Reviewerly/exHarmony huggingface dataset:
Solar3D: An Open-Source Tool for Estimating Solar Radiation in Urban Environments

Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground.
Detecting Violent Radical Accounts on Twitter

In the past few years and as a result of the enormous spreading of social media platforms worldwide, many radical groups tried to invade social media cyber space in order to disseminate their ideologies and destructive plans.This brutal invasion to society daily life style must be resisted as social media networks are interacted with on daily basis.As some violent radical groups such as ISIS has developed well designed propaganda strategies that enables them to recruit more members and supporters all over the world using social media facilities.So it is crucial to find an efficient way to detect the violent-radical accounts in social media networks.In this paper, an intelligent system that autonomously detects ISIS online community in Twitter social media platform is proposed.The proposed system analyzes both linguistic features and behavioral features such as hashtags, mentions and who they follow.The system consists of two main sub-systems, namely the crawling and the inquiring subsystems.The crawling subsystem uses the initially known ISIS-related accounts to establish an ISISaccount detector.The inquiring subsystem aims to detect Pro ISIS-accounts.
Bayesian mean-parameterized nonnegative binary matrix factorization

Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this context, probabilistic NMF assumes a generative model where the data is usually Bernoulli-distributed. Often, a link function is used to map the factorization to the [0, 1] range, ensuring a valid Bernoulli mean parameter. However, link functions have the potential disadvantage to lead to uninterpretable models. Mean-parameterized NMF, on the contrary, overcomes this problem. We propose a unified framework for Bayesian mean-parameterized nonnegative binary matrix factorization models (NBMF). We analyze three models which correspond to three possible constraints that respect the mean-parameterization without the need for link functions. Furthermore, we derive a novel collapsed Gibbs sampler and a collapsed variational algorithm to infer the posterior distribution of the factors. Next, we extend the proposed models to a nonparametric setting where the number of used latent dimensions is automatically driven by the observed data. We analyze the performance of our NBMF methods in multiple datasets for different tasks such as dictionary learning and prediction of missing data. Experiments show that our methods provide similar or superior results than the state of the art, while automatically detecting the number of relevant components.
Comment on “Circuit Ciphertext-Policy Attribute-Based Hybrid Encryption With Verifiable Delegation in Cloud Computing”

The scheme [1] is flawed because: (1) its circuit access structure is confusingly described; (2) the cloud server cannot complete the related computations; (3) some users can conspire to generate new decryption keys, without the help of the key generation authority.
Adaptive clip-limit-based bi-histogram equalization algorithm for infrared image enhancement

Infrared (IR) images are basically low-contrast in nature; hence, it is essential to enhance the contrast of IR images to facilitate real-life applications. This work proposes a novel adaptive clip-limit-oriented bi-histogram equalization (bi-HE) method for enhancing IR images. HE methods are simple in implementation but often cause over-enhancement due to the presence of long spikes. To reduce long spikes, this work suggests to apply a log-power operation on the histogram, where the log operation reduces the long spikes, and power transformation regains the shape of the histogram. First, a histogram separation point is generated applying the mean of the multi-peaks of the input histogram. After that, an alteration in the input histogram is done using the log-power process. Subsequently, a clipping operation on the altered histogram followed by redistribution of the clipped portion is performed to restrict over-enhancement. Next, the modified histogram is sub-divided using the histogram separation point. Finally, the modified sub-histograms are equalized independently. Simulation results show that the suggested method effectively improves the contrast of IR images. Visual quality evaluations and quantitative assessment demonstrate that the suggested method outperforms the state-of-the-art algorithms.
Understanding the Interaction between Older Adults and Soft Service Robots: Insights from Robotics and the Technology Acceptance Model

As the world's population increasingly ages, we need technological solutions such as robotics technology to assist older adults in their daily tasks.In this regard, we examine soft service robots' potential to help care for the elderly.To do so, we developed and tested the degree to which they would accept a soft service robot that catered to their functional needs in the home environment.We used embodied artificial to develop an in-house teleoperated human-sized soft service robot that performed object-retrieval tasks with a soft gripper.Using an extended technology acceptance model as a theoretical lens, we conducted a study with 79 older adults to examine the degree to which they would accept a soft service robot in the home environment.We found perceived ease of use, perceived usefulness, and subjective norms as significant predictors that positively influenced older adults' intention to adopt and use soft service robots.However, we also found that perceived anxiety and perceived likability did not significantly predict older adults' intention to adopt and use soft service robots.We discuss the implications, limitations, and future research directions that arise from these findings.
A Hybrid Chaotic Encryption Scheme for Wireless Body Area Networks

