Reviewerly/exHarmony huggingface dataset:
Optimal Trajectories for a Mobile Robot with Bounded Accelerations in the Presence of a Wall or a Bounded Obstacle

The time-optimal path planning problem in the plane for a point mass mobile robot navigating in an obstacle-presence environment is studied. The robot is subjected to maximal acceleration limits. The paper extends previous results which solved this problem in an obstacle-free environment. The problem is characterized as an optimal control problem with state-dependent obstacle constraints. Two equivalent ways of analysis are employed to obtain the necessary conditions for optimality under these constraints. The optimality conditions yield eight path primitives that form the time-optimal trajectories in the presence of obstacles. Representative examples are studied in order to demonstrate the method.
Enhancing the Conciseness of Linked Data by Discovering Synonym Predicates

In the meantime of the rapidly growing of Linked Data, the quality of these datasets is yet a challenge. A close examination of the quality of this data could be very critical, especially if important researches or professional decisions depend on it. Nowadays, several Linked Data quality metrics have been proposed which cover numerous dimensions of Linked Data quality such as completeness, consistency, conciseness and interlinking. In this paper, we propose an approach to enhance the conciseness of linked datasets by discovering synonym predicates. This approach is based, in addition to a statistical analysis, on a deep semantic analysis of data and on learning algorithms. We argue that studying the meaning of predicates can help to improve the accuracy of results. A set of experiments are conducted on real-world datasets to evaluate the approach.
Query-to-Communication Lifting Using Low-Discrepancy Gadgets

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 January 2020Accepted: 10 November 2020Published online: 18 February 2021Keywordscommunication complexity, query complexity, liftingAMS Subject Headings68Q17Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Avatar Selection for Live Performance in Virtual Reality: A Case Study

With the increasing number of commercial and open-source platforms for networked, multiplayer virtual reality, it is easier than ever to set up a virtual space where people from any geographic location can come together for shared immersive experiences, including live performance events. Very little UX research has been done on the specific needs of performers for such events. In this case study we explore how different types of avatars affect actors' experience of their own performance in VR. We observed that, counter-intuitively, more abstract avatars may make it easier for actors to connect with their character, although we also found that, with sufficient rehearsal, actors can learn to perform fluently through a wide variety of different kinds of avatars.
A New Insight Into Automatic First-Arrival Picking Based on Reinforcement Learning

Summary First-arrival picking is critical for static corrections and near-surface model building. We propose a fully automatic first-arrival picking methodology based on reinforcement learning. Unlike conventional methods, which usually limit the picking to a trace-by-trace procedure followed by a quality control step, we use the energy ratio of the whole shot gather as a reward map and formulate the picking procedure as an Markov decision process (MDP) in which the learning agent aims to find the first-arrival onsets of the shot gather by maximizing the total cumulative reward. Outliers and mis-picks associated with bad or dead traces can be avoided automatically in the proposed method. Both synthetic and real data examples demonstrate that this new technology enables geoscientists to automatically track first arrivals without ever using any prior knowledge, even in the presence of coherent noise and discontinuous first arrivals.
Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction

Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image.Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image.In this paper, a system for Copy Move Forgery detection is proposed.The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage.The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency.Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints.This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions.RANSAC is employed to find the superior group of matches to differentiate the manipulated parts.Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions.A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets.With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases.Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement.Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.
Microwave Engineer’s Guide to the Design of Superconducting Qubit Circuits

Superconducting qubit circuits are one of the prime candidates for building the quantum computer. In this guide I will provide a complete microwave description of the superconducting qubit circuits consisting of the most popular superconducting qubit the Transmon. Quantum interaction rate parameters related to simple impedance response formulas will allow microwave engineers to design and analyze superconducting qubit circuits without resorting to heavy machinery of quantum mechanical transformations. All formulas listed below are valid in the dispersive regime of low anharmonicity qubits. This work is a review of [1].
A Privacy-Preserving TPA-aided Remote Data Integrity Auditing Scheme in Clouds

