Reviewerly/exHarmony huggingface dataset:
An evaluation of time series summary statistics as features for clinical prediction tasks

Clinical prediction tasks such as patient mortality, length of hospital stay, and disease diagnosis are highly important in critical care research. The existing studies for clinical prediction mainly used simple summary statistics to summarize information from physiological time series. However, this lack of statistics leads to a lack of information. In addition, using only maximum and minimum statistics to indicate patient features fails to provide an adequate explanation. Few studies have evaluated which summary statistics best represent physiological time series.In this paper, we summarize 14 statistics describing the characteristics of physiological time series, including the central tendency, dispersion tendency, and distribution shape. Then, we evaluate the use of summary statistics of physiological time series as features for three clinical prediction tasks. To find the combinations of statistics that yield the best performances under different tasks, we use a cross-validation-based genetic algorithm to approximate the optimal statistical combination.By experiments using the EHRs of 6,927 patients, we obtained prediction results based on both single statistics and commonly used combinations of statistics under three clinical prediction tasks. Based on the results of an embedded cross-validation genetic algorithm, we obtained 25 optimal sets of statistical combinations and then tested their prediction results. By comparing the performances of prediction with single statistics and commonly used combinations of statistics with quantitative analyses of the optimal statistical combinations, we found that some statistics play central roles in patient representation and different prediction tasks have certain commonalities.Through an in-depth analysis of the results, we found many practical reference points that can provide guidance for subsequent related research. Statistics that indicate dispersion tendency, such as min, max, and range, are more suitable for length of stay prediction tasks, and they also provide information for short-term mortality prediction. Mean and quantiles that reflect the central tendency of physiological time series are more suitable for mortality and disease prediction. Skewness and kurtosis perform poorly when used separately for prediction but can be used as supplementary statistics to improve the overall prediction effect.
Risk management in the software life cycle: A systematic literature review

Risk management (RM) plays a key role in project management, as it allows identification and prompt management of threats that may arise during project execution. Furthermore, project management within the software industry is evolving rapidly nowadays, a fact that implies new challenges, because the emergence and use of fresh approaches has brought a greater degree of complexity to the RM process. The objective of this paper is to carry out a systematic literature review (SLR) in the field of software risk, in an attempt to characterize and present the state of the art of this field, identifying gaps and opportunities for further research. From the analysis of the results of this SLR it could be observed that interest on the part of the scientific community has turned away from the definition of research work that addressed an integrated risk management process, to pay attention to work that concentrates on specific activities of this process. It was also possible to see that there is a clear lack of scientific rigour as regards the process of validation in the different studies, and a deficiency in the use of standards or of de facto models to define these.
High-density Multi-tenant Bare-metal Cloud

Virtualization is the cornerstone of the infrastructure-as-a-service (IaaS) cloud, where VMs from multiple tenants share a single physical server. This increases the utilization of data-center servers, allowing cloud providers to provide cost-efficient services. However, the multi-tenant nature of this service leads to serious security concerns, especially in regard to side-channel attacks. In addition, virtualization incurs non-negligible overhead in the performance of CPU, memory, and I/O. To this end, the bare-metal cloud has become an emerging type of service in the public clouds, where a cloud user can rent dedicated physical servers. The bare-metal cloud provides users with strong isolation, full and direct access to the hardware, and more predicable performance. However, the existing single-tenant bare-metal service has poor scalability, low cost efficiency, and weak adaptability because it can only lease entire physical servers to users and have no control over user programs after the server is leased. In this paper, we propose the design of a new high-density multi-tenant bare-metal cloud called BM-Hive. In BM-Hive, each bare-metal guest runs on its own compute board, a PCIe extension board with the dedicated CPU and memory modules. Moreover, BM-Hive features a hardware-software hybrid virtio I/O system that enables the guest to directly access the cloud network and storage services. BM-Hive can significantly improve the cost efficiency of the bare-metal service by hosting up to 16 bare-metal guests in a single physical server. In addition, BM-Hive strictly isolates the bare-metal guests at the hardware level for better security and isolation. We have deployed BM-Hive in one of the largest public cloud infrastructures. It currently serves tens of thousands of users at the same time. Our evaluation of BM-Hive demonstrates its strong performance over VMs.
A Systematic Study to Improve the Requirements Engineering Process in the Domain of Global Software Development

