Reviewerly/exHarmony huggingface dataset:
Walk-Steered Convolution for Graph Classification

Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional neural networks as well as the powerful representation ability of random walk. Instead of deterministic neighbor searching used in previous graphical CNNs, we construct multi-scale walk fields (a.k.a. local receptive fields) with random walk paths to depict subgraph structures and advocate graph scalability. To express the internal variations of a walk field, Gaussian mixture models are introduced to encode principal components of walk paths therein. As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters. We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph can be well learned like the conventional pooling on image. The experimental results on several public datasets demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification.
On the estimation of latent distances using graph distances

We are given the adjacency matrix of a geometric graph and the task of recovering the latent positions. We study one of the most popular approaches which consists in using the graph distances and derive error bounds under various assumptions on the link function. In the simplest case where the link function is proportional to an indicator function, the bound matches an information lower bound that we derive.
On the use of the Stirling Transform in image steganography

Stirling Transform (ST) is primarily used in combinatorial mathematics to convert a polynomial sequence of n non-negative integers into another integer sequence. The source sequence can also be re-generated from the target sequence based on the principle of inverse Stirling Transform (IST). This property is considered as an interesting study and has been exploited for steganography in digital images. In this paper, the cover image is decomposed into the pair (n = 2) of neighboring pixel components in row major order. Stirling Transform (ST) converts the pair of pixel components into the pair of transformed components corresponding to red, green and blue channels. The obtained transformed components are always non-negative, which fabricates varying sizes of the secret image based on the specified payload in the range of 0.5 to 3 bpB (bits per Byte). To address the security issue, the secret image is scrambled through a simple scrambling process prior to embedding. The pixel adjustment process ensures the non-occurrence of overflow/underflow during embedding. Inverse Stirling Transform (IST) re-computes the pair of pixel components from the embedded pair of transformed components. This process is repeated till the secret information is concealed and the stego-image is produced. At the recipient end, the reverse procedure is applied to extract the concealed information from the stego-image based on the following parameters: shared PIN, system time and the one time password (OTP). Message digest obtained through MD5 algorithm is used to verify the authenticity. Simulation results ensures that the proposed scheme outperforms Chang et al.’s [17], Xia et al.’s [18], 3-LSB [2] and hybrid GEMD [7] schemes in terms of image quality with respect to increasing payloads.
Multiple pedagogical conversational agents to support learner-learner collaborative learning: Effects of splitting suggestion types

This study experimentally investigated the design of effective interactions using pedagogical conversational agents (PCAs) in a learner-learner collaborative learning activity. While dyads engaged in a concept explanation task (explaining the mechanism of computer processing), PCAs served as facilitators and provided metacognitive suggestions to better improve learning performance. Previous studies have shown that learners who received several types of suggestions from multiple PCAs were motivated to produce effective explanations; this study then further explored the effects of using multiple PCAs in different roles, providing different types of facilitation. It was predicted that by using two different PCAs to offer suggestions with a delay, learners may be able to process information more efficiently, for example by paying closer attention to each type of suggestion. To investigate this possibility, two types of facilitating content, namely provision of metacognitive suggestions and advice on effective coordination, were each implemented into two role-playing PCAs, named the "explanation adviser" and the "communication adviser" respectively. The results show that when learners used PCAs playing different roles and offering suggestions corresponding to these roles, learners generated explanations related to the suggestions and improved performance (efficacy of explanations) in several areas, including learning performance, for example better understanding the concept and becoming able to explain it using a greater range of technical words. This study shows empirically how multiple PCAs can be effectively designed to implement roles yielding different types of suggestions. The advantages of using such methods and implementing such functions of PCAs are further discussed.
Embedded implementation of multispectral satellite image encryption using a chaos-based block cipher

Image transmission from earth observation satellites to ground station is exposed to threats that can affect the confidentiality of data. One of the crucial tasks of on-board computer systems is to ensure the confidentiality of data transmitted from the satellites to the ground stations. Throughout the last two decades, Chaos based data confidentiality has reached a significant achievement. In this paper, we propose a new multispectral image encryption method based on Fridrich's scheme. The obtained experimental results show that the proposed method has a good security level with low hardware complexity and low power and can reach a throughput of 120 Mbit/s.
Performance improvement of a genetic algorithm using a novel restart strategy with elitism principle

