Reviewerly/exHarmony huggingface dataset:
Enriching Wikidata with Cultural Heritage Data from the COURAGE Project

Creating links manually between large datasets becomes an extremely tedious task. Although the linked data production is growing massively, the interconnecting needs improvement. This paper presents our work regarding detecting and extending links between Wikidata and COURAGE entities with respect to cultural heritage data. The COURAGE project explored the methods for cultural opposition in the socialist era (cc. 1950–1990), highlighting the variety of alternative cultural scenes that flourished in Eastern Europe before 1989. We describe our methods and results in discovering common entities in the two datasets, and our solution for automating this task. Furthermore, it is shown how it was possible to enrich the data in Wikidata and to establish new, bi-directional connections between COURAGE and Wikidata. Hence, the audience of both databases will have a more complete view of the matched entities.
Word reordering algorithm for poetry analysis

Abstract Automatic poetry analysis is usually examined as subtask of natural language processing but approaches which are good for prose texts are ineffective for poetry due to free semantic characteristics. It allows us to use large corpus for training words order and rather small text corpus amount. In this paper we suggest the way to adapt poetry texts to prose ones by changing word order without loosing of model and also use word2vec features to improve the classifier work.
Kernel Vulnerability Analysis: A Survey

Kernel vulnerability may lead to information disclosure, privilege escalation, rootkit planting, etc. Nowadays mobile devices and other smart products has been increasingly available. Modern operation systems need to support new functions like GSM, Bluetooth, Wi-Fi, GPS, Camera of the smart devices. As a result, the number and categories of vulnerabilities and attacks is dramatically increasing. Correspondingly, varieties of researches have gone into finding such vulnerabilities in kernel. The kernel vulnerability analysis methods are vastly diverse. This paper surveys the state of the art on kernel vulnerability analysis. We summarize, compare, evaluate existing methods and provide the features, strengths and weaknesses of each kind of approaches. Then we present the trend on kernel vulnerability analysis. First, analysis on the system call, file system and the driver code become increasingly important. Second, existing methods can only handle limited kinds of vulnerabilities, which is insufficient for kernels, which contains modules, drivers, core kernel etc.. Third, the method for kernel vulnerability analysis will continuously focused on restricted parts of kernels or limited types of vulnerabilities for the kernel is implemented differently according to their platforms.
Accelerating Stochastic Gradient Descent using Adaptive Mini-Batch Size

The following topics are dealt with: learning (artificial intelligence); feature extraction; natural language processing; pattern classification; text analysis; computer aided instruction; computer network security; Internet; neural nets; decision trees.
Possibility of HPC Application on Cloud Infrastructure by Container Cluster

Today, High Performance Computing (HPC) has been used to solve various domain problems including science and engineering field or streaming processing application. All of these domains require a lot of computation resources. For HPC application, wall time and accuracy of calculation are important factors. Therefore, cloud infrastructure was hardly acceptable to the HPC application, because cloud infrastructure has performance overhead as compared with native environments. However, new hardware acceleration devices, increased demand for large scale calculations using AI applications and evolution of container technology have increased the possibility of running HPC applications on cloud infrastructure. In this study, we evaluate and compare the performance of several applications such as ; Poisson's solver, ResNet50, and Recurrent Neural Networks (RNNs) with Long Short-Term Memory models (LSTM) - to confirm the possibility of running HPC applications on cloud infrastructure. We submit that there is only 0.05% overhead for ResNet50 on cloud infrastructure. It indicates the possibility of running special purpose HPC applications such as AI training or General-Purpose computing on Graphics Processing Units (GPGPU) oriented applications on cloud infrastructure. We also observe that there is no performance overhead for cache miss rate and InfiniBand latency.
heSRPT

Nearly all modern data centers serve workloads which are capable of exploiting parallelism. When a job parallelizes across multiple servers it will complete more quickly, but jobs receive diminishing returns from being allocated additional servers. Because allocating multiple servers to a single job is inefficient, it is unclear how best to share a fixed number of servers between many parallelizable jobs. In this paper, we provide the first closed form expression for the optimal allocation of servers to jobs. Specifically, we specify the number of servers that should be allocated to each job at every moment in time. Our solution is a combination of favoring small jobs (as in SRPT scheduling) while still ensuring high system efficiency. We call our scheduling policy high-efficiency SRPT (heSRPT).
Enhancing Eye Tracking of Source Code: A Specialized Fixation Filter for Source Code

