Reviewerly/exHarmony huggingface dataset:
Virtual Training Environments in a Printing Company

Although Virtual Reality technology has existed for decades, it has currently been experiencing a boom. This is - in particular, ensured by significant improvements in the availability and quality of Virtual Reality (VR) hardware that this technology requires. Virtual Reality is quickly expanding in many fields of human interests - scientific visualisation, the military, entertainment, healthcare, construction, etc. It is also increasingly being used in education and training. With the help of VR, industrial companies can train their employees to work more effectively, to improve their performance or to perform safe training programmes in high-risk scenarios. This paper sets out to describe proposed and implemented solutions for the use of VR for the new employee training process in a printing company. This solution includes the choice of a suitable VR headset and corresponding software tools. After that, the necessary graphical assets were prepared - (e.g. 3D models and their textures); these were then imported into the software in which the VR application was created. Its functionality was then tested in our laboratory and will be used in the relevant industrial environment.
Transparency and authenticity in the live arena

This chapter focuses on the interaction between the physical, visual, and audio elements of electronic music performance, and addresses questions regarding authenticity and transparency within the works of many current performers. For analytical purposes, the broad spectrum of existing methodologies are separated in this chapter into two categories: the 'Band with Backing Track' and 'Studio on Stage'. While the two methodologies discussed above are not without considerable merit, they also present clear shortcomings: the 'Band with Backing Track' offers strong visual elements but it is questionable whether these elements actually reflect what is being heard, which risks alienating audiences that demand authenticity of performance from an artist. With the drum sequencer handling the majority of the drum and percussion elements of a given performance, methods of performing the melodic and harmonic elements were also required, which for many electronic artists mainly consist of the use of synthesiser motifs and audio loops.
Authorship in Classical Rome

Even though the modern term “author” is derived from the Latin auctor, histories of authorship usually assume a paradigm shift from premodernity to the modern age, with the invention of printing with movable type as a major historical caesura. Yet they also set out from certain author concepts that rely on central premises concerning copyright: notably, the premise that, within a so-called premodern society less concerned with individuality, notions of property were unknown or at least insignificant; and that the entire theoretical baggage that has accrued around authorship in modern times was “naturally” unknown in antiquity and, hence, irrelevant. Two complementary prejudices take effect here: that art and life were supposedly not thought of as distinct before modernity, and that unity and identity, instead of rupture and change, dominated an author’s self-image and his or her works. But ancient thinkers were surely able to distinguish between art and life, with the emphasis on a conscious blending of the two rather than their prior unity.
Affective technologies for enchanting spaces and cultivating places

Thinking a place from postphenomenological thinking is to center the attention on a series of environmental, individual, and collective conditions and practices, looking into the identity and stories of a place. Also, inquire about the affects, feelings, and emotions grasped from the lived experience. In this chapter, we approach the concept of places and the opportunities that bring the deployment of smart technologies in cities through the lens of atmospheres. We look at spaces and their infrastructure (digital and material) by adopting the notion of enchantment as a means to cultivate better places.
Phishcasting: Deep Learning for Time Series Forecasting of Phishing Attacks

Phishing attacks remain pervasive and continue to be a source of significant monetary loss, identity theft, and malware. One of the challenges is that in most organizational settings, the detection paradigm is inherently about identifying and reacting to threats in real-time, as they are unfolding. As a way to complement these efforts with greater foresight, we introduce the idea of phishcasting — forecasting of phishing threat levels weeks or months into the future. Given that phishing attack volume time series data is noisy and devoid of traditional seasonal and cyclical trends, we extend the time series forecasting framework to utilize multiple time series, auxiliary information and alternate representations. We also introduce CoT-Net, a flexible, end-to-end CNN-LSTM based deep learning method for forecasting of complex phishing attack volume time series. CoT-Net uses time series embeddings to uncover correlations between organizational attack patterns within and across industry sectors. Using a publicly available test bed featuring multiple organizations’ attack volume over time, we find CoT-Net to outperform most state-of-the-art time series forecasting methods. By showing that phishcasting might be possible and practical, our work has important proactive implications for cybersecurity.
Bottom-up unranked tree-to-graph transducers for translation into semantic graphs

We develop a finite-state transducer for translating unranked trees into general graphs. This work is motivated by recent progress in semantic parsing for natural language, where sentences are first mapped into tree-shaped syntactic representations, and then these trees are translated into graph semantic representations. We investigate formal properties of our tree-to-graph transducers and develop a polynomial time algorithm for translating a weighted language of input trees into a packed representation, from which best-score graphs can be efficiently recovered.
Breaking the Interaction Wall: A DLPU-Centric Deep Learning Computing System

