Reviewerly/exHarmony huggingface dataset:
Real-time path planning to dispatch a mobile sensor into an operational area

This paper addresses problems of large planning time and cost uncertainty for informative path planning of a mobile sensor where the location of sensor deployment is different of that of an operational area. The first problem is that the cost has no term dependent on sensor state before arriving at the operational area and it causes large planning time. The information of the state of interest dissipates over time during the planning time and it degrades performance of sensing operation. The other problem is that the cost is dependent on the parameters to be estimated. To assess the cost, the target state in the future should be predicted by integrating the system model based on noisy initial estimate. The limitation of the informative path planning has a greater impact on performance in this specific problem. A strategy to cope with these problems is to devise a real-time path planning algorithm by using online optimization. The proposed algorithm is divided into two phases; determining the path to the boundary of the operational area and guiding the sensor by an informative potential field in the area. Detailed analysis on performance of the proposed algorithm compared to an optimal solution by nonlinear programming is given. The simulation results have demonstrated that the proposed algorithm can cope with performance degradation observed in the optimal solution.
Differentiating users by language and location estimation in sentiment analisys of informal text during major public events

In recent years there has been intense work on the analysis of social media to support marketing campaigns. A proper methodology for sentiment analysis is a crucial asset in this regard. However, when monitoring major public events the behaviour or social media users may be strongly biased by punctual actions of the participating characters and the sense of group belonging, which is typically linked to specific geographical areas. In this paper, we present a solution combining a location prediction methodology with an unsupervised technique for sentiment analysis to assess automatically the engagement of social network users in different countries during an event with worldwide impact. As far as the authors know, this is the first time such techniques are jointly considered. We demonstrate that the technique is coherent with the intrinsic disposition of individual users to typical actions of the characters participating in the events, as well as with the sense of group belonging.
A Dirichlet Process Based Type-1 and Type-2 Fuzzy Modeling for Systematic Confidence Bands Prediction

This paper presents a new methodology for fuzzy logic systems modeling based on the Dirichlet process Gaussian mixture models (DPGMM). The proposed method simultaneously allows for the systematic elicitation of confidence bands as well as the automatic determination of model complexity. This work is new since existing fuzzy model elicitation techniques use ad hoc methods for confidence band estimations, which do not meet the stringent requirements of today's challenging environments where data are sparse, incomplete, and characterized by noise as well as uncertainties. The proposed approach involves an integration of fuzzy and Bayesian topologies and allows for the generation of confidence bands based on both the random and linguistic uncertainties embedded in the data. Additionally, the proposed method provides a “right-first time approach” to fuzzy modeling as it does not require an iterative model complexity determination. In order to see how the proposed framework performs across a variety of challenging data modeling problems, the proposed approach was tested on a nonlinear synthetic dataset as well as two real multidimensional datasets generated by the authors from materials science and bladder cancer studies. Results show that the proposed approach consistently provides better generalization performances than other well-known soft computing modeling frameworks-in some cases, improvements of up to 20% in modeling accuracy were achieved. The proposed method also provides the capability to handle uncertainties via the generation of systematic confidence intervals for informing on model reliability. These results are significant since the generic methodologies developed in this paper should help material scientists as well as clinicians, for example, assess the risks involved in making informed decisions based on model predictions.
Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects’ localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.
Rock‐ring detection accuracy improvement in infrared satellite image with sub‐pixel edge detection

The projection of space circle can be used for relative pose measurement of satellite targets. The accuracy of elliptical parameters plays an important role in the accuracy of pose recovery. However, the quality of space visible and infrared image is poor. The traditional ellipse detection method is mainly based on the edge of pixel-accuracy-wise and the ellipse accuracy is low, resulting in pose measurement errors. In this study, a sub-pixel-accuracy-wise edge-based ellipse fitting method is proposed to improve the ellipse accuracy. To realise this goal, we improved the arc-based ellipse detection method and designed sub-pixel-edge-based ellipse detection method. Experimental results show that the ellipse accuracy fitted by sub-pixel edge coordinates is at least 50% higher than that by pixel edge coordinates, especially when the ellipse is incomplete. The author's method is the first one to propose and verify the sub-pixel edge coordinate which contributes to enhancing ellipse accuracy.
Multiple just-noticeable-difference-based no-reference stereoscopic image quality assessment

