Reviewerly/exHarmony huggingface dataset:
Detection of Reference Topics and Suggestions using Latent Dirichlet Allocation (LDA)

Pelatihan Aplikasi Teknologi Informasi (PATI) is an activity of training required for new students in Universitas Muhammadiyah Malang (UMM) to provide knowledge and training on UMM or information technology concerned about general technology. At the end of the training, the students give the conclusions and suggestions to PATI. During this event, the training Committee gave less concern in term of the inference from students to provide a material evaluation. The primary factor originated from the commenting processes which should be performed one by one. Therefore, the comprehensive method should be implemented by modelling using Latent Dirichlet Allocation (LDA) in order to facilitate the Committee to undertake an analysis of the conclusions and suggestions. LDA is a “generative probabilistic model” of a collection of composites made up of parts. In terms of topic modeling, the composites are documents and the parts are words and/or phrases (n-grams). Conclusions and suggestions are taken as many as 1025 data from PATI 2016/2017. Based on such research, modelling of LDA identifies the 7 topics in the overall data. The process of analysis is done by external details each comment contains what topics. The evaluation is done by testing 250 data to determine the results of the conformity between the results of the analysis of the system as well as actual results obtained from respondents. The test results obtained accuracy of 83.6%.
A Comparative Approach for Classification and Combined Cluster Based Classification Method for Tweets Data Analysis

The paper presents a problem in identifying duplicate detection of tweets from a social network or twitter. To accomplish the screening, we use clustering process for text-based classification, and this has to be applied not for test set but also employed on the training dataset. To incorporate the knowledge of discovery and performance, we use classifier and combined clustering with classification process in the detection of spam tweets or duplicate tweets on social media. We have performed experiments on TSVM/SVM and C-SVM classification approaches on tweets and have demonstrated the efficiency related to our approach. The texts performed on the integrated cluster classification will perform better than SVM (Support Vector Machine) classification.
Multi-Platform Architecture for Cooperative Pedestrian Navigation Applications

As the location-based services market is growing, the plethora of navigation applications is strongly increasing. The solutions are made for single purposes and platforms. And the communication and information exchange in between, for example, pedestrian navigation applications from different solution providers, remain non-existent. To fill this gap, we developed a software architecture. The architecture abstraction enables easy and modular replacement of map, object, method, user, communications and sensor implementations between applications for many navigation purposes and technology platforms. The prototype design was tested using inertial, GNSS and UWB technologies on a MATLAB environment using multiple users, methods and sensors.
Modulation Based Transfer Learning of Motivational Cues in Developmental Robotics

The modeling of utility is an important problem in many fields, including reinforcement learning. However, when considering a developmental approach to open-ended learning a new aspect arises. In these settings, the efficiency of the modeling process becomes a key aspect, as these processes usually take place in real time and, to increase survivability, it is necessary for the robot to be able to produce utility models as fast as possible. In this paper, we address this issue by proposing a modulation-based approach to the adaptation of the robot's experience, in the form of previously obtained ANN based utility models, to new situations. These previous utility models are perceptually recalled from a Long-Term Memory and combined to produce an initial guess to the new utility model. After this, modulatory structures are created that lead to the fine adaptation of these initial guesses to the real utility model of the new situation. Some initial results of experiments using a real robot are presented to clarify the approach. Specifically, three realistic problems that a Baxter "cooking robot" must solve are faced with this modulating approach. With them, it is clearly shown the increase in efficiency of the utility model learning in real time.
Blind Color Image Watermarking Based on 2-level Discrete Wavelet Transform, M-ary Modulation, and Logistic Map

With the fast development and growth in the communication of multimedia, the number of digital content issues about copyright protection has increased. Digital image watermarking is commonly used for copyright protection. This paper proposed new methodology using digital color image with existing m-ary modulation based spread spectrum and discrete wavelet transform by converting the host image into YCbCr color space. Logistic map function is used to shuffle the watermark before embedded into the host image. Perfomance of the proposed methodology is measured using Peak signal-to-noise ratio and normalized correlation to evaluate the imperceptibility and robustness. It is shown that the proposed watermarking methodology shows good imperceptibility of watermarked image with PSNR ±40dB and SSIM at least 0.997. The proposed methodology is also robust against noise attacks, filter attacks with filter size 3×3, histogram equalization, sharpening, JPEG compression, and image cropping.
Representation and generation of space-filling curves: a higher-order functional approach