The remote data integrity auditing technology can guarantee the integrity of outsourced data in clouds. Users can periodically run an integrity auditing protocol by interacting with cloud server, to verify the latest status of outsourced data. Integrity auditing requires user to take massive time-consuming computations, which would not be affordable by weak devices. In this paper, we propose a privacy-preserving TPA-aided remote data integrity auditing scheme based on Li et al.’s data integrity auditing scheme without bilinear pairings, where a third party auditor (TPA) is employed to perform integrity auditing on outsourced data for users. The privacy of outsourced data can be guaranteed against TPA in the sense that TPA could not infer its contents from the returned proofs in the integrity auditing phase. Our construction is as efficient as Li et al.’s scheme, that is, each procedure takes the same time-consuming operations in both schemes, and our solution does not increase the sizes of processed data, challenge and proof.
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures

A method of simultaneously optimizing both the structure of neural networks and the connection weights in a single training loop can reduce the enormous computational cost of neural architecture search. We focus on the probabilistic model-based dynamic neural network structure optimization that considers the probability distribution of structure parameters and simultaneously optimizes both the distribution parameters and connection weights based on gradient methods. Since the existing algorithm searches for the structures that only minimize the training loss, this method might find overly complicated structures. In this paper, we propose the introduction of a penalty term to control the model complexity of obtained structures. We formulate a penalty term using the number of weights or units and derive its analytical natural gradient. The proposed method minimizes the objective function injected the penalty term based on the stochastic gradient descent. We apply the proposed method in the unit selection of a fully-connected neural network and the connection selection of a convolutional neural network. The experimental results show that the proposed method can control model complexity while maintaining performance.
Bodo To English Statistical Machine Translation System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Action-Sensitive Phonological Dependencies

This paper defines a subregular class of functions called the tier-based synchronized strictly local (TSSL) functions. These functions are similar to the the tier-based input-output strictly local (TIOSL) functions, except that the locality condition is enforced not on the input and output streams, but on the computation history of the minimal subsequential finite-state transducer. We show that TSSL functions naturally describe rhythmic syncope while TIOSL functions cannot, and we argue that TSSL functions provide a more restricted characterization of rhythmic syncope than existing treatments within Optimality Theory.
Dr.VOT: Measuring Positive and Negative Voice Onset Time in the Wild

Voice Onset Time (VOT), a key measurement of speech for basic research and applied medical studies, is the time between the onset of a stop burst and the onset of voicing. When the voicing onset precedes burst onset the VOT is negative; if voicing onset follows the burst, it is positive. In this work, we present a deep-learning model for accurate and reliable measurement of VOT in naturalistic speech. The proposed system addresses two critical issues: it can measure positive and negative VOT equally well, and it is trained to be robust to variation across annotations. Our approach is based on the structured prediction framework, where the feature functions are defined to be RNNs. These learn to capture segmental variation in the signal. Results suggest that our method substantially improves over the current state-of-the-art. In contrast to previous work, our Deep and Robust VOT annotator, Dr.VOT, can successfully estimate negative VOTs while maintaining state-of-the-art performance on positive VOTs. This high level of performance generalizes to new corpora without further retraining. Index Terms: structured prediction, multi-task learning, adversarial training, recurrent neural networks, sequence segmentation.
A Note On Compliance Relations And Fixed Points.

We study compliance relations between behavioural contracts in a syntax independent setting based on Labelled Transition Systems.We introduce a fix-point based family of compliance relations, and show that many compliance relations appearing in literature belong to this family.
Geometry-Aware GAN for Face Attribute Transfer

In this paper, the geometry-aware GAN, referred to as GAGAN, is proposed to address the issue of face attribute transfer with unpaired data. The source and target images are not aligned and come from different individuals. The key idea is to deform the source image according to the geometry features and generate a high-resolution image with the desired attribute. To address the problem of the unpaired training samples, the CycleGAN architecture is applied to form an attribute adding and removing cycle, where the bilateral mappings between the source and target domains are learned. The geometry flow and occlusion mask are learned by the warping sub-network to capture the geometric variation between the two domains. In the attribute adding process, the spatial transformer network (STN) warps the source face into the desired pose and shape according to the flow, and the transfer sub-network hallucinates new components on the warped image. In the attribute removing process, the recover sub-network and the STN reverts the sample back to the source domain. Experiments on the benchmarks CELEBA and CELEBA-HQ datasets demonstrate the advantages of our method compared to the baselines, in terms of both quantitative and qualitative evaluation.
Wearable computing in libraries – applications that meet the needs of users and librarians