Due to the broad successes of deep learning, many CPU-centric artificial intelligent computing systems employ specialized devices such as GPUs, FPGAs, and ASICs, which can be named as Deep Learning Processing Units (DLPUs), for processing computation-intensive deep learning tasks. The separation between the scalar control operations mapped on CPUs and the vector computation operations mapped on DLPUs causes the frequent and costly interactions between CPUs and DLPUs, leading to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interaction Wall</i> . Moreover, the increasing algorithm complexity and DLPU computation speed would further aggravate the interaction wall substantially. To break the interaction wall, we propose a novel DLPU-centric deep learning computing system consisting of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exception-oriented programming (EOP) model</i> and the architectural support of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPULESS DLPU</i> . The EOP model processes scalar control operations of a deep learning task as exception handlers to maximally avoid stalling the crucial and dominated vector computation operations. Together with the CPULESS DLPU which integrates a scalar processing unit (SPU) for scalar control operations and the parallel processing unit (PPU) for vector computation operations into a fused pipeline, the proposed DLPU-centric system can cost-effectively leverage the EOP model to execute the two kinds of operations simultaneously without disturbing each other. Compared with a state-of-the-art commodity CPU-centric system with discrete V100 GPU via PCIe bus, experimental results show that our DLPU-centric system achieves 10.30× better performance and 92.99 percent energy savings, respectively. Moreover, compared with a CPU-centric version of DLPU system where the SPU serves as the host with integrated PPU, the proposed DLPU-centric system still achieves 15.60 percent better performance from avoided interactions.
The Design and Implementation of Corpus Labeling System for Terminology Identification in Fishery Field

Term recognition is the basic work in the field of natural fishery treatment. Now, the term recognition needs to be realized by machine learning algorithm, which requires a large number of corpus to train the model of the algorithm. At present, most corpus labeling needs manual labeling method, and the quality and speed of corpus labeling are problems to be solved in this field. Therefore, it is urgent to have a platform to assist in the evaluation of corpus labeling quality, so as to make the article labeling more efficient and accurate.
THE EAR IS A LABYRINTH: JULIO ESTRADA SEARCHING FOR THE MINOTAUR

Abstract This article explores Julio Estrada's musical thinking in his early and recent works. Estrada's early works precede the development of his graphication method in the 1980s, and his recent works follow the completion of his opera Murmullos del Páramo in 2006. It is his music of these three decades between 1980 and 2006 that is most widely scrutinised – a music centred on the extensive conception and manifestation of sound as a continuum and aided in the creational process by coloured drawings on graph paper – but this article focuses on lesser-known pieces from the 1970s like Melódica and Solo para uno , as well as recent works such as his opera-in-progress, a major project conceived as a book to be read and imagined, rather than performed. It is hoped that close examination of these pieces will significantly broaden the understanding of Estrada's creative drive and lifelong research, revealing how these works entangle with his better-known developments of the sound continuum and graphication method and share common concerns with work from across his career.
An Efficient Tiny Feature Map Network for Real-Time Semantic Segmentation

In this paper, we propose an efficient semantic segmentation network named Tiny Feature Map Network (TFMNet). This network significantly improves the running speed while achieves good accuracy. Our scheme uses a lightweight backbone network to extract primary features from input images of particular sizes. The hybrid dilated convolution framework and the DenseASPP module are used to alleviate the gridding problem. We evaluate the proposed network on the Cityscapes and CamVid datasets, and obtain performance comparable with the existing state-of-the-art real-time semantic segmentation methods. Specifically, it achieves $$72.9\%$$ mIoU on the Cityscapes test dataset with only 2.4M parameters and a speed of 113 FPS on NVIDIA GTX 1080 Ti without pre-training on the ImageNet dataset.
Typicality of Lexical Bundles in Different Sections of Scientific Articles

This paper proposes a method to quantify the typicality of lexical bundles in sections of academic articles, specifically in the field of Natural Language Processing papers. Typicality is defined as the product of individual KL-divergence scores and the probability of a bundle to appear in a type of section. An evaluation of our typicality measure against two other baselines shows slight improvements according to the Silhouette coefficient.
The experience of teaching «Inorganic Chemistry» course at an agricultural university

