Reviewerly/exHarmony huggingface dataset:
Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.
Curriculum self-paced learning for cross-domain object detection

Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e.g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced learning in a smart and efficient way, in this paper, we propose a novel self-paced algorithm that learns from easy to hard. Our method is simple and effective, without any overhead during inference. It uses only pseudo-labels for samples taken from the target domain, i.e. the domain adaptation is unsupervised. We conduct experiments on four cross-domain benchmarks, showing better results than the state of the art. We also perform an ablation study demonstrating the utility of each component in our framework. Additionally, we study the applicability of our framework to other object detectors. Furthermore, we compare our difficulty measure with other measures from the related literature, proving that it yields superior results and that it correlates well with the performance metric.
An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality

In all dimensions of education and all subjects, Smartphones have turned out to be broadly acknowledged technology. It plays an essential task in advanced online education systems. Because of smart devices' effortlessness and extension property, it is getting to be mandatory for portable applications. This paper analyses the research on Smart Devices (SD) to incorporate visual simulation into e-learning. The researchers created an Augmented Reality (AR) platform for e-learners to expand the coursebook with graphics and virtual multimedia applications. This paper recommends a Mobile e-Learning (MeL) application termed “MeL app". The advanced MeL app methods have been tested using Mann-Whitney ‘U' Test in the lecture hall using real-time learners. The proposed MeL app planned to create the learning practice easier, focusing on e-learner's requirements by encouraging e-learners and instructor relationships to maintain communicative development-based e-learning for Technical Higher Education (THE). Software engineering learners assess this proposed framework in THE. Future work in this investigation incorporates new highlights, testing the device in extreme situations, evaluating the instructive perspectives utilizing more significant and increasingly various understudy and beginner inhabitants, and at last, extending the application space.
Counterfactual state explanations for reinforcement learning agents via generative deep learning

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our first user study investigates if humans can discern if the counterfactual state explanations are produced by the actual game or produced by a generative deep learning approach. Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game. Our results indicate that counterfactual state explanations have sufficient fidelity to the actual game images to enable non-experts to more effectively identify a flawed RL agent compared to the nearest neighbor baseline and to having no explanation at all.
Entanglement-Assisted Communication Surpassing the Ultimate Classical Capacity

Entanglement underpins a variety of quantum-enhanced communication, sensing, and computing capabilities. Entanglement-assisted communication (EACOMM) leverages entanglement preshared by communicating parties to boost the rate of classical information transmission. Pioneering theory works showed that EACOMM can enable a communication rate well beyond the ultimate classical capacity of optical communications, but an experimental demonstration of any EACOMM advantage remains elusive. In this Letter we report the implementation of EACOMM surpassing the classical capacity over lossy and noisy bosonic channels. We construct a high-efficiency entanglement source and a phase-conjugate quantum receiver to reap the benefit of preshared entanglement, despite entanglement being broken by channel loss and noise. We show that EACOMM beats the Holevo-Schumacher-Westmoreland capacity of classical communication by up to 16.3%, when both protocols are subject to the same power constraint at the transmitter. As a practical performance benchmark, we implement a classical communication protocol with the identical characteristics for the encoded signal, showing that EACOMM can reduce the bit-error rate by up to 69% over the same bosonic channel. Our work opens a route to provable quantum advantages in a wide range of quantum information processing tasks.
An Enhanced IoT Based Tracing and Tracking Model for COVID -19 Cases

The COVID-19 pandemic has alarmed the world nations to impose strict curfews and emergencies to prevent the social transmission of the disease. In order to achieve this, effective Tracing and Tracking of the suspected COVID-19 cases need to be achieved. In view of the enormous number of cases being recorded each day, this process couldn't be performed effectively with simple manual tracing. Hence, we have proposed an Internet of Things (IoT) based automated Tracing and Tracking method for identification of the possible contacts with deployment of cost-effective RFID Tags and the mobile of the individuals which act as a reader. Thereby, tracing of persons who have crossed the subject would be possible even without the knowledge of the suspected cases. This would enable cent percent quarantine of possible primary and secondary contacts and monitoring of the same by the administrative agencies. This would augment the nations' capability of managing the pandemic.
Single Image Dehazing for a Variety of Haze Scenarios Using Back Projected Pyramid Network

Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.
Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding

In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage. In traditional methods, an object region that best matches the referring expression is picked out, and then the query sentence is reconstructed from the selected region, where the reconstruction difference serves as the loss for back-propagation. The existing methods, however, conduct both the matching and the reconstruction approximately as they ignore the fact that the matching correctness is unknown. To overcome this limitation, a discriminative triad is designed here as the basis to the solution, through which a query can be converted into one or multiple discriminative triads in a very scalable way. Based on the discriminative triad, we further propose the triad-level matching and reconstruction modules which are lightweight yet effective for the weakly-supervised training, making it three times lighter and faster than the previous state-of-the-art methods. One important merit of our work is its superior performance despite the simple and neat design. Specifically, the proposed method achieves a new state-of-the-art accuracy when evaluated on RefCOCO (39.21 percent), RefCOCO+ (39.18 percent) and RefCOCOg (43.24 percent) datasets, that is 4.17, 4.08 and 7.8 percent higher than the previous one, respectively. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/insomnia94/DTWREG</uri> .
Pyramidal Multiple Instance Detection Network With Mask Guided Self-Correction for Weakly Supervised Object Detection

Weakly supervised object detection has attracted more and more attention as it only needs image-level annotations for training object detectors. A popular solution to this task is to train a multiple instance detection network (MIDN) which integrates multiple instance learning into a deep convolutional neural network. One major issue of the MIDN is that it is prone to be stuck at local discriminative regions. To address this local optimum issue, we propose a pyramidal MIDN (P-MIDN) comprised of a sequence of multiple MIDNs. In particular, one MIDN performs proposal removal for its subsequent MIDN to reduce the exposure of local discriminative proposal regions to the latter during training. In this manner, it allows our MIDNs to focus on proposals which cover objects more completely. Furthermore, we integrate the P-MIDN into an online instance classifier refinement (OICR) framework. Combined with the P-MIDN, a mask guided self-correction (MGSC) method is proposed to generate high-quality pseudo ground-truths for training the OICR. Experimental results on PASCAL VOC 2007, PASCAL VOC 2010, PASCAL VOC 2012, ILSVRC 2013 DET and MS-COCO benchmarks demonstrate that our approach achieves state-of-the-art performance.
FPGA Implementation of Improved Security Approach for Medical Image Encryption and Decryption

Securing medical images is a great challenge to protect medical privacy. An image encryption model founded on a complex chaos-based Pseudorandom Number Generator (PRNG) and Modified Advanced Encryption Standard (MAES) is put forward in this paper. Our work consists of the following three main points. First, we propose the use of a complex PRNG based on two different chaotic systems which are the 2D Logistic map in a complex set and Henon’s system in the key generation procedure. Second, in the MAES 128 bits, the subbytes’ operation is performed using four different S-boxes for more complexity. Third, both shift-rows’ and mix-columns’ transformations are eliminated and replaced with a random permutation method which increases the complexity. More importantly, only four rounds of encryption are performed in a loop that reduces significantly the execution time. The overall system is implemented on the Altera Cyclone III board, which is completed with an SD card interface for medical image storage and a VGA interface for image display. The HPS software runs on μClinux and is used to control the FPGA encryption-decryption algorithm and image transmission. Experimental findings prove that the propounded map used has a keyspace sufficiently large and the proposed image encryption algorithm augments the entropy of the ciphered image compared to the AES standard and reduces the complexity time by 97%. The power consumption of the system is 136.87 mw and the throughput is 1.34 Gbit/s. The proposed technique is compared to recent image cryptosystems including hardware performances and different security analysis properties, such as randomness, sensitivity, and correlation of the encrypted images and results prove that our cryptographic algorithm is faster, more efficient, and can resist any kind of attacks.
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis

Synthesizing photo-realistic images based on text descriptions is a challenging task in the field of computer vision. Although generative adversarial networks have made significant breakthroughs in this task, they still face huge challenges in generating high-quality visually realistic images consistent with the semantics of text. Generally, existing text-to-image methods accomplish this task with two steps, that is, first generating an initial image with a rough outline and color, and then gradually yielding the image within high-resolution from the initial image. However, one drawback of these methods is that, if the quality of the initial image generation is not high, it is hard to generate a satisfactory high-resolution image. In this paper, we propose SAM-GAN, Self-Attention supporting Multi-stage Generative Adversarial Networks, for text-to-image synthesis. With the self-attention mechanism, the model can establish the multi-level dependence of the image and fuse the sentence- and word-level visual-semantic vectors, to improve the quality of the generated image. Furthermore, a multi-stage perceptual loss is introduced to enhance the semantic similarity between the synthesized image and the real image, thus enhancing the visual-semantic consistency between text and images. For the diversity of the generated images, a mode seeking regularization term is integrated into the model. The results of extensive experiments and ablation studies, which were conducted in the Caltech-UCSD Birds and Microsoft Common Objects in Context datasets, show that our model is superior to competitive models in text-to-image synthesis.
Predicting Camera Viewpoint Improves Cross-Dataset Generalization for 3D Human Pose Estimation

Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its effect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic differences in the distribution of camera viewpoints relative to a body-centered coordinate frame. Based on this observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization.
Consistency and diversity neural network multi-view multi-label learning

In multi-view multi-label learning, each object is represented by multiple heterogeneous data and is simultaneously associated with multiple class labels. Previous studies usually use shared subspaces to fuse multi-view representations. However, as the number of views increases, it is more challenging to capture the high-order relationships among multiple views. Therefore, a novel neural network multi-view multi-label learning framework is proposed, which is intended to solve the problem of consistency and diversity among views through a simple and effective method(CDMM). First, we build a separate classifier for each view based on the neural network method of the nonlinear kernel mapping function and require each view to learn a consistent label result. Then, we consider the diversity of individual views while learning a consistent representation among views. For this reason, we combine the Hilbert–Schmidt Independence Criterion with exploring the diversity among different views. Finally, the label correlation factor is in addition to the classification model, and the view contribution factor is added to the prediction model. A large number of comparative experiments with existing state-of-the-art solutions on benchmark multi-view multi-label learning data sets show the effectiveness of this method.
Policy-Based Deep Reinforcement Learning for Visual Servoing Control of Mobile Robots With Visibility Constraints

In this article, the image-based visual servoing (IBVS) problem for mobile robots with visibility constraints is studied by using a policy-based deep reinforcement learning (DRL) approach. First, the classical IBVS (C-IBVS) method and its feature-loss problem are introduced. Then, a DRL-based IBVS method is presented to solve the feature-loss problem and improve the servo efficiency.Specifically, the formulation of the C-IBVS controller is inherited by the designed controller to ensure the analytical stability, and a policy-based DRL algorithm is proposed to design an adaptive law for tuning the controller gain in the continuous space, which can maintain the feature in the field of the view of the camera as well as improving the servo efficiency. Finally, the effectiveness of the proposed method is demonstrated by various comparative experiments.
Enterprise adoption of cloud computing with application portfolio profiling and application portfolio assessment

Abstract With the continued appeal and adoption of cloud computing, an assessment of cloud run costs and migration affordability prior to adoption would assist enterprises that have several legacy applications targeted for cloud migration. However, as cloud migrations have become more prevalent, many have been characterised by unsuccessful migration or application modernisation attempts. The primary reason behind the failed attempts is insufficient planning upfront, to identify which legacy applications are suitable to realise the benefits of public or private cloud, leading to time and cost overruns. There is a need for strategic decision making for application portfolios to mitigate the risks of cost overruns and migration delays. Thus, a Rough Order of Magnitude (ROM) of cloud run costs for an application portfolio is required in the planning phase as an input into IT governance. To obtain the ROM cloud run costs, it is necessary to baseline application data, preferably through automated discovery, and perform quantitative analysis of the applications. Therefore, we propose an approach to (a) baseline application data using Application Portfolio Profiling (APP), and (b) perform quantitative analysis of applications using an Application Portfolio Assessment (APA), to inform the legacy application migration decision. APP and APA are proposed as part of a Cloud Computing Considerations for Companies (CCCC) framework that enables an enterprise to make an informed decision regarding which legacy applications are to be migrated as part of enterprise Cloud Computing adoption. This decision is important because of the change in operating model, infrastructure requirements, hidden costs and commercial models inherent with cloud computing adoption. We validate the proposed framework through applying it to a real-world use case scenario that provides the necessary coverage to test the proposed framework.
Extremely low-resource neural machine translation for Asian languages

Abstract This paper presents a set of effective approaches to handle extremely low-resource language pairs for self-attention based neural machine translation (NMT) focusing on English and four Asian languages. Starting from an initial set of parallel sentences used to train bilingual baseline models, we introduce additional monolingual corpora and data processing techniques to improve translation quality. We describe a series of best practices and empirically validate the methods through an evaluation conducted on eight translation directions, based on state-of-the-art NMT approaches such as hyper-parameter search, data augmentation with forward and backward translation in combination with tags and noise, as well as joint multilingual training. Experiments show that the commonly used default architecture of self-attention NMT models does not reach the best results, validating previous work on the importance of hyper-parameter tuning. Additionally, empirical results indicate the amount of synthetic data required to efficiently increase the parameters of the models leading to the best translation quality measured by automatic metrics. We show that the best NMT models trained on large amount of tagged back-translations outperform three other synthetic data generation approaches. Finally, comparison with statistical machine translation (SMT) indicates that extremely low-resource NMT requires a large amount of synthetic parallel data obtained with back-translation in order to close the performance gap with the preceding SMT approach.