Just-noticeable difference (JND) is an important characteristic of the human visual system (HVS), and some established JND models imitating the perception of human eyes already exist. However, their utilization in stereoscopic image quality assessment (SIQA) remains limited. To better simulate how HVS senses 3D images under a no-reference situation, a novel SIQA method based on multiple JND models is proposed in this paper. In our metric, the stereoscopic image pairs are decomposed into multi-scale monocular views and binocular views. Then, texture and edge information of these multi-scale images is extracted. Next, a monocular JND model, a binocular JND model, and a depth JND model are separately applied to the extracted features and the depth map. Finally, these features are synthesized and mapped to objective scores. Through experiment and comparison on public 3D image databases, the proposed method shows a competitive advantage over most state-of-the-art SIQA methods, which indicates that it has a promising prospect in practical applications.
Localize-It: Co-designing a Community-Owned Platform

One of the most difficult, yet undocumented, aspects of information and communications technologies and development (ICTD) projects is that of establishing partnerships around which researchers' interventions will develop, be tested and grow. Constraints on timing and funding usually lead to short-term projects, in which benefits are biased towards researchers rather than the partner community. In order to avoid empty and unethical promises and to increase the potential benefit for the community, we consider the process of developing participatory partnerships in ICTD projects. The objective is to make the project community owned, allowing the participants to develop what they value as important. Using the case of a township-based wireless community content sharing network, we describe the potential and some of the challenges with this approach. The paper highlights building blocks, such as ethical behaviour and trust, to avoid recreating the dichotomy between research and practice, and building a constructive collaboration.
Multi-target Attacks on the Picnic Signature Scheme and Related Protocols

Picnic is a signature scheme that was presented at ACM CCS 2017 by Chase et al. and submitted to NIST's post-quantum standardization project. Among all submissions to NIST's project, Picnic is one of the most innovative, making use of recent progress in construction of practically efficient zero-knowledge (ZK) protocols for general circuits. In this paper, we devise multi-target attacks on Picnic and its underlying ZK protocol, ZKB++. Given access to S signatures, produced by a single or by several users, our attack can (information theoretically) recover the $$\kappa $$ -bit signing key of a user in complexity of about $$2^{\kappa - 7}/S$$ . This is faster than Picnic's claimed $$2^{\kappa }$$ security against classical (non-quantum) attacks by a factor of $$2^7 \cdot S$$ (as each signature contains about $$2^7$$ attack targets). Whereas in most multi-target attacks, the attacker can easily sort and match the available targets, this is not the case in our attack on Picnic, as different bits of information are available for each target. Consequently, it is challenging to reach the information theoretic complexity in a computational model, and we had to perform cryptanalytic optimizations by carefully analyzing ZKB++ and its underlying circuit. Our best attack for $$\kappa = 128$$ has time complexity of $$T = 2^{77}$$ for $$S = 2^{64}$$ . Alternatively, we can reach the information theoretic complexity of $$T = 2^{64}$$ for $$S = 2^{57}$$ , given that all signatures are produced with the same signing key. Our attack exploits a weakness in the way that the Picnic signing algorithm uses a pseudo-random generator. The weakness is fixed in the recent Picnic 2.0 version. In addition to our attack on Picnic, we show that a recently proposed improvement of the ZKB++ protocol (due to Katz, Kolesnikov and Wang) is vulnerable to a similar multi-target attack.
Panchromatic image denoising by a log-normal-distribution-based anisotropic diffusion model

An anisotropic diffusion model based on a log-normal distribution of a local gray-level is used to propose a way to denoise the panchromatic images. The implication of the low-edge gradient of the feature space for denoising and smoothing the noisy image is adaptively adjusted by the adaptive threshold parameter in a diffusion coefficient function. Furthermore, to terminate the diffusion process, an entropy-based stopping criterion is implemented. The proposed model is compared with the existing models such as Perona–Malik (PM), adaptive PM, difference eigenvalue PM, modified PM, and Maiseli–Gao. In order to analyze the performance of the models, quantitative metrics such as standard deviation, entropy, and the signal-to-noise ratio of a two-dimensional line profile are used. For further analysis, the results of denoising models are segmented using entropy-based segmentation techniques such as Harvda, Renyi, Kapur, and Yen models. A misclassification error metric is used to evaluate the segmentation results. The metric results show that the proposed model effectively removes the noise and preserves the features of a panchromatic image.
Informal Language Learning Through Mobile Instant Messaging Among University Students in Korea