This paper discusses the experience of teaching Inorganic Chemistry course to the 1st-year-students of Microbiology, Biotechnology and Chemistry Department at Saratov State Agrarian University. The idea is substantiated that the content of the chemistry course is aimed at merging theory and practice. As practice shows, this course consists of giving lectures with the use of multimedia, performing laboratory work, practical exercises and test controls. The author proposes a method of organizing independent work among students, which leads to the successful development of the educational program of higher education. The paper contains some sample tasks of input control to control residual knowledge as well as sample test tasks and questions for students knowledge correct assessment. The author shows that such types of students knowledge control allow us to assess the level of material assimilation and identify problem points. The paper assesses the fact that a textbook developed by the department is used for a conscious and purposeful preparation for seminars, laboratory practice and exam. The author of the paper has come to the conclusion that the teacher acts as an assistant for students on their way to solve small scientific problems independently.
THE SPECIFICS OF THE PSYCHOLOGICAL ASPECT OF THE FORMATION OF STUDENTS’ PERSONAL AND METADISCIPLINARY COMPETENCIES IN TEACHING LITERATURE

The society is interested in individuals capable of showing flexibility of thinking, ability to navigate in a changing world, mobile and creative. Provisioning today must also respond to these challenges of the time: to provide lifelong learning needs. The refore, one of the key problems of the modern education system is the formation of metadisciplinary and personal competencies aimed at adapting the student in a rapidly changing world. At the sametime, it is important to understand the psychological aspect of the formation of these competencies. The implementation of education through training, the creation of conditions for self-realization are the most important components of modern education. In this article deals with defines such concepts as upbringing in the psychological aspect and the psychological aspect of upbringing, presents the principles for the selection of educational material for the formation of metadisciplinary and personal competences.
Sistem Informasi Multimedia Untuk Meningkatan UMKM Pada Kota Pagar Alam Berbasis WEB

Pagar Alam in gething the data of improvement of UMKM then use manual system, process of collecting data of number UMKM, kind of UMKM so that of collecting data, reporting data non effective note taking of total UMKM and UMKM activities ar activity routinely done by goverment in getting UMKM data consist of total of UMKM, kinds of industry system developing method which used is Web Engenering method with some steps: Costumers, Comunication, Planning, Modelling, Construction and development. By desingning this system information, get information system, dinamiclay and updating hopely. Beside that this system information can be us one of alternativ media and conveying information with faster for user commonly.
4D Light Field Disparity Map estimation using Krawtchouk Polynomials

This work presents an improved method to estimate disparity maps obtained from light field cameras using a novel edge detection algorithm based on Krawtchouk polynomials. The proposed method takes advantage of these polynomials to determine gradient information and find the edges based on automatically estimated weak and strong thresholds. The calculated edges in the gray scale epipolar plane image representation of a light field are then used to improve the accuracy of object boundaries in the the disparity map. The proposed method achieves better results when compared to other edge detection algorithms, both in terms of objective and subjective quality, specifically by reducing the mean squared error and the artifacts in the object boundaries. Furthermore, on average, the proposed method outperforms the state-of-the-art depth estimation algorithms, in terms of the objective quality of the final disparity map, namely for the commonly used HCI dataset.
Maintaining Population Diversity in Deterministic Geometric Semantic Genetic Programming by ϵ-Lexicase Selection

Genetic Programming (GP) is an evolutionary method for automatic programming. In recent years, crossover operators based on the semantics of programs have attracted much attention for improving the search efficiency. We have previously proposed a semantics-based crossover that deterministically generates an optimal offspring by utilizing the target semantics explicitly in symbolic regression problems. The GP method using this crossover is called Deterministic Geometric Semantic GP (D-GSGP). However, this operation may cause rapid convergence of the population. One of the ways to maintain diversity is to use an improved selection method. ε-Lexicase Selection is a method to select individuals based on their responses to a part of fitness cases. D-GSGP has a high affinity with ε-Lexicase Selection because the responses to a part of fitness cases are components of the semantics of the program. Therefore, in this research, we combine D-GSGP and ε-Lexicase Selection to maintain the diversity of the population. To verify the effectiveness of our proposed method, we applied the method to a practical symbolic regression problem, the Boston Housing Dataset.
Alpsoc Ant Lion: Particle Swarm Optimized Hybrid K-Medoid Clustering

