Reviewerly/exHarmony huggingface dataset:
A Study of Data Mining Methods for Identification Undernutrition and Overnutrition in Obesity

Indonesia is currently experiencing the Double Burden Nutrition problem. This is a problem when a significant proportion of the population is endangerd with the problem of malnutrition, but a significant other suffer from obesity or over-nutrition. People who are underweight, overweight, and obesity are included in the ten risks in terms of the global disease burden. In 2015 showing that the adult population in the world is overweight, with 38% of men and 40% of women. This study aims to detect over nutrition, undernutrition in obese sufferers to determine their nutritional status with 24 hours food recall method for assessing their consumption and using data mining algorithms, namely: K-Nearest Neighbor, Naïve Bayesian Classification, and Decision Tree for data mining calculations. From the results obtained with the help of RapidMiner tools, it can be concluded that not all obese sufferers are over nutritioned, they can also experience undernutrition or normal nutrition. From the results of this study, the best accuracy for calculating nutritional status is using the Naïve Bayesian Classification algorithm with an accuracy rate of 100 %, then the Decision Tree with an accuracy rate of 86.67% and the last is K-Nearest Neighbor with an accuracy rate of 73.33%.
Video Quality Assessment: Relationship between objective and subjective methods

In the last years the demand trend of multimedia services has rapidly increased. Hand in hand with that growth goes video quality assessment. Recently many studies deal with exploring the video quality using objective or subjective methods but not many examine the relationship between them. Therefore, the goal of this paper is to compare some of the well-known objective and subjective methods and to find out relationship between them. These results could be used for proposing a novel model which could determine the quality using selected objective metric without need to do subjective tests.
Cooperative object segmentation and recognition via restricted Boltzmann machine

The authors present a model where object segmentation and recognition are connected with a bottom-up inference and a top-down generation pathway so that the two models can communicate and cooperate with each other. They are integrated into an energy function and optimised at the same time. To achieve the cooperation, objects are modelled in two aspects: shape and appearance, so that the recognition result could feedback to the segmentation process. Restricted Boltzmann machine is employed to learn the shapes of the objects with corresponding labels and perform object recognition based on the object shapes. Another pathway involved with the appearance knowledge of the objects is also established so that both the shape and appearance information will guide and constrain the evolution of the segmentation developing towards the region of interest, which will further facilitate the performances of both tasks. Experiments demonstrate the effectiveness of the proposed model.
Towards a formative instrument to evaluate user experience in virtual reality serious games

Regardless the large efforts done in assessing user experience in virtual reality games, few instruments have been proposed to conduct these evaluations on VR serious games. Most proposals focus on evaluating final products or address different subsets of design aspects, providing few or no feedback to designers during the development of a game. In order to help developers assess their prototypes and determine the soundness of their interim designs during the development of VR serious games, this article proposes a formative evaluation instrument based on a literature review. The instrument has been particularly designed to evaluate user experience and provide feedback to designers for improving their proposals when required. The usability and usefulness of this instrument was evaluated with experts in the design of serious games, and the preliminary results are highly positive.
Ontology Based Document Clustering - An Efficient Hybrid Approach

Recent research results show that ontology can be used to improve the accuracy of document clustering. Previous studies mainly focused on the preprocessing part of text document using ontology. In this paper, we propose a hybrid approach, concentrating on both the preprocessing task as well as the clustering algorithm. This is with an objective of reducing the number of features and execution time, eliminate synonymous problems and enhance the accuracy of clustering. Cosine similarity is used as similarity measure. The preprocessing part uses a WordNet Ontology based feature extraction method. In clustering, the initial centroids are found by applying the Red Black Tree based sorting method. The data points are allocated to the suitable clusters using a novel approach, by maintaining the path of similarity between data points and nearest cluster centroids. Experimental results on some of the existing clustering algorithms with cosine similarity are compared with our novel clustering technique. Results show that the proposed hybrid approach executes better on the Newsgroup dataset with considerable improvements in dimensionality reduction, running time, and accuracy.
Performance Optimization for InfiniBand Virtualization on QEMU/KVM

