Reviewerly/exHarmony huggingface dataset:
Entailment for measure based belief structures

We discuss the Dempster–Shafer belief structure on finite universes and note its use for modeling variables that have both probabilistic uncertainty as well as imprecision. We note for these structures the probability that the variable lies in a subset cannot be precisely known but only be known to an interval value. We discuss methods for deducing this uncertainty interval. We next discuss the issue of entailment of belief structures, inferring the validity of additional belief model of a variable from an already established belief model of the variable. We next discuss a more general belief structure were the underling uncertainty rather tha0n being based on a probability distribution is based on a general measure type of uncertainty. We then extend the concept of entailment to the case where the belief structures are these more general measure based belief structures. In order to accomplish this we must extend the idea of containment from classic Dempster–Shafer belief structures to measure based belief structures.
Action Anticipation by Predicting Future Dynamic Images

Human action-anticipation methods predict what is the future action by observing only a few portion of an action in progress. This is critical for applications where computers have to react to human actions as early as possible such as autonomous driving, human-robotic interaction, assistive robotics among others. In this paper, we present a method for human action anticipation by predicting the most plausible future human motion. We represent human motion using Dynamic Images [1] and make use of tailored loss functions to encourage a generative model to produce accurate future motion prediction. Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.
A General Domain Specific Feature Transfer Framework for Hybrid Domain Adaptation

Heterogeneous domain adaptation needs supplementary information to link up different domains. However, such supplementary information may not always be available in real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation, in which different domains share some common features, but also have their own domain specific features. We leverage upon common features instead of supplementary information to achieve effective adaptation. We propose a general domain specific feature transfer framework, which can link up different domains using common features and simultaneously reduce domain divergences. Specifically, we learn the translations between common features and domain specific features. Then, we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. We instantiate the general framework to a linear case and a nonlinear case. Extensive experiments verify the effectiveness of the two cases.
Exploring vendor capabilities in the cloud environment: A case study of Alibaba Cloud Computing

Cloud computing is being viewed as an effective enabler for enhanced operational efficiency and flexibility in competitive business environments. Despite the increased knowledge on cloud computing, very few studies have been conducted to explore vendors' capabilities for service development and delivery. This gap prevents us from developing a full understanding of the service provision process and the actions through which vendors develop cloud services and create value for clients. In this paper, we present an in-depth qualitative case study of Alibaba Cloud Computing, China's biggest Infrastructure-as-a-Service (IaaS) vendor, and identify five important capabilities, namely, cloud platform development, cloud platform deployment, IaaS imitation, IaaS commercialization, and IaaS improvement. We suggest that these capabilities and the associated actions are central to vendors' adoption of cloud computing, acquisition of knowledge, and delivery and improvement of their cloud service provision. This paper contributes incrementally to the evolving scholarship on cloud computing and also offers useful guidelines for current and aspiring vendors.
Definitional proof-irrelevance without K

Definitional equality—or conversion—for a type theory with a decidable type checking is the simplest tool to prove that two objects are the same, letting the system decide just using computation. Therefore, the more things are equal by conversion, the simpler it is to use a language based on type theory. Proof-irrelevance, stating that any two proofs of the same proposition are equal, is a possible way to extend conversion to make a type theory more powerful. However, this new power comes at a price if we integrate it naively, either by making type checking undecidable or by realizing new axioms—such as uniqueness of identity proofs (UIP)—that are incompatible with other extensions, such as univalence. In this paper, taking inspiration from homotopy type theory, we propose a general way to extend a type theory with definitional proof irrelevance, in a way that keeps type checking decidable and is compatible with univalence. We provide a new criterion to decide whether a proposition can be eliminated over a type (correcting and improving the so-called singleton elimination of Coq) by using techniques coming from recent development on dependent pattern matching without UIP. We show the generality of our approach by providing implementations for both Coq and Agda, both of which are planned to be integrated in future versions of those proof assistants.
Adjusted Non-Local Regression and Directional Smoothness for Image Restoration

Image restoration (IR) problems are very important in many low-level vision tasks. Due to their ill-posed natures, image priors are widely used to regularize the solution spaces. Recently, patch-based non-local self-similarity has shown great potential in IR problems, leading to many effective non-local priors. Their performance largely depends on whether the non-local self-similarity of the underlying image can be fully exploited. However, most of these priors, including non-local regression (NLR), only utilize the center pixel of each patch to model the non-local feature, which is suboptimal. We propose an effective overlap-based non-local regression (ONLR) to fully exploit the non-local similar patches: first, the concept of overlap-based similar pixels group (OSPG) is introduced; second, for each pixel within an OSPG, the non-local weight is obtained via a novel similarity measurement method; third, based on the consistency assumption, the non-local fitting deviations (NLFDs) by using OSPGs are uniformly constrained. Because of the uniform constraints, the restoration may be poor in regions where OSPGs are not reliable. Consequently, a weighting scheme is proposed to measure the OSPG reliability, leading to a novel adjusted non-local regression (ANLR). In addition, the integral image technique (IIT) is adopted to speed up the similar patches search process. To further boost the ANLR, a local directional smoothness (DS) prior is proposed as a good complement of the non-local feature. Finally, a fast split Bregman iteration algorithm is designed to solve the ANLR-DS minimization problem. Extensive experiments on two typical IR problems, that is, image deblurring and super resolution, demonstrate the superiority of the proposed method compared to many state-of-the-art IR methods.
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving

Deep learning is a fast-growing machine learning approach to perceive and understand large amounts of data. In this paper, general information about the deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolutional Network (FCN) architectures obtained by modifying the Convolutional Neural Network (CNN) architectures based on deep learning. Experimental studies for the application are utilized 4 different FCN architectures named FCN-AlexNet, FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.
Quantum walks in external gauge fields

Describing a particle in an external electromagnetic field is a basic task of quantum mechanics. The standard scheme for this is known as "minimal coupling", and consists of replacing the momentum operators in the Hamiltonian by modified ones with an added vector potential. In lattice systems it is not so clear how to do this, because there is no continuous translation symmetry, and hence there are no momenta. Moreover, when time is also discrete, as in quantum walk systems, there is no Hamiltonian, only a unitary step operator. We present a unified framework of gauge theory for such discrete systems, keeping a close analogy to the continuum case. In particular, we show how to implement minimal coupling in a way that automatically guarantees unitary dynamics. The scheme works in any lattice dimension, for any number of internal degree of freedom, for walks that allow jumps to a finite neighborhood rather than to nearest neighbours, is naturally gauge invariant, and prepares possible extensions to non-abelian gauge groups.
Time-Bounded Activity Recognition for Ambient Assisted Living

Robust activity recognition in near real-time is a prerequisite for delivering the smartness intrinsic to the pragmatic realisation of smart homes, environments and so forth. Many of the physical devices necessary for equipping a smart home are already available as consumer electronic devices and certified for use by the public. Yet activity recognition remains the preserve of the research community, despite the array of machine learning and other AI techniques currently available. To-date, research has been dominated by the use of pre-segmented data, resulting in the recognition of an arbitrary activity subsequent to its completion. For assistive paradigms dependent on smart technologies, for example Ambient Assisted Living, such approaches are insufficient. The overall objective must be the identification of an activity within an appropriative confidence level as soon as possible after activity commencement. This paper presents a novel approach, Cumulatively Overlapping windowing approach for AmBient Recognition of Activities (COBRA), for near real-time activity recognition, specifically within 10, 30, 60 or 120 seconds of the commencement of an activity. COBRA utilizes an innovative combination of sliding windows augmented with a logistic regression model. The approach is evaluated using the well-established, open, CASAS dataset.
SRVis: Towards Better Spatial Integration in Ranking Visualization

Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.
Graph-Regularized Locality-Constrained Joint Dictionary and Residual Learning for Face Sketch Synthesis

Face sketch synthesis is a crucial issue in digital entertainment and law enforcement. It can bridge the considerable texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly to learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual specific features, which we call rare characteristics. In this paper, we propose a novel face sketch synthesis approach through residual learning. In contrast to traditional approaches, which aim to reconstruct a sketch image directly (i.e., learn the mapping relationship between the photo and sketch), we aim to predict the residual image by learning the mapping relationship between the photo and residual, i.e., the difference between the photo and sketch, given an observed photo. This technique will render optimizing the residual mapping easier than optimizing the original mapping and deriving rare characteristic information. We also introduce a joint dictionary learning algorithm by preserving the local geometry structure of a data space. Through the learned joint dictionary, we transform the face sketch synthesis from an image space to a new and compact space; the new and compact space is spanned by learned dictionary atoms, where the manifold assumption can be further guaranteed. Results show that the proposed method demonstrates an impressive performance in the face sketch synthesis task on three public face sketch datasets and various real-world photos. These results are derived by comparing the proposed method with several state-of-the-art techniques, including certain recently proposed deep learning-based approaches.
Hybrid Modulation Scheme Combining PPM With Differential Chaos Shift Keying Modulation

In conventional M-ary differential chaos shift keying modulation (DCSK) systems, the distance between constellation points gets closer as M increases, resulting in poor performance. A hybrid modulation scheme based on pulse position modulation (PPM) and DCSK is proposed in this letter to improve bit-errorrate (BER) performance. In this scheme, one part of the bits is modulated by PPM while the other part is modulated by DCSK. Thus, information bearing signals are simultaneously modulated by the information bit and the selected pulse position of PPM which is determined by extra information bits. Analytical BER performance of the proposed scheme is derived and verified by simulations. Results show that the considered scheme outperforms conventional M-DCSK, code index modulation DCSK, and commutation code index DCSK in additive white Gaussian noise and multipath Rayleigh fading channels.
Economic freedom and bilateral direct investment

