Reviewerly/exHarmony huggingface dataset:
Using Satellite and Aerial Imagery for Identification of Solar PV

Solar photovoltaic (PV) is the fastest growing form of energy generation today, and many countries are seeing significant uptake of distributed solar PV on the rooftops of homes and businesses. However, many of these systems are not accurately registered, and central records of distributed solar PV are often not up-to-date. At the same time, high levels of solar PV are introducing challenges for many stakeholders in the energy sector, such as market operators and network operators, who need to forecast total rooftop solar PV generation across entire regions. One possible solution to this problem is to identify existing solar PV generation systems using overhead satellite and aerial imagery. While there have been early promising attempts in this direction, there are nevertheless many important research challenges that remain to be addressed. In this paper we survey the state of the art in this nascent area, describe the challenges that exist, and advocate for novel research questions that are worthy of further exploration. By identifying these areas of interest we aim to generate greater awareness of the potential value of satellite and aerial imagery for identification of solar PV, which will ultimately facilitate large scale uptake of solar PV and other renewable generation technologies.
3D Skeletal Gesture Recognition via Discriminative Coding on Time-Warping Invariant Riemannian Trajectories

Learning 3D skeleton-based representation for gesture recognition has progressively stood out because of its invariance to the viewpoint and background dynamics of video. Typically, existing techniques use absolute coordinates to determine human motion features. The recognition of gestures, however, is irrespective of the position of the performer, and the extracted features should be invariant to body size. In addition, when comparing and classifying gestures, the problem of temporal dynamics can greatly distort the distance metric. In this paper, we represent a 3D skeleton as a point in the special orthogonal group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> product space that expressly models the 3D geometric relationships between body parts. As such, a gesture skeletal sequence can be described by a trajectory on a Riemannian manifold. Following that, we propose to generalize the transported square-root vector field to obtain a time-warping invariant metric for comparing these trajectories (identifying these gestures). Moreover, by specifically considering the labeling information with encoding, a sparse coding scheme of skeletal trajectories is presented to enforce the discriminant validity of atoms in the dictionary. Experimental results indicate that the proposed approach has achieved state-of-the-art performance on many challenging gesture recognition benchmarks.
Drinking Event Detection and Episode Identification Using 3D-Printed Smart Cup

Insufficient fluid intake is becoming an actual concern and problem of the elderly with dramatic increase of aging population. Poor fluid intake habits and Dehydration can cause long-term negative effects in mental and physical capabilities, and even death. Typical approaches to monitor individual fluid intake behavior and consumption of fluid quantities mainly rely on self-report, such as questionnaires. However, these typical approaches suffer issues in manual errors and cause additional burdens on the user. In recent years, advantages of wireless sensor networks and pervasive computing technologies have provided the opportunities to create automatic, objective and low-intrusive fluid intake monitoring systems. The aim of this work is to design a 3D-printed smart cup attached with a single accelerometer and drinking event detection algorithm that is feasible to spot fluid intake gestures, especially for detecting drinking events and recognizing complete periods of drinking. Such drinking information about timing and the frequency of fluid intake can reveal fluid intake habits and that is important for clinical assessment and intervention. The finding shows that the best system performance using k-nearest neighbors (k-NN) model with window size of 4s achieves 89.92% and 85.88% F-measure in event-defined and frame-defined evaluation approaches, respectively.
Semi-Federated Learning

Federated learning(FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less uplink transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.
Watershed-Based Superpixels With Global and Local Boundary Marching

Superpixels are widely used in computer vision applications, as they conserve the running costs of subsequent processing while preserving the original performance. In most of the existing algorithms, the boundary adherence and the compactness of superpixels are necessarily inter-inhibitive because the color/gradient information is balanced against the position constraints, and the set criteria define all pixels indiscriminately. In this paper, we present a two-phase superpixel segmentation method based on the watershed transformation. After designing a new approach for calculating the flooding priority, we propose a new strategy with two distinct criteria for global and local refinement of the boundary pixels. These criteria reduce the compromise between the boundary adherence and compactness. Unlike the indiscriminate standards, our method applies different treatments to pixels in different environments, preserving the color homogeneity in content-rich areas while improving the regularity of the superpixels in content-plain regions. The superior accuracy and computing time of our proposed method are verified in comparison experiments with several state-of-the-art methods.
FSSCaps-DetCountNet: fuzzy soft sets and CapsNet-based detection and counting network for monitoring animals from aerial images