The emergence of machine learning applications has brought the new demands for high performance computing in cloud environment. Besides accelerators, such as GPU or TPU, fast interconnection among and within computers becomes more and more important to achieve efficient training and learning. One of the high-bandwidth, low-latency interconnection architectures is InfiniBand. However, software based virtualization of InfiniBand in QEMU/KVM suffers large performance degradation owing to virtualization overhead and memory allocation problem. In this paper, two techniques, doorbell mapping and memlink, are proposed to optimize the performance the InfiniBand virtualization on QEMU/KVM. Doorbell mapping allows the applications in guest user-space to access the doorbell memory page directly so that the virtualization overhead is minimized. Memlink ensures memory contiguity after virtualization, which is a critical requirement for zero-copy between guest and host. Experiments show that the virtualized InfiniBand with mmap and memlink can achieve near native performance for large data transmissions. Comparing to the previous InfiniBand virtualization on QEMU/KVM, our implementation obtains over 3.5 times performance improvement in various benchmarks.
Combining Photometric Features and Relative Position to Detect and Track Target Person

Tracking a target person is a vital job in human-robot interaction. The robot must always notice a particular person as the interaction target partner. However, it is sometimes very hard to distinguish the target person because there are many other persons around the target. In this paper, we propose a target person detection and tracking system by combining person's frontal photometric features such as face and clothing color, and coordinate of the person's location in the real world. We apply an illumination invariant face recognition method named OptiFuzz. Hue-Saturation histogram (HS-histogram) is used to obtain the clothing color feature, and a location of the person is acquired from a calibrated single camera view. All these features are then fed into an algorithm of Naive Bayes to discriminate between the target person and others. Our experimental results indicate a successful outcome as it is always possible to detect and track the target person.
Low-precision CNN Model Quantization based on Optimal Scaling Factor Estimation

With the development of convolutional neural networks (CNNs), researchers have acquired satisfactory performances on computer vision tasks such as image classification and semantic segmentation. However, CNNs contain millons of weight parameters and demand huge storage and computation resources, making it hard to deploy on constrained hardwares. To compress the model size and accelerate the deployment speed, many model compression methods have been proposed, including quantization methods that aim to reduce network redundancy by decreasing the representational bits for weights (and activations). However, for low-bit quantization, the inevitable quantization error will lead to significant accuracy degradation and gradient mismatch. In this paper, we propose Scale Estimation Quantization (SEQ). To reduce the quantization error, we analyze the variance of error derived by the quantization process. By exploiting the distributions of network values, we reduce quantization error and estimate the optimal scale parameters for our proposed quantization function. Further more, to deal with gradient mismatch problem in backward propagation, we propose backward approximation. We apply our algorithm on image classification tasks. Our method achieves a close performance to full-precision counterparts on VGG-Small and AlexNet with 1-bit weights and 2-bit activations.
GPOL: Gradient and Probabilistic approach for Object Localization to understand the working of CNNs

Convolutional neural networks have been a revolution in the field of Computer Vision and are being extensively used for the purpose of image classification, object detection, generation of captions etc. CNNs are mostly considered black boxes where the internal functioning is not known. The objective of this work is to provide an explanation of the functioning of the the predictions made by the CNN. We propose a new technique for comprehending the functioning of the middle layers of the neural network and the classifier operations. The proposed approach is capable of analyzing multifarious models which are trained for applications such as object detection and recognition. In this work, probabilistic approach and gradient based approach have been used for the purpose of object localization. Geometric mean of heatmaps of both the approaches has been done. In the former approach, the true object's gradient's are made to flow into the last convolutional layer for the purpose of determining the most significant points which would help to predict that particular object. In the probabilistic approach, CNN's top down attention has been used which serves the purpose of generation of attention maps which are task specific. A probabilistic scheme (to select a significant neuron in the network) has been used during backpropagation of signals from top to down in the hierarchy of network. The proposed work has been executed on CLS-LOC dataset which is a part of Imagenet dataset. The proposed work is then compared with the previously developed techniques such as saliency maps, SmoothGrad, GradCam, Top Down Neural approach to exhibit the better accuracy of the proposed work.
HCI and User Experience Design Education