Genetic algorithm (GA) is one of the most widely used meta-heuristic optimization tools to solve a variety of problems. It is a powerful tool for global optimization. However, like other heuristic optimization techniques, it has a probabilistic guara
A foundation for probabilistic beliefs with or without atoms

We propose two novel axioms for qualitative probability spaces: (i) unlikely atoms , which requires that there is an event containing no atoms that is at least as likely as its complement; and (ii) third‐order atom‐swarming , which requires that for each atom, there is a countable pairwise‐disjoint collection of less‐likely events that can be partitioned into three groups, each with union at least as likely as the given atom. We prove that under monotone continuity , each of these axioms is sufficient to guarantee a unique countably‐additive probability measure representation, generalizing work by Villegas to allow atoms. Unlike previous contributions that allow atoms, we impose no cancellation or solvability axiom.
Anatomy of the Attraction Basins: Breaking with the Intuition

Solving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood structure that partially accounts for these properties. Considering a neighborhood, the space is usually interpreted as a natural landscape, with valleys and mountains. Under this perception, it is commonly believed that, if maximizing, the solutions located in the slopes of the same mountain belong to the same attraction basin, with the peaks of the mountains being the local optima. Unfortunately, this is a widespread erroneous visualization of a combinatorial landscape. Thus, our aim is to clarify this aspect, providing a detailed analysis of, first, the existence of plateaus where the local optima are involved, and second, the properties that define the topology of the attraction basins, picturing a reliable visualization of the landscapes. Some of the features explored in this article have never been examined before. Hence, new findings about the structure of the attraction basins are shown. The study is focused on instances of permutation-based combinatorial optimization problems considering the 2-exchange and the insert neighborhoods. As a consequence of this work, we break away from the extended belief about the anatomy of attraction basins.
On the steady state analysis of covariance matrix self-adaptation evolution strategies on the noisy ellipsoid model

This paper addresses the analysis of covariance matrix self-adaptive Evolution Strategies (CMSA-ES) on a subclass of quadratic functions subject to additive Gaussian noise: the noisy ellipsoid model. To this end, it is demonstrated that the dynamical systems approach from the context of isotropic mutations can be transferred to ES that also control the covariance matrix. Theoretical findings such as the component-wise quadratic progress rate or the self-adaptation response function can thus be reused for the CMSA-ES analysis. By deriving the steady state quantities approached on the noisy ellipsoid model for constant population size, a detailed description of the asymptotic CMSA-ES behavior is obtained. By providing self-adaptive ES with a population control mechanism, despite noise disturbances, the algorithm is able to realize continuing progress towards the optimum. Regarding the population control CMSA-ES (pcCMSA-ES), the analytical findings allow to specify its asymptotic long-term behavior and to identify influencing parameters. The finally obtained convergence rate matches the theoretical lower bound of all comparison-based direct search algorithms.
A Depth-Bin-Based Graphical Model for Fast View Synthesis Distortion Estimation

During 3-D video communication, transmission errors, such as packet loss, could happen to the texture and depth sequences. View synthesis distortion will be generated when these sequences are used to synthesize virtual views according to the depth-image-based rendering method. A depth-value-based graphical model (DVGM) has been employed to achieve the accurate packet-loss-caused view synthesis distortion estimation (VSDE). However, the DVGM models the complicated view synthesis processes at depth-value level, which costs too much computation and is difficult to be applied in practice. In this paper, a depth-bin-based graphical model (DBGM) is developed, in which the complicated view synthesis processes are modeled at depth-bin level so that it can be used for the fast VSDE with 1-D parallel camera configuration. To this end, several depth values are fused into one depth bin, and a depth-bin-oriented rule is developed to handle the warping competition process. Then, the properties of the depth bin are analyzed and utilized to form the DBGM. Finally, a conversion algorithm is developed to convert the per-pixel input depth value probability distribution into the depth-bin format. Experimental results verify that our proposed method is 8-$32\times $ faster and requires 17%-60% less memory than the DVGM, with exactly the same accuracy.
Designing Digital Vertigo Experiences

