening or endangering others by posting text or images that humiliate or harass people through the internet or other communication devices. According to a survey from Polling Indonesia and Asosiasi Penyelenggara Jasa Internet Indonesia (APJII) about cyberbullying, 49% of 5900 participants claimed they have been bullied. Therefore, this research was conducted with the intention to prevent cyberbullying acts, especially in Indonesia. We collected data from Twitter based on Twitter’s Trending keywords which correlated to cyberbully events. Then we combined it with the data from previous research. We obtained a total of 1425 tweets, consists of 393 data labeled as cyberbully and 1032 data labeled as non-cyberbully. Thereupon, we build a Doc2Vec model for features extraction, and a classifier model using the baseline classification method (SVM and RF) and CNN to detect cyberbully texts. The results show that the classifier using CNN and Doc2vec has the highest F1-score, 65.08%.
A Robust Vehicle Detection Method in Thermal Images Based on Deep Learning

Thermal cameras have been widely used in driver assistant system to provide supplementary visual information in darkness. As a consequence, vehicle detection in thermal images is highly required. Current methods for thermal image vehicle detection can hardly make a good balance between accuracy, speed and robustness. In this paper, we propose a deep-learning-based method for automatic vehicle detection in thermal images which employs yolov3-tiny as the main backbone with three major improvements. Firstly, an additional detection layer is designed and added to the original yolov3-tiny network for small vehicle detection. Secondly, a group of side-way blocks are introduced to extract and combine features in different layers so as to improve the overall detection accuracy. In the end, the Spatial Pyramid Pooling (SPP) module is adopted for multi-scale feature integration to further improve the detection performance. Experimental results show that the proposed method raises the mean Average Precision (mAP) by 4% compared to the current state-of-the-art method and 10% compared to the original yolov3-tiny network with little increase in time-consumption. In conclusion, the evaluation results suggest that our method is robust and effective for vehicle detection in thermal images.
Experience of training masters at Eastern Siberian State Institute of Culture

The experience of training in “Library and information activities” (specialty 51.04.06), profile “Theory and methodology of library pedagogical activities”, master program is analyzed. The history of librarianship master program at Eastern Siberian State Institute of Culture is reviewed; the faculty structure and learners are characterized. The curriculum comprises library, bibliographical, psychological and pedagogical and management disciplines. The tasks are specified: educating thoughtful professionals aware of library problems; developing theory and methodology; involving students into conceptualizing prospects for library and information industry. Methodological approaches and teaching methods are discussed as exemplified by the disciplines: “Methodology of library pedagogical activities”, “Library professiology”, “Organization and methodology of library, bibliographical and bibliological studies”, “Library environment and space”, “Business etiquette in library”. The focus is made on developing professional (rigorous) and personal (flexible) competences: critical thinking, empathy, communication skills, etc. The authors’ experience in applying pedagogical technologies is discussed (mind maps, puzzle technique, cases, discussions, presentations, etc.). The contents of practical activities and the subjects of final qualifying papers are characterized.
A Convolution Neural Network based MRI breast mass diagnosis using Zernike moments

The most important aspect of women's death worldwide is currently breast cancer. Advance engineering techniques of classifying the images and machine learning has generally been utilized for classifying the breast images. The association of computerized picture characterization permits the physicians and doctors second opinion hence saving their time. Median filter technique is used for image de-noise. To overcome the issues of the conventional imaging techniques (Mammography, Ultrasound, MRI) used in machine learning supervised algorithms such as small data size, feature introspection, nowadays deep learning techniques are used to make performance getting better as you feed them more information. In this study we develop a technique based on the CNN (LeNet-5) for the diagnosis of tumors in Zernike moments of breast cancer. The performance of the new CNN architecture is 83.33% 88.2 percent sensitivity and 76.92% accurateness, 83.3% sensitivity and 62.5% malignant growth accuracy.
Blockchain-Enabled IoT Integrated Autonomous Sewage Management System

