Reviewerly/exHarmony huggingface dataset:
High-performance operator evaluations with ease of use: libCEED's Python interface

libCEED is a new lightweight, open-source library for highperformance matrix-free Finite Element computations.libCEED offers a portable interface to high-performance implementations, selectable at runtime, tuned for a variety of current and emerging computational architectures, including CPUs and GPUs.libCEED's interface is purely algebraic, facilitating co-design with vendors and enabling unintrusive integration in new and legacy software.In this work, we present libCEED's newly-available Python interface, which opens up new strategies for parallelism and scaling in high-performance Python, without compromising ease of use.
A Priority-aware DASH-based Multi-View Video Streaming Scheme over Multiple Channels

The latest increase in multi-view video solutions, including those for telepresence, commercial conferencing, remote collaboration, etc. requires support for the high-quality delivery of large amounts of content data. Meanwhile, the extensive proliferation of wireless network access technology and multiple network interfaces on modern devices prompt the network transmission performance over various access networks. Diverse multipath-based multi-view streaming and adaptive delivery solutions were proposed, but they do not enable differentiation between streams. This paper proposes MVP-DASH, a priority-aware adaptive multi-view video streaming scheme based on the MPEG-DASH framework. MVP-DASH enables improved visual quality for high-priority streams, while maintaining acceptable quality levels for low-priority streams, primarily when delivered over a dynamic network environment. The experimental evaluation of the MVP-DASH demonstrates the effectiveness of the proposal in terms of achieving higher video quality and fewer video quality switches in comparison with alternative approaches.
Enhancing Employability Skills of Engineering Graduates

Engineering education demands numerous challenges in the present context due to the difficulties posed by the placement scenario. Demand for competent engineering professionals has made the selection process highly challenging giving rise to a need for comprehensive education pedagogy not just confined to inculcating hard core technical attributes but also human skills. Hence there is an urgent need to continuously upgrade the curriculum design of engineering courses so as to better equip the technical graduates with employability skills. BITS, Pilani, one of the premiere technical institutes in India has a well-designed pool of structured courses such as Business Communication, Technical communication, etc. which do incorporate modules such as Professional Presentations, Group Discussions, Interviews, etc. so as to hone the soft skills of the students. The paper attempts to deal with the experiential innovative methodology adopted in the course Business communication so as to enhance the employability skills of the Engineering students.
Availability of synchronous information in an additional sensory modality does not enhance the full body illusion

Abstract The Full body illusion (FBI) is an illusion in which participants experience a change in self-location to a body that is perceived from a third-person perspective. The FBI is usually induced through experimenter generated stroking but can also be induced through self-generated stroking. In four experiments (three preregistered) we compared a self-generated stroking induction condition to a self-generated movement condition, where the only difference between conditions was the presence or absence of touch. We investigated whether the illusion reflects an all-or-nothing phenomenon or whether the illusion is influenced by the availability of synchronous information in an additional sensory modality. As a prerequisite, we investigated whether the FBI can also be induced using just self-generated movement in the absence of synchronous touch. Illusion strength was measured through illusion statements. Participants reported an equally strong illusion for both induction methods in Experiments 1, 2 and 3. In the third experiment, we additionally measured the time of illusion onset. Like the illusion strength measures, the illusion onset times did not differ between the two induction methods. In the fourth experiment participants only completed the self-generated movement condition. Again, they reported the FBI, demonstrating that the findings of Experiments 1, 2 and 3 were not dependent on the presence of a condition that used synchronous touch. Together, these findings confirm the hypothesis that the FBI is an all-or-nothing phenomenon and that adding additional multisensory synchronicity does not help to enhance the strength, onset time or onset probability of the illusion.
Efficient Post-Quantum TLS Handshakes using Identity-Based Key Exchange from Lattices

Identity-Based Encryption (IBE) is considered an alternative to traditional certificate-based public key cryptography to reduce communication overheads in wireless sensor networks. In this work, we build on the well-known lattice-based DLP-IBE scheme to construct an ID-based certificateless authenticated key exchange for post-quantum Transport Layer Security (TLS) handshakes. We also propose concrete parameters for the underlying lattice computations and provide detailed implementation results. Finally, we compare the combined computation and communication cost of our ID-based certificate-less handshake with the traditional certificate-based handshake, both using lattice-based algorithms at similar postquantum security levels, and show that our ID-based handshake is 3.7× more energy-efficient, thus highlighting the advantage of ID-based key exchange for post-quantum TLS.
Risk Aware SUAS Path Planning in an Unstructured Wildfire Environment

