Reviewerly/exHarmony huggingface dataset:
A Hybrid Recommendation Model Based on Time Dimension for Academic Teams

Recommending academic teams for users of scholars’ social network systems is of great practical value for promoting communication among scholars. This paper proposed a hybrid recommendation model based on time dimension for academic teams. The model combines the three dimensions (the similarity of user and team, good friends and hot teams), and generates a list of team recommendation based on different weights given by the team’s creation time. Experiments on the SCHOLAT data set show that the proposed model can effectively improve the recommendation accuracy and coverage, and solve the cold start problem to a certain extent.
Adaptive learning and evolution framework of intelligent combat behavior modeling based on EXIT framework reasoning

The intelligent combat behavior modelling has always been a hot topic in the field of artificial intelligence and military. Aiming at the requirement of development of intelligent equipment in complex battlefield, this paper proposes an adaptive learning and evolution framework of intelligent combat behavior modeling based on EXIT framework reasoning, and provides theoretical, technical and methodological support for more efficient implementation of the evaluation and application verification of intelligent action plan.
Extended Mobility Procedures for EVPN-IRB

Procedure to handle host mobility in a layer 2 Network with EVPNcontrol plane is defined as part of RFC 7432. EVPN has since evolvedto find wider applicability across various IRB use cases that includedistributing both MAC and IP reachability via a common EVPN controlplane. MAC Mobility procedures defined in RFC 7432 are extensible toIRB use cases if a fixed 1:1 mapping between VM IP and MAC is assumedacross VM moves. Generic mobility support for IP and MAC that allowsthese bindings to change across moves is required to support a broaderset of EVPN IRB use cases, and requires further consideration. EVPNall-active multi-homing further introduces scenarios that requireadditional consideration from mobility perspective. This documentenumerates a set of design considerations applicable to mobilityacross these EVPN IRB use cases and defines generic sequence numberassignment procedures to address these IRB use cases.
The Data Dimensionality Reduction and Features Weighting in the Classification Process Using Forest Optimization Algorithm

The paper presentsKostrzewa, Daniel the dataBrzeski, Robert dimensionality reduction in the classification process, with a special presentation of using the ability of features weighting by determining the level of importance of a given attribute in the data vector. This reduction was implemented using the Forest Optimization Algorithm (FOA) and the use of a classifier allowing to enter the importance of each attribute for a data vector. The paper presents both, a description of the capability of using the FOA algorithm as well as the possibility of introducing modifications which allows to regulate the objective function between the obtained classification result and the number of reduced features. The conducted tests and obtained results were also presented. At the end of paper, a summary and the final conclusions are provided.
Precision Delivery in Critical Care: Balancing Prediction and Personalization

Recent developments in healthcare data availability, advanced analytic algorithms, and high-performance computing have produced incredible enthusiasm about a new age of data-driven healthcare [1–8]. When it comes to clinical care specifically, ‘precision delivery’ is an emerging term to describe the “routine use of patients’ electronic health record (EHR) data to predict risk and personalize care to substantially improve value” (Table 2.1) [7, 9, 10]. While clinical risk prediction tools have a long history in critical care, novel machine learning applications can offer improved predictive performance by maximally leveraging large-scale, complex EHR and other data [5]. Perhaps, even more importantly, these approaches may help overcome the problem of heterogeneity, which is routinely noted to be a hallmark of critical illness as well as a major barrier to improved treatment [11–13]. In this chapter, we discuss the overarching concept of ‘precision delivery’, the important balance between clinical risk prediction and personalization, and the future challenges and applications of data-driven critical care delivery.
Guess and determine cryptanalysis with variable sampling and its applications

Non-linear filtering generators, as a well-known family of stream ciphers, employ a filtering function to process the secret state bits and thus outputs binary keystream blocks of length m. In this study, the authors extend the framework of a generic cryptanalytic method applicable to non-linear filtering generators called generalised filter state guessing attacks (GFSGA), introduced as a generalisation of the filter state guessing attack method, by applying a variable sampling of the keystream bits in order to retrieve as much information about the secret state bits as possible. Two different modes that use a variable sampling of keystream blocks are presented and it is shown that in many cases these modes may outperform the standard GFSGA mode. They also demonstrate the possibility of employing GFSGA-like attacks to other design strategies such as non-linear feedback shift register-based ciphers (Grain family for instance). It is also indicated that the tap positions of Grain-128 are not chosen optimally with respect to this generic cryptanalytic method and provide a better selection of taps that gives higher resistance to GFSGA-like attacks.
Agricultural Information and Land Grant Libraries: Past, Present, and Future