With the advances in remote sensing, wild animals sprawling over a vast area can be easily and quickly captured using low-cost unmanned aerial vehicle imagery. We propose an aerial animal detection and counting network (DetCountNet) framework called FSSCaps-DetCountNet, using fuzzy soft sets (FSS) and capsule network (CapsNet). Similarity measures based on FSS have been used to discriminate the target animals from both nontargets and the background. Of particular interest to aerial images, CapsNet requires very few training data and is robust to rotation and affine transformation. With superpixel segmentation and attention maps, FSSCaps-DetCountNet works well on challenging image conditions, such as dense background with sparse animals and overlapping/cluttered animals. The model is trained and tested on benchmark aerial animal datasets, namely, the aerial elephants and the livestock datasets with an accuracy index of 99.84% and 99.86%, respectively. Also, the overall omission and commission errors are 0.02% and 0.03%, respectively. The experimental results and comparative study with other state-of-the-art conventional models demonstrate the effectiveness and robustness of FSSCaps-DetCountNet for real-time animal detection and counting from aerial images.
DO-RA: Data-oriented runtime attestation for IoT devices

Remote attestation is an excellent approach to confirm the security states of Internet of Things (IoT) devices. It allows an entity (verifier) to validate the integrity of a potentially compromised platform (prover). Most of the current attestation schemes are static, which verify only the software integrity of devices. Recently, some runtime attestation schemes based on the Control Flow Graph (CFG) of the program have been proposed to collect the runtime information. However, the algorithm for constructing CFG only focuses on the rationality of the programs' control flow, and ignores the possibility that attackers could compromise the control flow of the device by modifying key data. Some mitigation of runtime exploitation technologies take into account the Unique Code Target (UCT) property of control flow, but there are limitations to their algorithms abilities to find out the constraining data. In this paper, we present a Data-Oriented Control Flow Graph (DO-CFG) that can match a single legitimate target for each control-flow transfer, which guarantees both the rationality and the full uniqueness of programs' control flow. Furthermore, we propose a Data-Oriented Runtime Attestation (DO-RA) scheme based on DO-CFG. It collects some critical non-control data to enhance the detection ability of the attestation scheme, which further ensures the uniqueness of the control flow. We also present a detailed proof-of-concept implementation and analyze our protocol based on Raspberry Pi. We simulate several real applications to evaluate the security and performance of DO-RA, which demonstrates that our scheme provides a more comprehensive detection capability within an acceptable overhead.
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity. Our proposed re-ranking approach improves the results in two steps: in the sort-step, k-nearest neighbor search with soft-voting to sort the retrieved results based on their label similarity to the query images, and in the insert-step, we add additional samples from the dataset that were not retrieved by image-similarity. This approach allows overcoming the low visual diversity in retrieved images. In-depth experimental results show that the proposed approach significantly outperforms existing approaches on the challenging Google Landmarks Datasets. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge on Kaggle. Our code is publicly available here: https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution.
Preserve Security to Medical Evidences using Blockchain Technology

Advanced privacy preservation approaches are used to be protecting sensitive and important data of various application areas like Communication systems, Healthcare, Education and Financial Sector. In the Healthcare area, the important and sensitive task is a medical certificate that acts as medical evidence of a person which helps for many purposes. Deception in issuance as well as in verification of medical certificates has existed for a long time in health care centers due to lack of reliable mechanisms. In the issue of medical evidence to a person, needs to consider a method which has advanced features like transparency, immutable, reliable and decentralized properties. Hence, in this paper, blockchain technology is used to design and develop a model with advanced features for reducing forgery in medical pieces of evidence. In this approach, a regulatory body will authorize the Health care centers (Hospitals) for issuing medical certificates to the required persons in a decentralized approach. Here smart contract system helps to verify the certificate authenticity from any authorized one in the world. The main strength of the paper gives the Blockchain-based model as a solution to the issue related to proof of a certificate of medical evidence.
Disentangling, Embedding and Ranking Label Cues for Multi-Label Image Recognition

