Reviewerly/exHarmony huggingface dataset:
QoE Analysis of Spotify Audio Streaming and App Browsing

Spotify is the most-listened audio streaming provider in 2019 with 217 million active users per month. Providers are therefore interested in the quality and functionality of Spotify in order to provide their users with the best possible streaming quality. While video streaming services such as Netflix and their streaming approach have been extensively explored in previous research, audio streaming services like Spotify and their corresponding behavior at certain network conditions have not been considered in detail yet. In this paper, we perform a QoE analysis under various network conditions and examine the app browsing performance of the audio streaming platform Spotify using its native Android mobile application. We have developed a measurement tool that emulates a user listening to audio through Spotify. While streaming, application and network layer parameters are captured that have a high correlation to the user's QoE. The paper shows a baseline scenario including the streaming of a single song as well as playlist streaming behavior. Next, the effect of interruptions on the streaming behavior is evaluated and finally, the influence of network impairments on QoE key performance indicators such as initial delay is shown.
Dialogue based recommender system that flexibly mixes utterances and recommendations

Much of the prior research in the recommendations through dialogue separate dialogue and recommendations. However, since the accuracy of the recommendations themselves is not necessarily high, recommendation results rarely meet user needs. However, as human we can find the solutions that satisfy users by appropriately repeating the cycle of checking mismatched reasons and making another recommendations in our conversations. In this paper, we propose a system for leveraging a dialogue strategy for reinforcement learning using recommendation results based on user utterances. We constructed a dialogue system to perform adaptive behavior that naturally incorporates recommendations into conversation with users.
Leveraging Blockchain for Open Mobility-as-a-Service Ecosystems

The mobility landscape has changed over the last years with the advent of innovative transportation solutions which are being constantly deployed whereas more are set to emerge in the near future such as autonomous vehicles and flying taxis. In this landscape a new mobility paradigm has come to the forefront during the last years through Mobility as a Service (MaaS). This refers to a seamless integration of various forms of transport services accessible through one single digital platform. The concept of MaaS is aligned with Smart mobility, one of main Smart City components which refers to smart manners of people and goods mobility through the integration of all transport modes, ICT solutions, zero pollution and congestions and so on [1]. A key enabler for MaaS is technology that can provide the means for complex mobility ecosystems to be integrated, while allowing diverse service providers and related stakeholders (e.g. payment management organizations) to perform transactions between each other (for booking, invoicing, payment, etc.) as well as interact with the traveler, acquire and exchange operational data, and process it into information that can be used to support MaaS related business processes. Such an ecosystem can be centrally coordinated when all participants in the value chain have the same understanding of what needs to be delivered and accept to follow a common approach, which can be proprietary, customized and aligned to vendor specific guidelines. As the MaaS ecosystem expands and the coordination of an increasing number of mobility solutions comes into play, the complexity can be such that new decentralized solutions are required. In this work in progress paper we discuss the role of blockchain technology for enabling seamless mobility for MaaS.
On-chip implementation of the probabilistic quantum optical state comparison amplifier

Propagation losses in transmission media limit the transmission distance of optical signals.In the case where the signal is made up of quantum optical states, conventional deterministic optical amplification schemes cannot be used to increase the transmission distance as the copying of an arbitrary and unknown quantum state is forbidden.One strategy that can offset propagation loss is the use of probabilistic, or non-deterministic, amplification schemes -an example of which is the state comparison amplifier.Here we report a state comparison amplifier implemented in a compact, fiber-coupled femtosecond laser-written waveguide chip as opposed to the large, bulk-optical components of previous designs.This pathfinder on-chip implementation of the quantum amplifier has resulted in several performance improvements: the polarization integrity of the written waveguides has resulted in improved visibility of the amplifier interferometers; the potential of substantially-reduced losses throughout the amplifier configuration; and a more compact and environmentally-stable amplifier which is scalable to more complex networks.
Activity Recognition from Mobile Phone using Deep CNN

Achieving better performance has always been an important research target in the field of human activity recognition (HAR) based on mobile phone. The traditional activity recognition method mainly relies on artificial feature extraction, but the artificially selected features are not always effective, which affects the improvement of recognition accuracy. This paper mainly introduces a deep convolutional neural networks (CNN) model for human activity recognition, which can effectively improve the accuracy of human activity recognition. First of all, we manually collected 128-dimensional time domain sequence features from the accelerometer and gyroscope sensor data of the smartphone, and then we use a time domain to space domain transformation algorithm, namely Gramian Angular Fields transform algorithm, to convert these time domain signals into a 128×128 spatial signal of the image, which can take full advantage of the very effective deep learning model in the field of computer vision. Thus we can utilize the powerful feature representation capabilities of deep CNN, and then we construct an 8-layer convolutional neural network model for human activity recognition. Experimental results on UCI HAR dataset confirm the effectiveness of our method, the recognition accuracy are satisfactory and competitive compared with traditional and state of the art methods.
HaRD: a heterogeneity-aware replica deletion for HDFS