The present state of the art in gaze processing algorithms, for eye tracking data, targets general purpose application in studies using a variety of stimuli (prose, images, or video). This work proposes the development of a gaze processing algorithm specialized to support eye tracking in the field of software engineering. Enhancements to fixation identification with respect to source code tokens can have wide reaching implications for researchers and practitioners using eye tracking with software engineering tasks. This work will require infrastructure improvements to the iTrace framework and additional study to contextualize the relevance of individual source code tokens during comprehension activities. Details concerning the nature of the improvements and a plan research and development on this problem is presented.
An Approach for Automatic Aspect Extraction by Latent Dirichlet Allocation

Now a days internet has taken over all form of communication and activities. One of the most affected area is e-commerce which survives through internet. One of the vital process of business survival is customer feedback. There are several form of platforms where the feedbacks can be posted. The main task is to accumulate all the reviews and summarize them in a conceivable manner. The approach here is to summarize the reviews in aspect based manner. This representation will help future consumers to make well informed decision. LDA is one of the popular methods to extract latent topics comprising a document. The present approach intends to use that characteristic to extract aspects from review corpora. The current scope of work is to extract and improve the quality of topics. After several corpora of product reviews were processed through this method, the results were examined through graphically plotting the topics and also examining the dominant keywords of the topics. Finally the accumulated results of the present method are compared with the results of previously implemented Word2Vec based model and human extracted aspects.
How does Augmented Reality Improve the Play Experience in Current Augmented Reality Enhanced Smartphone Games?

This paper investigates the current state of handheld augmented reality (AR) gaming apps available on the App Store (iOS) and the Play Store (Android). To be able to directly compare the differences between games played with and without AR, only games in which the AR mode can be switched on/off were investigated. Because the main scope of this paper is on the evaluation of the experience provided by AR, parts of the game experience questionnaire (GEQ) have been included in the empirical study. It showed that AR has big potential to improve immersion or flow in the game-play. This paper also identifies differences in the implementation of AR features and investigates how and what parameter in the GEQ can be positively influenced.
A Geometry-Based Approach to Visualize High-Dimensional Data

Big Data has attracted extensive attention from industry, academia and governments around the world, employing various approaches from many fields such as machine learning, pattern recognition and data visualization. Data visualization is quite useful in the perception of relevant information by a human for gaining understanding and insight from data with high dimensionality. This paper presents a novel approach for dimensionality reduction called Polygonal Coordinate System (PCS), which is able to represent multi-dimensional data into a two-dimensional data. For this purpose, data are represented across a regular polygon or interface between the high dimensions and the 2D plane. PCS can deal with massive data sets by adopting an incremental and efficient dimensionality reduction. Statistical comparison using Spearman's rho correlation highlights the utility of PCS, outperforming the state-of-the-art t-Distributed Stochastic Neighbor Embedding (t-SNE) technique.
A Short Answer Grading System in Chinese by CNN

Short answer question is a common type of test in various level of learning. However, it is not common to test students with the short answer questions online, since automatic short answer grading is not easy. In this paper, we report a short answer grading system based on deep learning model CNN. We test the system on two corpus in Chinese. The first one is translated from a public available corpus in English. We also curate a corpus by ourselves. We get promising result on both data set.
Crime data warehousing and crime pattern discovery

The structure of crime activity logs stored in police databases is not designed for decision support systems and hence not for complex crime analysis. This paper shows how crime log data could be converted to significantly useful information using data warehousing and data mining techniques. Crime incident data and other relevant data are organized with applying data warehousing concepts. Spatial association rule mining is used for finding interesting local relationship patterns of crime incidents with other spatial features. This paper shows how to materialize spatial relationship information between crime activities and task-relevant other features into data marts, and to discover interesting crime patterns from the data mart using a spatial association rule mining technique. A proof of concept is carried out with real crime data and points of interest in a study area to illustrate and evaluate the proposed approach. The case study results show the usefulness of such data warehousing and spatial association rule mining for crime data analysis.
Probing Image and Video Steganography based On Discrete Wavelet and Discrete Cosine Transform

Now-a-days, video steganography has developed for a secured communication among various users. The two important factor of steganography method are embedding potency and embedding payload. Here, a Multiple Object Tracking (MOT) algorithmic programs used to detect motion object, also shows foreground mask. Discrete wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used for message embedding and extraction stage. In existing system Least significant bit method was proposed. This technique of hiding data may lose some data after some file transformation. The suggested Multiple object tracking algorithm increases embedding and extraction speed, also protects secret message against various attackers.
Fundamental Limits of Quantum-Secure Covert Communication over Bosonic Channels

