Reviewerly/exHarmony huggingface dataset:
The Emblem in the Visual Representation of a University: Classification and Semiotics of Visual Images

The article studies the main attribute of a university’s visual representation - its emblem. Among a few works devoted to the process of university branding, there is no сошріех analysis of their visual representation functioning. However, the observed growth of branding work in various universities increasingly involves the emblem as their graphic identifier. With the help of quantitative and qualitative methods, the structural and semiotic analysis of the emblems has been carried out on a соmріеіе sample of 917 Russian universities, their structure and range of their visual images being characterized, a typology of semiotic meanings being compiled. Consequently, the semiotic bases of Russian universities’ visual representation have been characterized, its functions in the process of university branding being defined. The research results have a wide practical application as a source of information on the use and purposes of university emblems within the higher education system, both for specialists involved in branding (marketing specialists, sociologists, designers etc.) and for PR departments, media centers, and administrations within a university. Nevertheless, the interpretative nature of the semiotic typology and the non-exclusive character of the classification of visual images allow but to designate the semantic directions of the universities positioning, beyond characterizing certain educational institutions in terms of their models of visual communication. Additional studies are necessary to determine the validity of the classification, as well as to clarify the semiotic bases for the typology given.
Analisis Pelatihan Penggunaan Media Pembelajaran CD Interaktif Berbasis Multimedia Dan Modul IPA SD Kelas III di Kecamatan Koto Tangah Kota Padang

Teachers and the world of education are generally expected to be able to create quality human resources both scientifically and positively. For this reason, in the learning process, the methods, strategies or learning activities carried out by the teacher should be something that is truly appropriate and meaningful, to obtain maximum results according to the stage of development of the child, then the strategies that the teacher uses in conveying something, both in the form of planting attitudes, mentality, behavior, personality and intelligence must be on target, seven proportional intelligence. In our lives we cannot deny that the ones who determine the attitudes, mentality, behavior, personality and intelligence of children are education, experience and exercises given and experienced by students since they were small. The results of the preliminary survey showed that several schools belonging to the Teacher Working Group (KKG) of Cluster V for science learning in SD III had not shown optimal learning. The results of the initial survey showed that the teacher working group (KKG) incorporated in the IV cluster V region of Koto Tangah Subdistrict, amounting to 11 schools, often experienced problems in the school learning process that had not been carried out optimally due to lack of learning media and science learning aids. The lecture activities carried out by the teacher were not wrong at all, but when lecturing, the teacher's interaction with students was not very running, the teacher only tended to lecture and did not pay attention to the situation and condition of students in the class. The lecture method also cannot be separated from the learning process. The lecture method will make students get maximum learning outcomes if packaged better and interestingly.
PPARM: Privacy Preserving Association Rule Mining Technique for Vertical Partitioning Database

With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this paper, we proposed Privacy Preserving Association Rule mining i.e. “PPARM” technique for multiparty computation of privacy preserving data model for aggregation, cryptographic security and association rule mining concept. In this process the data is secured using the cryptographic techniques and for providing the more secure mining technique the server generated random keys are used. Using the proposed technique the data is mined in similar manner as the association rule mining do, but for securing the data sensitivity the cryptographic technique is used at the client end. After mining of data the association rules are recoverable at client end also by the similar keys as produced by the server.
A comparison of two techniques of cell block preparation