The software organizations are outsourcing their development activities across the geographical border due to huge business gains.However, the adoption of the global software development (GSD) paradigm is not straightforward; various challenges are associated with it, particularly related to the requirements engineering (RE) process.The objective of this study is to identify the barriers to the RE process faced during GSD.To achieve this, we have conducted a systematic mapping study and questionnaire survey to identify and validate the barriers of the RE process with industry practitioners.A total of 20 barriers were identified and validated with the experts.Moreover, we have performed organization types (client and vendor), organization size (small, medium, and large) and experts' levels (junior, intermediate, and senior) based analysis to provide a clear understanding of the RE barriers in the three different context.Besides, we have also developed a theoretical framework by mapping the investigated barriers into six core knowledge areas of software process improvement.The mapping results indicated that project administration is the most significant knowledge area of investigated barriers.We believe that the findings of this study will provide a framework that assists the GSD practitioners in developing an effective plan and strategies to improve the RE process in the GSD context.
Performance analysis of quantum channels

We study the quality of service in quantum channels. We regard the quantum channel as a queueing system, and present queueing analysis of both the classical information transmission and quantum information transmission in the quantum channel. For the former, we link the analysis to the classical queueing model, and for the latter, we propose a new queueing model and investigate the limit queueing behavior. For both scenarios, we obtain tail distributions of the performance measures, that is, backlog, delay, and throughput.
ArchiText: Interactive Hierarchical Topic Modeling

Human-in-the-loop topic modeling allows users to explore and steer the process to produce better quality topics that align with their needs. When integrated into visual analytic systems, many existing automated topic modeling algorithms are given interactive parameters to allow users to tune or adjust them. However, this has limitations when the algorithms cannot be easily adapted to changes, and it is difficult to realize interactivity closely supported by underlying algorithms. Instead, we emphasize the concept of tight integration, which advocates for the need to co-develop interactive algorithms and interactive visual analytic systems in parallel to allow flexibility and scalability. In this article, we describe design goals for efficiently and effectively executing the concept of tight integration among computation, visualization, and interaction for hierarchical topic modeling of text data. We propose computational base operations for interactive tasks to achieve the design goals. To instantiate our concept, we present ArchiText, a prototype system for interactive hierarchical topic modeling, which offers fast, flexible, and algorithmically valid analysis via tight integration. Utilizing interactive hierarchical topic modeling, our technique lets users generate, explore, and flexibly steer hierarchical topics to discover more informed topics and their document memberships.
Recognition of Indian Sign Language Using ORB with Bag of Visual Words by Kinect Sensor

Recognition of Indian Sign Language (ISL) could bridge the gap between deaf-mute people and society. Hand recognition is a key requirement for ISL recognition system. In this paper, the hand region is segmented from the depth image using the Microsoft Kinect Sensor in the cluttered environment. The depth image obtained is then used to implement supervised machine learning by extracting and training the features of images. Here, by comparing various methods, it is depicted that ORB (Oriented FAST and Rotated BRIEF) outruns others in terms of accuracy. ORB is invariant to scale, rotation, and lighting conditions. ORB is also fused with various classification techniques to gain the optimum result. The method is applied to images of ISL 0–9 and is also compared with some standard datasets. Tuning of the ORB with k-NN classification produces an average recognition accuracy of 93.26% with ISL dataset.
Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

The number of web applications for both personal and business use will continue to increase. The popularity of web applications has grown, increasing the need to estimate Quality of Experience for web applications (Web QoE). Web QoE helps providers to understand how their end-users perceive quality and point towards areas to improve. Waiting time has been proven to have a significant influence on user satisfaction. Most studies in the field of Web QoE have focused on modelling Web QoE for the user's first interaction with the application, e.g., the waiting time for the first page load to complete. This does not include a user's subsequent interactions with the application. Users keep interacting with the application beyond the first page load resulting in an experience that consists of a series of waiting times. In this study, we have chosen web maps as a use case to investigate how to measure waiting time for a user's interactions across a web browsing session, and to measure the correlation between waiting time and user-reported perceived quality. We provide a short survey of existing Web QoE estimation metrics and models. We then propose two new measures: interactive Load Time (iLT) and Total Completed interactive Load (TCiL) to establish the waiting time associated with a web application user's interactions. A subjective study confirms a logarithmic relationship for interactive web application sessions between iLT and perceived quality. We compare the correlation between QoE for iLT and the state of the art, non-interactive equivalent, Page Load Time (PLT)/Waiting Time. We demonstrate how the iLT/QoE fitting curve deviates from PLT/QoE. The number of clicks in completing tasks and TCiL are explored to explain the connections between user's interactions behaviour and the perceived quality.
Lightweight and Fault-Resilient Implementations of Binary Ring-LWE for IoT Devices