Economic freedom (freedom from the intervention of government) is essential for doing business, so economic freedom of both the home country and the host country are important for bilateral foreign direct investment. However, though some literature has investigated the role of host country's economic freedom in bilateral direct investment, no literature has studied the role of home country's economic freedom. This paper has studied this issue in a gravity model with a sample of 155 countries. This paper has also employed some effective estimation techniques of gravity model to incorporate the zero observations and adopted quantile regression method. The findings indicate that economic freedom of both the home country and the host country are positively correlated with bilateral direct investment, and the economic freedom of home country has even stronger explanatory power for foreign direct investment. Hence, promoting the economic freedom may encourage more outward foreign direct investment than inward direct investment.
Dynamic interpolation for obstacle avoidance on Riemannian manifolds

This work is devoted to studying dynamic interpolation for obstacle avoidance. This is a problem that consists of minimising a suitable energy functional among a set of admissible curves subject to some interpolation conditions. The given energy functional depends on velocity, covariant acceleration and on artificial potential functions used for avoiding obstacles. We derive first-order necessary conditions for optimality in the proposed problem; that is, given interpolation and boundary conditions we find the set of differential equations describing the evolution of a curve that satisfies the prescribed boundary values, interpolates the given points and is an extremal for the energy functional. We study the problem in different settings including a general one on a Riemannian manifold and a more specific one on a Lie group endowed with a left-invariant metric. We also consider a sub-Riemannian problem.
A Localizability Constraint-Based Path Planning Method for Autonomous Vehicles

During autonomous navigation, environmental information and map noises at different locations may have dissimilar influence on a vehicle's localization process. This implies that the vehicle's localizability, i.e., the ability to localize itself in an environment using laser range finder (LRF) readings, varies over a given map. It is essential to take this factor into consideration when planning a path to avoid large localization errors or placing the vehicle at risk of failure to perform localization. We propose a localizability constraint (LC)-based path planning method for autonomous vehicles which plans the navigation path according to LRF sensor model of the vehicle in an effort to maintain a satisfactory level of localizability throughout the path, as well as to reduce the overall localization error. Our method is not limited to any specific algorithm in the optimization stage. Paths planned with and without LC are compared, and the influence of the LRF sensor model on planning outcomes is discussed through simulations. By conducting comparative experiments on a “JiaoLong” intelligent wheelchair in both indoor and outdoor environments, we show that the proposed method effectively lowers the localization error along the planned paths.
The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains

The success of Web-based applications depends on their ability to convert first-time users into recurring ones. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) providing useful recommendations to users without historical data. Traditionally, the systems assume that items biased by popularity, recency and positive ratings suit the interests of most first-time users. However, our studies confirm a contra-intuitive hypothesis, showing users consumption preferences biased to non-popular items. For this reason, we introduce two new RSs to mitigate this problem based on user coverage maximization: Max-Coverage and Category-Exploration. Offline experiments show that our recommendations complement the traditional ones. Thus, we found an opportunity for improvements on state-of-the-art RSs. We hypothesize that combining distinct non-personalized RSs can be better to conquer the most first-time users than traditional ones. An online study conducted with 236 real users in movie domain reinforced this hypothesis. Hence, this study provides a clear message: we should compose product pages that mix complementary non-personalized RSs.
Faithful Hierarchy of Genuine <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>n</mml:mi></mml:math> -Photon Quantum Non-Gaussian Light

Light is an essential tool for connections between quantum devices and for diagnostic processes in quantum technology. Both applications deal with advanced nonclassical states beyond Gaussian coherent and squeezed states. Current development requires a loss-tolerant diagnostic of such nonclassical aspects. We propose and experimentally verify a faithful hierarchy of genuine n-photon quantum non-Gaussian light. We conclusively witnessed three-photon quantum non-Gaussian light in the experiment. Measured data demonstrate a direct applicability of the hierarchy for a large class of real states.
API recommendation for event-driven Android application development

Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework. This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) functional APIs for implementing a functionality and (2) the event callback APIs that are to be overridden to contain the functional code. We carry out an empirical study on actual Android programming questions from StackOverflow to confirm the need of recommending callbacks. Then we build Android-specific API databases to contain the correlations among various functionalities and APIs, based on customized parsing of code snippets and natural language processing of texts in Android tutorials and SDK documents, and then textual and code similarity metrics are adapted for recommending relevant APIs. We have evaluated our prototype recommendation engine, named LibraryGuru, with about 1500 questions on Android programming from StackOverflow, and demonstrated that our top-5 results on recommending callbacks and functional APIs can on estimate achieve up to 43.5% and 50.9% respectively in precision, 24.6% and 32.5% respectively in mean average precision (MAP) scores, and 51.1% and 44.0% respectively in recall. We conclude that it is important and possible to recommend both functional APIs and callbacks for Android application development, and future work is needed to take more data sources into consideration to make more relevant recommendations for developers’ needs.