Multi-label image recognition is a fundamental but challenging computer vision and multimedia task. Great progress has been achieved by exploiting label correlations among these multiple labels associated with a single image, which is the most crucial issue for multi-label image recognition. In this paper, to explicitly model label correlations, we propose a unified deep learning framework to Disentangle, Embed and Rank (DER) the corresponding label cues. Specifically, we first obtain class-aware disentangled maps (CADMs) by reforming deep activations in accordance with the class-specific recognition weights. Then, after transforming CADMs into the corresponding label vectors, we propose an embedding operation from a metric learning perspective to pull the relevant label vectors together and push irrelevant label vectors away. Furthermore, a ranking operation is employed, which aims to accurately and robustly measure the similarity/dissimilarity of these label vectors. Our model can be trained in an end-to-end manner with only image-level supervision, during which the proposed embedding and ranking operations can contribute to the CADMs learning through back-propagation. In addition, the obtained CADMs are aggregated and further used as an essential feature stream for the final multi-label classification. We conduct extensive experiments on three commonly used multi-label benchmark datasets. Quantitative results show that our model can significantly and consistently outperform previous competitive methods. Moreover, qualitative analysis of our DER proposal also reveals the effectiveness of our proposed model.
DeepRacing: A Framework for Autonomous Racing

We consider the challenging problem of high speed autonomous racing in realistic dynamic environments. DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing. The virtual testbed is implemented using the realistic Formula One (F1) Codemasters game, which is used by many F1 drivers for training. We present AdmiralNet - a Convolution Neural Network (CNN) integrated with Long Short-Term Memory (LSTM) cells that can be tuned for the autonomous racing task in the highly realistic F1 game. We evaluate AdmiralNet’s performance on unseen race tracks, and also evaluate the degree of transference between the simulation and the real world by implementing end-to-end racing on a physical 1/10 scale autonomous racecar.
Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores

This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime.
Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition

This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which are composed of several repetitive and concurrent short movements having temporal dependencies. It is improbable to directly use sensor data to recognize these long-term composite activities because two examples (data sequences) of the same ADL result in largely diverse sensory data. However, they may be similar in terms of more semantic and meaningful short-term atomic actions. Therefore, we propose a two-level hierarchical model for recognition of ADLs. Firstly, atomic activities are detected and their probabilistic scores are generated at the lower level. Secondly, we deal with the temporal transitions of atomic activities using a temporal pooling method, rank pooling. This enables us to encode the ordering of probabilistic scores for atomic activities at the higher level of our model. Rank pooling leads to a 5–13% improvement in results as compared to the other popularly used techniques. We also produce a large dataset of 61 atomic and 7 composite activities for our experiments.
CTEA: Context and Topic Enhanced Entity Alignment for knowledge graphs

We study the problem of finding entities referring to the same real world object in multilingual knowledge graphs(KGs), i.e., entity alignment for multilingual KGs. Recently, embedding-based entity alignment methods get extended attention in this area. Most of them firstly embed the entities in low dimensional vectors space via relation structure of entities, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are defective in utilizing entity contexts and entity topic information. In this paper, we propose a novel entity alignment framework CTEA (Context and Topic Enhanced Entity Alignment), which integrates entity context information and entity topic information to help alignment. This framework learns entity topic distributions from their attributes with a specially designed topic model BTM4EA, and the learned entity topic distributions are used to filter some weakly correlated entities for each entity to be aligned. Meanwhile, we embed KGs to low dimensional vectors space via translation-based KG embedding model and mine context information from these vectors with an attention attached Convolutional Neural Network(CNN). The entity embeddings, entity contexts and entity topics are combined to get the final alignment results. Extended experiments reveal that our method achieves promising performances in most cases.
Comparative Analysis of Deep Convolutional Generative Adversarial Network and Conditional Generative Adversarial Network using Hand Written Digits