Purpose The subject of this paper is wearable computing technology. The purpose of this paper is to determine its potential in libraries to meet the needs of both users and librarians. The specific goals include: description of the main features of wearable computing from the perspective of its potential usefulness in libraries; analysis of areas of wearable computing use in public institutions; discussion of the potential of wearable computing for library users; and discussion of the potential of wearable computing for librarians to determine its potential in libraries to meet the needs of users and librarians. Design/methodology/approach The method of subject literature overview was used. The state of research from the period 2008 to 2018 on the use of wearable computing in libraries was established based on a search of selected sources Web of Science, Scopus, Elsevier and Emerald databases and LISTA database. Findings Wearable computing can be used in many areas of library activities to serve the needs of users and librarians. In the context of services for users, wearable computing can be used, among others, to help users navigate the library, to provide resources in new and interesting forms, and in education and entertainment. For the needs of librarians, additional data provided via wearable devices can accelerate the process of cataloguing resources and increase motivation to work via gamification options and features that make it possible to measure personal work efficiency. Librarians can also use the data obtained wearable computing devices to more quickly track, identify, select and organize resources. As with users, librarians can also use wearable computing in their education. Research limitations/implications The paper is a viewpoint based on subject literature. It presents only a sketch of potential wearable computing applications in libraries. The aim of the paper is to initiate further discussion on the possibilities of using wearable devices to improve both services for users and internal library processes. The author’s hope is that the concepts presented here will be tested in practice by librarians, which will allow further development of research on this subject. Practical implications The results can be widely used in practice as a framework for the implementation of wearable computing solutions in libraries. Social implications The paper can help to facilitate the debate on the role of the implementation of new technologies in libraries. Originality/value The issue of the use of wearable computing has not yet been widely discussed in library and information science journals. In the very few publications on similar topics, only the perspective on the application of new solutions in services for users is presented. This paper also shows the potential for improving libraries’ internal processes with the use of wearable computing.
Comparison Between LGBP and DCLBP for Non-frontal Emotion Recognition

Emotion recognition has captured the attention of many researchers these days. However most of the researches have happened in emotion recognition from frontal faces. In real-world conditions, we may not always be able to capture the frontal faces. Hence, emotion recognition from non-frontal faces is the new research area. The Local Binary Patterns (LBP) is an important feature extraction technique. In our effort to find better variant of LBP for non-frontal emotion detection, we have used two possible variants namely Local Gabor Binary Pattern (LGBP) and Diagonal Crisscross Local Binary Pattern (DCLBP). The LGBP is further implemented with and without Angle Classification; which leads to total three methods of feature extraction. These three methods are used to classify the images based on facial emotion expressions. An image is divided into number of blocks. Feature vectors are created by concatenating histograms computed from each sub-block. Multi-class SVMs are used to classify angles and expressions. A comparative analysis of the three methods for non-frontal emotion recognition has been carried out and is presented in this paper. By analyzing different variants of a LBP, we can understand the importance of feature representation in non-frontal emotion recognition. Our experimental studies show that the LGBP with angle classification outperforms other two variants.
Adversarial examples influence human visual perception