Space-Filling Curves (SFCs) map a multi-dimensional cellular space into one dimension. Although several algorithms have been introduced that can generate different SFCs, they barely consider deploying SFCs for non-cellular spaces. This article presents a new higher-order functional approach that enables a new level of abstraction which separates the SFC representation (i.e. a set of functions that define the SFC) from the algorithms that use it (e.g. SFC generator, point ordering, etc.). We describe the proposed approach and, as example cases, show how different SFCs can be used as the ordering mechanism for generating the K-d trees and R-trees for a set of arbitrarily distributed points using the proposed higher-order representation of the SFCs.
A Novel Wavelet Denoising Pre-processing Algorithm for TDOA Localization

In order to reduce the distance measurement errors during TDOA localization, a novel pre-processing algorithm based on wavelet denoising is proposed. The wavelet pre-processing algorithm is able to flexibly select the number of wavelet decomposition levels based on the energy coefficient, then different thresholds are set for positive and negative wavelet coefficients which do not require noise estimation. Next, a new threshold function is designed to deal with wavelet coefficients which can incorporate the advantages of soft thresholding and hard thresholding, and finally the signal is reconstruct. Simulation results show that the wavelet preprocessing algorithm reduces distance measurement errors and improves the positioning accuracy under different environments.
The Role of Activity and Similarity in Rating and Social Behavior in Social Recommender Systems

Social recommender systems exploit two sources of information for making recommendations, the historical rating behavior of users, and the social connections among them. The basic assumption is that if two users are friends, they are likely to share similar preferences. Many recommendation approaches are based on such correlations between the rating and the social behavior of users. However, there is little work in studying whether there actually exist such correlations and how strong they are. In our work, we look at the two views of user behavior, their social connections, and their history of ratings, and investigate two research questions. The first examines if strong activity in one view, e.g., having many friends, implies strong activity in the other view, e.g., having rated many items. The second investigates whether high similarity in one view, e.g., network similarity, implies high similarity in the other view, e.g., rating similarity. We employ various notions of activity and similarity, and identify those that appear to have the stronger impact. Specifically, to some degree, we find that rating behavior determines social behavior, and that the opposite relationship is weaker.
An Exploratory Study on Judicial Image Quality Assessment Based on Deep Learning

Images are important judicial materials. With the deepening of intelligent systems in the judicial area, image quality plays a vital role in the result of many judicial applications. This paper firstly introduces deep learning into judicial image quality assessment. Pre-trained convolutional neural network (CNN) models are fine-tuned and then used to extract image features. Based on the features extracted from CNN models, we convert them into specific numbers representing the quality. A preliminary experiment has been designed and conducted on three types of judicial images. The experimental results show that our approach can outperform the existing image processing technique. Images used as investigation materials are more distinctive than the other two types, and they need an independent model for analyzing.
Strategies and tactics in Skyrim through virtual reality: the VR–HMD as a model for disciplining in the twenty-first century

This article is about the Virtual Reality-Head Mounted Display (VR-HMD) as a model for contemporary ways of disciplining. The VR-HMD makes the observer discipline herself through the triggering of performance. Through specific strategies the VR-HMD addresses the body of the observer to perform in the mixed reality that is constructed in the interaction between the body and the VR-HMD. These strategies consist of approaches by the hardware developers and content creators to manage the subjectivity and visuality of the observer. Today’s body is not disciplined through formatted technics of the VR-HMD, but through self-disciplining of the observer’s active performance. This article will unravel the strategies within the game The Elder Scrolls V: Skyrim VR on the Playstation VR that are used to control the visuality and subjectivity. Through her interaction in the game the observer also becomes aware of her body and the ways that it is disciplined in. In the end, this article will argue that the VR-HMD should not only be understood as a strategic device that can discipline a neoliberal subject, but that the VR-HMD is a supercomplex intervention that could help us to become more corporeal literate of our bodies in the age of digital media.
47.3: A Novel Triangle Matching RGBW Algorithm

Last year we introduced a RGB‐to‐RGBW algorithm with minimized color distortion by keeping the color coordinate unchanged from the original color. Based on the similar principle, we optimized our method and proposed a more efficient color mapping algorithm according to the triangular color gamut of RGBW display. The algorithm mainly consists of three steps. Step 1: Image Enhancement which is a preprocessing operation of input Image. Step 2: Pixel Analysis which can obtain CIE chromaticity coordinates (x, y) and luminance (Y) of the input RGB data. Step 3: Triangle Matching which is the core of this algorithm that can calculate the values of the output RGBW signal according to the triangular gamut enclosed by RGBW display. The results indicated that our algorithm has superior performance upon conventional method and is more flexible in application.
A General Framework and Key Technology for Intelligent Railway Video Recognition Platform

