Reviewerly/exHarmony huggingface dataset:
Methodology

In this research, the proposed research methodology comprising of the following elements: (a) Mass Spring Model (MSM)—to model human liver as a collection of mass points linked by a network of springs, (b) Knowledge-based Fuzzy MSM Parameters Controller (FMPC)—to obtain the corresponding stiffness coefficients regarding different liver illnesses, (c) Utilization of Computer Unified Architecture (CUDA)—to utilize as a platform or framework for GPU and CPU distribution of computational works.
Decision Tree Induction: Using Frequency Tables for Attribute Selection

This chapter describes an alternative method of calculating the average entropy of the training (sub)sets resulting from splitting on an attribute, which uses frequency tables. It is shown to be equivalent to the method used in Chapter 5 but requires less computation. Two alternative attribute selection criteria, the Gini Index of Diversity and the $\chi^{2}$ statistic, are illustrated and it is shown how they can also be calculated using a frequency table. The important issue of inductive bias is introduced. This leads to a description of a further attribute selection criterion, Gain Ratio, which was introduced as a way of overcoming the bias of the entropy minimisation method, which is undesirable for some datasets.
SecPT: Providing Efficient Page Table Protection based on SMAP Feature in an Untrusted Commodity Kernel

Page tables are one of the key data structures in OS(Operating System) kernel. It plays an extremely important role in the memory access and protection. However, the page tables are fundamental weakness of operating system because they share the same address space with the vulnerable kernel, and thus subject to kernel data-only attack. To solve that, researchers have relied on the self-protection in the same kernel privilege level without introducing higher privilege layer for efficient world switch and effective page table protection. It needs to intercept and verify every update to kernel page tables. To improve the performance, it is required to reduce the time consumed for each interception as much as possible. In this paper, we propose an architecture to provide efficient page table protection based on Supervisor-mode Access Prevention (SMAP) hardware feature and Kernel Page Table Isolation (KPTI) from an untrusted kernel. SecPT maintains the kernel page tables which are actually used by the kernel in the protection domain and prevents the compromised kernel from subverting page table protection by abusing some privileged instructions. We have realized a prototype of the SecPT. The experimental results show that SecPT provides both effective and efficient page table protection.
Transgenerators

Pre-trained Transformers(GPT) are showed great performance in natural language generation task. This model was trained in a self-supervised manner on a large amount of text data crawled from the WEB. Such a dataset has not the highest quality, many sentences are prone to errors such as typos or grammar mistakes. As a result, text generated by GPTs consists of a lot of grammar incorrect sentences. While Transformers is also showed great performance in translation tasks, we propose the conception when a model can handle a generation and a translation task at the same time. But we propose a specific type of translation, in our method Transformer is training to translate a sentence with grammar errors to the same sentences without errors. In the full case, an incorrectly generated sentence can be corrected by the extended version of the same model, we call this type of model Transgenerator. We applied several experiments to estimate a generative power of Transgenerator based on GPT-2 architecture and the proposed method outperformed original GPT-2 model on the range of tasks
Mission Design of Mobile Manipulators in Cluttered Environments for Service Applications

The purpose of this paper is to present a mission design approach for a service mobile manipulator which is moving and manipulating objects in partly known indoor environments. The mobile manipulator is requested to pick up and place objects on predefined places (stations). The proposed approach is based on the Bump-Surface concept to represent robot's environment through a single mathematical entity. The solution of the mission design problem is searched on a higher dimension Bump-Surface in such a way that its inverse image into the actual robot environment satisfies the given objectives and constraints. The problem's objectives consist of determining the best feasible paths for both the mobile platform and for the manipulator's end-effector so that all the stations are served at the lowest possible cost. Simulation examples are presented to show the effectiveness of the presented approach.
Notes from Underground in Italian Mass Culture. The Case of Dylan Dog

In the XXth century Notes from Underground by F.M. Dostoevsky were translated several times into Italian and have had a significant influence on Italian literature, including such genres of mass literature as detective stories and horror. The article analyzes how Dostoevsky’s short novel influences the authors of the popular comic strip about Dylan Dog (the detective who investigates the paranormal) thematically and linguistically.
Review of Literature

Chapter is the literature review to identify the strengths and limitations of the current text and image-based email classification approaches wherein previous researchers are assessed and explored. It first elaborates on the concepts and definitions pertaining to the present research topic and describes the overview of spam detection, character segmentation, and character recognition methods. Furthermore, the chapter provides a detailed description of image-based email detection techniques. In addition, the chapter elucidates the concept of text-based email classification with various machine learning approaches including Naïve Bayes, Decision Tree and SVM (Support Vector Machine). The limitations of the previous researches examined are detailly explained in the research gap and the summary of the chapter.
Face Recognition Method Based on Deep-shallow Feature Fusion

