Reviewerly/exHarmony huggingface dataset:
Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems

A wide range of discrete planning problems can be solved optimally using graph search algorithms. However, optimal search quickly becomes infeasible with increased complexity of a problem. In such a case, heuristics that guide the planning process towards the goal state can increase performance considerably. Unfortunately, heuristics are often unavailable or need manual and time-consuming engineering. Building upon recent results on applying deep learning to learn generalized reactive policies, we propose to learn heuristics by imitation learning. After learning heuristics based on optimal examples, they are used to guide a classical search algorithm to solve unseen tasks. However, directly applying learned heuristics in search algorithms such as A∗ breaks optimality guarantees, since learned heuristics are not necessarily admissible. Therefore, we (i) propose a novel method that utilizes learned heuristics to guide Focal Search A∗, a variant of A∗ with guarantees on bounded suboptimality; (ii) compare the complexity and performance of jointly learning individual policies for multiple robots with an approach that learns one policy for all robots; (iii) thoroughly examine how learned policies generalize to previously unseen environments and demonstrate considerably improved performance in a simulated complex dynamic coverage problem.
Calibrated Stochastic Gradient Descent for Convolutional Neural Networks

In stochastic gradient descent (SGD) and its variants, the optimized gradient estimators may be as expensive to compute as the true gradient in many scenarios. This paper introduces a calibrated stochastic gradient descent (CSGD) algorithm for deep neural network optimization. A theorem is developed to prove that an unbiased estimator for the network variables can be obtained in a probabilistic way based on the Lipschitz hypothesis. Our work is significantly distinct from existing gradient optimization methods, by providing a theoretical framework for unbiased variable estimation in the deep learning paradigm to optimize the model parameter calculation. In particular, we develop a generic gradient calibration layer which can be easily used to build convolutional neural networks (CNNs). Experimental results demonstrate that CNNs with our CSGD optimization scheme can improve the stateof-the-art performance for natural image classification, digit recognition, ImageNet object classification, and object detection tasks. This work opens new research directions for developing more efficient SGD updates and analyzing the backpropagation algorithm.
Dressed quantum trajectories: novel approach to the non-Markovian dynamics of open quantum systems on a wide time scale

Abstract A new approach to theory and simulation of the non-Markovian dynamics of open quantum systems is presented. It is based on identification of a parameter which is uniformly bounded on wide time intervals: the occupation of the virtual cloud of quanta. By ‘virtual’ we denote those bath excitations which were emitted by the open system, but eventually will be reabsorbed before any measurement of the bath state. A useful property of the virtual cloud is that the number of its quanta is expected to saturate on long times, since physically this cloud is a (retarded) polarization of the bath around the system. Therefore, the joint state of open system and virtual cloud (we call it dressed state) can be accurately represented in a truncated basis of Fock states, on a wide time scale. At the same time, there can be an arbitrarily large number of the observable quanta (which survive up to measurement), especially if the open system is under driving. However, it turns out that the statistics of the bath-measurement outcomes is classical (in a suitable measurement basis): one can employ a Monte Carlo sampling of these outcomes. Therefore, it is possible to efficiently simulate the dynamics of the observable quantum field. In this work we consider the bath measurement with respect to the coherent states, which yields the Husimi function as the positive (quasi)probability distribution of the outcomes. The joint evolution of the dressed state and the corresponding outcome is called the dressed quantum trajectory. The Monte Carlo sampling of these trajectories yields a stochastic simulation method with promising convergence properties on wide time scales.
Entanglement-assisted communication in the absence of shared reference frame

Alice wants to convey the value of a parameter to Bob with whom she does not share a reference frame. What physical object can she use for this task? Shall she encode this value into the angle between two physical vectors such as the angle between two spins? Can she benefit from using entanglement? We investigate these questions here and show that an entangled state of two qubits has three parameters that are invariant under changes of the reference frame. We also calculate for specific examples the average information gain for different circumstances, where one of these parameters is used for communication. We compare our result with the special case of separable states and find that entanglement enhances the information gain.
Vector and line quantization for billion-scale similarity search on GPUs

