Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Job Scheduling Algorithms on Hadoop Multi-cluster Environment

In recent years, big data applications with scheduling algorithms have evolved lot due to the advancement of new technologies and techniques. We are living in digital data world where the data size is in terms of Exabyte or Pico Byte. This large volume of data is referred as big data. In today's business environment, the performance of applications largely depends on the efficient retrieval of relevant data on time; the data analysis and retrieval of relevant data need to be done at faster rate. The traditional scheduling algorithms will not be efficient to handle such huge volume of data, considering the above facts managing big data applications and scheduling of big data on distributed architecture has become a challenging research area in the last three–four years. To process such huge volume of data, efficient scheduling algorithms need to be adopted to achieve better performance. The existing MapReduce implementation on Hadoop framework on single node cluster limits themselves to implement all the jobs on single node cluster. In this paper, we will discuss different scheduling techniques and their performance effects on a multimode clusters. The parameters considered for performance evaluation are CPU time, physical memory, and virtual memory. The main aim is to provide survey of different scheduling algorithms that can be used across distributed architecture to achieve better performance in analysis of big data considering YouTube dataset. The results interpret that capacity-based scheduling algorithm is more efficient as compared to FIFO and FAIR in terms of CPU cycles, physical and virtual memory utilization.
Enhanced Character Recognition using Deep Neural Network-A Survey

The idea has been popped up suddenly 50 years ago. Neural Computing is a separate science and is very extensive. Sample recognition problems are solved by using Neural Networks. Character Recognition is one among those. The implementation of the Neural Network is easy for obtaining the solution for this problem. Artificial Intelligence uses techniques that gives Computer the capability to learn with data and without being explicitly programmed. The neural network recognizes characters, numbers and some special symbols. The solution is obtained by using MATLAB's Neural Network Toolbox. The accuracy of the output depends on the resolution of the input to the neural network.
"When the Elephant Trumps"

Orientation is an emerging issue in cinematic Virtual Reality (VR), as viewers may fail in locating points of interest. Recent strategies to tackle this research problem have investigated the role of cues, specifically diegetic sound effects. In this paper, we examine the use of sound spatialization for orientation purposes, namely by studying different spatialization conditions ("none", "partial", and "full" spatial manipulation) of multitrack soundtracks. We performed a between-subject mixed-methods study with 36 participants, aided by Cue Control, a tool we developed for dynamic spatial sound editing and data collection/analysis. Based on existing literature on orientation cues in 360º and theories on human listening, we discuss situations in which the spatialization was more effective (namely, "full" spatial manipulation both when using only music and when combining music and diegetic effects), and how this can be used by creators of 360º videos.
Barriers to End-User Designers of Augmented Fabrication

Augmented fabrication is the practice of designing and fabricating an artifact to work with existing objects. Although common both in the wild and as an area for research tools, little is known about how novices approach the task of designing under the constraints of interfacing with real-world objects. In this paper, we report the results of a study of fifteen novice end users in an augmented fabrication design task. We discuss obstacles encountered in four contexts: capturing information about physical objects, transferring information to 3D~modeling software, digitally modeling a new object, and evaluating whether the new object will work when fabricated. Based on our findings, we suggest how future tools can better support augmented fabrication in each of these contexts.
Blind Image Quality Prediction for Object Detection

Automatic video data analysis tools have become indispensable components in today's imaging applications. The accuracy of automatic analysis methods relies on the quality of images or videos that are processed. It is therefore essential to introduce objective metrics for predicting the quality of images as evaluated by automatic analysis algorithms. Object detection is the first and the most important step in the process of automatic video analysis. This paper proposes a new image quality model for predicting the performance of object detection. A video data set is constructed that considers different factors related to quality degradation in the imaging process, such as reduced image resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures such as PSNR and SSIM.
Blind quality assessment metric and degradation classification for degraded document images

Epochal documents suffer from several types of noises that accumulate and evolve over time. This significantly affects their quality and makes their storage and the interpretation of their visual content problematic. Digital preservation seems the most viable and the most promising. Moreover, measuring the amount of degradation and quality assessment of degraded documents is highly desirable for applications such as selecting the proper algorithms for enhancement and analysis of document images, filtering the damaged images, tuning the processing algorithms parameters, document repairing, psychological study, etc. The first contribution of this work is the proposition of an efficient Multi-distortion Document Quality Measure (MDQM) for quality assessment of physically degraded document images. The proposed MDQM metric is based on three sets of spatial and frequency image features. These features are extracted from two layers of text and non-text and mapped to the mean opinion scores (MOS) using the regression function. The second contribution of this work is to estimate the probability of four common document image distortion types, namely, paper translucency, stain, readers annotations and worn holes in the degraded images. In our experiment, the correlations of seven no-reference image quality assessment (NR-IQA) metrics with the MOS values are evaluated on two available datasets. It is shown that the performance of MDQM metric is significantly better than the state-of-the-art NR-IQA metrics. Moreover, the experimental results demonstrate that MDQM metric not only leads to high efficacy for classification of the various degradations but also maintains a remarkable run-time efficiency. It is worth to mention that the proposed method has been conducted for Arabic documents.
A Wee Bit More Interaction