The development of railway integrated video monitoring system has experienced analog TV monitoring, digital video monitoring and network monitoring. But it still stays in the application of supervision and control. This paper focuses on the development of the Intelligent Railway Video Recognition(IRVR), and designs a general framework for IRVR platform. Firstly, the necessity of IRVR platform is stated form value of staff, technology and standard operation, which is based on the current situation and requirements analysis of railway video monitoring. Secondly, a general framework for IRVR platform is designed to deal with the actual problems. Thirdly, object detection algorithm flow for IRVR platform is proposed based on the analysis of key technology, especially revolve around deep learning. Finally, the experimental environment is built to verify the feasibility of the object detection algorithm for IRVR platform.
Entangling two separable qubits using an entangled field state

In this paper, entanglement transfer from parity pair coherent states to separable qubits is studied. Parity pair coherent states are defined as superposition of pair coherent states. The entanglement of these states is obtained studying their entropy. We observe that the entanglement of the parity pair coherent states is a decreasing function of the photon number differences between the two modes. The interaction between the latter and the qubit system is studied considering Jaynes-Cummings model. The entanglement of the qubit system after interaction is calculated using concurrence. Our results reveal that the maximum of entanglement is achieved if the two modes have equal photon number. In addition, we observe that the maximum entanglement of the qubit system is an increasing (decreasing) function of the photon number difference between the two modes contingent on its odd (even) value.
A novel conditional anonymity scheme for vehicular communication networks

Summary Vehicular communication networks are traffic applications of the Internet of Things consisting of vehicle nodes, roadside units, service providers, and other components. To protect the vehicle privacy, some vehicular communication networks adopt anonymous schemes. However, in these anonymous schemes, anonymity abuse is inevitable. In order to protect vehicular privacy and prevent anonymity abuse, some conditional anonymous authentication schemes based on cryptography have been proposed. However, most of these methods have complex computation and large communication overhead, which makes them difficult to meet the actual requirement of high‐speed traffic in vehicular communication networks. In this paper, a novel conditional anonymity scheme called VKPCA (vehicular communication network based on kernel principal component analysis [KPCA]) is proposed, which can protect vehicle privacy while avoid anonymity abuse. The simulation results show that this scheme has lower computational complexity and communication overhead than the conventional anonymous authentication scheme.
Blockchain and Services – Exploring the Links

Blockchain is a novel distributed ledger technology which has attracted a wide range of interests for building the next generation of applications. The broad range of blockchain applications is made possible by so-called smart contracts, which transform a blockchain system into a general compute platform. As a new paradigm and technology platform in the integration space, this bears the question to which degree there is a connection to services, and in how far lessons learned on services can be applied to Blockchain. In the talk, I explored four different facets of this topic, as follows. First, application-level service interfaces for interaction with blockchain-based applications enable easy integration with existing infrastructure. Second, service composition can be achieved through smart contracts, and enable different approaches to orchestrations and choreographies. Third, Blockchain-aaS offerings cover infrastructure operation, but the idea presents further possibilities. And finally, some principles for designing SOA services and microservices can be applied to smart contract design.
Image‐independent optimal non‐negative integer bit allocation technique for the DCT‐based image transform coders

The optimum non-negative integer bit allocation (ONIBA) is an important technique, which provides optimal quantisation of transform coefficients for the image transform coders (ITCs). However, the existing ONIBA algorithms are still not popular for the discrete cosine transform (DCT)-based ITCs, due to their image-dependent nature and additional side information requirements. Therefore, this study presents a novel image-independent ONIBA (IIONIBA) technique to achieve efficient quantisation for the DCT-ITCs. For the development of the proposed IIONIBA technique, initially, an image-dependent ONIBA algorithm is proposed, which is then mapped into desired image-independent solution via utilisation of a prepared combined image and proposed modified step size mapping technique. Thereafter, a new lookup table for the elements of quantisation tables, obtained from the proposed IIONIBA technique, is established using non-linear regression analysis, to reduce the problem of additional side information requirements. Several experiments are performed to evaluate the performance of the proposed IIONIBA technique based on the visual quality assessment of reconstructed images and the image quality indexes peak signal to noise ratio (PSNR) and mean structural similarity index (MSSIM). The results show that the proposed IIONIBA technique delivers better quantisation and provides significant gains in the image quality indexes as compared to the recent quantisation techniques.
Syntax Tree Aware Adversarial Question Rewriting for Answer Selection