Billion-scale high-dimensional approximate nearest neighbor (ANN) search has become an important problem for searching similar objects among the vast amount of images and videos available online. The existing ANN methods are usually characterized by their specific indexing structures, including the inverted index and the inverted multi-index structure. The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs. However, the inverted index requires high memory overhead to index the dataset effectively. The inverted multi-index structure is difficult to implement for GPUs, and also ineffective in dealing with database with different data distributions. In this paper we propose a novel hierarchical inverted index structure generated by vector and line quantization methods. Our quantization method improves both search efficiency and accuracy, while maintaining comparable memory consumption. This is achieved by reducing search space and increasing the number of indexed regions. We introduce a new ANN search system, VLQ-ADC, that is based on the proposed inverted index, and perform extensive evaluation on two public billion-scale benchmark datasets SIFT1B and DEEP1B. Our evaluation shows that VLQ-ADC significantly outperforms the state-of-the-art GPU- and CPU-based systems in terms of both accuracy and search speed. The source code of VLQ-ADC is publicly available online.1
Guiding attention of faces through graph based visual saliency (GBVS)

In a general scenario, while attending a scene containing multiple faces or looking towards a group photograph, our attention does not go equal towards all the faces. It means, we are naturally biased towards some faces. This biasness happens due to availability of dominant perceptual features in those faces. In visual saliency terminology it can be called as 'salient face'. Human's focus their gaze towards a face which carries the 'dominating look' in the crowd. This happens due to comparative saliency of the faces. Saliency of a face is determined by its feature dissimilarity with the surrounding faces. In this context there is a big role of human psychology and its cognitive science too. Therefore, enormous researches have been carried out towards modeling the computer vision system like human's vision. This paper proposed a graphical based bottom up approach to point up the salient face in the crowd or in an image having multiple faces. In this novel method, visual saliencies of faces have been calculated based on the intensity values, facial areas and their relative spatial distances. Experiment has been conducted on gray scale images. In order to verify this experiment, three level of validation has been done. In the first level, our results have been verified with the prepared ground truth. In the second level, intensity scores of proposed saliency maps have been cross verified with the saliency score. In the third level, saliency map is validated with some standard parameters. The results are found to be interesting and in some aspects saliency predictions are like human vision system. The evaluation made with the proposed approach shows moderately boost up results and hence, this idea can be useful in the future modeling of intelligent vision (robot vision) system.
Security vulnerabilities related to web-based data

In this digital era, organizations and industries are moving towards replacing websites with web applications for many obvious reasons. With this transition towards web-based applications, organizations and industries find themselves surrounded by several threats and vulnerabilities. One of the largest concerns is keeping their infrastructure safe from attacks and misuse. Web security entails applying a set of procedures and practices, by applying several security principles at various layers to protect web servers, web users, and their surrounding environment. In this paper, we will discuss several attacks that may affect web-based applications namely: SQL injection attacks, cookie poisoning, cross-site scripting, and buffer overflow. Additionally, we will discuss detection and prevention methods from such attacks.
Academic performance prediction algorithm based on fuzzy data mining

This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction
Reliable Label-Efficient Learning for Biomedical Image Recognition

The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. To acquire accurate labels as the gold standard, multiple observers with specific expertise are required for both annotation and proofreading. This process can be time-consuming and labor-intensive, making high-quality, and large-annotated biomedical datasets difficult. To address this problem, we propose a deep active learning framework that enables the active selection of both informative queries and reliable experts. To measure the uncertainty of the unlabeled data, a dropout-based strategy is integrated with a similarity criterion for both data selection and random error elimination. To select the reliable labelers, we adopt an expertise estimator to learn the expertise levels of labelers via offline-testing and online consistency evaluation. The proposed method is applied to classification tasks on two types of medical images including confocal endomicroscopy images and gastrointestinal endoscopic images. The annotations are acquired from multiple labelers with diverse levels of expertise. The experiments demonstrate the efficiency and promising performance of the proposed method compared to a set of baseline methods.
Practical Methods for Computing Large Covering Tours and Cycle Covers with Turn Cost