Background: Cell block technique is a method of preparing cytological material so that it can be processed, sectioned, stained, and viewed as a histology section. The study compared two methods of cell block preparation, plasma-thromboplastin method and collodion bag technique of cell block preparation.&#x0D; Materials and Methods: Samples were randomly processed by plasma-thromboplastin technique or by collodion bag technique. Hematoxylin/Eosin stained cell block sections were examined and scored scaling from 1 to 3 for cellularity, clarity, nuclear features, cytoplasm, use of ancillary test and recovery of cell-cluster and fragments. Mann-Whitney U test was used for comparing two methods and establishing statistical significance.&#x0D; Results: The median cellularity score was 2.0 for plasma-thromboplastin and 1.0 for collodion bag. The median morphological preservation score and use of ancillary test score were 2.0 for both of the method while median score for recovery of cell cluster and fragments was 2.0 for collodion bag and 1.0 for plasma-thromboplastin. Statistically, both of the methods had similar results in cell block quality.&#x0D; The median total score for plasma-thromboplastin and collodion bag were 10.0 and 11.0 respectively. Diagnostic cell blocks were obtained in 46.51% of cell block by plasma-thromboplastin method and 60.52% of cell block by collodion bag method improving the diagnosis over conventional smear in 76.74% and 81.57% respectively.&#x0D; Conclusions: Cell block by plasma-thromboplastin and collodion bag method are cost effective, useful adjunct to FNA for diagnosis of mass lesion. Both techniques has no significant difference in quality of cell block produced.
Generative adversarial network based on chaotic time series

Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator and discriminator are evolving through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution, highly realistic scenes, human faces, among others have been generated. While GAN in general needs a large amount of genuine training data sets, it is noteworthy that vast amounts of pseudorandom numbers are required. Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data. We show that the similarity in proximity, which is a degree of robustness of the generated images with respects to a minute change in the input latent variables, is enhanced while the versatility as a whole is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also discuss the impact of utilizing chaotic time series in retrieving images from the trained generator.
Convolutions on Spherical Images

Applying convolutional neural networks to spherical images requires particular considerations. We look to the millennia of work on cartographic map projections to provide the tools to define an optimal representation of spherical images for the convolution operation. We propose a representation for deep spherical image inference based on the icosahedral Snyder equal-area (ISEA) projection, a projection onto a geodesic grid, and show that it vastly exceeds the state-of-the-art for convolution on spherical images, improving semantic segmentation results by 12.6%.
An Efficient and Geometric-Distortion-Free Binary Robust Local Feature

An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency.
Big Data and Development of Smart City

Phenomenon of Smart City had its around-twenty-year development path influenced by changes in technological, social and business environment.Big Data, together with Internet of Things and Cloud Computing, has tremendous and profound impact on the changes of Smart City, transforming its shape and achievable goals and redefining its requirements and challenges.The aim of this paper is to study the aspects of Big Data within the Smart City concept.We analyze the past and present Smart City pilot projects, giving special attention to the evolution of its traditional applications (traffic, healthcare, power grid, water & waste treatment and public safety) in the context of present technological maturity.We also describe feasible Smart City applications based on Big Data, followed by summary of benefits and main challenges, especially the growing ones, such as security of infrastructure or security of private data.The final result of the study is comprehensive two-dimensional chart (technological maturity of environment vs. stage of adoption of Smart City) with identification of typical development states.The chart may help city planners selecting or creating optimized development plans of their communities.
A joint image dehazing and segmentation model

Objects and their feature identification in hazy or foggy weather conditions has been of interest in the last decades. Improving image visualization by removing weather influence factors for easy image postprocessing, such as object detection, has benefits for human assistance systems. In this paper, we propose a novel variational model that will be capable of jointly segmenting and dehazing a given image. The proposed model incorporates atmospheric veil estimation and locally computed denoising constrained surfaces into a level set function by performing a robust and efficient image dehazing and segmentation scheme for both gray and color outdoor images. The proposed model not only shows efficient segmentation of objects in foggy images by outperforming state-of-the-art methods but also produces dehazed object results in the same time.
Exploring Bias in GAN-based Data Augmentation for Small Samples

