Reviewerly/exHarmony huggingface dataset:
Attention-Guided Label Refinement Network for Semantic Segmentation of Very High Resolution Aerial Orthoimages

The recent applications of fully convolutional networks (FCNs) have shown to improve the semantic segmentation of very high resolution (VHR) remote-sensing images because of the excellent feature representation and end-to-end pixel labeling capabilities. While many FCN-based methods concatenate features from multilevel encoding stages to refine the coarse labeling results, the semantic gap between features of different levels and the selection of representative features are often overlooked, leading to the generation of redundant information and unexpected classification results. In this article, we propose an attention-guided label refinement network (ALRNet) for improved semantic labeling of VHR images. ALRNet follows the paradigm of the encoder–decoder architecture, which progressively refines the coarse labeling maps of different scales by using the channelwise attention mechanism. A novel attention-guided feature fusion module based on the squeeze-and-excitation module is designed to fuse higher level and lower level features. In this way, the semantic gaps among features of different levels are declined, and the category discrimination of each pixel in the lower level features is strengthened, which is helpful for subsequent label refinement. ALRNet is tested on three public datasets, including two ISRPS 2-D labeling datasets and the Wuhan University aerial building dataset. Results demonstrated that ALRNet had shown promising segmentation performance in comparison with state-of-the-art deep learning networks. The source code of ALRNet is made publicly available for further studies.
Modality adaptation in multimodal data

Recently, multimodal data has received much attention. In classical machine learning, it is assumed that all data comes from one modality while in multimodal machine learning, the information comes from different modalities. In multimodal machine learning, transiting, or fusing knowledge from different modalities is an important step. Hence, in these steps, the different marginal distributions between different modalities should be taken into account. However, in recent years, modality adaptation has not gotten enough attention. The motivation of this work is to consider modality adaptation to effectively encode the shared common or complementary knowledge in multimodal data. To reduce the modality shift, we present a new perspective on the modality adaptation algorithm. In multimodal data, by applying the existing domain adaptation techniques to reduce the modality shift, a problem arises because of the insufficient capability of those techniques in preserving complementary knowledge. Our proposed modality adaptation is designed such that it simultaneously considers both the shared and complementary knowledge of each modality while preserving the discriminative ability of each modality in the label space. To evaluate the proposed approach, we have applied it to two different multimodal applications: multi-view object detection and RGBD image semantic segmentation. Our results show that the proposed modality adaptation technique is successful in transferring and fusing knowledge.
Moving Vehicle Tracking with a Moving Drone Based on Track Association

The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of moving vehicles with a small drone. A moving object detection scheme has been developed based on frame registration and subtraction followed by morphological filtering and false alarm removing. The center position of the detected object area is the input to the tracking target as a measurement. The Kalman filter estimates the position and velocity of the target based on the measurement nearest to the state prediction. We propose a new data association scheme for multiple measurements on a single target. This track association method consists of the hypothesis testing between two tracks and track fusion through track selection and termination. We reduce redundant tracks on the same target and maintain the track with the least estimation error. In the experiment, drones flying at an altitude of 150 m captured two videos in an urban environment. There are a total of 9 and 23 moving vehicles in each video; the detection rates are 92% and 89%, respectively. The number of valid tracks is significantly reduced from 13 to 10 and 56 to 26 in the first and the second video, respectively. In the first video, the average position RMSE of two merged tracks are improved by 83.6% when only the fused states are considered. In the second video, the average position and velocity RMSE are 1.21 m and 1.97 m/s, showing the robustness of the proposed system.
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.
Social-Ecological Predictors of Homophobic Name-Calling Perpetration and Victimization Among Early Adolescents

Bias-based aggression at school in the form of homophobic name-calling is quite prevalent among early adolescents. Homophobic name-calling is associated with low academic performance, higher risky sexual behaviors, and substance abuse, among other adverse outcomes. This longitudinal study examined risk and protective factors across multiple domains of the social ecology (individual, peer, family, school and community) and levels of analysis (within- and between-person) associated with homophobic name-calling perpetration and victimization. Students from four middle schools in the U.S. Midwest (N = 1,655; X¯ age = 12.75; range = 10-16 years) were surveyed four times (Spring/Fall 2008, Spring/Fall 2009). For homophobic name-calling perpetration, significant risk factors included impulsivity, social dominance, traditional masculinity, family violence, and neighborhood violence; while empathy, peer support, school belonging, and adult support were significant protective factors. For homophobic name-calling victimization, significant risk factors included empathy (between-person), impulsivity, traditional masculinity, family violence, and neighborhood violence, while empathy (within-person), parental monitoring, peer support, school belonging, and adult support were significant protective factors.
Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks

Human Activity Recognition is a field concerned with the recognition of physical human activities based on the interpretation of sensor data, including one-dimensional time series data. Traditionally, hand-crafted features are relied upon to develop the machine learning models for activity recognition. However, that is a challenging task and requires a high degree of domain expertise and feature engineering. With the development in deep neural networks, it is much easier as models can automatically learn features from raw sensor data, yielding improved classification results. In this paper, we present a novel approach for human activity recognition using ensemble learning of multiple convolutional neural network (CNN) models. Three different CNN models are trained on the publicly available dataset and multiple ensembles of the models are created. The ensemble of the first two models gives an accuracy of 94% which is better than the methods available in the literature.
Two-stage classifier for face recognition using HOG features

Face recognition is mainly used in biometric applications, surveillance systems and computer vision. The major problem while recognizing faces arises due to pose variations, background illumination invariants and facial expressions. The proposed two-stage classifier aims to recognize given input face images using k-NN and SVM. The use of two separate classifiers, one after the other, improves the system recognition accuracy. The pre processed face images are then utilized to extract prominent features by Histogram of Oriented Gradients (HOG). In the initial stage, k-NN classifier was used and afterwards, unrecognized face images were tested with SVM classifier. It is found that recognition accuracy increased by cascading k-NN followed by SVM. The two-stage classifier achieved a recognition accuracy of 95.2%.
A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naïve bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.
rkt-io

The shielding of applications using trusted execution environments (TEEs) can provide strong security guarantees in untrusted cloud environments. When executing I/O operations, today's shielded execution frameworks, however, exhibit performance and security limitations: they assign resources to the I/O path inefficiently, perform redundant data copies, use untrusted host I/O stacks with security risks and performance overheads. This prevents TEEs from running modern I/O-intensive applications that require high-performance networking and storage.
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition

Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embedding space. We encode signals into images and extract features using a deep residual CNN. Using triplet loss, we learn a feature embedding. The resulting encoder transforms features into an embedding space in which closer distances encode similar actions while higher distances encode different actions. Our approach is based on a signal level formulation and remains flexible across a variety of modalities. It further outperforms the baseline on the large scale NTU RGB+D 120 dataset for the one-shot action recognition protocol by 5.6%. With just 60% of the training data, our approach still outperforms the baseline approach by 3.7%. With 40% of the training data, our approach performs comparably well to the second follow up. Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data. Furthermore, our inter-joint and inter-sensor experiments suggest good capabilities on previously unseen setups.
IoT and Machine Learning based Peer to Peer Platform for Crop Growth and Disease Monitoring System using Blockchain

Farmer Suicides are a major issue in India due to various factors. One of the major concerns being the financial aspect of managing and growing the crops while still trying to turn a profit. This paper proposes a decentralised platform for buying and selling of agriculture produce by connecting farmers with individuals interested in investing in their fields with the continuous monitoring of the quality and crop health using IOT and Machine Learning for predicting diseases in the agricultural produce.
2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data

Deep neural networks have been widely applied and achieved great success in various fields. As training deep models usually consumes massive data and computational resources, trading the trained deep models is highly-demanded and lucrative nowadays. Unfortunately, the naive trading schemes typically involves potential risks related to copyright and trustworthiness issues, e.g., a sold model can be illegally resold to others without further authorization to reap huge profits. To tackle this problem, various watermarking techniques are proposed to protect the model intellectual property, amongst which the backdoor-based watermarking is the most commonly-used one. However, the robustness of these watermarking approaches is not well evaluated under realistic settings, such as limited in-distribution data availability and agnostic of watermarking patterns. In this paper, we benchmark the robustness of watermarking, and propose a novel backdoor-based watermark removal framework using limited data, dubbed WILD. The proposed WILD removes the watermarks of deep models with only a small portion of training data, and the output model can perform the same as models trained from scratch without watermarks injected. In particular, a novel data augmentation method is utilized to mimic the behavior of watermark triggers. Combining with the distribution alignment between the normal and perturbed (e.g., occluded) data in the feature space, our approach generalizes well on all typical types of trigger contents. The experimental results demonstrate that our approach can effectively remove the watermarks without compromising the deep model performance for the original task with the limited access to training data.
Content4All Open Research Sign Language Translation Datasets

Computational sign language research lacks the large-scale datasets that enables the creation of useful real-life applications. To date, most research has been limited to prototype systems on small domains of discourse, e.g. weather forecasts. To address this issue and to push the field forward, we release six datasets comprised of 190 hours of footage on the larger domain of news. From this, 20 hours of footage have been annotated by Deaf experts and interpreters and is made publicly available for research purposes. In this paper, we share the dataset collection process and tools developed to enable the alignment of sign language video and subtitles, as well as baseline translation results to underpin future research.
Supporting digitally enhanced learning through measurement in higher education: Development and validation of a university students' digital competence scale