Mobile technologies and platforms that facilitate communication across different languages are increasingly relevant in a world characterised by the global flows of diverse populations and disparate digital environments. This qualitative study explores communication between native and non-native speakers of Korean, mediated through mobile instant messaging in the sophisticated digital environment of South Korea. This article reports on two studies that examined the experiences of English-speaking language instructors (Study 1) and international students (Study 2) in a private university situated in South Korea during 2012 and 2015, respectively. This is done by focusing on face-to-face and screen-to-screen communication, and the co-dependencies arising between language instructors and international students. Between 2012 and 2015, large upgrades in the local digital environment saw the emergence of ubiquitous low cost or free high-speed Wi-Fi coverage and a near universal national adoption of a local mobile instant messaging service. The participants in Study 2 were widely found to be digitally literate and demonstrated an acuity with mobile instant messaging. They developed creative solutions for communication and language learning by blending sociolinguistic and linguistic competencies within mobile instant messaging chat rooms. This article highlights the changes that have occurred between 2012 and 2015 and demonstrates the need for further research into how mobile instant messaging services support communication between people from different language and culture backgrounds, particularly for people without access to formal language instruction.
Heterogeneous Recommendation via Deep Low-rank Sparse Collective Factorization

A real-world recommender usually adopts heterogeneous types of user feedbacks, for example, numerical ratings such as 5-star grades and binary ratings such as likes and dislikes. In this work, we focus on transferring knowledge from binary ratings to numerical ratings, facing a more serious data sparsity problem. Conventional Collective Factorization methods usually assume that there are shared user and item latent factors across multiple related domains, but may ignore the shared common knowledge of rating patterns. Furthermore, existing works may also fail to consider the hierarchical structures in the heterogeneous recommendation scenario (i.e., genre, sub-genre, detailed-category). To address these challenges, in this paper, we propose a novel Deep Low-rank Sparse Collective Factorization (DLSCF) framework for heterogeneous recommendation. Specifically, we adopt low-rank sparse decomposition to capture the common rating patterns in related domains while splitting the domain-specific patterns. We also factorize the model in multiple layers to capture the affiliation relation between latent categories and sub-categories. We propose both batch and Stochastic Gradient Descent (SGD) based optimization algorithms for solving DLSCF. Experimental results on MoviePilot, Netfilx, Flixter, MovieLens10M and MovieLens20M datasets demonstrate the effectiveness of the proposed algorithms, by comparing them with several state-of-the-art batch and SGD based approaches.
The Effectiveness of Teaching Materials for Graphic Organizers in Reading in Elementary School Students

A teacher must be able to develop teaching materials in learning because it is a professional demand that can provide meaningful and enjoyable learning. The purpose of this study was to describe the effectiveness of developing graphic organizer teaching materials in reading in grade 4 of elementary school. The graphic organizer is a learning media used to read learning. This is a development research with consisting of 4 stages namely defining, designing, developing, and disseminating research results. Aspects namely need analysis and student analysis. In needs analysis, some analyzes were carried out covering curriculum, concepts, and assignments. While in the analysis of students looking at the suitability of teaching materials developed with the level of development of students. The design phase of teaching materials was made to plan the implementation of learning indicators, the average indicator was 95, and the learning objectives aspect was 93. The development phase was tested in SDN 38 Kota Pekanbaru with an average reading ability of 87.5, so it can be concluded that the effectiveness of teaching materials for graphic organizer in reading is very decent to use.
Regular switching components

We consider a problem of Discrete Tomography which consists of reconstructing a finite lattice set S⊂Z2 from given horizontal and vertical X-rays (i.e. with prescribed numbers of points in each row and column). Without additional requirements, the problem can be solved in polynomial time. Many variants require the solution to be in a chosen class A. For instance, the problem is NP-complete for the class A=H∩V of HV-convex lattice sets and it is polynomial for the class A=H∩V∩P of HV-convex polyominoes. Twenty years after these results, the problem's complexity remains unknown for A=C, the class of C-convex lattice sets (i.e. two-dimensional lattice polytopes). The main difficulty to solve this problem comes from combinatorial structures called switching components. Switching components are closed path with horizontal and vertical edges such that the solutions S are composed of either the elements of even or of odd indices. This binary choice can be encoded by a Boolean variable associated with the switching component and the convexity constraints are simply encoded by SAT clauses (2-clauses for HV-convexity and 3-clauses for C-convexity). The purpose of the paper is to investigate the properties of the switching components and the consequences of the convexity requirements. We divide the switching components in two classes: regular if their turning angle is constant, irregular otherwise. We prove that adjacent regular switching components have the same Boolean values. This property allows us to merge them into extended switching components. We prove that if all switching components are regular, then the extended switching components are all independent (then the number of solutions with the considered feet is 2n, where n is the number of extended switching components). Finally, we prove that they are geometrically ordered.
Adaptive Gaussian Copula ABC

Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ABC approaches --- regression ABC and sequential ABC. The key idea is that rather than learning the posterior directly, we first target another auxiliary distribution that can be learned accurately by existing methods, through which we then subsequently learn the desired posterior with the help of a Gaussian copula. During this process, the complexity of the model changes adaptively according to the data at hand. Experiments on a synthetic dataset as well as three real-world inference tasks demonstrates that the proposed method is fast, accurate, and easy to use.
Automatic 3D reconstruction of SEM images based on Nano-robotic manipulation and epipolar plane images

This paper reports a new and general 3D reconstruction algorithm by using the light field reconstruction theory to effectively construct 3D SEM images in a large range. Firstly, a nano-robotic system was employed to automatically capture a group of SEM images along a linear path with a fixed step size, which allowed the 3D SEM images to be reconstructed beyond the field of view (FOV) of SEM. Then, the epipolar-plane images (EPI) were generated, and the depth image was reconstructed based on the specific linear structures emerging in EPI and the automatic depth estimation algorithm. After that, the depth image was stitched and the dense 3D point cloud was obtained by using the delaunay technology. Depth reconstruction with the proposed algorithm does not depend on the matching corresponding points technology. This means nearly all kinds of SEM samples, even those with a simple texture structure or an almost flat surface, can be reconstructed. In addition, the proposed method allows constructing the 3D images out of the FOV of SEM with the assistance of nanorobot. The performance of the proposed algorithm was tested using our self-built database with several microscopic samples. The results demonstrate that the proposed algorithm is general and effective and it is particularly suitable for reconstructing highly complex micro surfaces with a flat surface in a large range.
CFHider: Control Flow Obfuscation with Intel SGX

When a program is executed on an untrusted cloud, the confidentiality of the program's logics needs to be protected. Control flow obfuscation is a direct approach to obtain this goal. However, existing methods in this direction cannot achieve both high confidentiality and low overhead. In this paper, we propose CFHider, a hardware-assisted method to protect the control flow confidentiality. By combining program transformation and Intel Software Guard Extension (SGX) technology, CFHider moves branch statement conditions to an opaque and trusted memory space, i.e., the enclave, thereby offering a guaranteed control flow confidentiality. Based on the design of CFHider, we developed a prototype system targeting on Java applications. Our analysis and experimental results indicate that CFHider is effective in protecting the control flow confidentiality and incurs a much reduced performance overhead than existing software-based solutions (by a factor of 8.8).
Linear entropy of multiqutrit nonorthogonal states

We derive a closed-form analytical expression for the linear entropy of a multipartite qutrit state, providing a quantitative measure for quantum entanglement within the class of n-mode nonorthogonal qutrit states with any n. Conditions for enhanced and maximum quantum entanglement of multipartite qutrit states are identified. The usefulness of the introduced multipartite qutrit states as quantum communication channel resources is analyzed. The Hamiltonians allowing for the generation of multipartite qutrit states can be attained by combining optomechanical cavities with sequences of tunable beam splitters.
A Combined Approach for the Binarization of Historical Tibetan Document Images

It is common that historical Tibetan documents belonging to historical collections are poorly preserved and are prone to degradation processes. This causes many challenges that can be addressed by image binarization, the most common of which is stains. A lack of uniform standard datasets makes it difficult to evaluate binarization effects. Motivated by the poor effects and difficulty of evaluating the binarization of historical Tibetan document images, a combined approach is proposed that aims to improve overall performance. The method includes the following parts: first, image generation through standard binarization and the background extraction of color images, which are both used for image synthesis in preparing for an evaluation. Then, preliminary binarization processing is implemented through channel combination in the lab color space and through local binarization. The synthetic images are used to select the coefficient when the channels are combined. Furthermore, Local Binary Pattern (hereafter LBP) and image smoothing is carried out after the combination of the channels to obtain the outline of the text area. Finally, the final binarization image is obtained by combining the preliminary binarization image and the text area contour image. Our method achieved top performance compared to other methods after a large number of synthetic image tests with a variety of background types.