Reviewerly/exHarmony huggingface dataset:
Modeling global geometric spatial information for rotation invariant classification of satellite images

The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.
Unsupervised Curricula for Visual Meta-Reinforcement Learning

In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast and effective reinforcement learning (RL) strategies. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting these task distributions can be challenging and time-consuming. Can ``useful'' pre-training tasks be discovered in an unsupervised manner? We develop an unsupervised algorithm for inducing an adaptive meta-training task distribution, i.e. an automatic curriculum, by modeling unsupervised interaction in a visual environment. The task distribution is scaffolded by a parametric density model of the meta-learner's trajectory distribution. We formulate unsupervised meta-RL as information maximization between a latent task variable and the meta-learner’s data distribution, and describe a practical instantiation which alternates between integration of recent experience into the task distribution and meta-learning of the updated tasks. Repeating this procedure leads to iterative reorganization such that the curriculum adapts as the meta-learner's data distribution shifts. Moreover, we show how discriminative clustering frameworks for visual representations can support trajectory-level task acquisition and exploration in domains with pixel observations, avoiding the pitfalls of alternatives. In experiments on vision-based navigation and manipulation domains, we show that the algorithm allows for unsupervised meta-learning that both transfers to downstream tasks specified by hand-crafted reward functions and serves as pre-training for more efficient meta-learning of test task distributions.
Knowledge Extraction with No Observable Data

Knowledge distillation is to transfer the knowledge of a large neural network into a smaller one and has been shown to be effective especially when the amount of training data is limited or the size of the student model is very small. To transfer the knowledge, it is essential to observe the data that have been used to train the network since its knowledge is concentrated on a narrow manifold rather than the whole input space. However, the data are not accessible in many cases due to the privacy or confidentiality issues in medical, industrial, and military domains. To the best of our knowledge, there has been no approach that distills the knowledge of a neural network when no data are observable. In this work, we propose KegNet (Knowledge Extraction with Generative Networks), a novel approach to extract the knowledge of a trained deep neural network and to generate artificial data points that replace the missing training data in knowledge distillation. Experiments show that KegNet outperforms all baselines for data-free knowledge distillation. We provide the source code of our paper in https://github.com/snudatalab/KegNet.
A visually secure asymmetric image encryption scheme based on RSA algorithm and hyperchaotic map

This paper proposes a visually secure asymmetric image encryption scheme based on Rivest–Shamir–Adlemen (RSA) algorithm and hyperchaotic map. It combines the advantages of the RSA and chaos-based image encryption algorithms. First, to fully encrypt the image information, the plain image is encrypted into a cipher image by applying the row and column encryption algorithms. Second, the image secret key is converted into a ciphertext by utilizing the RSA algorithm to increase the security, and then the ciphertext is transformed into an image and permuted using the Arnold map to enhance the robustness of the embedded image. We then obtain the key cipher image. Finally, the cipher image and the key cipher image are embedded into a carrier image (CI) to obtain a visually secure image. It simultaneously achieves the image information security and image visually security. To further improve the security, the RSA keys management technique is applied and the initial conditions of the hyperchaotic map are updated in real time based on the plain image. In addition, the encryption scheme is suitable for multi-user image transmission with high security requirements and image transmission without the secure channel for key transmission. The simulation results and performance analysis validate the effectiveness, security and robustness of the proposed scheme.
Learning deep graph matching with channel-independent embedding and Hungarian attention

Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete. Until very recently, deep graph matching methods start to resort to deep networks to achieve unprecedented matching accuracy. Along this direction, this paper makes two complementary contributions which can also be reused as plugin in existing works: i) a novel node and edge embedding strategy which stimulates the multi-head strategy in attention models and allows the information in each channel to be merged independently. In contrast, only node embedding is accounted in previous works; ii) a general masking mechanism over the loss function is devised to improve the smoothness of objective learning for graph matching. Using Hungarian algorithm, it dynamically constructs a structured and sparsely connected layer, taking into account the most contributing matching pairs as hard attention. Our approach performs competitively, and can also improve state-of-the-art methods as plugin, regarding with matching accuracy on three public benchmarks.
A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Software defect prediction is one of the emerging research areas of software engineering.The prediction of defects at early stage of development process can produce high quality software at lower cost.This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results.The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection.The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments.The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: "Naïve Bayes (NB), Multi-Layer Perceptron (MLP).Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)".Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.
Effective metric learning with co-occurrence embedding for collaborative recommendations

In recommender systems, matrix factorization and its variants have grown up to be dominant in collaborative filtering due to their simplicity and effectiveness. In matrix factorization based methods, dot product which is actually used as a measure of distance from users to items, does not satisfy the inequality property, and thus may fail to capture the inner grained preference information and further limits the performance of recommendations. Metric learning produces distance functions that capture the essential relationships among rating data and has been successfully explored in collaborative recommendations. However, without the global statistical information of user–user pairs and item–item pairs, it makes the model easy to achieve a suboptimal metric. For this, we present a co-occurrence embedding regularized metric learning model (CRML) for collaborative recommendations. We consider the optimization problem as a multi-task learning problem which includes optimizing a primary task of metric learning and two auxiliary tasks of representation learning. In particular, we develop an effective approach for learning the embedding representations of both users and items, and then exploit the strategy of soft parameter sharing to optimize the model parameters. Empirical experiments on four datasets demonstrate that the CRML model can enhance the naive metric learning model and significantly outperforms the state-of-the-art methods in terms of accuracy of collaborative recommendations.
360-Degree Videos, VR Experiences and the Application of Education 4.0 Technologies in Malaysia for Exposure and Immersion