Generative adversarial network framework has recently emerged as a promising generative modeling approach. It is composed or made up of a generative network and a discriminative network. There have been various types of adversarial networks present today. Among these types of networks, the most popular network is Deep Convolutional Generative Adversarial Network (DCGAN) for performing on the convolutional networks without using multilayer perceptrons. The multilayer layer perceptrons have the hidden layers because of this we have to more bind to extract the data with the parameters, because of this we also study the Conditional Generative Adversarial Network (CGAN) to add an extra label to the generator and the discriminator. We study the comparative analysis between these two popular networks to highlight the main differences and similarities using the handwritten image datasets.
Robust Deep Reservoir Computing Through Reliable Memristor With Improved Heat Dissipation Capability

Deep neural networks (DNNs), a brain-inspired learning methodology, requires tremendous data for training before performing inference tasks. The recent studies demonstrate a strong positive correlation between the inference accuracy and the size of the DNNs and datasets, which leads to an inevitable demand for large DNNs. However, conventional memory techniques are not adequate to deal with the drastic growth of dataset and neural network size. Recently, a resistive memristor has been widely considered as the next generation memory device owing to its high density and low power consumption. Nevertheless, its high switching resistance variations (cycle-to-cycle) restrict its feasibility in deep learning. In this work, a novel memristor configuration with the enhanced heat dissipation feature is fabricated and evaluated to address this challenge. Our experimental results demonstrate our memristor reduces the resistance variation by ~ 30% and the inference accuracy increases correspondingly in a similar range. The accuracy increment is evaluated by our deep delay-feed-back reservoir computing (Deep-DFR) model. The design area, power consumption, and latency are reduced by ~48%, ~42%, and ~67%, respectively, compared to the conventional static random-access memory technique (6T). The performance of our memristor is improved at various degrees (~13%-73%) compared to the state-of-the-art memristors.
Blockchain-based e-Tendering System

The tendering process is generally used by governments and companies to procure goods or services from manufacturing companies or service providers. However, e-tendering being the mostly used procurement method, there are various security implications present. Blockchain technology can be used to solve these security implications as it heavily focuses on the decentralization of information and is secured by encryption integrated with undeniable block-based architecture for transaction management. In this paper, how smart contracts (based on ethereum blockchain) can be employed to design a distributed e-tendering system is explored. The project is divided into four sections, 1. Tender creation and publishing process, 2.Bidding process on the tender, 3.Evaluation, and Negotiation of the bid and 4.Selection of the Winning bid. Different algorithms are used to implement each process. The security and audibility challenges are evaluated and compared to the current tendering process. The main aim of this paper is to implement a fair, transparent and open tendering scheme.
Supporting Smartphone-Based Image Capture of Rapid Diagnostic Tests in Low-Resource Settings

Rapid diagnostic tests (RDTs) provide point-of-care medical diagnosis without sophisticated laboratory equipment, making them especially useful for community health workers (CHWs). Because the procedure for completing a malaria RDT is error-prone, CHWs are often asked to carry completed RDTs back to their supervisors. Doing so makes RDTs susceptible to deterioration and introduces inefficiencies in the CHWs' workflow. In this work, we propose a smartphone-based RDT capture app, RDTScan, that facilitates the collection of high-quality RDT images to support CHWs in the field. RDTScan does not require an external adapter to control the image capture environment, but instead provides real-time guidance using image processing to obtain the best image possible. During our evaluation study, we found that RDTScan had 98.1% sensitivity and 99.7% specificity against visual inspection of the RDTs. RDTScan helped CHWs capture high-quality RDT images within 18 seconds while enabling a better RDT workflow.