Many people enjoy “vertigo” sensations caused by intense playful bodily activities such as spinning in circles, and riding fairground rides. Game scholar Caillois calls such experiences “vertigo play,” elucidating that these enjoyable activities are a result of confusion between sensory channels. In HCI, designers are often cautious to avoid deliberately causing sensory confusion in players, but we believe there is an opportunity to transition and extend Caillois’ thinking to the digital realm, allowing designers to create novel and intriguing digital bodily experiences inspired by traditional vertigo play activities. To this end, we present the Digital Vertigo Experience framework. Derived from four case studies and the development of three different digital vertigo experiences, this framework aims to bring the excitement of traditional vertigo play experiences to the digital world, allowing designers to create more engaging and exciting body-based games, and provides players with more possibilities to enjoy novel and exciting play experiences.
Quantum accelerated approach to the thermal state of classical all-to-all connected spin systems with applications to pattern retrieval in the Hopfield neural network

We explore the question as to whether quantum effects can yield a speedup of the non-equilibrium evolution of spin systems towards a classical thermal state. In our approach we exploit the fact that the thermal state of a spin system can be mapped onto a node-free quantum state whose coefficients are given by thermal weights. This perspective permits the construction of a dissipative -- yet quantum -- dynamics which encodes in its stationary state the thermal state of the original problem. We show for the case of an all-to-all connected Ising spin model that an appropriate transformation of this dissipative dynamics allows to interpolate between a regime in which the order parameter obeys the classical equations of motion under Glauber dynamics, to a quantum regime with an accelerated approach to stationarity. We show that this effect enables in principle a speedup of pattern retrieval in a Hopfield neural network.
Supervised fuzzy partitioning

Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features, enabling the method to capture more complex patterns, identify significant features, and yield better performance facing high-dimensional data. An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum. Extensive classification experiments on synthetic, real-world, and high-dimensional datasets demonstrate that the predictive performance of SFP is competitive with state-of-the-art algorithms such as SVM and random forest. SFP has a major advantage over such methods, in that it not only leads to a flexible, nonlinear model but also can exploit any convex loss function in the training phase without compromising computational efficiency.
Upper and Lower Bounds for Continuous Non-Malleable Codes

Recently, Faust et al. (TCC'14) introduced the notion of continuous non-malleable codes (CNMC), which provides stronger security guarantees than standard non-malleable codes, by allowing an adversary to tamper with the codeword in a continuous way instead of one-time tampering. They also showed that CNMC with information theoretic security cannot be constructed in the 2-split-state tampering model, and presented a construction in the common reference string (CRS) model from collision-resistant hash functions and non-interactive zero-knowledge proofs. In this work, we ask if it is possible to construct CNMC from weaker assumptions. We answer this question by presenting lower as well as upper bounds. We show that it is impossible to construct 2-split-state CNMC, with no CRS, for one-bit messages from any falsifiable assumption, thus establishing the lower bound. We additionally provide an upper bound by constructing 2-split-state CNMC for one-bit messages, assuming only the existence of a family of injective one way functions. We note that in a recent work, Ostrovsky et al. (CRYPTO'18) considered the construction of a relaxed notion of 2-split-state CNMC from minimal assumptions. We also present a construction of 4-split-state CNMC for multi-bit messages in CRS model from the same assumptions. Additionally, we present definitions of the following new primitives: (1) One-to-one commitments, and (2) Continuous Non-Malleable Randomness Encoders, which may be of independent interest.
Shape-aware label fusion for multi-atlas frameworks

Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures.
Automated support to capture verbal just-in-time requirements via audio mining and cluster-based visualization