A thorough and robust understanding of responsible and ethical practices in UX is essential to user experience designers. Ethical UX practices include authentic inclusion, vulnerability in participants, the inclusion of IRB-protected groups in research, tolerance for risk and motivation, truth in research, fair and respectful conduct, informed consent, cultural sensitivity, accessibility in UX processes, stigmatized populations, idiosyncratic populations, right to privacy, and an understanding of ADA-compliant accommodations by UX researchers and participants. This chapter will introduce these principles as well as demonstrate one possible application in an undergraduate design classroom.
CFRF: Cloud Forensic Readiness Framework – A Dependable Framework for Forensic Readiness in Cloud Computing Environment

Cloud computing is an important paradigm of information technology that entirely depends upon virtualization technique. Multi-tenancy is one of the most important characteristic and it is applicable in all deployment models. Another significant feature of cloud is its distributed nature which includes jurisdictions of various geographical areas. Due to these two characteristics, it is a complex and tedious task to collect data, proof and evidences in cloud whenever compared with traditional digital forensic. New mechanisms and frameworks are required to apply digital forensic techniques in cloud computing environment. In this research paper, an effort has been made to expound prominent techniques that can be applied to perform digital forensic analysis and investigation in cloud computing environment.
Improving the QoE of OTT Multimedia Services in Wireless Scenarios

One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered, as the Internet has now become the `de facto` path for video transmission, with video data representing over 75% of the entire network traffic, and the users' desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clients' QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrate-adaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.
Matching Digital Copies of Documents Based on OCR

The paper deals with methods for comparison of digitized copies of business documents. This task arises when comparing two copies of documents signed by two parties in order to find possible modifications made by one party. For example, this task is quite actual in the banking sector when concluding contracts in paper form. The comparison method of two digitized images based on the combination of several ways of comparing the words images that are descriptors of text feature points is proposed. The proposed method is well proven on own dataset (Russian). The results show the high quality and reliability of differences search in two samples of the same document.
Sherpa: Leveraging User Attention for Computational Steering in Visual Analytics

We present Sherpa, a computational steering mechanism for progressive visual analytics that automatically prioritizes computations based on the analyst's navigational behavior in the data. The intuition is that navigation in data space is an indication of the analyst's interest in the data. Sherpa implementation provides computational modules, such as statistics of biological inferences about gene regulation. The position of the navigation window on the genomic sequence over time is used to prioritize computations. In a study with genomic and visualization analysts, we found that Sherpa provided comparable accuracy to the offline condition, where computations were completed prior to analysis, with shorter completion times. We also provide a second example on stock market analysis.
CSNN: Contextual Sentiment Neural Network

Although deep neural networks are excellent for text sentiment analysis, their applications in real-world practice are occasionally limited owing to their black-box property. In response, we propose a novel neural network model called contextual sentiment neural network (CSNN) model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable. The CSNN has the following interpretable layers: the word-level original sentiment layer, word-level sentiment shift layer, word-level local contextual sentiment layer, word-level global importance layer, and word-level global contextual sentiment layer. Because of these layers, this network can explain the process of its document-level sentiment analysis results in a human-like way using these layers. Realizing the interpretability of each layer in the CSNN is a crucial problem in the development of this CSNN because the general back-propagation method cannot realize such interpretability. To realize this interpretability, we propose a novel learning strategy called initialization propagation (IP) learning. Using real textual datasets, we experimentally demonstrate that the proposed IP learning is effective for improving the interpretability of each layer in CSNN. We then experimentally demonstrate that both the predictability and explanation ability of the CSNN are high.
ROS Hybrid Behaviour Planner: Behaviour Hierarchies and Self-organisation in the Multi-Agent Programming Contest