Aiming at the problem of limited discrimination of features extracted by traditional shallow features and rapid decline of recognition rate in complex environments, a face recognition algorithm that fuses the deep and shallow features is proposed in this paper. We extract the HOG feature and PCANet feature of the face image respectively, and used Principal Component Analysis (PCA) method to reduce the dimensionality of the feature vectors. After fusing the processed feature vectors, we used Linear Discriminant Analysis (LDA) to further reduce the dimensionality and select the discriminative information. And SVM classifier is adopted for classification. The experimental results on AR and LFW dataset show that our algorithm has higher recognition rate and stronger robustness to complex environments than the single feature method.
Shaping Applied Mathematical Competences of College Students Using Information Technology

Authors of the paper explore the problem of developing applied mathematical competences of students from an economics college under conditions of the contemporary digital economy. Individual competences form various systems of capabilities and manifest themselves at various levels when solving academic and professional tasks relevant for the individual. Teaching mathematical disciplines can intentionally develop certain students’ competences. Passive, active and interactive ways of teaching mathematics and its applied aspects to students create conditions for developing intellectual competences. Contemporary digital economy requires the integration of mathematical and computer modeling into a single whole to solve complex production tasks. Development of production and society in the twenty first century shapes a certain kind of thinking among students – good perception of fragments within massive information flows and simultaneously complications with establishing logical links. Students are currently challenged to create abstract models of real processes and phenomena. They are hard put to perform mathematical operations without using computing devices when solving applied tasks. This is related to the fact that such activity requires a theoretical type of thinking. The pedagogical task is finding the ways of developing applied mathematical competences required for successful adaptation in the information and technology space.
Fuzzy Integrals

While the Choquet and Sugeno integrals are the most widely adopted when it comes to applying fuzzy measures for aggregation, there are a number of alternative approaches to integration with respect to measures that may not be necessarily additive. This chapter gives an overview of some of the main types proposed along with their properties, semantic interpretations and generalisations. For each of the integrals presented, we outline important considerations for computation and provide examples.
Image Processing

In this chapter, we become familiar with image analysis and processing. We learn definition and computation of two of the most popular 2D orthogonal image transformations, the 2D discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT). We see how to use them for image filtering in frequency domain. Next, we find out the definition of 2D convolution of matrices and their usage for image filtering, i.e. calculation of local weighted mean values of image pixels. We learn 2D filter weights, the most frequently used for image smoothing (de-noising) and edge detection (masks of Sobel, Prewitt, Roberts, Laplacian). We become also familiar of some non-linear image processing with median and morphological filters (erosion, dilatation). In this chapter, principles of 2D wavelet transform of an image using pair of wavelet low- and high-pass filter are presented in an image processing example/exercise in an image processing example/exercise. Basics of JPEG standard (compression of still pictures) and MPEG standard (compression of moving pictures, i.e., movies) are summarized. Finally, example of image watermarking is shown.
Image Segmentation

Image segments are basically the useful parts of an image of out interest that is possible because of image segmentation. This image which is broken into useful parts is known as sets mathematically whereas the segments are called this set’s subsets. The segmentation of objects meets all the laws of the set theory. Such subsets or segments give us an idea to better understand the picture. The image segmentation process is important for extraction of higher-level features, and this process depends on the characteristics of an object such as point, line, edge, and area. Object segmentation plays a vital role, but it is tough because object segmentation accuracy determines the success or failure of an image’s computerized analysis method. Here are the approaches that can divide an object into corresponding partitions using methods based on edge detection, threshold and area.
Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment

This chapter illustrates how case-based blended learning ecosystem can strengthen the bridge between age-old precision approaches with modern omics-driven approaches to deliver precision healthcare and reduce overdiagnosis and overtreatment. The online component of this blended learning ecosystem began on email groups, and then shifted to social media groups such as “Tabula Rasa”. “The idea of sharing and learning around patients has been alive since the beginning of medicine, when physicians would present their cases to a large audience to primarily learn from the inputs of other physicians.” SS is a postgraduate resident physician managing critically ill patients, as well as chronic patients in the outpatient department as part of her formal residency program in medicine. “Precision Medicine”—an abundant term in medical literature—refers to the tailoring of medical treatment guided by genomic or molecular features of the disease and not by the clinicopathological features.
Advanced Model of Complex Information System