Wireless body area network is a low-power and high-security wireless communication protocol specially used in the medical environment. It requires a highly secure encryption algorithm to ensure that information related to a patient's vital signs cannot be accessed by an attacker. In this article, a security scheme based on chaotic mapping for WBAN is proposed. This algorithm makes use of the hybrid chaotic system obtained by the combination of Logistic and Tent mapping as the sequence generator, which can select the effective precision according to the importance of the transmitted information to ensure the reasonable allocation of computing resources, including floating-point encryption, fixed-point 32-bit encryption, fixed-point 24-bit encryption, and fixed-point 16-bit encryption. In the randomness test of the sequence, it can be seen that the sequence autocorrelation coefficient generated by the combined chaotic system is close to 0 and has a flat power spectrum. At the same time, the sequences produced by the floating-point system and 32-bit fixed-point system have passed the NIST test, which proves that combined chaos overcomes the influence of sequence length and finite precision. Experimental analysis and performance comparison show that the proposed encryption algorithm has sufficient security to resist common attacks.
Evaluation of Inertial Sensor Data by a Comparison with Optical Motion Capture Data of Guitar Strumming Gestures

Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups.
Optimizing the scheme of bidirectional controlled quantum teleportation with a genuine five-qubit entangled state

A bidirectional controlled quantum teleportation (BCQT) scheme was proposed by Yan Chen [Int. J. Theor. Phys. 53, 1454 (2014)] with a genuine five-qubit entangled state as quantum channel. In the scheme two remote participants have to perform some two-qubit joint operations to accomplish the BCQT task. Such joint operations are nonlocal and usually regarded as serious faults in some sense. To overcome them, in this paper we put forward an optimized version. Our scheme has the distinct advantages of degrading the necessary-operation complexity (i.e., both intensity and difficulty) and consuming fewer classical resources as well as owing higher intrinsic efficiency in contrast to Chen’s. Besides, it is feasible in experiment and applicable in the intending quantum network.
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a fewshot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators.We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains.Our models achieve an overall test accuracy ranging between 61% and 69% for the hotels domain and between 64% and 78% for restaurants domain, which compares favorably to 69% and 80% obtained for English parser trained on gold English data and a few examples from validation set.We show our approach outperforms the previous state-of-theart methodology by more than 30% for hotels and 40% for restaurants with localized ontologies for the subset of languages tested.Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours.Our code is released open-source.
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models.Specifically, we present a universal training framework named Pretrain-KGE consisting of three phases: semanticbased fine-tuning phase, knowledge extracting phase and KGE training phase.Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.
Self-supervised Motion Representation via Scattering Local Motion Cues

Motion representation is key to many computer vision problems but has never been well studied in the literature. Existing works usually rely on the optical flow estimation to assist other tasks such as action recognition, frame prediction, video segmentation, etc. In this paper, we leverage the massive unlabeled video data to learn an accurate explicit motion representation that aligns well with the semantic distribution of the moving objects. Our method subsumes a coarse-to-fine paradigm, which first decodes the low-resolution motion maps from the rich spatial-temporal features of the video, then adaptively upsamples the low-resolution maps to the full-resolution by considering the semantic cues. To achieve this, we propose a novel context guided motion upsampling layer that leverages the spatial context of video objects to learn the upsampling parameters in an efficient way. We prove the effectiveness of our proposed motion representation method on downstream video understanding tasks, e.g., action recognition task. Experimental results show that our method performs favorably against state-of-the-art methods.
A quantum interior-point predictor–corrector algorithm for linear programming