We study the problem of computing provably optimal and near-optimal solutions for the NP-hard problem of finding covering tours and cycle covers with turn cost, which are of practical importance for a variety of applications, such as pest control and precision farming. Previous work has largely focused on theoretical aspects, such as complexity and approximation. We develop a number of algorithm engineering techniques and refinements to make such theoretical insights practically useful, resulting in a comprehensive study for solving a wide spectrum of large instances. We compute provably optimal solutions for instances with more than 1000 pixels, from the largest previous solved instance size of 76 (de Assis and de Souza 2011). Making use of additional algorithm engineering techniques for handling very large instances, we also compute near-optimal solutions for instances with up to 300 000 pixels, for which we give solutions that are typically within a few percent of our computed lower bounds. We also provide an experimental comparison of a practically refined version of our new theoretical approach with the approximation technique of Arkin et al. that dates back to 2001; we show that our new LP/IP-based approximation method closes 70% of the remaining optimality gap to the lower bound.
Additivity of entanglement of formation via an entanglement-breaking space

We study the entanglement-breaking (EB) space, such that the entanglement of formation (EOF) of a bipartite quantum state is additive when its range is an EB subspace. We systematically construct the EB spaces in the Hilbert space ${\mathbb{C}}^{m}\ensuremath{\bigotimes}{\mathbb{C}}^{3}$, and the two-dimensional EB space in ${\mathbb{C}}^{2}\ensuremath{\bigotimes}{\mathbb{C}}^{n}$. We characterize the expression of two-qubit states of rank two with nonadditive EOF, if they exist. We further apply our results to construct EB spaces of arbitrarily given dimensions. We show that the example in Phys. Rev. Lett. 89, 027901 (2002) is a special case of our results. We further work out the entanglement cost of a qubit-qutrit state in terms of the two-atom system of the Tavis-Cummings model.
An Empirical Examination on Trade Openness and Economic Growth Nexus in Africa

African countries have experienced disparities in the growth of their economies. The literature provides different propositions on the causality of economic growth which has given rise to debates on the alternative course that countries need to pursue to enhance their respective growths economically. The paper has therefore examined the effects of trade openness on economic growth for the African countries. In order to analyse the link between trade openness and economic growth by using the growth model, the instrumental variables and two stage least squares (2SLS) for panel data models approach is employed. Later an examination of the direction and strength of causality is conducted by using such techniques as Johansen cointergration test and short run granger causality test. The paper uses data from the World Bank database. A sample size consists of 49 African countries with a sample period of 20 years. Empirical results reveal that of all the variables that were included in the growth model, gross capital formation revealed a great impact on economic growth for African economies, followed by openness and then FDI flows. A test of granger causality for individual countries so as to explain the economic disparities, reveals that majority of the African countries (in the sample) supports the export led growth hypothesis. Implications are that the export led hypothesis would be much more advantageous for the African countries if it would not only result into increased real incomes but also economic structural transformations.
PENGEMBANGAN BUKU DIGITAL MELALUI APLIKASI SIGIL PADA MATA KULIAH COOKIES DAN CANDYS

The development of digital book through SIGIL application in cookies and candys lessons was motivated by the lack of the interactive learning media for students. The purposes of this study were: (1) to develop digital book through Sigil Application concerning to the understanding and processing candys subject; (2) to know the feasibility of digital book based on the expertâ€™s assessment and studentâ€™s view. This study used research and development (R&amp;D) which was modified through ADDIE development model.TheÂ phases of study were: 1) Analysis (needs identification &amp; book prototype formulation), 2) Design ( digital book designing), 3)Development (digital book validation by experts, reading test and media opinion by students, analysis, and revision), 4) Implementation (digital book application for learning), and 5) Evaluation (digital book evaluation). This study was conducted up to third phase - development phases.The results of the study were: 1)Â the development of digital book through Sigil Application concerning to the understanding and processing candys, 2) the result from the experts assessment of digital book feasibility is in average percentageÂ 95,23% . As a learning media in cookies and candys lessons, it was estimated â€œextremely feasibleâ€.However, the result of students assessment in media opinion was regarded positive as it had 91,43% in average percentage which assumed that the digital books were able to be used as cookies and candys learning media.
Threat Modeling for Cloud Infrastructures