Computer vision models are vulnerable to adversarial examples: small changes to images that cause models to make mistakes. Adversarial examples often transfer from one model to another, making it possible to attack models that an attacker has no access to. This raises the question of whether adversarial examples similarly transfer to humans. Clearly, humans are prone to many cognitive biases and optical illusions, but these generally do not resemble small perturbations, nor are they generated by optimization of a machine learning loss function. Thus, adversarial examples has been widely assumed – in the absence of experimental evidence – to not influence human perception. A rigorous investigation of the above question creates an opportunity both for machine learning and neuroscience. If we knew that the human brain could resist certain classes of adversarial examples, this would provide an existence proof for a similar mechanism in machine learning security. On the other hand, if we knew that the brain could be fooled by adversarial examples, this phenomenon could lead to a better understanding of brain function. Here, we investigate this question by leveraging three ideas from machine learning, neuroscience, and psychophysics[1]. First, we use black box adversarial example construction techniques to generate adversarial examples. Second, we adapt machine learning models to mimic the initial visual processing of humans. Third, we evaluate classification decisions of human observers in a time-limited setting to limit the brain’s utilization of recurrent and top-down processing pathways[2]. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers. [1] A version of this work is accepted, but not yet presented, as a conference paper at NIPS 2018. [2] M. Potter et al. Detecting meaning in rsvp at 13 ms per picture. Attention, Perception, Psychophysics, 76(2):270–279, 2014.
Indoor–Outdoor Scene Classification with Residual Convolutional Neural Network

In this paper, we demonstrate the effectivenessKumari, Seema of a customized ResNetJha, Ranjeet Ranjan to address the problem of indoor–outdoor scene classificationNigam, Aditya both for color images as well as depth imagesBhavsar, Arnav. Such an approach can serve as an initial step in a scene classification/retrieval pipeline or a single-image depth estimation task. The classification framework is developed based on Residual Convolutional Neural Network (ResNet-18) to classify any random scene as indoor or outdoor. We also demonstrate the invariance of the classification performance with respect to different weather conditions of outdoor scenes (which one can commonly encounter). The performance of our classification strategy is analyzed on different varieties of publicly available datasets of indoor and outdoor scenes that also have corresponding depth maps. The suggested approach achieves almost an ideal performance in many scenarios, for both color and depth images, across datasets. We also show positive comparisons with other state-of-the-art methods.
Scalable and Efficient Data Authentication for Decentralized Systems.

Public blockchains like Ethereum use Merkle trees to verify transactionsreceived from untrusted servers before applying them to the blockchain. Weempirically show that the low throughput of such blockchains is due to the I/Obottleneck associated with using Merkle trees for processing transactions. Wepresent RAINBLOCK, a new architecture for public blockchains that increasesthroughput without affecting security. \sysname achieves this by tackling theI/O bottleneck on two fronts: first, decoupling transaction processing fromI/O, and removing I/O from the critical path; second, reducing I/Oamplification by customizing storage for blockchains. RAINBLOCK uses a novelvariant of the Merkle tree, the Distributed Sharded Merkle tree (DSM-TREE) tostore system state. We evaluate RAINBLOCK using workloads based on publicEthereum traces (including smart contracts) and show that \sysname processes20K transactions per second in a geo-distributed setting with four regionsspread across three continents.
INFaaS: A Model-less Inference Serving System

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain key challenges. Developers must manually match the performance, accuracy, and cost constraints of their applications to decisions about selecting the right model and model optimizations, suitable hardware architectures, and auto-scaling configurations. These interacting decisions are difficult to make for users, especially when the application load varies, applications evolve, and the available resources vary over time. Thus, users often end up making decisions that overprovision resources. This paper introduces INFaaS, a model-less inference-as-a-service system that relieves users of making these decisions. INFaaS provides a simple interface allowing users to specify their inference task, and performance and accuracy requirements. To implement this interface, INFaaS generates and leverages model-variants, versions of a model that differ in resource footprints, latencies, costs, and accuracies. Based on the characteristics of the model-variants, INFaaS automatically navigates the decision space on behalf of users to meet user-specified objectives: (a) it selects a model, hardware architecture, and any compiler optimizations, and (b) it makes scaling and resource allocation decisions. By sharing models across users and hardware resources across models, INFaaS achieves up to 150x cost savings, 1.5x higher throughput, and violates latency objectives 1.5x less frequently, compared to Clipper and TensorFlow Serving.