The Hadoop distributed file system (HDFS) is responsible for storing very large data-sets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients with high throughput. Data replication is a trade-off between better data availability and higher disk usage. Recent studies propose different data replication management frameworks that alter the replication factor of files dynamically in response to the popularity of the data, keeping more replicas for in-demand data to enhance the overall performance of the system. When data gets less popular, these schemes reduce the replication factor, which changes the data distribution and leads to unbalanced data distribution. Such an unbalanced data distribution causes hot spots, low data locality and excessive network usage in the cluster. In this work, we first confirm that reducing the replication factor causes unbalanced data distribution when using Hadoop's default replica deletion scheme. Then, we show that even keeping a balanced data distribution using WBRD (data-distribution-aware replica deletion scheme) that we proposed in previous work performs sub-optimally on heterogeneous clusters. In order to overcome this issue, we propose a heterogeneity-aware replica deletion scheme (HaRD). HaRD considers the nodes' processing capabilities when deleting replicas; hence it stores more replicas on the more powerful nodes. We implemented HaRD on top of HDFS and conducted a performance evaluation on a 23-node dedicated heterogeneous cluster. Our results show that HaRD reduced execution time by up to 60%, and 17% when compared to Hadoop and WBRD, respectively.
A decade of Semantic Web research through the lenses of a mixed methods approach

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.
An augmented estimation procedure for EHR-based association studies accounting for differential misclassification

Abstract Objectives The ability to identify novel risk factors for health outcomes is a key strength of electronic health record (EHR)-based research. However, the validity of such studies is limited by error in EHR-derived phenotypes. The objective of this study was to develop a novel procedure for reducing bias in estimated associations between risk factors and phenotypes in EHR data. Materials and Methods The proposed method combines the strengths of a gold-standard phenotype obtained through manual chart review for a small validation set of patients and an automatically-derived phenotype that is available for all patients but is potentially error-prone (hereafter referred to as the algorithm-derived phenotype). An augmented estimator of associations is obtained by optimally combining these 2 phenotypes. We conducted simulation studies to evaluate the performance of the augmented estimator and conducted an analysis of risk factors for second breast cancer events using data on a cohort from Kaiser Permanente Washington. Results The proposed method was shown to reduce bias relative to an estimator using only the algorithm-derived phenotype and reduce variance compared to an estimator using only the validation data. Discussion Our simulation studies and real data application demonstrate that, compared to the estimator using validation data only, the augmented estimator has lower variance (ie, higher statistical efficiency). Compared to the estimator using error-prone EHR-derived phenotypes, the augmented estimator has smaller bias. Conclusions The proposed estimator can effectively combine an error-prone phenotype with gold-standard data from a limited chart review in order to improve analyses of risk factors using EHR data.
A Mask RCNN based Automatic Reading Method for Pointer Meter

Automatic reading of pointer instrument is of great significance to realize intelligent monitoring of substation. Many scholars have proposed kinds of schemes based on the traditional image processing methods. However, the environmentally sensitive issues have not been effectively solved, which prevents the promotion of automatic reading under complex environmental conditions. In recent years, convolutional neural networks (CNN) have been proved to be suitable for image recognition tasks. The results of image object detection by deep learning methods are greatly improved compared with the conventional methods. Based on the premise that deep learning methodology is developing rapidly, this paper proposes an automatic reading method for pointer meter with keypoint detection. The main idea is to first apply improved Mask R-CNN to detect the key points of the scale and pointer on the meter, then utilize the detected key points to fit the circle composed of the tick marks and the straight line of the pointer, and finally calculate the reading value based on the deflection angle of the pointer relative to the scales. The experimental results demonstrate that the proposed method is better than the traditional Hough Transform based algorithms in terms of accuracy and robustness.
A Framework for Evaluating Snippet Generation for Dataset Search

Reusing existing datasets is of considerable significance to researchers and developers. Dataset search engines help a user find relevant datasets for reuse. They can present a snippet for each retrieved dataset to explain its relevance to the user's data needs. This emerging problem of snippet generation for dataset search has not received much research attention. To provide a basis for future research, we introduce a framework for quantitatively evaluating the quality of a dataset snippet. The proposed metrics assess the extent to which a snippet matches the query intent and covers the main content of the dataset. To establish a baseline, we adapt four state-of-the-art methods from related fields to our problem, and perform an empirical evaluation based on real-world datasets and queries. We also conduct a user study to verify our findings. The results demonstrate the effectiveness of our evaluation framework, and suggest directions for future research.
Lifelong Mapping using Adaptive Local Maps