Abstract Digital competence is critical for university students to adapt to and benefit from digitally enhanced learning. Prior studies on its measurement mostly focus on educators and relied on factor analyses. However, there is a lack of valid and convenient tools to measure university students' digital competence. This study aimed to develop a digital competence scale for university students (DC‐US) in digitally enhanced learning with robust psychometric properties. An initial DC‐US with 23 items was proposed to measure the single latent trait of digital competence. It was validated and refined continuously through a pilot study, a main study and a predictive validity study in three datasets involving 825 participants altogether, using factor analyses, Rasch analyses and the partial least squares modelling. The final DC‐US turned out to comprise two subscales: technical literacy and digital skills, with 10 items retained, and manifested high internal consistency, unidimensionality and measurement invariance. The scale also demonstrated strong predictive validity, with technical literacy greatly predicting digital skills, which negatively predicted technostress. The DC‐US enables instructors and school administrators to conveniently obtain preliminary information of university students' digital competence, informing their digital class preparation and development of timely interventions for addressing digital deficiencies.
Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification

Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN.
A Composite Framework of Co-located Asymmetric Virtual Reality

As the variety of possible interactions with virtual reality (VR) continues to expand, researchers need a way to relate these interactions to users' needs and goals in ways that advance understanding. Existing efforts have focused mainly on the symmetric use of technology, which excludes a rising form of interaction known as asymmetric VR, in which co-located participants use different interfaces to interact with a shared environment. There must be a clear path to creating asymmetric VR systems that are rooted in previous work from several fields, as these systems have use cases in education, hybrid reality teams (using VR and other technologies to interact online and face to face), accessibility, as well as entertainment. Currently, there is no systematic way to characterize 1) how a system may be asymmetric, 2) how the different mediation technology and affordances within asymmetric VR support (or do not support) users' goals, and 3) the relationships and collaborative capabilities between users of these different technologies. In this paper, the authors use a scoping review to explore relevant conceptual frameworks for asymmetric interaction, mediation technology, and computer supported cooperative work to clarify the dimensions of asymmetry and synthesize the literature into a Composite framework for Asymmetric VR (CAVR). The paper concludes with suggestions of ways to test and expand the framework in order to guide future research as it identifies the most-beneficial interaction paradigms for co-located asymmetric VR.
Inverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity

In this Letter, we present a deep-learning-based method using neural networks (NNs) for inverse design of photonic nanostructures. We show that by using dimensionality reduction in both the design and the response spaces, the computational complexity of the inverse design algorithm is considerably reduced. As a proof of concept, we apply this method to design multi-layer thin-film structures composed of consecutive layers of two different dielectrics and compare the results using our techniques to those using conventional NNs.
On the Use of Multi-sensory Cues in Symmetric and Asymmetric Shared Collaborative Virtual Spaces

Physical face-to-face collaboration with someone gives a higher-quality experience compared to mediated communication options, such as a phone- or video-based chat. Participants can share rich sensory cues to multiple human senses in a physical space. Also, the perceptual sensing of the surrounding environment including other peoples' reactions can influence human communication and emotion, and thus collaborative performance. Shared spaces in virtual environments provide degraded sensory experiences because most commercial virtual reality systems typically provide only visual and audio feedback. The impact of richer, multi-sensory feedback on joint decision-making tasks in VR is still an open area of research. Two independent studies exploring this topic are presented in this paper. We implemented a multi-sensory system that delivers vision, audio, tactile, and smell feedback, and we compared the system to a typical VR system. The scenario placed two users in a virtual theme-park safari ride with a number of non-player character (NPC) passengers to simulate realistic scenarios compared to the real-world and we varied the type and complexity of NPCs reactions to participants. In Experiment 1, we provided both users with either multi-sensory or typical sensory feedback symmetrically as a between-subjects factor, and used NPC reaction type as a within-subjects factor. In Experiment 2, we provided sensory feedback asymmetrically to each user (i.e., one had multi-sensory cues and the other had typical sensory cues) as a between-subjects factor, and used NPC reaction type as a within-subjects factor. We found that the number of sensory channels and NPC reactions did not influence user perception significantly under either symmetric or asymmetric sensory feedback conditions. However, after accounting for individual personality traits (e.g., assertive, passive), as well as any existing relationship between the pairs, we found that increasing the number of sensory channels can significantly improve subjective responses.