Today's businesses are increasingly relying on the cloud as an alternative IT solution due to its flexibility and lower cost.Compared to traditional enterprise networks, a cloud infrastructure is typically much larger and more complex.Understanding the potential security threats in such infrastructures is naturally more challenging than in traditional networks.This is evidenced by the fact that there are limited efforts on threat modeling for cloud infrastructures.In this paper, we conduct comprehensive threat modeling exercises based on two representative cloud infrastructures using several popular threat modeling methods, including attack surface, attack trees, attack graphs, and security metrics based on attack trees and attack graphs, respectively.Those threat modeling efforts may provide cloud providers useful lessons toward better understanding and improving the security of their cloud infrastructures.In addition, we show how hardening solution can be applied based on the threat models and security metrics through extended exercises.Such results may not only benefit the cloud provider but also embed more confidence in cloud tenants by providing them a clearer picture of the potential threats and mitigation solutions.
A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping

To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model's performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.
Report on EARS'18

This is a report on the first edition of the International Workshop on ExplainAble Recommendation and Search (EARS 2018), co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) held in Ann Arbor, Michigan, USA on July 8-12, 2018. The workshop was held on July 12, 2018, co-chaired by Yongfeng Zhang, Yi Zhang, and Min Zhang, with invited keynote speeches delivered by Prof. Paul Resnick from University of Michigan, and Dr. Qingsong Hua from Alibaba Inc. An invited panelist committee including Professors Matthew Lease, Paul Resnick, Mark Sanderson, Wlodek Zadrozny and Dr. Qingsong Hua made fruitful discussions about the past, present, and future on the research of explainable recommendation and search. The workshop accepted six contributed papers, and attracted over 80 registered participants. The scope of the workshop spans from technical approaches to explainable recommendation and search, to policy debates on the "principle of transparency" and the "right to explanation" of algorithmic decisions implied in recent EU General Data Protection Regulation (GDPR) and other similar regulations such as The California Consumer Privacy Act of 2018.
A study on differences in human perception between a real and an <scp>AR</scp> scene viewed in an <scp>OST‐HMD</scp>

Abstract With the recent growth in the development of augmented reality (AR) technologies, it is becoming important to study human perception of AR scenes. In order to detect whether users will suffer more from visual and operator fatigue when watching virtual objects through optical see‐through head‐mounted displays (OST‐HMDs), compared with watching real objects in the real world, we propose a comparative experiment including a virtual magic cube task and a real magic cube task. The scores of the subjective questionnaires (SQ) and the values of the critical flicker frequency (CFF) were obtained from 18 participants. In our study, we use several electrooculogram (EOG) and heart rate variability (HRV) measures as objective indicators of visual and operator fatigue. Statistical analyses were performed to deal with the subjective and objective indicators in the two tasks. Our results suggest that participants were very likely to suffer more from visual and operator fatigue when watching virtual objects presented by the OST‐HMD. In addition, the present study provides hints that HRV and EOG measures could be used to explore how visual and operator fatigue are induced by AR content. Finally, three novel HRV measures are proposed to be used as potential indicators of operator fatigue.
Provably Secure NTRUEncrypt over Any Cyclotomic Field

NTRUEncrypt is generally recognized as one of candidate encryption schemes for post quantum cryptography, due to its moderate key sizes, remarkable performance and potential capacity of resistance to quantum computers. However, the previous provably secure NTRUEncrypts are only based on prime-power cyclotomic rings. Whether there are provably secure NTRUEncrypt schemes over more general algebraic number fields is still an open problem. In this paper, we answer this question and present a new provably IND-CPA secure NTRUEncrypt over any cyclotomic field. The security of our scheme is reduced to a variant of learning with errors problem over rings (Ring-LWE). More precisely, the security of our scheme is based on the worst-case approximate shortest independent vectors problem (SIVP $$_\gamma $$ ) over ideal lattices. We prove that, once the field is fixed, the bounds of the reduction parameter $$\gamma $$ and the modulus q in our scheme are less dependent on the choices of plaintext spaces. This leads to that our scheme provides more flexibility for the choices of plaintext spaces with higher efficiency under stronger security assumption. Furthermore, the probability that the decryption algorithm of our scheme fails to get the correct plaintext is much smaller than that of the previous works.