The Internet and its implementation have become an important part of the human lifestyle of today. In every way, it has become an integral device. Researchers went through connecting just computers to the Internet because of the immense need for demand. In order to prevent serious injury and even loss of life, operations produced under unsafe conditions, such as sewage cleaning and mining, need active investment from the industry. Safety measures are taken to incorporate current sensor technologies and wireless communication system based on Internet of Things (IoT) that uses cloud computing for transmission and storage of wireless module parameters. Protection and understanding of sewage cleaning are now a significant issue. The health of sewage cleaners is at risk primarily because of the release of poisonous gases or oxygen insufficiency. In this project, a continuous monitoring system will be built to monitor environmental parameters such as temperature, carbon monoxide (poisonous gases), and humidity. The parameters are continually detected by different sensors; the admin will take appropriate measures to handle the situation if any irregular condition occurs. For the registration of new IoT devices and users, blockchain network will help in authentication and identification. Blockchain technology also manages the sensitive events happening in the IoT network system and notifies to users.
Mean-Squared Accuracy of Good-Turing Estimator

The brilliant method due to Good and Turing allows for estimating objects not occurring in a sample. The problem, known under names “sample coverage” or “missing mass” goes back to their cryptographic work during WWII, but over years has found has many applications, including language modeling, inference in ecology and estimation of distribution properties. This work characterizes the maximal mean-squared error of the Good-Turina estimator, for any sample and alphabet size.
Contrast Study of Side Scan Sonar Image Enhancement Methods

Aiming at the problems of grayscale distortion, low contrast, and unobtrusive targets of side-scan sonar images, this paper uses five image enhancement methods for comparative experimental analysis. Analyzed five image enhancement algorithms: histogram equalization (HE), grayscale stretching (GS), mean filter (MF), Retinex (MSR) and wavelet transform (WT). Experiments were performed on three side scan sonar images. Experiments show that the three methods HE, GS, and MSR can effectively improve the image quality to a certain extent. The image enhancement effect of the MF method is not obvious, and the effect of the WT method is poor.
KPRM: Kernel Page Restriction Mechanism to Prevent Kernel Memory Corruption

An operating system (OS) comprises a mechanism for sharing the kernel address space with each user process. An adversary’s user process compromises the OS kernel through memory corruption, exploiting the kernel vulnerability. It overwrites the kernel code related to security features or the kernel data containing privilege information.Process-local memory and system call isolation divide one kernel address space into multiple kernel address spaces. While user processes create their own kernel address space, these methods leave the kernel code vulnerable. Further, an adversary’s user process can involve malicious code that elevates from user mode to kernel mode.Herein, we propose the kernel page restriction mechanism (KPRM), which is a novel security design that prohibits vulnerable kernel code execution and prevents writing to the kernel data from an adversary’s user process. The KPRM dynamically unmaps the kernel page of vulnerable kernel code and attack target kernel data from the kernel address space. This removes the reference of the unmapped kernel page from the kernel page table at the system call invocation. The KPRM achieves that an adversary’s user process can not employ the reference of unmapped kernel page to exploit the kernel through vulnerable kernel code on the running kernel. We implemented KPRM on the latest Linux kernel and showed that it successfully thwarts actual proof-of-concept kernel vulnerability attacks that may cause kernel memory corruption. In addition, the KPRM performance results indicated limited kernel processing overhead in software benchmarks and a low impact on user applications.
The Development Characteristics of Virtual Reality After "The Year of VR"

After "The Year of VR" in 2016, virtual reality has shown a pace of returning to rationality and calm development. The technology is becoming more and more mature, while the penetration and expansion of the virtual reality industry have begun to accelerate and the application has begun to show diversification. Virtual reality technology stem from multi-disciplinary integration and it will continue to advance the integration of virtual reality and various industries under the context of "VR+", deeply and widely. The frequency of the iterative update of hardware products intensified, while the product design getting closer and closer to user needs.
Parallel Attention with Weighted Efficient Network for Video-Based Person Re-Identification