Land-grant libraries contribute to the national corpus of research through holdings of journals, books, e-books, conference proceedings, government documents, and more. Impacts on land-grant libraries and agricultural information and implications for the future landscape include a number of topics ranging from the role of journal package deals on collection development budgets to the importance of agriculturally focused professional societies. The agricultural information outlook for the future is being cultivated today. New and current information is critical but the preservation of the agricultural heritage is a necessary part of the plan.
Research on Weibo Emotion Classification Based on Context

In order to classify the speech and information published on the social network platform, this paper proposes an emotion classification method, based on text word vector and deep learning. According to the characteristic of weibo short text itself, the corpus is preprocessed. This paper uses word2vec to obtain the text vector of weibo short text, and classifies emotion through the classification model which is based on XGBoost. The experimental results for NLPCC corpus show that this method achieves a good emotion classification results, and can effectively improve the accuracy of sentiment classification.
The Value of Data—The Qatar Geologic Mapping Project

The State of Qatar is in a period of rapid development, modernization, and population growth. One of the most important factors influencing the long-term success and sustainability of future development is a comprehensive understanding of the region's geologic regime, geotechnical conditions, natural resources, and environmental constraints. To obtain this understanding, the Ministry of Municipality and Environment (MME) of the State of Qatar has undertaken the Qatar Geological Mapping Project (QGMP). The project was developed with strategic foresight to compile and utilize existing subsurface data collected as part of its massive infrastructure and development projects. Recently, the MME, in consultation with Gannett Fleming Inc. (GF) and the United States Geological Survey (USGS) concluded the data collection and analysis phase (Phase I) of the two-phase QGMP. Phase I included: the development of a comprehensive geotechnical relational database (GRDB) populated with data digitized from more than 13,000 subsurface data logs; a detailed data quality analysis and distribution assessment; an extensive gap analysis and needs assessment; and careful design of the geologic mapping and subsurface investigation programs for the next phase of the project. The comprehensive GRDB will allow the MME, other government agencies, and their representatives, the ability to quickly search, query, and assess available geological and geotechnical data for use in site evaluations, project planning, and decision making.
One Core Task of Interpretability in Machine Learning — Expansion of Structural Equation Modeling

Structural equation modeling (SEM) is a system of two kinds of equations: a linear latent structural model (SM) and a linear measurement model (MM). The latent structure model is a causal model from the latent parent node to the latent child node. Meanwhile, MM’s link is from latent variable parent node to observed variable child node. However, researchers should determine the initial causal order between variables based on experience when applying SEM. The main reason is that SEM does not fully construct causal models between observed variables (OVs) from big data. When the artificial causal order is contrary to the fact, the causal inference from SEM is doubtful, and the implicit causal information between the OVs cannot be extracted and utilized. This study first objectively identifies the causal order of variables using the DirectLiNGAM method widely accepted in recent years. Then traditional SEM is converted to expanded SEM (ESEM) consisting of SM, MM and observation model (OM). Finally, through model testing and debugging, ESEM with good fit with data is obtained.
Real time human action recognition from RGB clips using local motion histogram

This paper proposes a method to human action recognition from RGB video clips. The method is based on capturing the local motion information from smaller size video clips. Local motion information is captured through accumulation of motion in different shape and size of patches of spatial domain. T he motion information is then transformed to motion histograms. Further, all the histograms are concatenated to make the proposed feature vector. Bagging ensemble technique, in form of random forest, is used for classification. The idea is further extended to real time human action recognition mechanism. To show the robustness and efficiency of proposed algorithm, it is performed on publicly available human action datasets Joint-annotated Human Motion Data Base (JHMDB) [29] and University of Rzeszów (UR) Fall detection dataset [19]. The results are also compared with other state of art methods.
A Brief Introduction to R, R Studio and MicrofiniteR

This chapter provides a short introduction to the R programming language and the RStudio environment to facilitate familiarity with the computer codes in the later chapters of the book. Basic instructions on relevant installations, demonstrations of some R commands, a description of microfiniteR (developed for this book) and a list of references for further consultations are presented.
A Digital Map as a Representational Tool Implications for the Instructional Design Process