Occupancy mapping enables a mobile robot to make intelligent planning decisions to accomplish its tasks. Adaptive local maps is an algorithm which represents the occupancy information as a set of overlapping local maps anchored to poses in the robot's trajectory. At any time, a global occupancy map can be rendered from the local maps to be used for path planning. The advantage of this approach is that the occupancy information stays consistent despite the changes in the pose estimates resulting from loop closures and localization updates. The disadvantage, however, is that the number of local maps grows over time. For long robot runs, or for multiple runs in the same space, this growth will result in redundant occupancy information, which will in turn increase the time it takes to render the global map, as well as the memory footprint of the system. In this paper, we propose a novel approach for the maintenance of an adaptive local maps system, which intelligently prunes redundant local maps, ensuring the robustness and stability required for lifelong mapping.
Salient Object Detection: Integrate Salient Features in the Deep Learning Framework

Salient object detection in complex environments brings the challenge from the collections of large number of training images for deep learning algorithm. It is difficult to collect the enough number of training data for varied salient objects in different scenes, and furthermore the salient objects are usually compared with the background. This paper proposes a novel method to integrate the salient features into the deep learning framework, and design a parallel multi-scale structure of the neural network to enhance the ability to detect salient objects. In addition, a multiple stage method is proposed to optimize the training process and make the datasets more prominent in the commonality of the salient features, which effectively reduces the difficulty of correctly distinguishing salient objects in complex environments. Experiments show that the proposed approach enhances the ability of neural networks to learn specified features and improves the detection effect of salient objects in complex scenes.
A holistic review on gravitational search algorithm and its hybridization with other optimization algorithms

A Gravitational search algorithm is a physics-based heuristic algorithm inspired by Newton's gravity law. GSA is good at finding the global optimum but has the drawbacks of slow convergence speed and getting stuck in local minima in last iterations. To overcome these problems, the GSA is hybridized with other swarm based optimization algorithms and it results in the increase in searching capability, problem-solving and application domains of the gravitational search algorithm. The GSA has been used to solve various optimization problems in different application areas such as clustering, classification, feature subset selection, load power dispatch, routing, etc. and it shows better performance than other swarm intelligence algorithms. This paper gives information about the GSA and its hybridization with other meta-heuristic algorithms.
An intelligent healthcare system with data priority based on multi vital biosignals

Background and Objective: Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction. Methods: The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients’ urgency. Results: Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow. Conclusion: The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.
Application of augmented reality to surgical practice: A pilot study using the ODG R7 Smartglasses

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Hardware Efficient Integer Discrete Cosine Transform for Efficient Image/Video Compression

With the introduction of high efficiency video coding (HEVC) standard which provides super compression efficiency, there has been a lot of research works on integer transform matrices that can provide good approximation to the discrete cosine transform (DCT) used in HEVC.Not only maintaining the coding performance, the hardware and power of the circuit to implement the derived integer DCT (Int-DCT) needs to be minimized.To address these multiple design considerations, a new multi-objective optimization algorithm is proposed in this paper to search for efficient Int-DCT matrix, which has the coding performance as close as possible to the transform in HEVC but implemented with reduced hardware and power.Experimental results show that the approximated Int-DCT matrix generated by the proposed algorithm can achieve almost the same coding performance as the transforms in HEVC measured in terms of BjØntegaard Delta rate.Meanwhile, the experiments demonstrate that the proposed 16-point Int-DCT can produce at least 15.5% and 26.8% lower circuit area in FPGA and ASIC respectively, compared with other state-of-the-art Int-DCT realizations which can provide similar coding performance.
Evaluating data-flow coverage in spectrum-based fault localization

Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data- and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data- and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Data-flow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL.
QaldGen: Towards Microbenchmarking of Question Answering Systems over Knowledge Graphs

Over the last years, a number of Knowledge Graph (KG) based Question Answering (QA) systems have been developed. Consequently, the series of Question Answering Over Linked Data (QALD1–QALD9) challenges and other datasets have been proposed to evaluate these systems. However, the QA datasets contain a fixed number of natural language questions and do not allow users to select micro benchmarking samples of the questions tailored towards specific use-cases. We propose QaldGen, a framework for microbenchmarking of QA systems over KGs which is able to select customised question samples from existing QA datasets. The framework is flexible enough to select question samples of varying sizes and according to the user-defined criteria on the most important features to be considered for QA benchmarking. This is achieved using different clustering algorithms. We compare state-of-the-art QA systems over knowledge graphs by using different QA benchmarking samples. The observed results show that specialised micro-benchmarking is important to pinpoint the limitations of the various QA systems and its components. Resource Type: Evaluation benchmarks or Methods Repository: https://github.com/dice-group/qald-generator License: GNU General Public License v3.0