We introduce a new quantum optimization algorithm for dense linear programming problems, which can be seen as the quantization of the interior point predictor–corrector algorithm [1] using a quantum linear system algorithm [2]. The (worst case) work complexity of our method is, up to polylogarithmic factors, for n the number of variables in the cost function, m the number of constraints, −1 the target precision, L the bit length of the input data, an upper bound to the Frobenius norm of the linear systems of equations that appear, ||M||F, and an upper bound to the condition number κ of those systems of equations. This represents a quantum speed-up in the number n of variables in the cost function with respect to the comparable classical interior point algorithms when the initial matrix of the problem A is dense: if we substitute the quantum part of the algorithm by classical algorithms such as conjugate gradient descent, that would mean the whole algorithm has complexity , or with exact methods, at least . Also, in contrast with any quantum linear system algorithm, the algorithm described in this article outputs a classical description of the solution vector, and the value of the optimal solution.
Depth Uncertainty in Neural Networks

Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines.
Learning Physical Graph Representations from Visual Scenes

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs' success on tasks that require structured understanding of visual scenes. To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts. Bound to each node is a vector of latent attributes that intuitively represent object properties such as surface shape and texture. We also describe PSGNet, a network architecture that learns to extract PSGs by reconstructing scenes through a PSG-structured bottleneck. PSGNet augments standard CNNs by including: recurrent feedback connections to combine low and high-level image information; graph pooling and vectorization operations that convert spatially-uniform feature maps into object-centric graph structures; and perceptual grouping principles to encourage the identification of meaningful scene elements. We show that PSGNet outperforms alternative self-supervised scene representation algorithms at scene segmentation tasks, especially on complex real-world images, and generalizes well to unseen object types and scene arrangements. PSGNet is also able learn from physical motion, enhancing scene estimates even for static images. We present a series of ablation studies illustrating the importance of each component of the PSGNet architecture, analyses showing that learned latent attributes capture intuitive scene properties, and illustrate the use of PSGs for compositional scene inference.
LoCo: Local Contrastive Representation Learning

Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and is not biologically plausible. Recent advances in unsupervised contrastive representation learning point to the question of whether a learning algorithm can also be made local, that is, the updates of lower layers do not directly depend on the computation of upper layers. While Greedy InfoMax separately learns each block with a local objective, we found that it consistently hurts readout accuracy in state-of-the-art unsupervised contrastive learning algorithms, possibly due to the greedy objective as well as gradient isolation. In this work, we discover that by overlapping local blocks stacking on top of each other, we effectively increase the decoder depth and allow upper blocks to implicitly send feedbacks to lower blocks. This simple design closes the performance gap between local learning and end-to-end contrastive learning algorithms for the first time. Aside from standard ImageNet experiments, we also show results on complex downstream tasks such as object detection and instance segmentation directly using readout features.
R2AD: Randomization and Reconstructor-based Adversarial Defense on Deep Neural Network

Machine learning (ML) has been widely adopted in a plethora of applications ranging from simple time-series forecasting to computer security and autonomous systems. Despite the robustness by the ML algorithms against random noise, it has been shown that inclusion of specially crafted perturbations to the input data termed as adversarial samples can lead to a significant degradation in the ML performance. Existing defenses to mitigate or minimize the impact of adversarial samples including adversarial training or randomization are confined to specific categories of adversaries, compute-intensive and/or often lead to reduce performance even without adversaries. To overcome the shortcomings of the existing works on adversarial defense, we propose a two-stage adversarial defense technique (R2AD). To thwart the exploitation of the deep neural network by the attacker, we first include a random nullification (RNF) layer. The RNF nullifies/removes some of the features from the input randomly to reduce the impact of adversarial noise and minimizes attacker's feasibility to extract the model parameters. However, the removal of input features through RNF leads to a reduction in the performance of the ML. As an antidote, we equip the network with a Reconstructor. The Reconstructor primarily contributes to reconstructing the input data by utilizing an autoencoder network, but based on the distribution of the normal samples, thereby improving the performance, and also being robust to the adversarial noise. We evaluated the performance of proposed multi-stage R^2AD on the MNIST digits and Fashion-MNIST datasets against multiple adversarial attacks including FGSM, JSMA, BIM, Deepfool, and CW attacks. Our findings report improvements as high as 80% in the performance compared to the existing defenses such as adversarial training and randomization-based defense.
GEVO

GPUs are a key enabler of the revolution in machine learning and high-performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU’s computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of general-purpose GPU programs and machine learning (ML) models on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline by an average of 51.08%. For the ML workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24×) and the a9a income prediction (2.93×) datasets with no loss of model accuracy. GEVO achieves 1.79× kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.