The dimension of designing is one of the essential skills included in the professional teacher’s profile. Nowadays the complexity of the classrooms requires a specific competence in the design of learning disciplinary paths that take into account the different students’ needs. The article describes a prototype of a graphic organizer designed within the European DEPIT project, to support teachers and the need of an inclusive approach in the design process and students who can take advantage of a full visualization of the whole learning path.
Secure Data Deduplication on Cloud Storage

Deduplication is the process of removing duplicate data by storing only one copy of the original data and replacing the others simply as a reference to the original. When data is stored on cloud storage, client-side deduplication helps in reducing storage and communications overheads both from the client as well as the server perspective. Secure deduplication is the practice by which the data stored on the cloud is secured from external influences such that the clients maintain the privacy of their data, and the server also gets to take advantage of deduplication. This is done by encrypting the data using different schemes into ciphertext, which makes sense only to the original client. The schemes created for secure deduplication on cloud storage provide a solution to the problem of duplication detection in encrypted ciphertext. This chapter provides a brief overview of secure deduplication used on cloud storage along with the issues encountered during its implementation. The chapter also includes a literature review and comparison of some deduplication techniques.
Preserving Privacy in Data Analytics

Data Analytics is becoming an essential business tool for many data intensive companies and organizations. However, the increased use of such methods comes with the threat of data disclosure. Privacy-preserving methods have been developed with varying degrees of efficiency with the main goal of protecting individuals' privacy. This tutorial aims at presenting models and techniques of preserving privacy in machine learning and data mining.
Practical Use of Google Forms in the Examination Course for Mechanical Design Engineers Grade 3

In this paper, we introduce the practical use of Google Forms in the examination course for Mechanical Design Engineers Grade 3. We consider it an important goal to improve teaching methods to increase the number of successful mechanical design engineer candidates from the Aichi University of Technology. A clicker is often used in this context to enable a bidirectional lecture and enhance educational effects;however, it can be costly to use. Thus, we used Google Forms instead, enabling bidirectional lectures incorporating smartphones. A survey conducted at Aichi University of Technology found that bidirectional lectures incorporating smartphones were almost universally accepted by the students.
Analysis on the deformation monitoring theory of fabricated high-rise residential buildings

Under the influence of various factors, the assembled high-rise residential buildings will undergo different degrees of deformation in the horizontal and vertical directions. The amount of deformation is within the allowable range and the building can be used safely. However, if the amount of deformation exceeds the allowable range, it may have a greater impact on the integrity and stability of the fabricated high-rise apartment, and may even form a disaster. Therefore, the deformation law of the building during a certain period of time is studied, and the deformation prediction analysis of the assembled high-rise residential building is carried out according to the relevant data, thereby ensuring the safe operation of the assembled high-rise residential building. This paper first introduces the development status and development opportunities of fabricated buildings in China, discusses the main contents of the deformation monitoring of fabricated high-rise buildings, various methods, monitoring technical solutions, and analyzes the various existing forecasting methods and their respective advantages and disadvantages.
Automated Monitoring and Forecasting of the Development of Educational Technologies

This chapter will describe an approach to monitoring and forecasting the development of innovative educational technologies based on text stream analysis. The approach will involve detecting education-related events in the stream of text documents, constructing situations, determining possible scenarios of further development of situations, and generating recommendations for successful introduction of detected innovations into the educational process of the university. The authors will propose a multicriteria model of an event reflecting its key aspects. The chapter will describe an event detection method based on incremental clustering, as well as a scenario generation method based on the principle of historical analogy. The authors will discuss several experiments to evaluate the quality of the methods.
ASAP: A Source Code Authorship Program

Source code authorship attribution is the task of determining who wrote a computer program, based on its source code, usually when the author is either unknown or under dispute. Areas where this can be applied include software forensics, cases of software copyright infringement, and detecting plagiarism. Numerous methods of source code authorship attribution have been proposed and studied. However, there are no known easily accessible and user-friendly programs that perform this task. Instead, researchers typically develop software in an ad hoc manner for use in their studies, and the software is rarely made publicly available. In this paper, we present a software tool called A Source Code Authorship Program (ASAP), which is suitable to be used by either the layperson or the expert. An author can be attributed to individual documents one at a time, or complex authorship attribution experiments can easily be performed on large datasets. In this paper, the interface and implementation of the ASAP tool is presented, and the tool is validated by using it to replicate previously published authorship attribution experiments.
Automatic liver segmentation with CT images based on 3D U-net deep learning approach

