Reviewerly/exHarmony huggingface dataset:
Beyond Load Balancing: Package-Aware Scheduling for Serverless Platforms

Fast deployment and execution of cloud functions in Function-as-a-Service (FaaS) platforms is critical, for example, for microservices architectures. However, functions that require large packages or libraries are bloated and start slowly. An optimization is to cache packages at the worker nodes instead of bundling them with the functions. However, existing FaaS schedulers are vanilla load balancers, agnostic of packages cached in response to prior function executions, and cannot properly reap the benefits of package caching. We study the case of package-aware scheduling and propose PASch, a novel scheduling algorithm that seeks package affinity during scheduling so that worker nodes can re-use execution environments with preloaded packages. PASch leverages consistent hashing and the power of 2 choices, while actively avoiding worker overload. We implement PASch in a new scheduler for the OpenLambda framework and evaluate it using simulations and real experiments. When using PASch instead of a least loaded balancer, tasks perceive an average speedup of 1.29×, and 80th percentile latency that is 23× faster. Furthermore, for the workload studied in this paper, PASch outperforms consistent hashing with bounded loads-a state-of-the-art load balancing algorithm-yielding a 1.3× average speedup, and a speedup of 1.5× at the 80th percentile.
Weather recognition via classification labels and weather-cue maps

Although it is of great importance to recognize weather conditions automatically, this task has not been explored thoroughly in practice. Generally, most approaches in the literature simply treat it as a common image classification task, i.e., assigning a certain weather label to each image. However, there are significant differences between weather recognition and common image classification, since several weather conditions tend to occur simultaneously, like foggy and cloudy. Obviously, a single weather label is insufficient to provide a comprehensive description of the weather conditions. In this case, we propose to utilize auxiliary weather-cues, e.g., black clouds and blue sky, for comprehensive weather description. Specifically, a multi-task framework is designed to jointly deal with the weather-cue segmentation task and weather classification task. Benefit from the intrinsic relationships lying in the two tasks, exploring the information of weather-cues can not only provide a comprehensive description of weather conditions, but also help the weather classification task to learn more effective features, and further improve the performance. Besides, we construct two large-scale weather recognition datasets equipped with both weather labels and segmentation masks of weather-cues. Experiment results demonstrate the excellent performance of our approach. The constructed two datasets will be available at https://github.com/wzgwzg/Multitask_Weather.
Using Session Modeling to Estimate HTTP-Based Video QoE Metrics From Encrypted Network Traffic

Understanding the user-perceived quality of experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based adaptive streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model an HAS session and estimate video QoE metrics, such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth collected using a lab testbed for video sessions from three popular services, two video on demand (VoD) and one Live. eMIMIC estimates re-buffering ratio within 1% point of ground truth for up to 75% sessions in VoD (80% in Live) and average bitrate with error under 100 Kb/s for up to 80% sessions in VoD (70% in Live). We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics. Finally, we show that eMIMIC can estimate real-time QoE metrics with at least 89.6% accuracy in identifying buffer occupancy state and at least 85.7% accuracy in identifying average bitrate class of recently downloaded chunks.
MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants

We introduce MCScript2.0,a machine comprehension corpus for the end-to-end evaluation of script knowledge.MCScript2.0contains approx.20,000 questions on approx.3,500 texts, crowdsourced based on a new collection process that results in challenging questions.Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge.We give a thorough analysis of our corpus and show that while the task is not challenging to humans, existing machine comprehension models fail to perform well on the data, even if they make use of a commonsense knowledge base.
Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM

In this paper, we study the graph Laplacian estimation problem under a given connectivity topology. We aim at enriching the unified graph learning framework proposed by Egilmez et al. and improve the optimality performance of the combinatorial graph Laplacian (CGL) case. We apply the well-known alternating direction method of multipliers (ADMM) and majorization-minimization (MM) algorithmic frameworks and propose two algorithms, namely, GLE-ADMM and GLE-MM, for graph Laplacian estimation. Both algorithms can achieve an optimality gap as low as 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , around three orders of magnitude more accurate than the benchmark. In addition, we find that GLE-ADMM is more computationally efficient in a dense topology (e.g., an almost complete graph), while GLE-MM is more suitable for sparse graphs (e.g., trees). Furthermore, we consider exploiting the leading eigenvectors of the sample covariance matrix as a nominal eigensubspace and propose a third algorithm, named GLENE, which is also based on ADMM. Numerical experiments show that the inclusion of a nominal eigensubspace significantly improves the estimation of the graph Laplacian, which is more evident when the sample size is smaller than or comparable to the problem dimension.
Development of augmented‐reality applications in otolaryngology–head and neck surgery