K-medoids clustering aims at partitioning a numerous data points into different K medoids based on the similarity distance between object pair. Cluster efficiency varies with respect to medoid initialization and may results in local optima traps. Various evolutionary swarm based approaches are adopted to obtain enhanced performance. Suitable combination of K-medoids with optimization technique does not operate well as expected while considering computational time. No such techniques are developed to solve entire clustering drawbacks. Assurance is not provided to any method in attaining success by solving the overall issues. Hence in this, an improved K-medoids integrated with Ant lion optimization and Particle swarm optimization algorithm commonly referred as ALPSOC is proposed to obtain optimized cluster centroid in which the computational complexity is preserved with better improvements in performance. Further the intra-cluster distance, F-measure, Rand Index, Adjusted Rand Index, Entropy and Normalized Mutual Information is evaluated for different datasets adopting the presented approach. The proposed algorithm is simulated on different datasets and is compared with different existing techniques based on above performance metric. From the observed results, it is shown that the proposed method functions better in all cases maintaining to solve clustering limitations.
Interoperability and discursive process about categories

With interdisciplinary contexts and various epistemic points of view, Digital Humanities implies to take into account social dimensions in the production of tools, methods and archive processes. From a pragmatic point of view, Digital Humanities activities involve complex interpretative processes, linking human (individual and collective understanding) and algorithmic computation spaces. This articulation is emphasized in Prospéro software, but still has to support community dialog processes, which can be understood according to vocabulary alignment paradigm or socio-anthropological usage of language paradigm. In order to do so, we give some exploratory guidelines to express and historicize taxonomic processes and define the conditions by which algorithms could support such developments.
Design and Implementation of Information Management System of an Electric Material Testing Center

A scheme of an enterprise-level information management system of an electric material testing center is proposed, with the core goal of achieving large-scale testing operations. An information management system with a data center at the level of the electric material testing center is constructed. The core business functions are separated from traditional department business, and reconstructed into seven core business units. The core business units can be accessed by front-end applications. A panoramic display interface for four front-end applications is established based on the BIM platform. The information management system improves the response speed of customer service and technical support.
Efficient Reversible Data Hiding Using Dynamic Variance Mean Sorting and Fitting Weight Rhombus Predictor

Reversible data hiding (RDH) together with sorting is one of the key goals for reducing image distortion. This paper presents an improvement in sorting based on the double-layered RDH algorithm by modifying the dynamic variance mean (DVM) sorting models and applying an effective predictor. Seven models are investigated to achieve suitable sorting with the best prediction error (PE) for each capacity and image. The fitting weight rhombus (FWR) predictor and two-pass testing (TPT) strategy are significantly utilized to achieve small PEs and reduce distortion. The performance of the proposed RDH algorithm is compared with other similar methods using eight different images. Experiment results demonstrate that the sorting performance of the proposed method is superior to those of the previous methods.
Continuing professional training as a factor of teachers’ professional and career growth

The article provides the analysis of modern trends in the development of education that call for steady professional advancement and improvement of professional competencies of teachers. The main vectors of teacher’s professional and career growth are defined. The role of continuing professional training as a leading factor of improving pedagogical activity in the modern changing world is emphasized. The main directions and goals of the development of continuing training within the field of study “Pedagogical Education” and the experience of implementation in Armavir State Pedagogical University are considered.
A Baseline Accuracy Classification Method to Over-come the Over-Fitting Problem for Class- Imbalanced Defect-Prone Datasets Model

Software Engineers are battling always with defects from the earliest starting point of the software history. The defect has significant commitment and assumes an important job in the hour of freeing software. Software defect- prone might be recognized before the software announcement or after the announcement. Although, the desire for designers is to give defect free software to their customers. Hence, recognizing the software defect-prone before announcement makes a decent preferred position for the designers. Here, the difficult assignment is to discover the defect-prone region or defective module of software. On the off chance that defective modules are distinguished before the arrival of software, at that point it could be effectively counteracted and fixed by the designer. In most recent two decades scientists have concentrated on software deformity prophecy issue by applying a few statistical and machine learning systems. The software defect-prone information experiences the class-imbalance issue due to the slanted conveyance of defective and non-defective software modules. Generally, machine learning calculations consider equivalent circulation of information tests in each class and expect the misclassification cost of each class is similarly significant. We have used SMOTE with ONE-R by using its MinBucketsize number n=1, 2, 3, 4, 5 & 6 for over-fit the data and resultant we got that minbucketsize n1 & 2 are highly good for base accuracy and efficiency for defect-prone datasets model. We have used datasets model in three different ways and we observed that use training datasets is very suitable for our experiments as compare to other two ways. We also observed that al evaluation measures results are highly good at use of training datasets model.