Overactive Bladder (OAB) is a widespread condition, affecting 20% of the population. Even though it is a treatable condition, people often do not seek treatment. In this paper, we describe how we co-designed and evaluated with 30 stakeholders (9 medical professionals and 21 end-users) an OAB mobile health application that aims to increase adherence to self-managed treatment. Our results support previous research that visualizing progress, setting goals, receiving reminders and feedback increases use. We discovered that games could be used successfully as a distraction technique for urge suppression. Contrary to the current research direction, automatically calculated features could be a detriment to app interaction. Regarding evaluation, we found that designers may not want to rely only on questionnaires when assessing the success of a game and its emotional impact on users.
AGREE: Attention-Based Tour Group Recommendation with Multi-modal Data

Tour recommendation aims to design a sequence of Points of Interest (POIs) for a tourist that suits his/her preference. Most existing tour recommenders mainly focus on recommending a POI sequence to a single tourist but cannot be applied to the tour group, which is a common way to travel. Designing a tour group recommender is more challenging in aggregating group preference and tracking influence changes during a tour. Hence we propose a novel approach named AGREE (Attention-based Tour Group Recommendation), which leverages the attention mechanism, to adjust members' influence dynamically. Specifically, our model aggregates group's preference based on members' history data in different modalities, utilizing attention sub-networks to focus on influential ones in each modality across a POI sequence. Then we adopt a bi-directional recurrent unit (Bi-GRU) to generate the POI sequence. Experimental results show that the proposed scheme outperforms benchmark methods on a real-world dataset.
Experience with telepathology in combination with diagnostic assistance systems in countries with restricted resources

Introduction We describe the use of telepathology in countries with restricted resources using two diagnosis assistance systems (Isabel and Memem7) in addition to the diagnoses made by experts in pathology via the iPath-Network. Methods A total of 156 cases, largely from Afghanistan, were analysed; 18 cases had to be excluded because of poor image quality. Results Of the remaining 138 cases (100%), a responsible physician provided a tentative diagnosis for 61.6% of them. With a diagnosis from a consultant pathologist, it was then possible to make a definite diagnosis in 84.8% of cases on the basis of images taken from hematoxylin and eosin staining sections alone. The use of the diagnosis assistance systems resulted in an ordered list of differential diagnoses in 82.6% (IsabelHealth) and in 74.6% (Memem7) of cases, respectively. Adding morphological terminology reduced the list of possible diagnoses to 52.2% (72 cases, Memem7), but improved their quality. Discussion In summary, diagnosis assistance systems are promising approaches to provide physicians in countries with restricted resources with lists of probable differential diagnoses, thus increasing the plausibility of the diagnosis of the consultant pathologist.
Characteristics and detectability of Windows auto-start extensibility points in memory forensics

Computer forensics is performed during a security incident response process on disk devices or on the memory of the compromised system. The latter case, known as memory forensics, consists in dumping the memory to a file and analyzing it with the appropriate tools. Many security incidents are caused by malware that targets and persists as long as possible in a Windows system within an organization. The persistence is achieved using Auto-Start Extensibility Points (ASEPs), the subset of OS and application extensibility points that allow a program to auto-start without any explicit user invocation. In this paper, we propose a taxonomy of the Windows ASEPs, considering the features that are used or abused by malware to achieve persistence. This taxonomy splits into four categories: system persistence mechanisms, program loader abuse, application abuse, and system behavior abuse. We detail the characteristics of each extensibility point (namely, write permissions, execution privileges, detectability in memory forensics, freshness of system requirements, and execution and configuration scopes). Many of these ASEPs rely on the Windows Registry. We also introduce the tool Winesap, a Volatility plugin that analyzes the registry-based Windows ASEPs in a memory dump. Furthermore, we state the order of execution of some of these registry-based extensibility points and evaluate the effectiveness of our tool in memory dumps taken from a Windows OS where extensibility points were used. Winesap was successful in marking all the registry-based Windows ASEPs as suspicious registry keys.
Exploiting Side Information for Recommendation

Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.
Bookly

We introduce Bookly, an interactive artifact that physically represents the accumulated time of users' reading activity through abstract volumetric changes. Bookly accumulates the time of actions (e.g., picking up and putting down books) that users performed for reading and provides a designated space for the ongoing book being read. The results of our 2-week in-field study with six participants showed that continuous exposure to volumetric changes representing the accumulated time of reading activities helped the users to understand their unsettled reading patterns. Bookly also motivated the users to improve their reading behavior by gradually making reading part of their schedules. Additionally, the definite distinction of the ongoing book improved its visual affordance and accessibility for the users to start reading books. Based on the findings, we confirmed the possibility of making intangible data physical for self-reflection to enhance changes in behaviors that are difficult to perform due to weak motivation.
Vision based Vehicle/Pedestrian Detection in Traffic Surveillance System