Augmented reality (AR) allows for the addition of transparent virtual images and video to one's view of a physical environment. Our objective was to develop a head-worn, AR system for accurate, intraoperative localization of pathology and normal anatomic landmarks during open head and neck surgery.Face validity and case study.A protocol was developed for the creation of three-dimensional (3D) virtual models based on computed tomography scans. Using the HoloLens AR platform, a novel system of registration and tracking was developed. Accuracy was determined in relation to actual physical landmarks. A face validity study was then performed in which otolaryngologists were asked to evaluate the technology and perform a simulated surgical task using AR image guidance. A case study highlighting the potential usefulness of the technology is also presented.An AR system was developed for intraoperative 3D visualization and localization. The average error in measurement of accuracy was 2.47 ± 0.46 millimeters (1.99, 3.30). The face validity study supports the potential of this system to improve safety and efficiency in open head and neck surgical procedures.An AR system for accurate localization of pathology and normal anatomic landmarks of the head and neck is feasible with current technology. A face validity study reveals the potential value of the system in intraoperative image guidance. This application of AR, among others in the field of otolaryngology-head and neck surgery, promises to improve surgical efficiency and patient safety in the operating room.2b Laryngoscope, 129:S1-S11, 2019.
An improved differential evolution algorithm for learning high-fidelity quantum controls

Precisely and efficiently designing control pulses for the preparation of quantum states and quantum gates are the fundamental tasks for quantum computation. Gradient-based optimal control methods are the routine to design such pulses. However, the gradient information is often difficult to calculate or measure, especially when the system is not well calibrated or in the presence of various uncertainties. Gradient-free evolutionary algorithm is an alternative choice to accomplish this task but usually with low-efficiency. Here, we design an efficient mutation rule by using the information of the current and the former individuals together. This leads to our improved differential evolution algorithm, called daDE. To demonstrate its performance, we numerically benchmark the pulse optimization for quantum states and quantum gates preparations on small-scale NMR system. Further numerical comparisons with conventional differential evolution algorithms show that daDE has great advantages on the convergence speed and robustness to several uncertainties including pulse imperfections and measurement errors.
EPISODE: Efficient Privacy-PreservIng Similar Sequence Queries on Outsourced Genomic DatabasEs

Nowadays, genomic sequencing has become much more affordable for many people and, thus, many people own their genomic data in a digital format. Having paid for genomic sequencing, they want to make use of their data for different tasks that are possible only using genomics, and they share their data with third parties to achieve these tasks, e.g., to find their relatives in a genomic database. As a consequence, more genomic data get collected worldwide. The upside of the data collection is that unique analyses on these data become possible. However, this raises privacy concerns because the genomic data uniquely identify their owner, contain sensitive data about his/her risk for getting particular diseases, and even sensitive information about his/her family members.
Generic metadata representation framework for social-based event detection, description, and linkage

Various methods have been put forward to perform automatic social-based event detection and description. Yet, most of them do not capture the semantic meaning embedded in online social media data, which are usually highly heterogeneous and unstructured, and do not identify event relationships (e.g., car accident temporally occurs after storm, and geographically occurs near soccer match). To address this problem, we introduce a generic Social-based Event Detection, Description, and Linkage framework titled SEDDaL, taking as input: a collection of social media objects from heterogeneous sources (e.g., Flickr, YouTube, and Twitter), and producing as output a collection of semantically meaningful events interconnected with spatial, temporal, and semantic relationships. The latter are required as the building blocks for event-based Collective Knowledge (CK) organization, where CK underlines the combination of all known data, information, and metadata concerning a given concept or event. SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. We believe this is the first study to provide a generic model for describing semantic-aware events and their relationships extracted from social metadata on the Web. Experimental results confirm the quality and potential of our approach.
Improving Hyperconnected Logistics With Blockchains and Smart Contracts