In this paper, we propose a new way to solve the problems of temporal and spatial independence, shallow feature extraction, and large computation which are not solved by traditional video-based Re-ID methods. Insufficient ability to extract features based on traditional networks can cause problems with bad ripple effect later, therefore we design an attention network named Parallel Spatio-Temporal Attention (PSTA) to fuse spatio-temporal features. After extracting deep features, existed methods need stack convolutional operation to model large receptive fields, so we use Non-local operation to capture long-range dependencies directly. For Non-local method, we propose an Attention-Like Similarity (ALS) to learn the weights of similarity matrix adaptively, then filter out redundant similarities. To solve the high complexity brought by Non-local method and maintain accuracy, we perform Spatial Pyramid Pooling (SPP) in Non-local structure to reduce complexity and combine multi-scale features. Extensive experiments with ablation analysis show the effectiveness of our methods, and state-of-the-art results are achieved on large-scale video datasets.
Entangled photon factory: How to generate quantum resource states from a minimal number of quantum emitters

Photonic graph states are a crucial resource for quantum networks and measurement-based quantum computing. We present a general, efficient algorithm for determining how to generate any graph state from a minimal number of quantum emitters.
Optimal Rate-Distortion-Leakage Tradeoff for Single-Server Information Retrieval

Private information retrieval protocols guarantee that a user can privately and losslessly retrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates in the single server scenario, i.e., all files are stored uncoded on a single server. In particular, we derive the optimal tradeoff between download rate, distortion, and information leakage when the file size is infinite and the information leakage is measured in terms of the average success probability for the server of correctly guessing the identity of the requested file. Moreover, we present a novel approach based on linear programming to construct schemes for a finite file size and an arbitrary number of files. When the database contains at most four bits, this approach can be leveraged to find provably optimal schemes.
Blockchain for green smart cities

Green smart cities are being implemented and under development in various countries across the world. The main aim of the green smart city is to improve the quality of the resident’s life, improve urban system functionalities, and enhance various industries and businesses with the help of modern technology for the efficient performance, overall cost minimization, and to reduce resource deterioration. One of such modern technology that has gained popularity on recent years is blockchain technology, which can be adapted into the green smart cities. Blockchain is a distributed ledger on a decentralized network, it plays a major role in handling large volume of data, highly tamper resistant against various attacks, immutable, simplify verification and auditing requirements, highly cost-efficient, and provides security to the sensitive data. Also the traditional cyber-security protection to the smart devices is not enough due to its dynamic, heterogeneity, and scalability characteristics. Thus, blockchain can be used as a solution to handle various challenges in the green smart city development. In this chapter, current overview of green smart cities is studied to find the challenges in the green smart city and how various blockchain-based applications could solve the issues in a green smart city are discussed, creating a way to analyze the better usage of blockchain in the research and development of future green smart cities.
Disciplinary Differences in Student Use of Library-Provided Materials in Dissertations

Citation analysis is a valuable approach for collection managers to provide evidence of collection use and direct future collection growth. Researchers at East Carolina University reviewed nearly 23,000 citations from 171 doctoral dissertations written by candidates in three programs: Education, English, and Kinesiology. This study focused on library-provided access to journals and books, the format and ages of these items, and titles that were cited by more than one author and across disciplines. Future research possibilities include the impact of open access on library collections in both practical and philosophical terms.
Empowering women wearing plus-size clothing through co-design

