Reviewerly/exHarmony huggingface dataset:
Enhanced answer selection in CQA using multi-dimensional features combination

Community Question Answering (CQA) in web forums, as a classic forum for user communication, provides a large number of high-quality useful answers in comparison with traditional question answering. Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects, and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multi-dimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks: (a) classifying answers as good, bad, or potentially associated with a question, and (b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.
Continuous variable quantum key distribution with multi-mode signals for noisy detectors

This paper proposes a multi-mode Gaussian modulated continuous variable quantum key distribution (CV-QKD) scheme able to operate at high bandwidth despite using conventional noisy, coherent detectors. We demonstrate enhancement in shotnoise sensitivity as well as reduction in the electronic noise variance of the coherent receiver of the multi-mode CV-QKD system. A proof-of-concept simulation is presented using multiple modes; this demonstrates an increase in signal-to-noise ratio and secure key rate at various transmission distances with increasing signal modes.
Quantum Fisher information of atomic system interacting with a single cavity mode in the presence of Kerr medium

The quantum Fisher information of an atomic system interacting with a single cavity mode in the presence of Kerr medium is discussed. It is shown that quantum Fisher information for an initial separable atomic system is larger than that depicted for the initial entangled atomic system. For initial vacuum state of the cavity mode, the quantum Fisher information with respect to the Kerr medium and the phase decoherence parameter is larger than that displayed for the detuning parameter. Both phase decoherence and Kerr medium have the same effect on the decay of quantum Fisher information, while they have an opposite effect on its maximum values.
Energy-Aware Autonomic Resource Scheduling Framework for Cloud

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.
Conceptual Approach for an Extension to a Mushroom Farm Distributed Process Control System: IoT and Blockchain

Collecting, storing, integrating and transforming data, together with the problem of security and privacy, are topics that present great challenges for society. The needs of the industry in general and agri-food in particular in the mushroom production sector, due to their specificities, requires the adoption of emerging technologies to make them more productive and more competitive in this global market. In this type of industry it is important, and essential, the control of the environmental variables of the production areas and the way they are presented, because the information associated with these variables would provide an important complement to the established production control system. In this paper we propose a conceptual approach for an extension to a mushroom farm distributed process control system with IoT and blockchain integration that not only allows to collect distributed data on the environmental indicators inherent to the mushrooms production, but also complement the already existent production control system, which is extremely important for the overall success of the farm management information system used by the group managers.
Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition

Near-infrared (NIR) facial expression recognition is resistant to illumination change. In this paper, we propose a three-stream three-dimensional convolution neural network with a squeeze-and-excitation (SE) block for NIR facial expression recognition. We fed each stream with different local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to different local features to further improve recognition accuracy. The experimental results on the Oulu-CASIA NIR facial expression database showed that the proposed method has a higher recognition rate than some state-of-the-art algorithms.
PnP

Frequently used parallel iterative graph analytics algorithms are computationally expensive. However, researchers have observed that applications often require point-to-point versions of these analytics algorithms that are less demanding. In this paper we introduce the PnP parallel framework for iterative graph analytics that processes a stream of point-to-point queries with each involving a single source and destination vertex pair. The efficiency of our framework is derived from the following two novel features: online Pruning of graph exploration that eliminates propagation from vertices that are determined to not contribute to a query's final solution; and dynamic direction Prediction for solving the query in either forward (from source) or backward (from destination) direction as their costs can differ greatly. PnP employs a two-phase algorithm where, Phase 1 briefly traverses the graph in both directions to predict the faster direction and enable pruning; then Phase 2 completes query evaluation by running the algorithm for the chosen direction till it converges. Our experiments show that PnP responds to queries rapidly because of accurate direction selection and effective pruning that often offsets the runtime overhead of direction prediction. PnP substantially outperforms Quegel, the only other point-to-point query evaluation framework. Our experiments on multiple benchmarks and graphs show that PnP on a single machine is 8.2× to 3116× faster than Quegel on a cluster of four machines.
Mesh-Based Computation for Solving Photometric Stereo With Near Point Lighting

We tackle the problem of dense reconstruction with a practical system, in which near point lighting (NPL) is employed. Different from the conventional formulation of photometric stereo (PS) that assumes parallel lighting, PS under the NPL condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. After obtaining the locations of point lights by a calibration process, we develop a new framework to solve this nonlinear reconstruction problem via mesh deformation, in which each facet is corresponding to a pixel in the image captured by the camera. In our framework, mesh deformation is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under NPL in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.
A bottom-up summarization algorithm for videos in the wild

Video summarization aims to provide a compact video representation while preserving the essential activities of the original video. Most existing video summarization approaches relay on identifying important frames and optimizing target energy by a global optimum solution. But global optimum may fail to express continuous action or realistically validate how human beings perceive a story. In this paper, we present a bottom-up approach named clip growing for video summarization, which allows users to customize the quality of the video summaries. The proposed approach firstly uses clustering to oversegment video frames into video clips based on their similarity and proximity. Simultaneously, the importance of frames and clips is evaluated from their corresponding dissimilarity and representativeness. Then, video clips and frames are gradually selected according to their energy rank, until reaching the target length. Experimental results on SumMe dataset show that our algorithm can produce promising results compared to existing algorithms. Several video summarizations results are presented in supplementary material.
Generic Constructions of Revocable Identity-Based Encryption