The Physical Internet and hyperconnected logistics concepts promise an open, more efficient, and environmentally friendly supply chain for goods. Blockchain and Internet of Things (IoT) technologies are increasingly regarded as main enablers of improvements in this domain. We describe how blockchain and smart contracts present the potential of being applied to hyperconnected logistics by showing a concrete example of its implementation.
Geoscience and decarbonization: current status and future directions

At the 2015 United Nations International Climate Change Conference in Paris (COP21), 197 national parties committed to limit global warming to well below 2°C.But current plans and pace of progress are still far from sufficient to achieve this objective.Here we review the role that geoscience and the subsurface could play in decarbonizing electricity production, industry, transport and heating to meet UK and international climate change targets, based on contributions to the 2019 Bryan Lovell meeting held at the Geological Society of London.Technologies discussed at the meeting involved decarbonization of electricity production via renewable sources of power generation, substitution of domestic heating using geothermal energy, use of carbon capture and storage (CCS), and more ambitious technologies such as bioenergy and carbon capture and storage (BECCS) that target negative emissions.It was noted also that growth in renewable energy supply will lead to increased demand for geological materials to sustain the electrification of the vehicle fleet and other low-carbon technologies.The overall conclusion reached at the 2019 Bryan Lovell meeting was that geoscience is critical to decarbonization, but that the geoscience community must influence decision-makers so that the value of the subsurface to decarbonization is understood.
Scamming and the reputation of drug dealers on Darknet Markets

In this paper I investigate the role of e-reputation mechanisms on illegal platforms that specialize in drug sales. I ask whether online reputation systems can limit the risk of scamming (i.e. fraud) by dishonest sellers, and thus prevent Akerlof-like market destruction. I do so by analyzing all published offers on the second-largest platform operating on March 18th 2017 (Hansa). Three types of drugs show relatively low scamming risks, with the average probability that a random seller effectively send the ordered good of over 83%. The recent shutdowns of the two leading platforms are likely to increase this probability by 2.7 to 9.7%. Endogeneity may either lead us to overestimate the effect of e-reputation mechanisms (e.g., unobserved heterogeneity in sellers) or underestimate it (e.g., better-functioning markets may attract more scammers).
A Novel Architecture for Tamper Proof Electronic Health Record Management System using Blockchain Wrapper

In this paper, we present a novel architecture of blockchain-based tamper-proof electronic health record (EHR) management system. Recording electronic health data in cloud-based storage systems always pose a threat to information security. Intruders can delete or tamper EHR of patients, giving benefits to insurance companies or hiding medical malpractices (e.g. misdiagnosis and delayed diagnosis). A tamper-proof EHR management system is required that would essentially solve such issues. The blockchain is an emerging technology that can be adapted to develop a tamper-proof data management system. However, establishing a new blockchain based system replacing the existing system is expensive. In our proposed architecture, we introduce a wrapper layer integration mechanism, named as the blockchain handshaker, between the existing cloud-based EHR management system and public blockchain network to develop a tamper-proof health record management system. We implement a prototype to provide evidence on the feasibility of the proposed concept.
Random subsampling improves performance in lexicase selection

Lexicase selection has been proven highly successful for finding effective solutions to problems in genetic programming, especially for test-based problems where there are many distinct test cases that must all be passed. However, lexicase (as with most selection schemes) requires all prospective solutions to be evaluated against most test cases each generation, which can be computationally expensive. Here, we propose reducing the number of per-generation evaluations required by applying random subsampling: using a subset of test cases each generation (down-sampling) or by assigning test cases to subgroups of the population (cohort assignment). Tests are randomly reassigned each generation, and candidate solutions are only ever evaluated on test cases that they are assigned to, radically reducing the total number of evaluations needed while ensuring that each lineage eventually encounters all test cases. We tested these lexicase variants on five different program synthesis problems, across a range of down-sampling levels and cohort sizes. We demonstrate that these simple techniques to reduce the number of per-generation evaluations in lexicase can substantially improve overall performance for equivalent computational effort.
Self-Supervised Learning of Face Representations for Video Face Clustering