This chapter presents an original approach to information representation, transmission, and processing together with their features that yield into basic principles of informatics. Models of complex systems are based on knowledge from information science that has been gathered over the years in classical physics, a specialized part of which is called information physics. At present, this discipline is still in its infancy, but many discoveries have already been and some scientists have realized that without basic theories in this area, the further development of human knowledge will not be possible.
Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using Particle Swarm Optimization

An association rule is classified as sensitive if its thread of revelation is above certain confidence value. If these sensitive rules were revealed to the public, it is possible to deduce sensitive knowledge from the published data and offers benefit for the business competitors. Earlier studies in privacy preserving association rule mining focus on binary data and has more side effects. But in practical applications the transactions contain the purchased quantities of the items. Hence preserving privacy of quantitative data is essential. The main goal of the proposed system is to hide a group of interesting patterns which contains sensitive knowledge such that modifications have minimum side effects like lost rules, ghost rules, and number of modifications. The proposed system applies Particle Swarm Optimization to a few clusters of particles thus reducing the number of modification. Experimental results demonstrate that the proposed approach is efficient in terms of lost rules, number of modifications, hiding failure with complete avoidance of ghost rules.
Patch Group-based Simultaneous Sparse Model for Image Denoising

This paper presents a patch group-based simultaneous sparse model (PG-SSM) for image denoising, which can accurately characterize image information and further recover the high-quality images from their noisy observations. In the proposed PG-SSM, to tap both the local and the nonlocal sparsity simultaneously, two adaptive dictionaries are learned to sparsely represent similar patch group. With an insight into the distribution of the simultaneous sparse coefficients, the Exponential distributions with different parameters are exploited to model the sparse coefficients, and thus an adaptive threshold function is derived to solve the sparse coefficients, thereby achieving denoising. Moreover, a preprocessing step is introduced to overcome the influence caused by noise, including the collection of patch groups and the parameter estimations of the Exponential distribution. Experimental results demonstrate that PG-SSM outperforms not only the related sparse-based works, but also the low-rank- based as well as the deep learning-based methods.
A Study of Normalized Population Diversity in Particle Swarm Optimization

The values and velocities of a Particle swarm optimization (PSO) algorithm can be recorded as a series of matrix and its population diversity can be considered as an observation of the distribution of matrix elements. Each dimension is measured separately in the dimension-wise diversity. On the contrary, the element-wise diversity measures all dimensions together. In this chapter, the PSO algorithm is first represented in the matrix format. Then, based on the analysis of the relationship between pairs of vectors in the PSO solution matrix, different normalization strategies are utilized for dimension-wise and element-wise population diversity, respectively. Experiments on benchmark functions are conducted. Based on the simulation results of 10 benchmark functions (including unimodal/multimodal function, separable/non-separable function), the properties of normalized population diversities are analyzed and discussed.
Motion-augmented Change Detection for Video Surveillance

The main task of change detection is to segment moving objects from the background. Recently, the deep learning-based change detection method has attracted much attention, but it tends to classify stationary objects as foreground. The reason for this phenomenon is that it identifies objects mainly based on appearance features while the same object has a similar appearance both in motion and stationary state. To address this problem, we propose a novel network called Motion-augmented Change Detection Network (MCDNet) to distinguish moving objects using both motion and appearance information. To achieve this purpose, firstly, we introduce a Motion-augmented Background Model (MBM) to simulate scene background without any foreground objects which can be dynamically updated by the predicted mask. In this way, the motion information can be implicitly highlighted by comparing the current frame and the background. Secondly, we design an Attention Memory Module (AMM) to store past features and use it to guide the segmentation of the current frame, facilitating the extraction of motion and appearance features. Experiments on two challenging public benchmarks (i.e. AGVS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and CDnet2014) demonstrate that our proposed method achieves compelling performance against state-of-the-art methods.
Research on Improved Stereo Matching Algorithm for Autonomous Obstacle Avoidance of UAV in Complex Indoor Environment

The stereo matching of unmanned aerial vehicle (UAV) in complex indoor environment is affected by illumination changes, mutual occlusion between objects and other factors, and there are some defects in color/brightness differences, noise, tilt plane and so on, which seriously affect the matching effect. To solve these problems, an optimization algorithm based on SIFT is proposed. Then the FAST algorithm is used to extract the feature points quickly. The improved SIFT is used to describe the feature points accurately. In the matching process, the Manhattan distance is used to calculate the similarity between the reference image and the image to be matched; Finally, the improved RANSAC algorithm is used to check the consistency and eliminate the mismatching points, which further reduces the time consumed by the algorithm. The EuRoC indoor binocular data set is used to verify the algorithm .Experimental results show that, compared with the SIFT algorithm, the proposed method not only improves the correct matching rate, but also ensures the matching performance, which has a certain engineering application value.
Deep Learning Based Image Enhancement using Super Resolution