Revocable identity-based encryption (RIBE) is an extension of IBE which can support a key revocation mechanism, and it is important when deploying an IBE system in practice. Boneh and Franklin (Crypto’01) presented the first generic construction of RIBE, however, their scheme is not scalable where the size of key updates is linear in the number of users in the system. Then, Boldyreva, Goyal and Kumar (CCS’08) presented the first scalable RIBE which significantly reduces the size of key update to logarithmic in the number of users. In this paper, we first present a generic construction of scalable RIBE from any IBE in a black-box way which solves the open problem presented by Seo and Emura (PKC’13). Our construction has some merits both in theory and practice. In theory, we can obtain the first RIBE scheme from quadratic residues modulo composite and the first adaptive-ID secure RIBE scheme from lattices if we instantiate the underlying IBE with IBE schemes from quadratic residues modulo composite and adaptive-ID secure IBE schemes from lattices, respectively. In practice, public parameters size and secret keys size of our construction can be same as those of the underlying (H)IBE scheme. Our construction is naturally server-aided where the overheads of decryption computation for receivers is the same as that of underlying IBE schemes. Inspired by recent work of Katsumata et al. (PKC’19), we present a generic construction of RIBE with decryption key exposure resistance by using hierarchical IBE (HIBE) and IBE schemes. Finally, we reduce the ciphertext size to constant by using two HIBE schemes.
We Did It Right, But It Was Still Wrong

HCI interventions often fall short of delivering lasting impact in resource-constrained contexts. We reflect on a project where we followed the "right" steps of needs-based, human-centered design, yet failed to deliver impact to the community. We introduce a framework that evaluates an intervention's potential for sustainable impact by maximizing use of assets in the community and minimizing novelty. We propose assets-based design as an approach that starts with what a community has, leveraging those assets in a design, as opposed to a needs-based approach that focuses on adding what a community lacks.
Defensive peripersonal space is modified by a learnt protective posture

Abstract The Hand Blink Reflex (HBR) is a subcortical defensive response, elicited by the electrical stimulation of the median nerve. HBR increases when the stimulated hand is inside the defensive peripersonalspace (DPPS) of the face. However, the presence of a screen protecting the face could reduce the amplitude of this response. This work aimed to investigate whether the learning of a posture intended to protect the head could modulate the HBR responses. Boxing athletes learn a defensive posture consisting of blocking with arms opponent’s blow towards the face. Two groups were recruited: 13 boxers and 13 people naïve to boxing. HBR response was recorded and elicited in three hand positions depending on the distance from the face. A suppression of HBR enhancement in the static position close to the face was observed in boxer group, contrary to the control group. Also, the higher years of practice in boxing, the higher suppression occurred. However, this suppression was not observed when boxers were asked to move the hand up-to/down-from the face. These findings might suggest that the sensorimotor experience related to a previously learnt protective posture can modify the HBR and thus shape the dimension of the DPPS.
How AI-Chatbots Can Make Dubai Smarter?

Dubai has been the leading Smart City in the MENA region for the past decade. The strong commitment of the government to emerging technology, in particular, Artificial Intelligence was the main reason for utilizing Chatbots in a number of government entities in the past three years. This study, after a short introduction to Chatbots and their applications in governmental sectors around the world, reviews the performance of existing Chatbots in the Dubai government and makes recommendations on how the Dubai government extend the deployment of Chatbots even further to serve its residents better.
A Method for Calibrating a Digital Image Correlation System for Full-Field Strain Measurements during Large Deformations

Numerous variables can introduce errors into the measurement chain of a digital image correlation (DIC) system. These can be grouped into two categories: measurement quality and the correlation principle. Although previous studies have attempted to investigate each error source in isolation, there are still no comprehensive, standardized procedures for calibrating DIC systems for full-field strain measurement. The aim of this study, therefore, was to develop an applied experimental method that would enable a DIC practitioner to perform a traceable full-field measurement calibration to evaluate the accuracy of a particular system setup in a real-world environment related to their specific application. A sequence of Speckle Pattern Boards (SPB) that included artificial deformations of the speckle pattern were created, allowing for the calibration of in-plane deformations. Multiple deformation stages (from 10% to 50%) were created and measured using the GOM ARAMIS system; the results were analysed and statistical techniques were used to determine the accuracy. The measured strain was found to be slightly over-estimated (nominally by 0.02%), with a typical measurement error range of 0.34% strain at a 95% confidence interval. Location within the measurement volume did not have a significant effect on error distributions. It was concluded that the methods developed could be used to calibrate a DIC system in-situ for full-field measurements of large deformations. The approach could also be used to benchmark different DIC systems against each other or allow operators to better understand the influence of particular measurement variables on the measurement accuracy.
Quantum proof systems for iterated exponential time, and beyond