While the Internet of Things (IoT) shapes the future of the Internet, communications among nodes must be secured by employing cryptographic schemes such as public-key encryption (PKE). However, classic PKE schemes, such as RSA and elliptic curve cryptography (ECC) suffer from both high complexity and vulnerability to quantum attacks. During the past decade, post-quantum schemes based on the learning with errors (LWEs) problem have gained high attention due to the lower complexity among PKE schemes. In addition to resistance against theoretical (quantum and classic) attacks, every practical implementation of any cryptosystem must also be evaluated against different side-channel attacks such as power analysis or fault injection ones. In this article, we analyze the vulnerability of binary ring learning with error (Ring-LWE) scheme regarding (first-order) fault attacks, such as randomization, zeroing, and skipping faults. We show that previous implementations can be easily broken by employing such fault attacks. Moreover, we propose fault-resilient software implementations of binary Ring-LWE on 8- and 32-b lightweight microcontrollers, namely, AVR ATxmega128A1 and ARM Cortex-M0 that are ideal for IoT devices. Furthermore, we formally prove the resilience of the proposed implementations against different fault attacks. To the best of our knowledge, this article is the first one to propose fault-resilient binary Ring-LWE implementations on resource-constrained microcontrollers. Our implementations on AVR ATxmega128A1 require only 80 and 120 ms for encryption and decryption, respectively.
DeepTrailerAssist: Deep Learning Based Trailer Detection, Tracking and Articulation Angle Estimation on Automotive Rear-View Camera

Trailers are commonly used for transport of goods and recreational materials. Even for experienced drivers, manoeuvres with trailers, especially reversing can be complex and stressful. Thus driver assistance systems are very useful in these scenarios. They are typically achieved by a single rear-view fisheye camera perception algorithms. There is no public dataset for this problem and hence there is very little academic literature on this topic. This motivated us to present all the trailer assist use cases in detail and propose a deep learning based solution for trailer perception problems. Using our proprietary dataset comprising of 11 different trailer types, we achieve a reasonable detection accuracy using a lightweight real-time network running at 30 fps on a low power embedded system. The dataset will be released as a companion to our recently published dataset [24] to encourage further research in this area.
Q‐learning for noise covariance adaptation in extended KALMAN filter

Abstract The extended Kalman filter (EKF) is a widely used method in navigation applications. The EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice. This paper attempts to suppress the unfavorable effect of the noise covariance uncertainty to the EKF in the framework of reinforcement learning. The proposed state estimation algorithm combines the EKF and a Q‐learning method, where a covariance adaptation strategy is designed based on the Q‐values, leading to a gradual improvement in the estimation performance. The resultant algorithm is called the Q‐learning extended Kalman filter (QLEKF), which is less sensitive to the noise covariance uncertainty. To evaluate the estimation error behavior in nonlinear uncertain systems, the stability of the filtering algorithm is investigated. A numerical simulation for spacecraft autonomous navigation is implemented to demonstrate the efficiency of the QLEKF. It is shown that the presented algorithm outperforms a traditional EKF and an adaptive extended Kalman filter (AEKF) in estimation accuracy.
Robust extreme learning machine in the presence of outliers by iterative reweighted algorithm

Extreme learning machine (ELM) is widely used to derive the single-hidden layer feedforward neural networks. However, ELM faces a great challenge in the presence of outliers, which can result in the sensitivity and poor robustness. To overcome this dilemma, a non-convex 2-norm loss function is developed to reduce these negative influences by setting a fixed penalty on any potential outliers. A novel robust ELM is proposed in this paper, and the resultant optimization can be implemented by an iterative reweighted algorithm, called IRRELM. In each iteration, IRRELM solves a weighted ELM. Several artificial datasets, real-world datasets and financial time series datasets are employed in numerical experiments, which demonstrate that IRRELM has superior generalization performance and robustness for modeling datasets in the presence of outliers, especially at the higher outlier levels.
Performance of Open Autonomous Vehicle Platforms: Autoware and Apollo

Fatalities due to human reckless driving and technological advancements makes all automobile companies to work towards autonomous vehicles. Making autonomous car platforms from scratch would be difficult therefore some middleware framework like Robot Operating System (ROS) which consists of pre-built software libraries can be used for self-driving. This paper throws light upon the main existing ROS based open software platforms, Autoware and Apollo and their algorithms required for scene understanding, path planning, and vehicle control.
Making Chat at Home in the Hospital: Exploring Chat Use by Nurses