This paper addresses path planning for small unmanned aerial vehicles (SUAS) in an unstructured environment with poorly understood and time varying obstacles. These environmental characteristics manifest in an evolving wildfire, which is used as the test-case for this work. An evidential framework is employed to estimate the current state of wildfire and the resulting heat aura at flight level. This approach accounts for ignorance in estimation that can result from frequent conflict among sensors operating in a harsh environment combined with a computational forecasting agent that must operate with poor models of fire evolution. The objective of SUAS mission design is to visit such points of high conflict in order to provide additional situational awareness. A novel unsupervised classification algorithm based on statistical formalism is developed to identify distinct keep-out zones within the estimated heat aura. Boundaries of identified obstacles are modeled as probabilistic barriers. The planning problem is posed as a chance-constrained optimal control problem, which is transcribed to a nonlinear program via pseudospectral discretization. It is shown that by actively varying the probability of violating obstacle boundaries, a family of solutions can be generated that elicit the risk associated with each mission design.
Privacy-preserving Payment Channel Networks using Trusted Execution Environment

Payment channel networks (PCN) have demonstrated its significant advantages in improving the scalability of blockchain. However, the existing work of PCN leads to serious privacy leakage problem that intermediate nodes along a payment path can collude to obtain the payment amounts and payment receivers. To address this problem, we propose to move PCN-related modules into the Trusted Execution Environment (TEE) commonly available on modern CPUs, so that adversaries cannot access the critical payment information protected by TEE, even though they compromise the software (e.g., blockchain clients or operating system) outside of TEE. An additional challenge is that adversaries can still infer payment receivers by observing the pattern of message transmissions among nodes. To hide payment receivers, we further propose to send redundant transactions to pseudo receivers to confuse adversaries. A fast algorithm with provable approximation ratio has been proposed to maximize the level of privacy protection under the constraint of communication overhead. Both experiments on a small-scale testbed and large-scale simulations are conducted to evaluate our proposal. The results show that our proposed solution outperforms existing work significantly.
Sensor Fusion for Quadrotor Autonomous Navigation

Autonomous navigation requires the availability of all vehicle states. Since there is not a single sensor capable of measuring translational and rotational positions and velocities, sensor fusion becomes necessary. This paper proposes a translational velocity observer and a position scale factor estimator based on inertial and visual measurements. The observer/estimator design follows the Immersion and Invariance method. Numerical simulations and real-time experiments are presented to validate the proposed observer/estimator.
Lateral Thinking in Learning Computational Fluid Dynamic Methods

Although a traditional engineering education system is good in imparting the intended know-how, it does not focus much on the delivery system. Applying lateral thinking techniques can provide a value addition to delivery as illustrated in this work, for the course of Computational Fluid Dynamics (CFD). CFD is a powerful engineering simulation tool and a trending elective course taught at several universities. CFD methods build on numerical methods to resolve flows and design flow related equipment. Here several methods form the core of the course, making teaching and learning them all a rigorous mathematical exercise. Hence lateral thinking techniques were applied to sustain and enhance student interest. A basic CFD method (Lax-Wendroff) was first taught. It was then illustrated how suspended judgment, generation of alternatives and fractionation were applied to further elucidate closely related CFD methods, namely those of Maccormack and Richtmyre. Hence lateral thinking was successfully applied for value addition to delivery in the class room, and a framework was thereby suggested to explore other CFD methods in this way. The work demonstrates the possibilities in amalgamating lateral thinking with analytical content in engineering education.
Dynamic Reduced-Round Cryptography for Energy-Efficient Wireless Communication of Smart IoT Devices

