Reviewerly/exHarmony huggingface dataset:
Regional-scale mapping for determining geochemical background values in soils of the Itacaiúnas River Basin, Brazil: The use of compositional data analysis (CoDA)

This study aims at providing high quality soil geochemical data at a regional scale (1 sample per 25 km2) to understand the source and distribution of potentially toxic elements (PTEs: Al, As, Ba, Cd, Cu, Co, Cr, Fe, Hg, Mo, Mn, Ni, Pb, Se, U, V, and Zn) and to define their geochemical background threshold values (GBTVs) in the Itacaiúnas River Basin (IRB), Brazilian Amazon. A total of 2958 soil samples were collected at two depths (surface: 0–10 cm; bottom: 30–50 cm) and the fine fractions (<0.117 mm) were analyzed by ICP-MS and ICP-AES after aqua-regia digestion. The compositional nature of geochemical data was evaluated using the log-ratio approach and compared with traditional methods. Results show that compared to raw/log-transformation, centred log-ratio (clr) transformation improves spatial mapping and also better separated variables in Principal Component Analysis (PCA). Correlation analysis based on the isometric log-ratio (ilr) transformation in heat-maps provides reliable and better structure of the results. The spatial maps (clr) indicate that most of the elements in surface and bottom soils are mainly governed by underlying bedrock geology (parent lithologies and mineralization), while anthropogenic factors are insignificant. The resulting clr-based PCA and cluster analyses associated with geological information indicate that geochemical patterns of Fe-Ti-V-Cr-Sc can be linked to mafic to intermediate metavolcanic rocks and banded-iron formations, which are dominant in the Carajás basin, while Ni and Cr distribution pattern is attributed to mafic and ultramafic lithologies, and Cu anomalous values are mainly related to hydrothermal mineralized copper belts. Among the statistical methods applied for estimation of GBTVs, TIF produced the highest BTVs, followed by 98th and mMAD, with few exceptions, while mMAD was considered the more suitable for defining GBTVs. The number of potential anomalies of PTEs identified by these methods follow the order of MAD > 98th > TIF or MAD > TIF > 98th, and overall the anomalies are mostly related to the local lithology/mineralization with no indication of anthropogenic contamination. Compared with Brazilian guidelines (CONAMA Resolution No. 420/2009), the GBTVs determined for Cr, Co, Ni, and Cu by TIF, MAD, and 98th percentile mostly exceeded both IV and PV limits. However, these high values are due to the influence of local lithology. This reveals that the Brazilian CONAMA guidelines of these elements for the IRB are unrealistic because that suggests a large number of sites requiring investigation, which is not consistent with the geochemical evidence. Therefore, site-specific GBTVs must be considered for the accurate evaluation of anthropogenic contamination, as well as the ecological and human health risks of PTEs in this region.
Secure Robust JPEG Steganography Based on AutoEncoder With Adaptive BCH Encoding

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security.
Double-layer-clustering differential evolution multimodal optimization by speciation and self-adaptive strategies

Multimodal optimization aims to find and maintain as many global and local optima of a function as possible. Niching techniques based on multi-populations and clustering proved to be efficient for tackling multimodal optimization problems. The main focus of this work is to enhance the diversity of the population and improve the global search ability to locate more optima. A Double-Layer-Clustering Speciation Differential Evolution (DLCSDE) algorithm for multimodal optimization is proposed. We also show how the DLCSDE can be improved by integrating with a self-adaptive strategy to form the Self-adaptive DLCSDE (SDLCSDE). Based on speciation, first layer clustering divides the entire population into multiple subpopulations to locate global and local optima. The seeds from each species then form a sub-population to search globally during the second layer clustering to find peaks missed during the first layer clustering search process. To test the performance, both DLCSDE and SDLCSDE are compared with 17 state-of-art niching algorithms on 29 multimodal problems with different dimensions. The experiment results demonstrate that both the proposed algorithms outperform or perform comparably to the 17 niching algorithms on all the test functions.
Machine Learning Based Early Fall Detection for Elderly People with Neurological Disorder Using Multimodal Data Fusion

Fall is deemed to be one of the critical problems for the elderly patient having neurological disorders as it may cause injury or death. It turns to be a public health concern and attracts researchers to detect fall using sensing devices wearable, portable, and imaging. With the availability of low cost pervasive sensing elements, advancement of ubiquitous computing and better understanding of machine learning approaches, researchers have employing various machine learning approaches in detecting fall from the sensor data. In this paper, we have proposed a recurrent neural network (RNN)-based framework for detecting fall/daily activity of a patient having a neurological disorder using Internet of things and then manage the patient by referring to doctor. If an anomaly is detected in the daily activity and notify caregiver/family member if fall is detected. The RNN based fall detection model fused knowledge from both the smartphone/wearable and camera installed on the wall and ceiling. The proposed RNN is trained with open-labeled and UR data-sets and is compared with the support vector machine and random forest for these two data-sets. The performance evaluation shows the proposed method is effecting and outperforms its counterparts.
Seenomaly

GUI animations, such as card movement, menu slide in/out, snackbar display, provide appealing user experience and enhance the usability of mobile applications. These GUI animations should not violate the platform's UI design guidelines (referred to as design-don't guideline in this work) regarding component motion and interaction, content appearing and disappearing, and elevation and shadow changes. However, none of existing static code analysis, functional GUI testing and GUI image comparison techniques can "see" the GUI animations on the scree, and thus they cannot support the linting of GUI animations against design-don't guidelines. In this work, we formulate this GUI animation linting problem as a multi-class screencast classification task, but we do not have sufficient labeled GUI animations to train the classifier. Instead, we propose an unsupervised, computer-vision based adversarial autoencoder to solve this linting problem. Our autoencoder learns to group similar GUI animations by "seeing" lots of unlabeled real-application GUI animations and learning to generate them. As the first work of its kind, we build the datasets of synthetic and real-world GUI animations. Through experiments on these datasets, we systematically investigate the learning capability of our model and its effectiveness and practicality for linting GUI animations, and identify the challenges in this linting problem for future work.
PatientDataChain: A Blockchain-Based Approach to Integrate Personal Health Records

Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients’ traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients’ medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players.
Challenges for the Repeatability of Deep Learning Models

Deep learning training typically starts with a random sampling initialization approach to set the weights of trainable layers. Therefore, different and/or uncontrolled weight initialization prevents learning the same model multiple times. Consequently, such models yield different results during testing. However, even with the exact same initialization for the weights, a lack of repeatability, replicability, and reproducibility may still be observed during deep learning for many reasons such as software versions, implementation variations, and hardware differences. In this article, we study repeatability when training deep learning models for segmentation and classification tasks using U-Net and LeNet-5 architectures in two development environments Pytorch and Keras (with TensorFlow backend). We show that even with the available control of randomization in Keras and TensorFlow, there are uncontrolled randomizations. We also show repeatable results for the same deep learning architectures using the Pytorch deep learning library. Finally, we discuss variations in the implementation of the weight initialization algorithm across deep learning libraries as a source of uncontrolled error in deep learning results.
EnAET: A Self-Trained Framework for Semi-Supervised and Supervised Learning With Ensemble Transformations

Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many methods for semi-supervised learning have been proposed and achieved excellent performance. In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information. To our best knowledge, all current semi-supervised methods improve performance with prediction consistency and confidence ideas. We are the first to explore the role of self-supervised representations in semi-supervised learning under a rich family of transformations. Consequently, our framework can integrate the self-supervised information as a regularization term to further improve all current semi-supervised methods. In the experiments, we use MixMatch, which is the current state-of-the-art method on semi-supervised learning, as a baseline to test the proposed EnAET framework. Across different datasets, we adopt the same hyper-parameters, which greatly improves the generalization ability of the EnAET framework. Experiment results on different datasets demonstrate that the proposed EnAET framework greatly improves the performance of current semi-supervised algorithms. Moreover, this framework can also improve supervised learning by a large margin, including the extremely challenging scenarios with only 10 images per class. The code and experiment records are available in https://github.com/maple-research-lab/EnAET.
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1$\mu\mathrm{s}$ on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the $\mathtt{hls4ml}$ library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.
Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.
A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment

Abstract Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.
Social Engineering Attacks During the COVID-19 Pandemic

The prevailing conditions surrounding the COVID-19 pandemic has shifted a variety of everyday activities onto platforms on the Internet. This has led to an increase in the number of people present on these platforms and also led to jump in the time spent by existing participants online. This increase in the presence of people on the Internet is almost never preceded by education about cyber-security and the various types of attacks that an everyday User of the Internet may be subjected to. This makes the prevailing situation a ripe one for cyber-criminals to exploit and the most common type of attacks made are Social Engineering Attacks. Social Engineering Attacks are a group of sophisticated cyber-security attacks that exploit the innate human nature to breach secure systems and thus have some of the highest rate of success. This paper delves into the particulars of how the COVID-19 pandemic has set the stage for an increase in Social Engineering Attacks, the consequences of this and some techniques to thwart such attacks.
Accelerating Federated Learning for IoT in Big Data Analytics With Pruning, Quantization and Selective Updating

The ever-increasing number of Internet of Things (IoT) devices are continuously generating huge masses of data, but the current cloud-centric approach for IoT big data analysis has raised public concerns on both data privacy and network cost. Federated learning (FL) recently emerges as a promising technique to accommodate these concerns, by means of learning a global model by aggregating local updates from multiple devices without sharing the privacy-sensitive data. However, IoT devices usually have constrained computation resources and poor network connections, making it infeasible or very slow to train deep neural networks (DNNs) by following the FL pattern. To address this problem, we propose a new efficient FL framework called FL-PQSU in this paper. It is composed of 3-stage pipeline: structured pruning, weight quantization and selective updating, that work together to reduce the costs of computation, storage, and communication to accelerate the FL training process. We study FL-PQSU using popular DNN models (AlexNet, VGG16) and publicly available datasets (MNIST, CIFAR10), and demonstrate that it can well control the training overhead while still guaranteeing the learning performance.
State of Art for Semantic Analysis of Natural Language Processing

Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.
Towards NIR-VIS Masked Face Recognition

Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities. Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain newly-emerged difficulties during the pandemic of COVID-19, since people are supposed to wear facial masks to cut off the spread of the virus. We define this task as NIR-VIS masked face recognition, and find it problematic with the masked face in the NIR probe image. First, the lack of masked face data is a challenging issue for the network training. Second, most of the facial parts (cheeks, mouth, nose etc.) are fully occluded by the mask, which leads to a large amount of loss of information. Third, the domain gap still exists in the remaining facial parts. In such scenario, the existing methods suffer from significant performance degradation caused by the above issues. In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method. Specifically, we propose a novel heterogeneous training method to maximize the mutual information shared by the face representation of two domains with the help of semi-siamese networks. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image. Resorting to these practices, our solution provides the domain-invariant face representation which is also robust to the mask occlusion. Extensive experiments on three NIR-VIS face datasets demonstrate the effectiveness and cross-dataset-generalization capacity of our method.
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&amp;Temples dataset demonstrate the effective generalization ability of the proposed method.