Reviewerly/exHarmony huggingface dataset:
Attribution-Based Confidence Metric For Deep Neural Networks

We propose a novel confidence metric, namely, attribution-based confidence (ABC) for deep neural networks (DNNs). ABC metric characterizes whether the output of a DNN on an input can be trusted. DNNs are known to be brittle on inputs outside the training distribution and are, hence, susceptible to adversarial attacks. This fragility is compounded by a lack of effectively computable measures of model confidence that correlate well with the accuracy of DNNs. These factors have impeded the adoption of DNNs in high-assurance systems. The proposed ABC metric addresses these challenges. It does not require access to the training data, the use of ensembles, or the need to train a calibration model on a held-out validation set. Hence, the new metric is usable even when only a trained model is available for inference. We mathematically motivate the proposed metric and evaluate its effectiveness with two sets of experiments. First, we study the change in accuracy and the associated confidence over out-of-distribution inputs. Second, we consider several digital and physically realizable attacks such as FGSM, CW, DeepFool, PGD, and adversarial patch generation methods. The ABC metric is low on out-of-distribution data and adversarial examples, where the accuracy of the model is also low. These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
Parameter-free Sentence Embedding via Orthogonal Basis

We propose a simple and robust nonparameterized approach for building sentence representations.Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence.We model the semantic meaning of a word in a sentence based on two aspects.One is its relatedness to the word vector subspace already spanned by its contextual words.The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace.Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations.This approach requires zero parameters, along with efficient inference performance.We evaluate our approach on 11 downstream NLP tasks.Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
Virtual Reality � Opportunities and Challenges

In today's world, we tend to are encircled by blends of technologies. We've non heritable majorities of those technologies into our everyday lives. More and more, these varied technologies are becoming integrated to produce US with new talents and services. And most frequently a pc is that the heart of such integration. This is often the case with video game a therefore referred to as technology that truly may be a terribly subtle integration of variety of technologies. Video game may be a technology supported pc that assimilates specialized input and output devices by permitting the user to move with and skill a manmade created surroundings as if they were within the universe. A video game system is additionally permitting the user to go looking and move with a 3 dimensional virtual or artificial surroundings created by the designer. Within the virtual world, the user will do all things just like routine as throwing a ball or as glorious as flying through house. And every one these items is created to occur solely owing to a hand gesture or a nod. Augmented Reality is a three dimensional PC based intelligent condition which recreate reality. Computer generated Reality can bring us into a fictional universe which shows up precisely like our very own reality. For planning an augmented experience framework, one need to manages thoughts of spatial connections and PC designs which thus are subsidiary to science, material science, expressions and furthermore human brain science. In these virtual or manufactured conditions, one need to consider different material science laws like gravity, air obstruction, and speed and so on.
Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.
Task Assignment Algorithm for Road Patrol by Multiple UAVs With Multiple Bases and Rechargeable Endurance

Increasing attention has been paid to the application of Unmanned Aerial Vehicles (UAVs) in traffic field.Existing studies on UAVs in road patrol task assignment rarely integrate the actual requirements of multiple bases, charging problems during inspection and multiple types of UAVs into the patrol model, and rarely consider the fastest time to complete the patrol of all task points as the optimization target.In addition, most existing solving algorithms are difficult to use to directly solve problem models meeting the above requirements and objectives, and some existing algorithms need to be improved in terms of solving quality.In order to solve those problems, a task assignment model for road patrol by multiple UAVs with multiple bases and rechargeable endurance is established, and a time-priority immune clonal selection algorithm with optimization-revision is proposed.The optimal sequence of task points is obtained by the immune clonal selection algorithm, and the time-priority method is adopted to divide the sequence of task points.The optimal UAV paths are further optimized and modified.With 20 task points, the experimental results show that the earliest completion time obtained by the proposed algorithm decreased by 1.071 and 2.1209 compared with the earliest reachable time particle swarm optimization algorithm and the improved dynamic programming ant colony algorithm, respectively.The results indicate that proposed algorithm achieves better performance in terms of solution quality.In addition, experiments with 50 and 100 task points show that the algorithm is also suitable for medium and large scale problems.
An Efficient Framework for Animal Breeds Classification Using Semi-Supervised Learning and Multi-Part Convolutional Neural Network (MP-CNN)