The retail landscape includes a vast array of clothing choices, yet style options remain extremely limited for Canadian women in the plus-size category (sizes 14W–40W). Our study empowered women who wear size 20+ by bringing them into the conversation about plus-size apparel design and development. Few studies have identified clothing solutions utilized by plus-size women or how clothing impacts their feelings about themselves, and there is no research on the clothing needs of women in the upper plus-size range. We recruited participants through Facebook posts to plus-size communities and clothing swap groups located in a major Canadian city. Our research design had a human-centred focus and included co-design methods. Activities included body mapping, body scanning and co-design activities with sixteen women in a full-day workshop to unpack their ideas about plus-size clothing in a body-positive space to foster confidence, strength and autonomy. Body maps allowed our participants to embrace creativity as a tool to communicate meaning in an empowering way. Body scanning provided a quick way to electronically capture body shape and size through circumferential measurements. Co-design activities included drawing and writing. Proposed clothing designs were drawn on body templates derived from participant’s personal body scans. Participants elaborated on their clothing ideas by completing a needs and features chart to share perceived problems and propose solutions. Emergent themes included participants’ ideas about meaning and empowerment, proposed clothing designs, detailed information regarding clothing fit and selection challenges, as well as their feelings about the co-design process. Consultation with people, using co-design methods is a way to reveal fashion gaps and an opportunity to improve customer satisfaction and increase sales and thus is important to designers and retailers specializing in the plus-size market.
Accumulator-free Hough Transform for Sequence Collinear Points

The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory.
Learning From Negative Links

Recently, graph convolutional networks (GCNs) and their variants have achieved remarkable successes for the graph-based semisupervised node classification problem. With a GCN, node features are locally smoothed based on the information aggregated from their neighborhoods defined by the graph topology. In most of the existing methods, the graph typologies only contain positive links which are deemed as descriptions for the feature similarity of connected nodes. In this article, we develop a novel GCN-based learning framework that improves the node representation inference capability by including negative links in a graph. Negative links in our method define the inverse correlations for the nodes connected by them and are adaptively generated through a neural-network-based generation model. To make the generated negative links beneficial for the classification performance, this negative link generation model is jointly optimized with the GCN used for class inference through our designed training algorithm. Experiment results show that the proposed learning framework achieves better or matched performance compared to the current state-of-the-art methods on several standard benchmark datasets.
Deep Classifier for News Text Classification Using Topic Modeling Approach

Classification of text documents present over Internet has become vital and most crucial part for discovering the knowledge about text. Varieties of approaches have been proposed by researchers in this field of natural language processing (NLP). In most of existing approaches, semantics of the text is not taken in consideration, which in turn is not always realistic. In this paper, proposal of keeping the semantic relationship intact by not removing the stop words in feature extraction step and then applying deep learning is given. Most relevant features are extracted using bi-gram and topic modeling. Next, comparative study of different machine learning and deep learning classifiers is carried out. This paper shows that proposed method of feature extraction, deep learning classifier, and long short-term memory is better and shows accuracy of 96.1% than other classification techniques.
A Training System for Vascular Interventional Surgeons based on Local Path Planning

Because the internal environment of the blood vessels in the brain is complex, and the cerebrovascular itself is very fragile, the catheter and guide wire are easy to puncture the cerebrovascular in the process of cerebrovascular interventional surgery, which brings great harm to patients. In order to reduce the number of collisions between catheter and guide wire with vascular wall and improve the safety of surgery. This paper proposes a local dynamic path planning based physician training system for vascular interventional surgery. The system uses the improved rapidly-exploring random tree (RRT) algorithm to plan the real-time local path of the catheter guide wire in the vascular interventional surgeon training system. Experimental results show that the algorithm has fast convergence speed and makes the planned path closer to the optimal path.
Cyclic Hypersequent Calculi for Some Modal Logics with the Master Modality

At LICS 2013, O. Lahav introduced a technique to uniformly construct cut-free hypersequent calculi for basic modal logics characterised by frames satisfying so-called ‘simple’ first-order conditions. We investigate the generalisation of this technique to modal logics with the master modality (a.k.a. reflexive-transitive closure modality). The (co)inductive nature of this modality is accounted for through the use of non-well-founded proofs, which are made cyclic using focus-style annotations. We show that the peculiarities of hypersequents hinder the usual method of completeness via infinitary proof-search. Instead, we construct countermodels from maximally unprovable hypersequents. We show that this yields completeness for a small (yet infinite) subset of simple frame conditions.