The detection and the evaluation of the shape of liver from abdominal computed tomography (CT) images are fundamental tasks in the computer-assisted liver surgery planning such as radiation therapy. However, the segmentation of the liver still remains many challenges to be solved, such as ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we developed an automatic liver segmentation model based on 3D U-net network. Some preprocessing steps were done to elevate the performance of our protocol first. Also, an approximate liver map was generated by calculating the gradient of CT images. The area which had high possibility to be liver was select as the training set to make sure the balance of data. Then, a deep learning U-net structure was applied for the processed training data. Finally, some post-processing methods, which include k-means clustering and morphology algorithms, was applied in our protocol. Our protocol showed the results with high structure similarity index (SSIM), dice score coefficient and peak signal-to noise ratio (PSNR) of liver segmentation model, demonstrating the potential clinical applicability of the proposed approach.
Art upon Art: The Spatial Augmented Reality as New Value for Contemporary Art and Educational Tool for Art History

Contemporary Art is not always well acknowledged even when work of successful talents. Forgotten, if not abandoned by its own community, it is often not recognised as Art by the potential users, as the Italian educational system does not always provide the right tools for its understanding. This paper focuses on two case studies which final aim is to increase the value of Contemporary Art and its educational foundations using the Spatial Augmented Reality (SAR), well known as video mapping, a performative action that allows to “augmenting” the existing contents. Today this artistic form is undergoing a split: on the one hand a growing graphic data processing are turning into independent performance whose immersiveness recalls that of gaming, on the other its use as a means of enhancement and education for Art, including the contemporary one, without taking anything away from the artistic performance. Here we want to show two preparatory performances made recently by the Studio Glowarp that follows the second mentioned front.
The Extracting of 2D Plot Knowledge in Chart Automatic Generalization

In order to keep the facticity of generalization process, more attention should be paid on the feature of datum. According to the analysis of familiar 2D plot knowledge, the graphic and geographic feature can be pre-digested in the electronic chart automatic generalization process. At the same time, the maintenance of facticity should be considered.
Sound Banks – a Priceless Aid in Contemporary Music Writing

Abstract Sound banks are collections of sound samples from musical instruments of the symphonic orchestra, traditional instruments from various areas of the world and sounds of virtual devices, such as synthesizers, which are increasingly present in contemporary musical creations. Sound banks are loaded in a device called sampler, which can edit and play them. The article describes analog and especially virtual samplers, complex devices that can store or play sounds from specific libraries of sound banks. It also defines and catalogs the main types of digital virtual instruments (that include traditional symphonic orchestra instruments, ones with modern electronic instruments/percussion instruments, and ethnic collections for various geographic areas. Our research on digital applications used in music writing relies on 20 years of experience. Currently, applications are valuable tools for composers and musicians, and for everyone in the contemporary music industry. In 2006, I created the first collection of sound banks made in Romania “The Essence of Panflute”, library containing sound samples 583, grouped in 33 virtual instruments. This is the most complex virtual version of the Romanian pan flute, played by the renowned Cătălin Tîrcolea. The library is designed and edited by Cătălin Răsvan, for the company S.C. Canira Music Internațional. This collection of sound banks presents in minute detail the laborious process of recording and editing this virtual library. “The Essence of Panflute” has seen international acclaim, is distributed by the German company Best Service, one of the major companies in the world, was reviewed in the most prestigious magazine in this field, Sound on Sound, and has opened the door for current/future creators of music. We hope that it is only the beginning for our work in the research and development of digital virtual sound, which is a special category for the instruments in our country.
The Location-Centric Approach to Employee's Interaction Pattern Detection

The task of the insider threat detection is one of the most sophisticated problems of the information security. The analysis of the logs of the access control system may reveal on how employees move and interact providing thus better understanding on how personnel observe security policies and established business processes. The paper presents an approach to the detection of the location-centric employees' interaction patterns. The authors propose the formal definition of the interaction patterns and present the visualization-driven technique to the extraction of the patterns from the data when any prior information about existing interaction routine and procedures is not available. The proposed approach is demonstrated on the data set provided within VAST MiniChallenge-2 2016 contest.
Top 100 Occupations in Demand at Modern Russian Enterprises: Interaction and Communication in the 21st century

Today labour market reflects hi-tech and innovative economic development of the XXI century. The system of professional occupations is subject to the effect of various factors, prestige being one of them. The paper analyses 100 occupations highly demanded by Russian enterprises. These nominations represent the synergetic nature of professional sphere in the life of society involving major advances in science and engineering. The paper offers subject-based classification of prospective occupations and provides characteristic of their communicative potential.