Obstacles, both imagined and real, continue to hinder the wider adoption of Education 4.0 learning technologies although these technologies are available in the mainstream consumer market.At the same time, the boom in Industry 4.0 manufacturing has brought down the prices of these technological tools making them generally more affordable.A case in point is 360-degree spherical video cameras and software that can record, render and playback immersive 'real life' contents.360-degree spherical video contents can then be rendered and post-processed into VR (virtual reality) experiences that are not just immersive but also allow for limitless exposure time to learners.Both technologies allow for learner-driven learning to happen in the truest sense, for instance for second and foreign language learners.This research paper examines data collected from 560 undergraduates (n = 560) who were immersed and exposed to 360-degree videos and VR experiences for degree level courses on English (as a second language), Mandarin (Chinese) (as a third or foreign language, and Arabic (as a third or foreign language) at four public university campuses in Peninsular Malaysia.The benefits of 360-degree videos and VR experiences in second and foreign languages content delivery were evident to support learner-driven learning: Contents were developed by local lecturers and then uploaded for free online and learners could learn anytime, anywhere; total immersion could be achieved using cheap VR goggles powered by learners' smartphones; and most beneficially, weaker learners who desperately need more time to understand and practice difficult degree level second or foreign languages now have the freedom to revise and upskill themselves at their own pace.
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising

Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on "internal" image statistics, by searching for similar patterns within the input image itself. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns. Given a pair of noisy patches, our network predicts whether different sub-band coefficients of the original noise-free patches are similar. The denoising algorithm then aggregates matched coefficients to obtain an initial estimate of the clean image. Finally, this estimate is provided as input, along with the original noisy image, to a standard regression-based denoising network. Experiments show that our method achieves state-of-the-art color image denoising performance, including with a blind version that trains a common model for a range of noise levels, and does not require knowledge of level of noise in an input image. Our approach also has a distinct advantage when training with limited amounts of training data.
Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering

With the development of population aging, the recognition of elderly activity in smart homes has received increasing attention. In recent years, single-resident activity recognition based on smart homes has made great progress. However, few researchers have focused on multi-resident activity recognition. In this paper, we propose a method to recognize two-resident activities based on time clustering. First, to use a de-noising method to extract the feature of the dataset. Second, to cluster the dataset based on the begin time and end time. Finally, to complete activity recognition using a similarity matching method. To test the performance of the method, we used two two-resident datasets provided by Center for Advanced Studies in Adaptive Systems (CASAS). We evaluated our method by comparing it with some common classifiers. The results show that our method has certain improvements in the accuracy, recall, precision, and F-Measure. At the end of the paper, we explain the parameter selection and summarize our method.
3-D Human Behavior Understanding Using Generalized TS-LSTM Networks

This paper addresses the problems of skeleton feature representation and the modeling of temporal dynamics to recognize human actions consisting of poses. In contrast to traditional methods which generally used relative coordinate systems dependent on some joints, or modeled only the long-term dependency, we attempt to understand 3D human behavior with observation by taking temporally different windows. Instead of taking raw skeletons as the input, we transform the skeletons into another coordinate system to obtain the robustness to scale, rotation and translation, and extract motion features between adjacent skeletons, which finally constructs an efficient hybrid-stream combining both pose and motion streams. We propose novel generalized Temporal Sliding Long Short-term Memory (TS-LSTM) networks. The proposed networks are composed of multiple TS-LSTM networks with various hyper-parameters, which can capture various temporal dynamics of actions. We also propose a novel hyper-parameter searching method, which finds decent hyper-parameters of generalized TS-LSTM to handle temporal dynamics of actions. In the experiment, we evaluate the proposed networks to verify the effectiveness of the proposed methods, and compare them with the other methods on three challenging datasets. Additionally, we analyze a relation between the recognized actions and the hyper-parameters, and visualize the layers of the proposed models.
Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these forward models do not admit tractable densities forcing practitioners to make use of approximations. This work introduces a novel approach to address the intractability of the likelihood and the marginal model. We achieve this by learning a flexible amortized estimator which approximates the likelihood-to-evidence ratio. We demonstrate that the learned ratio estimator can be embedded in MCMC samplers to approximate likelihood-ratios between consecutive states in the Markov chain, allowing us to draw samples from the intractable posterior. Techniques are presented to improve the numerical stability and to measure the quality of an approximation. The accuracy of our approach is demonstrated on a variety of benchmarks against well-established techniques. Scientific applications in physics show its applicability.
MergeNAS: Merge Operations into One for Differentiable Architecture Search