Securing the wireless Internet of Things (IoT) is a challenging issue do to their technological constraints: limited computing power, restricted batteries or inconsistent energy supply. With more than 26 billion devices connected in 2019, the expected 75 billion things by 2025 will require an even higher energy supply. Meanwhile, as smarts cities, industry and healthcare represent more than 75% of the IoT market share, these devices must be secured while limiting the impact on energy consumption. The lifetime of specific devices such as Wearable or Implantable Medical Devices (WMDs, IMDs) can then be significantly impacted. In this paper, we propose a generic design that dynamically reduces the energy consumption required by the addition of security within the IoT networks, according to the local level of battery use. This self-monitored, fully-automated, low-cost and remotely configurable mechanism adjusts the number of encryption rounds of the cryptographic primitive while guaranteeing the minimum level of security required. This method has been integrated into the Constrained Application Protocol (CoAP) with the Datagram Transport Layer Security (DTLS) using the AES-128 encryption standard, with 10 rounds (full) to 7, and can be implemented on other protocol stacks. We show a reduction in CPU power consumption of a Raspberry Pi of 19.67%. Finally, we estimate its efficiency by simulating the discharge of multiple batteries with different capacities. Our mechanism increases operating time up to 33 minutes and 15 seconds for a 10,000mAh Raspberry Pi battery when 150 messages of 4Kb per second are exchanged with an operator.
The Intelligent Diagnostic System for Common Diseases using the Optimized Medical Knowledge Graph

With the development of artificial intelligence technology, Clinical Decision Support System (CDSS) has been widely used. This paper presents a CDSS system, which called the Intelligent Auxiliary Diagnostic System for Common Disease (CD-IADS). CD-IADS assists the primary doctors in the diagnosis of common diseases with the patient's symptoms, signs, tests and personal background, which improves the primary doctors' working efficiency and reduces their misdiagnosis rate. In CD-IADS, medical experts construct the Medical Knowledge Graph according to medical literature and clinical experience firstly. Then, information entropy and particle swarm optimization (PSO) algorithms are used to optimize the special weights based on medical records. The system combines the advantages of the medical expert system and the artificial intelligence algorithm to support the diagnosis of common diseases. The paper takes the common diseases of the respiratory department as an example, and the TOP-1 and TOP-3 diagnostic accuracy rates are up to 64.6% and 86.0% respectively.
Dense Block U-net for Dynamic Scene Deblurring

Motion blur often arises in a single image because of the camera shake, the objects motion and the depth variation. The image deblurring is a challenging task due to its ill-posed nature. To remove these blurriness, the conventional energy optimization based methods always rely on the assumption such that the blur kernel is uniform across the entire image. With the development of the deep neural network, the learning based methods were proposed to tackle with the non-uniform blur cases. In this paper, we propose a U-Net network containing dense blocks for dynamic scene deblurring. By passing the kernel estimation, our model significantly reduces the inference time. The extensive experiments on both synthetic and real blurred images demonstrate that our method outperforms the state-of-the-art blind deblurring algorithms.
Navigation in Large Groups of Robots

As robots become more ubiquitous, research in large-scale robot navigation has gained more focus given their potential real-world applications. This review intends to provide a summary of recent advances on the field of multi-robot navigation, focusing on cases where scale is an important attribute of the system. Experimental evaluation of a large number of robots is complex due to size and cost constraints. Successful applications of large-scale robot navigation approaches are in structured and known environments where robots are centrally controlled. Advances in the standardization of robot hardware and software are increasing interest for research in navigation for large groups of robots. We present a review on navigation approaches for multi-robot systems. Then, we focus on recent articles that deal with the problem of moving a large number of robots in virtual or physical space. Finally, we summarize our main findings and emphasize the challenges.
Shared Control of an Electric Wheelchair Considering Physical Functions and Driving Motivation

Individuals with severe physical impairments have difficulties operating electric wheelchairs (EWs), especially in situations where fine steering abilities are required. Automatic driving partly solves the problem, although excessive reliance on automatic driving is not conducive to maintaining their residual physical functions and may cause more serious diseases in the future. The objective of this study was to develop a shared control system that can be adapted to different environments by completely utilizing the operating ability of the user while maintaining the motivation of the user to drive. The operating characteristics of individuals with severe physical impairments were first analyzed to understand their difficulties when operating EWs. Subsequently, a novel reinforcement learning-based shared control method was proposed to adjust the control weight between the user and the machine to meet the requirements of fully exploiting the operating abilities of the users while assisting them when necessary. Experimental results showed that the proposed shared control system gradually adjusted the control weights between the user and the machine, providing safe operation of the EW while ensuring full use of the control signals from the user. It was also found that the shared control results were deeply affected by the types of users.
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient

Convex composition optimization is an emerging topic that covers a wide range of applications arising from stochastic optimal control, reinforcement learning and multistage stochastic programming. Existing algorithms suffer from unsatisfactory sample complexity and practical issues since they ignore the convexity structure in the algorithmic design. In this paper, we develop a new stochastic compositional variance-reduced gradient algorithm with the sample complexity of O((m + n)log(1/ε) + 1/ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) where m + n is the total number of samples. Our algorithm is near-optimal as the dependence on m + n is optimal up to a logarithmic factor. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the new algorithm.
Bricklayer: Resource Composition on the Spot Market

AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.
Automatic question generation

Abstract Automatic generation of semantically well‐formed questions from a given text can contribute to various domains, including education, dialogues/interactive question answering systems, search engines, and more. It is well‐known as a challenging task, which involves the common obstacles of other natural language processing (NLP) activities. We start this advanced review with a brief overview of the most common automatic question generation (AQG) applications. Then we describe the main steps of a typical AQG pipeline, namely question construction, ranking, and evaluation. Finally, we discuss the open challenges of the AQG field that still need to be addressed by NLP researchers. This article is categorized under: Algorithmic Development &gt; Text Mining
Stroke-Hover Intent Recognition for Mid-Air Curve Drawing Using Multi-Point Skeletal Trajectories

Abstract Drawing curves is a fundamental task in mid-air interactive applications such as 3D sketching, geometric modeling, hand-writing recognition, and authentication. Existing research in mid-air drawing is solely focused on determining what the user drew assuming that the intended curve is segmented from the continuous user-generated trajectory. In this work, our aim is to address the complementary problem: to determine when the user actually intended to draw without the use of any prescribed gestures or hand-held controllers (e.g., Wii remote, HTC Vive). In our previously published work, we demonstrated that in mid-air drawing tasks, not only it is possible to statistically learn drawing intent from hand motion, but it is also perceived to be more natural by users. Our idea was to simply classify each instance of hand trajectories as either a stroke or a hover. Our current work investigates new representations of the users’ motion beyond a single point (such as a tracked palm) to richer multi-point trajectories obtained with other skeletal joints such as wrist and elbow. We trained several binary classifiers on five such trajectory representations obtained from 3D drawing data from 25 users using a hand tracking device. We compare these representations and the corresponding classifiers for predicting user intent for mid-air drawing. Our extended approach resulted in improved prediction accuracy (mean: 80.17%, min: 79.92%, max: 91.30%) with respect to our earlier work (mean: 76.75%, min: 74.23%, max: 84.01%).
Script Induction as Association Rule Mining

We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. ( 2012) can be unified in a general framework of narrative chain likelihood maximization.We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event.The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics.We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.
Photomicrograph-Based Neuropathology Consultation in Tanzania

Abstract Objectives Since neuropathologic diagnosis in the developing world is hampered by limitations in technical infrastructure, trained laboratory personnel, and subspecialty-trained pathologists, the use of telepathology for diagnostic support, second-opinion consultations, and ongoing training holds promise as a means of addressing these challenges. This study aims to assess the utility of static teleneuropathology in improving neuropathologic diagnoses in low- and middle-income countries. Methods Consecutive neurosurgical biopsy and resection specimens obtained at Muhimbili National Hospital in Tanzania between July 1, 2018, and June 30, 2019, were selected for retrospective, blinded static-image neuropathologic review followed by on-site review by an expert neuropathologist. Results A total of 75 neuropathologic cases were reviewed. The agreement of static images and on-site glass diagnosis was 71% with strict criteria and 88% with less stringent criteria. This represents an overall improvement in diagnostic accuracy from 36% by general pathologists to 71% by a neuropathologist using static telepathology (or from 76% to 88% with less stringent criteria). Conclusions Telepathology offers a promising means of providing diagnostic support, second-opinion consultations, and ongoing training to pathologists practicing in resource-limited countries. Moreover, static digital teleneuropathology is an uncomplicated, cost-effective, and reliable way to achieve these goals.