We show that any language solvable in nondeterministic time exp( exp(⋯exp(n))), where the number of iterated exponentials is an arbitrary function R(n), can be decided by a multiprover interactive proof system with a classical polynomial-time verifier and a constant number of quantum entangled provers, with completeness 1 and soundness 1 − exp(−Cexp(⋯exp(n))), where the number of iterated exponentials is R(n)−1 and C>0 is a universal constant. The result was previously known for R=1 and R=2; we obtain it for any time-constructible function R.
Improved Differentially Private Analysis of Variance

Abstract Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the restrictions of differential privacy. We show that the F -statistic, the optimal test statistic in the public setting, is no longer optimal in the private setting, and we develop a new test statistic F 1 with much higher statistical power. We show how to rigorously compute a reference distribution for the F 1 statistic and give an algorithm that outputs accurate p -values. We implement our test and experimentally optimize several parameters. We then compare our test to the only previous work on private ANOVA testing, using the same effect size as that work. We see an order of magnitude improvement, with our test requiring only 7% as much data to detect the effect.
Face Completion with Semantic Knowledge and Collaborative Adversarial Learning

Unlike a conventional background inpainting approach that infers a missing area from image patches similar to the background, face completion requires semantic knowledge about the target object for realistic outputs. Current image inpainting approaches utilize generative adversarial networks (GANs) to achieve such semantic understanding. However, in adversarial learning, the semantic knowledge is learned implicitly and hence good semantic understanding is not always guaranteed. In this work, we propose a collaborative adversarial learning approach to face completion to explicitly induce the training process. Our method is formulated under a novel generative framework called collaborative GAN (collaGAN), which allows better semantic understanding of a target object through collaborative learning of multiple tasks including face completion, landmark detection and semantic segmentation. Together with the collaGAN, we also introduce an inpainting concentrated scheme such that the model emphasizes more on inpainting instead of autoencoding. Extensive experiments show that the proposed designs are indeed effective and collaborative adversarial learning provides better feature representations of the faces. In comparison with other generative image inpainting models and single task learning methods, our solution produces superior performances on all tasks.
Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine

In this paper we propose a method for recognizing hand drawn electronic circuit diagrams.The proposed method first detect and classify each components present in the hand drawn circuit diagram.For the purpose of component recognition, we have constructed the feature vector by combining Local Binary Pattern (LBP) and statistical features based on pixel density.Classification of components is done by using support vector machine (SVM) classifier.Upon detection and recognition of components, the proposed method subsequently uses the position and sequence of arrangement of components to determine the type of circuit.For the purpose of establishing the sequence of components we have used finite state machine.The proposed method represents the sequence of recognized components as a string.This string representation of circuit is fed to a Finite State Machine (FSM) to detect type of circuit.The proposed method has been tested on about 100 hand written circuit diagrams of varying complexities and of different types.The proposed component detection method gives over 99% accuracy whereas, the circuit recognition method has recognition rate of over 85% recognition rate for the circuit type recognition.
Many-particle entanglement in multiple quantum nuclear-magnetic-resonance spectroscopy

We use multiple quantum (MQ) NMR dynamics of a gas of spin-carrying molecules in nanocavities at high and low temperatures for an investigation of many-particle entanglement. A distribution of MQ NMR intensities is obtained at high and low temperatures in a system of 201 spins 1/2. The second moment of the distribution, which provides a lower bound on the quantum Fisher information, sheds light on the many-particle entanglement in the system. The dependence of the many-particle entanglement on the temperature is investigated. Almost all spins are entangled at low temperatures.
Hybrid regression and isophote curvature for accurate eye center localization

Abstract The eye center localization is a crucial requirement for various human-computer interaction applications such as eye gaze estimation and eye tracking. However, although significant progress has been made in the field of eye center localization in recent years, it is still very challenging for tasks under the significant variability situations caused by different illumination, shape, color and viewing angles. In this paper, we propose a hybrid regression and isophote curvature for accurate eye center localization under low resolution. The proposed method first applies the regression method, which is called Supervised Descent Method (SDM), to obtain the rough location of eye region and eye centers. SDM is robust against the appearance variations in the eye region. To make the center points more accurate, isophote curvature method is employed on the obtained eye region to obtain several candidate points of eye center. Finally, the proposed method selects several estimated eye center locations from the isophote curvature method and SDM as our candidates and a SDM-based means of gradient method further refine the candidate points. Therefore, we combine regression and isophote curvature method to achieve robustness and accuracy. In the experiment, we have extensively evaluated the proposed method on the two public databases which are very challenging and realistic for eye center localization and compared our method with existing state-of-the-art methods. The results of the experiment confirm that the proposed method outperforms the state-of-the-art methods with a significant improvement in accuracy and robustness and has less computational complexity.
MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.