Differentiable architecture search (DARTS) has been a promising one-shot architecture search approach for its mathematical formulation and competitive results. However, besides its caused high memory utilization and a large computation requirement, many research works have shown that DARTS also often suffers notable over-fitting and thus does not work robustly for some new tasks. In this paper, we propose a one-shot neural architecture search method referred to as MergeNAS by merging different types of operations e.g. convolutions into one operation. This merge-based approach not only reduces the search cost (about half a GPU day), but also alleviates over-fitting by reducing the redundant parameters. Extensive experiments on different search space and various datasets have been conducted to verify our approach, showing that MergeNAS can converge to a stable architecture and achieve better performance with fewer parameters and search cost. For test accuracy and its stability, MergeNAS outperforms all NAS baseline methods implemented on NAS-Bench-201, including DARTS, ENAS, RS, BOHB, GDAS and hand-crafted ResNet.
Neural Kernels Without Tangents

We investigate the connections between networks and simple building blocks in kernel space. In particular, using well established feature space tools such as direct sum, averaging, and moment lifting, we present an algebra for creating kernels from bags of features. We show that these operations correspond to many of the building blocks of neural tangent kernels (NTK). Experimentally, we show that there is a correlation in test error between network architectures and the associated kernels. We construct a simple network architecture using only 3x3 convolutions, 2x2 average pooling, ReLU, and optimized with SGD and MSE loss that achieves 96% accuracy on CIFAR10, and whose corresponding compositional kernel achieves 90% accuracy. We also use our constructions to investigate the relative performance of networks, NTKs, and compositional kernels in the small dataset regime. In particular, we find that compositional kernels outperform NTKs and networks outperform both kernel methods.
ThumbUp: Identification and Authentication by Smartwatch using Simple Hand Gestures

The widespread creative application and smart devices call for convenient and secure interaction with human users. We propose, design, and implement a smartwatch-based two-factor real-time identification and authentication system named ThumbUp, where smartwatch users can identify and authenticate themselves by some simple hand and finger gestures, such as thumb-up. ThumbUp leverages the signal collected from the Inertial Measurement Unit (IMU) in Commercial Off-The-Shelf (COTS) smart devices and discovers the unique fingerprint pattern produced by each user’s simple hand gestures using a carefully crafted deep learning model. We implement our system and conduct extensive experiments to evaluate its efficacy and efficiency with 65 different users over a period of more than 3 months. It reaches an accuracy of 97% for identification, and EER 0.014 for authentication using only one simple gesture. We also survey the users’ acceptance of our system and discuss how the proficiency of gestures affects authentication accuracy.
Drones, Augmented Reality and Virtual Reality Journalism: Mapping Their Role in Immersive News Content

Drones are shaping journalism in a variety of ways including in the production of immersive news content. This article identifies, describes and analyzes, or maps out, four areas in which drones are impacting immersive news content. These include: 1) enabling the possibility of providing aerial perspective for first-person perspective flight-based immersive journalism experiences; 2) providing geo-tagged audio and video for flight-based immersive news content; 3) providing the capacity for both volumetric and 360 video capture; and 4) generating novel content types or content based on data acquired from a broad range of sensors beyond the standard visible light captured via video cameras; these may be a central generator of unique experiential media content beyond visual flight-based news content.
A Text Generation and Prediction System: Pre-training on New Corpora Using BERT and GPT-2

Using a given starting word to make a sentence or filling in sentences is an important direction of natural language processing. From one aspect, it reflects whether the machine can have human thinking and creativity. We train the machine for specific tasks and then use it in natural language processing, which will help solve some sentence generation problems, especially for application scenarios such as summary generation, machine translation, and automatic question answering. The OpenAI GPT-2 and BERT models are currently widely used language models for text generation and prediction. There have been many experiments to verify the outstanding performance of these two models in the field of text generation. This paper will use two new corpora to train OpenAI GPT-2 model, used to generate long sentences and articles, and finally perform a comparative analysis. At the same time, we will use the BERT model to complete the task of predicting intermediate words based on the context.
A systematic review of cyber-resilience assessment frameworks

Cyber-attacks are regarded as one of the most serious threats to businesses worldwide. Organizations dependent on Information Technology (IT) derive value not only from preventing cyber-attacks, but also from responding promptly and coherently when cyber-attacks happen so as to minimize their disruptive effect on operations. This capacity is known as cyber-resilience. As multiple cyber-resilience frameworks (CRF) have been proposed in literature, an increased clarity about the scope, characteristics, synergies and gaps in existing CRFs will facilitate scientific research advancement in this area. This paper uses a systematic literature review to identify extant research on CRFs. The analysis is based on a sample representing 36 different industries and 25 different research areas. Through the use of descriptive analysis and thematic categorization, this paper makes a contribution by identifying CRFs as either strategic or operational, by the hierarchy of their decision influence, by the attacks addressed, and through the methods used and the places and institutions doing CRF research. As a result, this work presents an overview map of the current CRF research landscape, identifies relevant research gaps, highlights similarities and synergies between CRFs, and proposes opportunities for interdisciplinary research, as a contribution to guide future research in this area.