The automatic classification of animal images is an onerous task due to the challenging image conditions, especially when it comes to animal breeds. In this paper, we built a semi-supervised learning based Multi-part Convolutional Neural Network (MP-CNN) that classifies 35,992 animal images from ImageNet into 27 different classes of animals. The proposed model classifies the animals on both generic and fine-grained level. The animal breeds are accurately classified using Multi-part Convolutional Neural Network with a hybrid feature extraction framework of Fisher Vector based Stacked Autoencoder. Furthermore, with Semi-supervised learning based pseudo-labels, the model classifies new classes of unlabeled images too. Modified Hellinger Kernel classifier has been used to re-train the misclassified classes of animals and thereby improve the performance obtained from MP-CNN. The model has experimented with varied tasks to analyze its performance in each of the cases. The experimental results have proved that the coalesced approach of MP-CNN with pseudo-labels can accurately classify animal breeds and we have achieved an accuracy of 99.95% from the proposed model.
Analysis of the Cryptocurrency Adoption Decision: Literature Review

Cryptocurrency is a recent and significant innovation in the financial industry. The goal is to offer a currency that is not tied, created, or backed by a government. Cryptocurrency use the Blockchain technology as the financial platform. Cryptocurrency adoption level has increased, and the market has grown dramatically. There have not been enough literature investigating the adoption and acceptance of the cryptocurrency by users. The aim of this paper is to fill the gap in the current literature by investigating the current cryptocurrency adoption level, adoption-influencing factors, providing an in-depth analysis of these factors and discussing some pitfalls surrounding the cryptocurrency adoption. We believe that despite the difficulty to find out an accurate number of cryptocurrency users, a good estimate can be made by studying the number of cryptocurrency exchange sites' users. In addition, the paper suggests that the main factors driving the adoption decision revealed from the literature review are the investment opportunity cryptocurrency forms, the anonymity of the transactions and privacy, the acceptance by businesses as a payment method, the fast transfer of funds, the low cost of transactions, and technological curiosity. The research findings help researchers, regulators, and cryptocurrency developers to better understand their consumer's intention toward cryptocurrency adoption.
DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation

Indoor scene segmentation is a difficult task in computer vision.We propose an indoor scene segmentation framework, called DFMNet, incorporating RGB and complementary depth information to establish indoor scene segmentation.We use the squeeze-and-excitation residual network as encoder to simultaneously extract features from RGB and depth data and fuse them in the decoder.Multiple average pooling layers and transposed convolution layers are used to process the encoded outputs and fuse their outputs over several decoder layers.To optimize the network parameters, we use a pyramid supervision training scheme, which applies supervised learning over different layers in the decoder to prevent vanishing gradients.We evaluated the proposed DFMNet on the NYU Depth V2 dataset, which consists of 1449 cluttered indoor scenes, achieving competitive results compared to state-of-the-art methods.
Mining clinical phrases from nursing notes to discover risk factors of patient deterioration

Early identification and treatment of patient deterioration is crucial to improving clinical outcomes. To act, hospital rapid response (RR) teams often rely on nurses' clinical judgement typically documented narratively in the electronic health record (EHR). We developed a data-driven, unsupervised method to discover potential risk factors of RR events from nursing notes.We applied multiple natural language processing methods, including language modelling, word embeddings, and two phrase mining methods (TextRank and NC-Value), to identify quality phrases that represent clinical entities from unannotated nursing notes. TextRank was used to determine the important word-sequences in each note. NC-Value was then used to globally rank the locally-important sequences across the whole corpus. We evaluated our method both on its accuracy compared to human judgement and on the ability of the mined phrases to predict a clinical outcome, RR event hazard.When applied to 61,740 hospital encounters with 1,067 RR events and 778,955 notes, our method achieved an average precision of 0.590 to 0.764 (when excluding numeric tokens). Time-dependent covariates Cox model using the phrases achieved a concordance index of 0.739. Clustering the phrases revealed clinical concepts significantly associated with RR event hazard.Our findings demonstrate that our minimal-annotation, unsurprised method can rapidly mine quality phrases from a large amount of nursing notes, and these identified phrases are useful for downstream tasks, such as clinical outcome predication and risk factor identification.
End-to-End Unpaired Image Denoising with Conditional Adversarial Networks

Image denoising is a classic low level vision problem that attempts to recover a noise-free image from a noisy observation. Recent advances in deep neural networks have outperformed traditional prior based methods for image denoising. However, the existing methods either require paired noisy and clean images for training or impose certain assumptions on the noise distribution and data types. In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. The critical component of our model is a noise learning module based on a conditional Generative Adversarial Network (cGAN). The model learns the noise distribution from the input noisy images and uses it to transform the input clean images to noisy ones without any assumption on the noise distribution and data types. This process results in pairs of clean and pseudo-noisy images. Such pairs are then used to train another denoising network similar to the existing denoising methods based on paired images. The noise learning and denoising components are integrated together so that they can be trained end-to-end. Extensive experimental evaluation has been performed on both synthetic and real data including real photographs and computer tomography (CT) images. The results demonstrate that our model outperforms the previous models trained on unpaired images as well as the state-of-the-art methods based on paired training data when proper training pairs are unavailable.
3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels

Crowd counting has been studied for decades and a lot of works have achieved good performance, especially the DNNs-based density map estimation methods. Most existing crowd counting works focus on single-view counting, while few works have studied multi-view counting for large and wide scenes, where multiple cameras are used. Recently, an end-to-end multi-view crowd counting method called multi-view multi-scale (MVMS) has been proposed, which fuses multiple camera views using a CNN to predict a 2D scene-level density map on the ground-plane. Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones. Compared to 2D fusion, the 3D fusion extracts more information of the people along z-dimension (height), which helps to solve the scale variations across multiple views. The 3D density maps still preserve the 2D density maps property that the sum is the count, while also providing 3D information about the crowd density. We also explore the projection consistency among the 3D prediction and the ground-truth in the 2D views to further enhance the counting performance. The proposed method is tested on 3 multi-view counting datasets and achieves better or comparable counting performance to the state-of-the-art.
InstaNAS: Instance-Aware Neural Architecture Search

Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy. However, a single architecture may not be representative enough for the whole dataset with high diversity and variety. Intuitively, electing domain-expert architectures that are proficient in domain-specific features can further benefit architecture related objectives such as latency. In this paper, we propose InstaNAS—an instance-aware NAS framework—that employs a controller trained to search for a “distribution of architectures” instead of a single architecture; This allows the model to use sophisticated architectures for the difficult samples, which usually comes with large architecture related cost, and shallow architectures for those easy samples. During the inference phase, the controller assigns each of the unseen input samples with a domain expert architecture that can achieve high accuracy with customized inference costs. Experiments within a search space inspired by MobileNetV2 show InstaNAS can achieve up to 48.8% latency reduction without compromising accuracy on a series of datasets against MobileNetV2.
Visual Agreement Regularized Training for Multi-Modal Machine Translation

Multi-modal machine translation aims at translating the source sentence into a different language in the presence of the paired image. Previous work suggests that additional visual information only provides dispensable help to translation, which is needed in several very special cases such as translating ambiguous words. To make better use of visual information, this work presents visual agreement regularized training. The proposed approach jointly trains the source-to-target and target-to-source translation models and encourages them to share the same focus on the visual information when generating semantically equivalent visual words (e.g. “ball” in English and “ballon” in French). Besides, a simple yet effective multi-head co-attention model is also introduced to capture interactions between visual and textual features. The results show that our approaches can outperform competitive baselines by a large margin on the Multi30k dataset. Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.
A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. However, excessive virtual machine consolidation can lead to dangerous Service Level Agreement (SLA) violations. Therefore, how to balance between effective energy consumption and SLA violations avoidance effectively is a paradox to be mediated. The virtual machine consolidation problem is NP-hard. The traditional heuristic algorithm is easy to fall into the local optimal and some meta-heuristic algorithms can help to avoid it. However, the existing meta-heuristic algorithms are with high complexity. Therefore, we propose a lower complexity multi-population ant colony system algorithm with the Extreme Learning Machine (ELM) prediction (ELM_MPACS). The algorithm firstly predicts the host state employing ELM and then the virtual machine on the overloaded host will be migrated to the normal host, while the virtual machine on the underloaded host will be consolidated to another underloaded host with higher utilization. Multiple populations concurrently construct migration plans and local search further optimizes the results obtained by each population to reduce SLA violations. We compare ELM_MPACS with the benchmark, heuristic and meta-heuristic algorithms. The experimental results have shown that compared with these algorithms, our algorithm reduces energy consumption, migration times and SLA violations effectively.
Jamming Prediction for Radar Signals Using Machine Learning Methods

Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.
Large-scale audience participation in live music using smartphones

We present a study and reflection about the role and use of smartphone technology for a large-scale musical performance involving audience participation. We evaluated a full design and development process from initial ideation to a final performance concept. We found that the smartphone became the design tool, the technical device and the musical instrument at the same time. As a technical device that uses ultrasound communication as interaction technique, the smartphone became inspirational for the artist's creative work. In aiming to support the artist, we observed pervasive importance of retaining artistic control to realise artistic intent. This concerns the co-design process and the resulting concept of audience participation and supports recommendations for such participatory work.
Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory

During human–robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The pressure data of nine standing postures were obtained from each participant. The human standing postures were discriminated by seven classification algorithms. The results of the best three algorithms were fused using the Dempster–Shafer evidence theory to improve the accuracy and robustness. In a cross-validation test, the best method achieved an average accuracy of 99.96%. The convolutional neural network classifier and data-fusion algorithm can feasibly classify the standing postures of human workers.