Analyzing the story behind TV series and movies often requires understanding who the characters are and what they are doing. With improving deep face models, this may seem like a solved problem. However, as face detectors get better, clustering/identification needs to be revisited to address increasing diversity in facial appearance. In this paper, we address video face clustering using unsupervised methods. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, and thus can also be applied to image collections. We evaluate our proposed method on three video face clustering datasets. The experiments show that our methods outperform current state-of-the-art methods on all datasets. Video face clustering is lacking a common benchmark as current works are often evaluated with different metrics and/or different sets of face tracks. The datasets and code are available at https://github.com/vivoutlaw/SSIAM.
Comparison of blockchain frameworks for healthcare applications

Though a relatively new technology, blockchain has become a very trendy topic in recent times, thanks to bitcoin and other popular cryptocurrencies which are built on the blockchain. With features such as decentralized consensus and data immutability, blockchain transactions are known to be transparent, secure, and trustworthy. For these reasons, blockchain is increasingly being adopted in different industries and for diverse use cases, especially where security and trust are important concerns. Health care is one such industry that offers several use cases for applying blockchain technology. Even so, blockchain‐based healthcare applications have yet to become widespread. This is mostly because initial efforts were focused on developing blockchain frameworks for cryptocurrencies and not for general purpose applications, such as health care. Recently, general‐purpose blockchain frameworks, which may be used to develop healthcare applications, have begun to emerge. However, there is no consensus on which framework is most suitable for developing healthcare applications. In light of this, this paper compares the popular general‐purpose blockchain frameworks, vis‐a‐vis the requirements for healthcare systems, in order to guide health informatics researchers and practitioners in selecting the appropriate platform for developing and experimenting with blockchain‐based healthcare applications.
A novel learning-based global path planning algorithm for planetary rovers

Autonomous path planning algorithm is significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. In this paper, a novel learning-based algorithm is proposed to deal with global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping. Moreover, the planning procedure requires no prior knowledge about planetary surface terrains. Finally, experimental results demonstrate that DB-CNN achieves better performance on global path planning and faster convergence during training compared with the existing Value Iteration Network (VIN).
Semantic segmentation via highly fused convolutional network with multiple soft cost functions

Semantic image segmentation is one of the most challenging tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: downsampling, fused upsampling and multiple predictions. We adopt a VGG-net based downsampling structure, followed by multiple steps of upsampling. Feature maps in each pair of corresponding pooling layers and unpooling layers are combined. We also bring out multiple pre-outputs, each is generated from an unpooling layer by a one-step upsampling operation. Finally, we concatenate these pre-outputs to get the final output. As a result, our proposed network makes high use of the feature information by fusing and reusing features in low layers. In addition, when training our model, we add multiple soft cost functions on pre-outputs and the final output. In this way, we can reduce the loss reduction in backpropagation. We evaluate our model on three public segmentation datasets: CamVid, PASCAL VOC, and ADE20K. We achieve considerable segmentation performance on PASCAL VOC dataset and ADE20K dataset. Especially on CamVid dataset, we achieve state-of-the-art performance.
Temporal Hockey Action Recognition via Pose and Optical Flows

In this paper, a novel two-stream architecture has been designed to improve action recognition accuracy for hockey using three main components. First, pose is estimated via the Part Affinity Fields model to extract meaningful cues from the player. Second, optical flow (using LiteFlownet) is used to extract temporal features. Third, pose and optical flow streams are fused and passed to fully-connected layers to estimate the hockey player's action. A novel publicly available dataset named HARPET (Hockey Action Recognition Pose Estimation, Temporal) was created, composed of sequences of annotated actions and pose of hockey players including their hockey sticks as an extension of human body pose. Three contributions are recognized. (1) The novel two-stream architecture achieves 85% action recognition accuracy, with the inclusion of optical flows increasing accuracy by about 10%. Thus, demonstrating the complementary nature of pose estimation and optical flow. (2) The unique localization of hand-held objects (e.g., hockey sticks) as part of pose increases accuracy by about 13%. (3) For pose estimation, a bigger and more general dataset, MSCOCO, is successfully used for transfer learning to a smaller and more specific dataset, HARPET, achieving a PCKh of 87%.