We investigate the fundamental limit of quantumsecure covert communication over the lossy thermal noise bosonic channel, the quantum-mechanical mode underlying many practical channels. We assume that the adversary has unlimited quantum information processing capabilities as well as access to all transmitted photons that do not reach the legitimate receiver. Given existence of noise that is uncontrolled by the adversary, the square root law (SRL) governs covert communication: up to c√(n) covert bits can be transmitted reliably in n channel uses. Attempting to surpass this limit results in detection with unity probability as n → ∞. Here we present the expression for c, characterizing the SRL for the bosonic channel. We also prove that discrete-valued coherent state quadrature phase shift keying (QPSK) constellation achieves the optimal c, which is the same as that achieved by a circularly-symmetric complex-valued Gaussian prior on coherent state amplitude. Finally, while binary phase shift keying (BPSK) achieves the Holevo capacity for non-covert bosonic channels in the low received signal-to-noise ratio regime, we show that it is strictly sub-optimal for covert communication.
Simulation-Based Energy Assessment of PV Systems Installed in an Urban Environment

In this paper, a stochastic simulation procedure for energy assessment of PV systems is developed and tested. The method is especially adequate for urban applications since the impact of surrounding obstacles both for direct and diffuse irradiance is taken into account. The main input is the system location along with its monthly average irradiance and temperature statistics. Based on the input data, the program generates a set of random years represented by hourly sequences of global horizontal irradiance and ambient temperature. The generated sequences are then converted into the module plane-of-array irradiance and module temperature time series and, consequently, the AC energy production is estimated for each simulated year. The final results are presented in a form of probability density function of the system annual energy output.
An Improved Feature Fusion for Speaker Recognition

Speech is the most effective way of communication for humans. Its uniqueness is the basis for speaker recognition. It is a research focus on finding speaker's distinctive speech features for better system performance. In this paper, we propose a novel approach of feature fusion for the speaker recognition. It is based on the ratio of the inter and intra class variance, which is calculated from the different dimensions of traditional features, such as Linear Prediction Cepstral Coefficients (LPCC) and Perceptual Linear Prediction (PLP). The ratio is higher, the component is more significant, which are combined to a new feature. The pitch period and spectral centroid features are added to obtain extra speech features. Experimental results show that the speaker recognition's accuracy by proposed feature fusion can improve 13.26% and 8.74% compared to traditional LPCC and PLP features with a GMM classifier.
Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Computer Vision

This paper explores Domain-Specific Deep Learning Architectures for GPU Computer Vision through a "brainstorming" approach on selected hands-on topics in the area. We intend to discuss Deep Neural Networks (DNNs) to image classification problems through tools, frameworks and data pipelines commonly used to train and deploy DNNs in GPUs and Domain-Specific Architectures (DSAs).
Survey on context‐aware tour guide systems

As a result of the pervasiveness of smartphones and improvement of context-aware systems, developers have designed and implemented a variety of context-aware tour guide systems. These systems focus on tourist attractions and provide services for tourists in order to support them before, during and after the trip. This survey aims to summarise, classify and investigate these systems from three standpoints of general, design and context-related issues. To this end, after introducing the related concepts, a framework for investigating context-aware tour guide systems is proposed. The aim is the theoretic consideration and classification of these projects with regard to distributed and pervasive computing patterns and the identification of the current situation and challenges. The proposed framework includes three axes of general, system design and context- awareness, each of which consists of some parameters. Afterwards, the existing projects are categorised according to the proposed framework. Finally, concluding remarks as well as new trends are discussed in order to illuminate future research directions.
Parameters Analysis for PSO based Task Scheduling in Cloud Computing

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Q-NAS Revisited: Exploring Evolution Fitness to Improve Efficiency