Being able to quickly and accurately capture requirements is crucial when using agile methodologies. Requirements, to that end, are often captured in an as-needed and informal manner, with continuous stakeholder interaction. Techniques such as interviews, user stories, rapid feedback, and text mining are commonly used in the industry to gather such informal requirements, which are often congruent with the concept of ‘just-in-time’ (JIT)—informally represented and continuously refined—requirements. Oral communication is an integral part of most of these techniques, and the loss or misinterpretation of verbally communicated requirements is a daunting problem. To address this issue, In this invited paper, we propose a novel framework to assist developers in capturing verbal requirements in complete manner. In particular, our framework automatically captures, transcribes, and mines verbal communication, which in turn produces a set of loosely formulated candidate requirements for further elaboration. We also conducted a survey among practitioners concerning requirements in agile methodologies and our framework. The results of this survey provide positive feedback on the prospects of the framework and also indicate the prevalence of JIT requirements in closed source agile projects. In addition, we proposed a new framework for visual exploration of JIT requirements and presented our JITREvisu prototype tool.
Multi-stage cascaded deconvolution for depth map and surface normal prediction from single image

Understanding the 3D perspective of a scene is imperative in improving the precision of intelligent autonomous systems. The difficulty in understanding is compounded when only one image of the scene is available at disposal. In this regard, we propose a fully convolutional deep framework for predicting the depth map and surface normal from a single RGB image in a common architecture. The DenseNet CNN architecture is employed to learn the complex mapping between an input RGB image and its corresponding 3D primitives. We introduce a novel approach of multi-stage cascaded deconvolution, where the output feature maps of one dense block are reused by concatenating with the feature maps of the corresponding deconvolution block. These combined feature maps are progressed along the deep network in a pre-activated manner to construct the final output. The network is trained separately for estimating depth and surface normal while keeping the architecture same. The suggested architecture, compared to the counterparts, uses fewer training samples and model parameters. Exhaustive experiments on benchmark dataset not only reveal the efficacy of the proposed multi-stage scheme over the one-way sequential deconvolution but also outperform the state-of-the-art methods.
Constraint-Based Visual Generation

In the last few years the systematic adoption of deep learning to visual generation has produced impressive results that, amongst others, definitely benefit from the massive exploration of convolutional architectures. In this paper, we propose a general approach to visual generation that combines learning capabilities with logic descriptions of the target to be generated. The process of generation is regarded as a constrained satisfaction problem, where the constraints describe a set of properties that characterize the target. Interestingly, the constraints can also involve logic variables, while all of them are converted into real-valued functions by means of the t-norm theory. We use deep architectures to model the involved variables, and propose a computational scheme where the learning process carries out a satisfaction of the constraints. We propose some examples in which the theory can naturally be used, including the modeling of GAN and auto-encoders, and report promising results in image translation of human faces.
Daily irradiance test signal for photovoltaic systems by selection from long-term data

When performing tests on a photovoltaic energy conversion system, it is useful to have a daily-term irradiance profile which, in some way, represents the typical behavior of this variable at a desired geographic location, including fast changes caused by weather. Therefore, the objective of this work is to propose a simple methodology, to choose a representative daily-duration irradiance signal, from long-term irradiance data. This is accomplished, by means of quantifying the irradiance variability, and considering the total daily energy. A method to combine these two factors, is proposed and employed in the selection the most representative day; it is also used to search for extreme condition days.
Estimates of clear-sky solar irradiances over Nigeria

This study attempts to circumvent the problem of paucity of input data required in climatology mapping of clear-sky solar irradiance in Nigeria by computing beam normal (Ebn) and diffuse (Ed) irradiances using a high performance broadband radiative model in the country climate zones. Air temperature, relative humidity and global datasets of ozone thickness and angstrom turbidity were used as input parameters. The biases in the Ebn estimates with NASA datasets across Nigeria (11–25%) are of similar magnitudes with NASA observations with ground measurements. The estimates show persistent negative biases that increased from tropical savannah to semi-arid climate zones (−8 to −24%). The bias in the Ed estimates is only of similar magnitude with NASA in semi-arid climate zone (10%). The Ed estimates show persistent negative biases that increase from semi-arid to tropical savannah across Nigeria (−7 to −54%). Also, the estimates in each climate zone correspond to the expected climatology of water vapour, aerosol turbidity and absolute optical mass. Lastly, the response of Ebn to water vapour absorption and aerosol extinction signals is mostly active in monsoon zone while the response to the signals by Ed are active in all the zones.