Answer selection is an important method to achieve better user experience in question answering (QA) systems and it is essential in ensuring better QA matching performance. Improving mutual information between QA pairs is a useful way to obtain the matching degree improvement and question rewriting has been proven a helpful way in utilizing mutual interaction between questions and answers. In this research, we focus on syntax tree aware question rewriting inspired by the thought of integrating syntactic information into question answering. Besides, to improve the quality of rewriting, we employ the generative adversarial network for rewriting optimization, which consists of a syntax tree aware rewriting model and a discriminator. The quality information given by the discriminator guides the optimizing of the rewriting model in the training phase. The experimental study has shown the effectiveness of syntax tree aware question rewriting and utilizing the generative adversarial network for rewriting.
Face Image Deblurring Based on Iterative Spiral Optimazation

The motion blurred image is caused by the relative motion between the target and the capturing device during the exposure time. It’s difficult to analyze the face information of the motion blurred face image, therefore motion deblurring is needed. However, the existing algorithms cannot deal with the diversity of motion blur kernels well. Based on that, this paper proposes an iterative spiral optimization algorithm for blind motion blurring. The algorithm makes the blurred image spirally approximate the sharp image by calling the deblurring generator multiple times. It is proved that the algorithm can effectively restore the motion blurred image with diverse blurred kernels in the approximate natural state, and improve the visual effect of the image.
Image Super-resolution Reconstruction Based on an Improved Generative Adversarial Network

To solve the problem that images reconstructed by traditional super-resolution reconstruction (SR) techniques are smooth and lack good details, in this paper, we have presented an improved generative adversarial network for image super-resolution. The improved method was based on deep neural networks whose generative model contained a multi-layer convolution module and multi-layer deconvolution module, in which a layer hopping connection and a loss function was added to the perceptual loss. The discriminant model was made up of a multi-layer neural network whose loss function was based on the discriminant model loss function that was generated from the generative adversarial network. Finally, we selected PSNR and SSIM as the indicator in the experiments. In the experiments, the PSNR value of 2x, 3x and 4x magnification factor are improved on average by 1.125, 2.175 and 2.075 respectively and the SSIM value of 2x, 3x and 4x magnification factor are basically improved. Compared with the existing image super-resolution reconstruction methods, the effectiveness of the method that we proposed in image super-resolution reconstruction was proven.
Using VR for Fitness Training – Pilot Study

In the digitalized era, we do less and less physical work, which in turn leads to less physical activities. Many different wearables and applications try to motivate us to exercise more but people stop using them after a couple of months. One of the reasons people stop using them is because they serve as a post-activity viewing and not as a motivator. Meanwhile, the gaming industry has spent years of perfecting the art of creating addictive games. One idea is to utilize other technology such as virtual reality (VR) to encourage people to exercise more. VR is a computer-generated world, which tracks users and can give users an illusion of participating in a synthetic environment. This paper aims to introduce a Fitness VR application, which was developed and evaluated. The application contains three exercises and a mindfulness scene, the evaluation focus on how different gamification and interaction techniques can be used to motivate users to exercise more. Initial findings show that the participants find the Fitness VR application fun and motivating.
Key Point Detection Techniques

Image registration is a process to find the offset or misalignment between two or more images for a certain area to determine the required geometrical transformation that aligns points in one image with its corresponding in the other one. Generally, the operational goal of the registration process is a geometrical transformation for the input leading to geometrically agreement for input images, so that the matched pixels in the input images refer to the same region of the captured area. So, image registration can be applied in many applications such as change detection, mosaicking, creating super-resolution images etc. Registration process is divided into two categories: (1) Traditional methods and (2) Automated methods. For the traditional methods, the anchor, control, points are selected manually and applying the transformation model leading to time consuming and low accuracy. So, automatically detection of these points helps to recover the performance of manual selection. Registration process deals with many problems such as illumination changes, intensity variations, Different sensors, noise etc. So, its applications are mainly dependent on errors (multi temporal, multi view, or multi modal) occurred during capturing process. Feature detection, as a step of image registration process, aims to find a set of stable (invariant) distinctive key points or regions under varying conditions. Also, it is critical for the detector to be robust to changes in viewpoint, brightness, and other distortions. The goal of current paper is discussion and exhibition of the common corner detectors helping to be familiar with the various applied techniques for feature detection.