In this paper, we examine WhatsApp use by nurses in India. Globally, personal chat apps have taken the workplace by storm, and healthcare is no exception. In the hospital setting, this raises questions around how chat apps are integrated into hospital work and the consequences of using such personal tools for work. To address these questions, we conducted an ethnographic study of chat use in nurses' work in a large multi-specialty hospital. By examining how chat is embedded in the hospital, rather than focusing on individual use of personal tools, we throw new light on the adoption of personal tools at work — specifically what happens when such tools are adopted and used as though they were organisational tools. In doing so, we explicate their impact on invisible work [77] and the creep of work into personal time, as well as how hierarchy and power play out in technology use. Thus, we point to the importance of looking beyond individual adoption by knowledge workers when studying the impact of personal tools at work.
Single-shot bidirectional pyramid networks for high-quality object detection

Recent years have witnessed significant advances in deep learning based object detection. Despite being extensively explored, most existing detectors are designed to detect objects with relatively low-quality prediction of locations, i.e., they are often trained with the threshold of Intersection over Union (IoU) set as 0.5. This can yield low-quality or even noisy detections. Designing high quality object detectors which have a more precise localization (e.g. IoU > 0.5) remains an open challenge. In this paper, we propose a novel single-shot detection framework called Bidirectional Pyramid Networks (BPN) for high-quality object detection. It comprises two novel components: (i) Bidirectional Feature Pyramid structure and Anchor Refinement (AR). The bidirectional feature pyramid structure aims to use semantic-rich deep layer features to enhance the quality of the shallow layer features, and simultaneously use the spatially-rich shallow layer features to enhance the quality of deep layer features, leading to a stronger representation of both small and large objects for high quality detection. Our anchor refinement scheme gradually refines the quality of pre-designed anchors by learning multi-level regressors, giving more precise localization predictions. We performed extensive experiments on both PASCAL VOC and MSCOCO datasets, and achieved the best performance among all single-shot detectors. The performance was especially superior in the regime of high-quality detection.
Deep Learning based Prediction Model for Adaptive Video Streaming

Improving the overall Quality of Experience (QoE) of a client for a given video streaming session is a challenging task in a continuously varying network environment. A dynamic bit-rate adaptation strategy at the client side that can maximize the overall QoE for a session is required irrespective of the bandwidth allocation scheme adopted in-network. Even though there are a plethora of state-of-the-art strategies for adaptive bit-rate streaming, they suffer from a few key shortcomings which may significantly restrict the overall QoE potentially achievable by an end-user. For instance, QoE management using a fixed set of rules may not always guarantee optimal bandwidth utilization, video quality enhancement and accurate buffer estimation, especially in the face of severely varying and often unpredictable bandwidth fluctuations. To handle these issues across a wide range of varying network conditions and QoE parameters, machine learning strategies are being used in recent times. ML approaches train the QoE models based on diverse observations related to network bandwidth, actually received bitrate, segment size, etc, corresponding to video segments received in the past. In this work, we propose a Deep Neural Network based model that selects the appropriate video bit-rates in order to maximize the overall QoE of a user. The overall QoE is obtained as a linear combination of various input parameters such as perceived video quality, buffering time and smoothness of the video session. Experimental results reveal that the proposed architecture improves the average QoE achieved by a client by ≈8.84% when compared against Pensieve, a state-of-the-art ML based approach.
Research on Chinese Naming Recognition Model Based on BERT Embedding

Named entity recognition (NER) is one of the foundations of natural language processing(NLP). In the method of Chinese named entity recognition based on neural network, the vector representation of words is an important step. Traditional word embedding method map words or chars into a single vector, which can not represent the polysemy of words. To solve this problem, a named entity recognition method based on BERT Embedding model is proposed. The method enhances the semantic representation of words by BERT(Bidirectional Encoder Representations from Transformers) pre-trained language model. BERT can generates the semantic vectors dynamically according to the context of the words, and then inputs the word vectors into BiGRU-CRF for training. The whole model can be trained during training. It is also possible to fix the BERT and train only the BiGRU-CRF part. Experiments show that the two training methods of the model reach 95.43% F1 and 94.18% F1 in MSRA corpus, respectively, which are better than the current optimal Lattice-LSTM model.