With the ever-growing field of microscopy there is pretty much a necessity of high - resolution microscopic images. A microscope may have powerful magnifying lenses, but if the resolution is poor, the magnified image is just blur and no useful insights can be gained from such images. Traditional techniques like Structured Illumination Microscopy (SIM) are not feasible enough for proper use and current solutions based on deep learning assume that the input image is noise free. Based on our research and existing applications related to deep learning-based image enhancement, our proposed solution of deep learning based General Adversarial Network (GAN), will help jointly denoise and super-resolved microscopy images. Thus, this project has competitive applications in different research areas including biomedical microscopy, medical diagnosis, astronomical research, surveillance or investigation, etc., and many other areas as well.
Release of Trajectory Data based on Space Segmentation using Differential Privacy

The applications of location-based services(LBS) are increasingly important in people's daily life. However, these services may lead to privacy leakage, which causes more and more users to worry about privacy issues. Although the differential privacy technology has solved some location privacy problems, most of the current models are still unable to resist complex background-knowledge attacks. In this paper, we propose a novel approach to protect privacy for trajectory based on prefix tree. Firstly, we use the Hilbert curve to split the trajectory position points which the optimal divisions are selected by the index mechanism, and obtain the center point of each area. Secondly, we build a noisy prefix tree to store location points, it should be noted that the nodes of the tree store the polymerization location points. Finally, an arithmetic distribution method is applied to the privacy budget, meanwhile the amount of noise is limited by a threshold. The proposed algorithm is compared with related algorithm on the original dataset. Experimental results show that the algorithm proposed in this paper improves data availability while ensuring data privacy.
Improving Customer Relationship Management Through Social Listening

In the dynamic world of social media strategy, developing an effective approach to customer relationship management (CRM) online is challenging. With best practices for CRM on social media still being uncovered, the value of social listening is becoming recognizable in contemporary social CRM. This chapter presents a case study that shares the actions, insights, and experiences of using social media for CRM at the academic library at a mid-size American university located in northeast Florida. Using specific examples of how social media is used to engage in social listening and to enhance CRM, the social listening practices and social media strategy of this library are highlighted in relation to how they influence and potentially improve CRM. By examining the practices of this individual institution's library, a better understanding of how academic libraries engage with customers using social media as a CRM platform comes to light. In addition, ideas for future research on the intersection of social listening, CRM, and social media strategy are discussed.
Risamálheildin

Í maí 2018 var ný textamálheild, Risamálheildin, opnuð á vefnum málheildir.árnastofnun.is. Risamálheildin er leitarbær í leitarvél sem er sérsmíðuð fyrir málheildir og aðlöguð að íslenskum textum en einnig er hægt að sækja alla textana sem í henni eru, markaða og lemmaða, til nota við þróun á máltæknibúnaði eða í rannsóknir. Fyrsta útgáfa málheildarinnar inniheldur yfir 1200 milljón lesmálsorð í textum úr ýmsum áttum. Stefnt er að því að gefa út nýja útgáfu málheildarinnar árlega, þar sem fleiri textum hefur verið bætt við og hún mörkuð og lemmuð með nýjustu aðferðum.
Reviews

Elizabeth Parang reviews Reengineering the Library: Issues in Electronic Resources Management. Scott Johnson reviews Academic Library Management: Case Studies.
Research on Security Analysis and Assessment of Quantum Secure Communication System

The application of quantum private communication system can improve the security protection capability of power information system. However, the application of quantum private communication system may introduce new security risks. Therefore, it is necessary to analyze and evaluate the security of quantum private communication system, relying on relevant national standards and regulations, and using traditional Security risk assessment means for risk assessment and security analysis of quantum private communication systems. This project conducts a security risk assessment for a quantum private communication system. With reference to relevant national standards, the security risk points of quantum private communication systems are studied, and the quantum private communication systems are evaluated from various aspects such as Quantum secure communication system network security, quantum key distribution equipment host security, quantum key application software security, quantum channel security. Focus on finding security risks and improve the system's own security protection capabilities. Achieve the integrity, confidentiality and usability of a quantum private communication system.