For machine learning task, lacking sufficient samples mean the trained model has low confidence to approach the ground truth function. Until recently, after the generative adversarial networks (GAN) had been proposed, we see the hope of small samples data augmentation (DA) with realistic fake data, and many works validated the viability of GAN-based DA. Although most of the works pointed out higher accuracy can be achieved using GAN-based DA, some researchers stressed that the fake data generated from GAN has inherent bias, and in this paper, we explored when the bias is so low that it cannot hurt the performance, we set experiments to depict the bias in different GAN-based DA setting, and from the results, we design a pipeline to inspect specific dataset is efficiently-augmentable with GAN-based DA or not. And finally, depending on our trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based DA application.
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neural networks (BNNs), the BNN analogue of this has not yet been explored. This paper derives BNN architectures mirroring such kernel combinations. Furthermore, it shows how BNNs can produce periodic kernels, which are often useful in this context. These ideas provide a principled approach to designing BNNs that incorporate prior knowledge about a function. We showcase the practical value of these ideas with illustrative experiments in supervised and reinforcement learning settings.
A State-of-the-Arts and Prospective in Neural Style Transfer

Artistic style transfer is one the enormous research fields in computer vision. It is a technique to synthesize a content image and the style together to form an stylized image. Various popular applications like Prisma, Deepart, Pikazoapp provide a platform to synthesis images in order to produce very exciting artistic style. These applications use Convolutional Neural Networks to transform the images in an artistic style. Prisma photo editor has more than 250 modern art filters and 110 million users. Hence, the amount of images being synthesized is huge and computation requires to perform the task efficiently is large. Abundant CNN architectures like VGG16, VGG19, GoogleNet etc are introduced to solve this problem of heavy computations. The system helps users to transform their images into artworks using style of famous artists for stirring image filters, and it might be useful for artists to have an indication about the art. This paper presents the fundamental concepts, classification of Style Transfer and major progress towards Neural Style Transfer.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF PADA MATA PELAJARAN KOMPUTER DAN JARINGAN DASAR

This Article describes how to develope of interactive learning media, that it can be used in teaching and learning activities in school. This development aims to design an Interactive Learning media that is suitable for Komjardas (Komputer dan Jaringan Dasar) in Vocational Schools. The method used is the Instructional Development Institute (IDI), namely by determining the principle of the system approach which includes three stages including define, develop, and evaluate. Interactive learning media is designed using Lectora Inspire Software, Adobe Flash Cs 6 and Wondershare Quiz Creator that function as makers of evaluation questions. The results of designing instructional media in the form of an application that can be run on all computers / laptops of each student.Keywords: Interactive Learning Media, Instructional Development Institute (IDI).
Special Issue Proposal for the Parallel Computing Journal: HeteroPar 2016 and HCW 2016 Workshops

Training a Deep Neural Network (DNN) is a significant computing task since it places high demands on computing resources and memory bandwidth. Many approaches have been proposed to compress the network, while maintaining high model accuracy, reducing the computational demands associated with large-scale DNN training. One attractive approach is to leverage Block Circulant Matrices (BCM), compressing the linear transformation layers, e.g., convolutional and fully-connected layers, that heavily rely on performing General Matrix Multiplications (GEMM). By using BCMs, we can reduce the weight storage for a linear transformation layer from O(N2) to O(N). BCMs are also more efficient in terms of computational complexity, improving algorithmic complexity from O(N2) to O(Nlog(N)).Previous work has only evaluated DNNs using BCMs targeting FPGAs for inference. There has been little prior work that considers the potential benefits of using BCMs for accelerating DNN training on GPUs. In this paper, we explore acceleration of DNNs using BCM on a state-of-the-art GPU. First, we identify the challenges posed by using BCMs. Next, we perform both general and GPU-specific optimizations that impact: (i) the decomposition and interaction of individual operations, and (ii) the overall GPU kernel design. We modify the algorithmic steps to remove redundant computations, while maintaining mathematical integrity. We also leverage multiple GPU kernel optimizations, considering performance factors, such as occupancy, data sharing/reuse patterns, and memory coalescing. We evaluate the performance of DNN training on an NVIDIA Tesla V100, providing insights into the benefits of our proposed kernel optimizations on a state-of-the-art GPU. Based on our results, we can achieve average speedups of 1.31× and 2.79× for the convolutional layers and fully-connected layers, respectively for AlexNet. We can also achieve average speedups of 1.33× and 3.66× for the convolutional layers and fully-connected layers, respectively for VGGNet-16.
LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs

This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Especially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). Lastly, various experimental results confirm the superiority of the proposed equalizer over some existing ones, particularly when the statistics of the channel state information are unclear.
Rewriting, new drafts, new editions: authorial and ecdotic practices

The lexicon about ‘rewriting’, usually employed by Italian scholars, is not shared by all them: in fact, terms such as ‘redazione’, ‘stesura’, ‘revisione’, ‘rielaborazione’, ‘rifacimento’, can be used to denote both similar and completely different case-studies or concepts. The aim of this paper is to elucidate this complex situation by means of two case studies and the terminology used in the field of library sciences. We also suggest some possible solutions to this vagueness, trying to identify the liaison points between theoretical studies, editorial criteria and author’s choice.
EVALUASI TATA KELOLA TEKNOLOGI INFORMASI (TI) BERBASIS TESCA PADA UNIVERSITAS BALIKPAPAN

Corporate governance of information technology (it Governance) has been widely implemented by institutions in the various Colleges of the world. Information technology (it Governance) plays an important role in supporting the achievement of the mission, vision, objectives such Colleges in adapting new technology changes, innovation and environmental change in minimizing the risks and address the vulnerabilities Information technology security. To enhance the role of TI against the continuity of the process of college work, then it takes a measurement of it governance (IT Governance). TESCA is a Framework the governance of information technology as a standard framework in information technology on an utilization of an institution/organisisi. And is used to control the it governance.TESCA is in drawing up the instrument and assess the maturity level of the utilization of information technology on campus. The initiative sponsored by the PT. Telkom Tbk and Economic News. This Insturmen as material guidelines and guidance in conducting an evaluation or assessment of the implementation of IT in college. The goal of the research is to Implement IT Governance/corporate governance of information technology on information systems at the University of balikpapan and its information technology infrastructure that supports the information system, to make it more maximize teaching and learning through the system, determine the level of maturity (maturity level) in information systems and information technology infrastructure that supports information systems at the University of Balikpapan and Make a recommendation of the management of it in accordance with the business strategy and objectives of the University of Balikpapan. The results of the research that based on the results Obtained in just my Assesment that the University entrance 49.004 value of Kerosene into the category of Associate Level (SCORE 40.00-use of 59.99). where the existence of the IT management recommendations that can serve as the IT management considerations how should the management of IT to support the performance of information systems
NEURAL NETWORKS FOR SHAPE RECOGNITION BY MEDIAL REPRESENTATION

Abstract. The article is dedicated to the development of neural networks that process data of a special kind – a medial representation of the shape, which is considered as a special case of an undirected graph. Methods for solving problems that complicate the processing of data of this type by traditional neural networks – different length of input data, heterogeneity of its structure, unordered constituent elements – are proposed. Skeletal counterparts of standard operations used in convolutional neural networks are formulated. Experiments on character recognition for various fonts, on classification of handwritten digits and data compression using the autoencoder-style architecture are carried out.
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words

Word embeddings are one of the most useful tools in any modern natural language processing expert's toolkit. They contain various types of information about each word which makes them the best way to represent the terms in any NLP task. But there are some types of information that cannot be learned by these models. Emotional information of words are one of those. In this paper, we present an approach to incorporate emotional information of words into these models. We accomplish this by adding a secondary training stage which uses an emotional lexicon and a psychological model of basic emotions. We show that fitting an emotional model into pre-trained word vectors can increase the performance of these models in emotional similarity metrics. Retrained models perform better than their original counterparts from 13% improvement for Word2Vec model, to 29% for GloVe vectors. This is the first such model presented in the literature, and although preliminary, these emotion sensitive models can open the way to increase performance in variety of emotion detection techniques.