While the decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP) has been used in a wide variety of projects, newer features have not yet been tested in complex scenarios. One of those features allows creating multiple independent levels of decision-making by encapsulating a separate behaviour network into behaviours. Another one is an extension for implicit coordination through self-organisation. This paper discusses our system that was developed for the multi-agent contest 2018 using RHBP, while especially making use of newer features wherever possible. Our team TUBDAI achieved the shared top spot in the contest, showing that RHBP and in particular the new features can be used successfully in a complex scenario and measures up to the multi-agent frameworks, other teams have used. Especially, when a last-minute change to the contest environment required us to integrate substantial strategy changes in last-minute, it turned out that RHBP fostered adaptiveness during our development.
Sentiment Analysis of Train Derailment in India: A Case Study from Twitter Data

The services of Indian Railway are availed by many people in the country. It is an important mode of transportation. Most of the users of Indian Railway express their views about it on different social media sites like Twitter, Facebook etc. It leads to generation of large amount of data and sentimental analysis of that data can be very helpful in understanding public opinions towards Indian Railway and in decision making. In this paper, the lexicon based sentimental analysis technique has been applied to the twitter data collected corresponding to three train accidents namely Puri-Haridwar-Kalinga Utkal Express, Delhi-bound Kaifiyat Express and Mumbai-Nagpur Duranto Express which took place on 19/08/2017, 23/08/2017 and 29/08/2017 respectively. Further, tweets are classified into different categories and analyzed in terms of percentage frequency. The results present the pattern how the sentiments of the public fluctuate with time as when derailment happens the negative tweets has high frequency of occurrence but with passage of time frequency of occurrence of neutral tweets become high.
Security Enforcement on Web Services Compositions

Web services (WS) composition is the center of many different types of information systems such as e-commerce, financial and healthcare systems, where sensitive data are shared, which raises important security problems. WSBPEL is a standard to specify business processes, however, this orchestration language does not support users to meet their security requirements. This paper proposes a formal and automatic approach for enforcing security policies in WS composition. More precisely, given a composition of WSs written in WS-BPEL and a security policy specified in the LTL logic, this paper aims to generate a new version of the services that respects the security policy. The LTL policy is transformed to a new service that can monitor some activities of the others.
Quality Estimation for Facial Biometrics

Over past decade, behavioral biometric systems based on face recognition became leading commercial systems that meet the need for fast and efficient confirmation of a person's identity. Facial recognition works on biometric samples, like image or video frames, to recognize people. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. In this chapter, the authors propose a quality estimation method based on a linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The authors evaluated the quality estimation model on the Extended Yale Database B, finally formulating a data set of samples which will enable efficient implementation of biometric facial recognition.
Based on Embedded Zerotree Wavelet Coding Data Compression Algorithm Research of NC Machine Tool Control System

the core of intelligent manufacturing is Cyber-Physical system. In order to establish high frequency sampling of Cyber-Physical system, massive data is needed. It is necessary to study the efficient transmission technology of real-time data. Data compression is through the omitted part is not necessary or redundant, transfer and storage in finite strip width, and storage space for more information of a technical means. Embedded Wavelet coding (Embedded Zero tree Wavelet, EZW) is based on Wavelet transform multi-resolution analysis technology, extract the Embedded Zero tree, and variable length of quantization and coding compression, transmission and decompression algorithm. This paper mainly studies the numerical control machine tool electric control instruction domain representative of the current loop of data in the data for one dimensional data was designed based on the EZW algorithm binary tree structure of zero tree and Huffman encoding to achieve further compression. Verification experiment and application results showed that, the algorithm on the basis of the guarantee accuracy has good compression ratio and efficiency.
Using Semantic Information for Coreference Resolution with Neural Networks in Russian

This paper describes an experiment aimed at improving the quality of coreference resolution for Russian by combining one of the most recent developments in the field, employment of neural networks, with benefits of using semantic information. The task of coreference resolution has been the target of intensive research, and the interest at using neural networks, successfully tested in other tasks of natural language processing, has been gradually growing. The role that semantic information plays for the task of coreference resolution has been recognized by researchers, but the impact of semantic features on the performance of neural networks has not been yet described in detail. Here we describe the process of integrating features derived from open-source semantic information into the coreference resolution model based on a neural network, and evaluate its performance in comparison with the base model. The obtained results demonstrate quality on par with state-of-the-art systems, which serves to re-establish the importance of semantic features in coreference resolution, as well as the applicability of neural networks for the task.