Vehicle detection and counting plays a major role in Intelligent Transportation Systems, which continuously provides the traffic information. This detection process may face many challenges like different climatic conditions and illumination changes. Detection of Pedestrians is a main concern of car manufacturers to have an automated system which must be able to detect the pedestrian in the surrounding of vehicles. There is a performance degradation with increase in occlusion level. This paper presents the Vehicle and Pedestrian Detection by Haar Cascade Classifier and Background Separation method respectively. Haar Cascade classifier is an efficient method for object detection, first proposed by Viola-Jones. Background Separation method uses K-NN algorithm to identify the pedestrians.
Guessing probability in quantum key distribution

Abstract On the basis of the existing trace distance result, we present a simple and efficient method to tighten the upper bound of the guessing probability. The guessing probability of the final key k can be upper bounded by the guessing probability of another key $${\bf{k}}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> , if $${\bf{k}}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> can be mapped from the final key k . Compared with the known methods, our result is more tightened by thousands of orders of magnitude. For example, given a 10 −9 -secure key from the sifted key, the upper bound of the guessing probability obtained using our method is 2 × 10 −3277 . This value is smaller than the existing result 10 −9 by more than 3000 orders of magnitude. Our result shows that from the perspective of guessing probability, the performance of the existing trace distance security is actually much better than what was assumed in the past.
Pedestrian Detection Using Pixel Difference Matrix Projection

Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance when a small-medium scale of training data with low-resolution images is available. To address this issue, a difference matrix projection (DMP) is developed to compute aggregated multi-oriented pixel differences using global matrix operations. Both the pixel differences and aggregation are computed using global matrix projection to avoid the laborious iterative operations. We tested our method on the INRIA, Daimler Chrysler classification (Daimler-CB), NICTA, and Caltech Pedestrian datasets. The experiments on these benchmark data sets show encouraging results in terms of detection performance, particularly for image datasets with low-resolution pedestrians.
A New Method of Clothoid-Based Path Planning Algorithm for Narrow Perpendicular Parking Spaces

Autonomous driving and its practical applications for mobile robotics (e.g. automotive industry) are one of the most futuristic plans of engineering industry which will change the way of using vehicles. Having a fully autonomous vehicle on public roads is not something for the short future. To bring the autonomous driving technology to real life, the very first step would be bringing the applications of autonomous driving gradually in people's routine driving style. An automated parking system for autonomous vehicles is one of these examples in real life applications. Planning an optimum path for all types of parking scenarios under a reasonable maximum running time is an important task to have a deterministic trajectory planner. Here we worked on some of the common and critical parking scenarios for narrow perpendicular parking use cases in which the autonomous vehicle needs to handle the collision-free parking maneuver with a limited number of maneuvers to park in a tight parking space. The current planner algorithms plan several unnecessary maneuvers for narrow parking slots. Our proposed method is using Clothoid curves to plan a three-goals maneuver to park the car perfectly and avoid unnecessary maneuvers. We have implemented this method for several parking scenarios and could find a three goals collision free maneuver to park for all use cases with a reasonable average performing time (~700 ms).
Overgrown

Ambient notifications are an essential element to support users in their daily activities. Designing effective and aesthetic notifications that balance the alert level while maintaining an unobtrusive dialog, require them to be seamlessly integrated into the user's environment. In an attempt to employ the living environment around us, we designed Overgrown, an actuated robotic structure capable of supporting a plant to grow over itself. As a plant endoskeleton, Overgrown aims to engage human empathy towards living creatures to increase effectiveness of ambient notifications while ensuring better integration with the environment. In a focus group, Overgrown was identified with having personality, showed potential as a user's ambient avatar, and was suited for social experiments.
A Modular Framework for Motion Planning Using Safe-by-Design Motion Primitives

In this paper, we present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low-level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multirobot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters.
Understanding the Occupational Therapists Method to Inform the Design of Technologies for People with Dementia

This paper describes a PhD research project involving people with dementia and practitioners who work primarily with people with dementia to support engagement in meaningful activities and activities of everyday living. The aim of this work is to develop a technology which adapts to changing cognitive demands of people with dementia in order to facilitate continuous engagement in meaningful activities. In depth, semi-structured interviews were conducted with practitioners to understand their methods for personalization of activities and the implications for design of future adaptive technologies. Preliminary results from interviews with Occupational Therapists are presented.
Depth-based Obstacle Avoidance through Deep Reinforcement Learning

Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. In this paper, we propose an end-to-end deep neural network to derive control commands directly from the raw depth images using deep reinforcement learning. The convolutional neural networks are used to extract the feature representation from the input depth images and the fully connected neural networks subsequently map the features to Q-values for determination of the optimal action. To improve the performance of the network, we adopt a two-stage method so that noisy fully connected layers are employed at the beginning while conventional ones are utilized during the second stage of training. Compared to the existing method, our proposed model exhibits much better performance in avoiding obstacles and converges faster during training.