Over the last decade, the scientific community has witnessed the success of deep neural networks in a variety of tasks. However, the design of these new structures still demands expert knowledge and significant time. In this scenario, the idea of automating the design of such complex networks has inspired many researchers. New algorithms have been proposed to address the neural architecture search problem, but computational cost is a major drawback. Q-NAS (Quantum-inspired Neural Architecture Search) is an evolutionary algorithm recently proposed with the idea of improving efficiency, but with minor humanbias introduced in the search. This work extends the analysis of Q-NAS, focusing on fitness behavior during evolution. We want to verify if good individuals are obtained early in evolution, so we could apply an early-stopping mechanism. We experiment with a simple early-stopping technique, and results indicated an evolution time reduction of more than 45% in most cases. This new feature can improve efficiency, making Q-NAS very competitive compared to other algorithms.
A Study of Usability Improvement in Immersive VR Programming Environment

Visual programming environments such as Scratch have been proposed for beginners. In those environments, programming is possible by arranging function blocks expressed on two dimensions. In order to improve the browsability of many blocks, an environment for programming by arranging functional blocks with hand gesture interaction in immersive VR space has been proposed. However, operation error by Leap Motion when setting the value and the usability of the environment was not good due to the error of estimation of hand gesture. In this study, we propose an operation method using VR controller for programming in an immersive VR environment and compare the results. We will incorporate these results into future development.
Convolutional networks for appearance-based recommendation and visualisation of mascara products

Abstract In this work, we explore the problems of recommending and visualising makeup products based on images of customers. Focusing on mascara, we propose a two-stage approach that first recommends products to a new customer based on the preferences of other customers with similar visual appearance and then visualises how the recommended products might look on the customer. For the initial product recommendation, we train a Siamese convolutional neural network, using our own dataset of cropped eye regions from images of 91 female subjects, such that it learns to output feature vectors that place images of the same subject close together in high-dimensional space. We evaluate the trained network based on its ability to correctly identify existing subjects from unseen images, and then assess its capability to identify visually similar subjects when an image of a new subject is used as input. For product visualisation, we train per-product generative adversarial networks to map the appearance of a specific product onto an image of a customer with no makeup. We train models to generate images of two mascara formulations and assess their capability to generate realistic mascara lashes while changing as little as possible within non-lash image regions and simulating the different effects of the two products used.
Hamiltonian polygonal path in assembly graph of FTTM

Fuzzy Topographic Topological Mapping (FTTM) consists of four topological spaces that are homeomorphic to each other. A sequence of FTTMn is a combination of n terms of FTTM. An assembly graph is a graph with all vertices have valency of one or four. A Hamiltonian path is a path that visits every vertices of a graph exactly once. In this paper, we prove an assembly graphs exists in FTTMn and relation between the Hamiltonian polygonal paths and assembly graph of FTTMn. Several definitions and theorems are developed for the purpose.
Design of Mobile Robot Motion Framework based on Modified Vector Field Histogram

This paper presents a design of the mobile robot motion framework using slightly modified Vector Field Histogram (VFH) algorithm. The VFH algorithm provides both local motion planning and obstacle avoidance based on on-board sensors measurements. This framework utilizes Robot Operating System (ROS) for the software implementation and simulation purposes. The effectiveness of the proposed framework was verified in both static and dynamic unknown environments. The obtained simulation results indicate a proficiency of the VFH algorithm in navigating the mobile robot from the start to the goal position avoiding collision with obstacles. Trajectories obtained using the designed framework are smooth and without oscillations even in the presence of moving obstacles.
Structure Learning of Sparse GGMs Over Multiple Access Networks

A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from a dataset distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In the Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received data. In the Uncoded method, data symbols are scaled and transmitted through the channel. The central machine uses the received noisy symbols to recover the structure. Theoretical results show that both methods can recover the structure with high probability for a large enough sample size. Experimental results indicate the superiority of the Signs method over the Uncoded method under several circumstances.
Simplify Testing of Mobile Medical Applications by Using Timestamps for Remote, Automated Evaluation

To simplify the evaluation and certification process for mobile medical applications, a methodology for simple testing has been developed. For this purpose, certain actions of the apps are provided with timestamps in order to be able to reproduce the entire behavior of the testers within the scope of the test study. Therefore, the tester does not have to come to a test laboratory to fulfill certain tasks that are set by the test leader. Nevertheless, all main and secondary functions are tested naturally, as is the entire app.With the help of these asynchronous remote tests, the testers can test the medical app in their familiar environment. Thus the test environment is not present and the testers act naturally. The timestamps are not noticed by the testers and require only little additional programming effort. The results are automatically transferred to a database of the developer and evaluated in terms of usability, compliance, risk factors, learning curve and validity. It is possible to automatically compare the timestamp with a previously defined Happy Path and other previously defined reference values.