Reviewerly/exHarmony huggingface dataset:
Hydrogeochemistry of porphyry-related solutes in ground and surface waters; an example from the Casino Cu–Au–Mo deposit, Yukon, Canada

The Casino Cu–Au–Mo deposit is one of the largest and highest-grade porphyries of its kind in Canada, residing in an unglaciated region of west-central Yukon. A batch of 22 stream water samples and eight groundwater samples were collected proximal to the deposit for the purpose of identifying the most diagnostic trace element and isotopic pathfinders associated with the hydrothermal mineralization, as well as establishing natural hydrogeochemical baselines for the area. Water chemistry around this deposit was investigated because: (i) the deposit has not yet been disturbed by mining; (ii) the deposit was known to have metal-rich waters in local streams; and (iii) the deposit has atypically preserved ore zones. Surface and ground waters around the Casino deposit are anomalous with respect to Cd (up to 5.4 µg l –1 ), Co (up to 64 µg l –1 ), Cu (up to 1657 µg l –1 ), Mo (up to 25 µg l –1 ), As (up to 17 µg l –1 ), Re (up to 0.7 µg l –1 ) and Zn (up to 354 µg l –1 ) concentrations. Sulfur and Sr isotopes are consistent with proximal waters interacting with the Casino rocks and mineralization; a sulfide-rich bedrock sample from the deposit has δ 34 S = −1.2‰ and proximal groundwaters are only slightly heavier (−0.3 to 3.1‰). These geochemical and isotopic results indicate interaction and dispersion of porphyry-related solutes in ground and surface waters and point to the suitability of hydrogeochemistry as a medium for mineral exploration for porphyry-style mineralization in the Yukon, and elsewhere in Canada. Supplementary material: Dissolved concentration data for major and trace elements for the ground and stream water samples from Casino are available at https://doi.org/10.6084/m9.figshare.c.5778911 Thematic collection: This article is part of the Hydrochemistry related to exploration and environmental issues collection available at: https://www.lyellcollection.org/cc/hydrochemistry-related-to-exploration-and-environmental-issues
Measured solar irradiance data for resource assessment at four sites in Geba catchment, Tigray, North Ethiopia

Properly recorded solar radiation data are very important in providing accurate information on solar radiation intensity and potential for the application of any solar energy technology. Since such type of data is hardly available in most developing countries like Ethiopia, analysis of temporal and spatial variations of solar radiation is essential for exploring the true potential of a specific area. This scientific data article is, therefore, related to the research work entitled "Temporal and Spatial Solar Resource Variation by Analysis of Measured Irradiance in Geba Catchment, North Ethiopia" (https://doi.org/10.1016/j.seta.2021.101110). In this work, we present the solar radiation measurement data collected for five years (from January 2011 to December 2015) from four sites of the Geba catchment (Dera, May Derhu, Hagere Selam, and Mekelle University) located in the Northern part of Ethiopia. Data were measured at ten-minute intervals using Pyranometers mounted on wind masts. The data was used for the analysis of the temporal variation and spatial distribution performed using MS Excel spreadsheet and Inverse Distance Weight (IDW) method of the ArcGIS software, respectively. Accordingly, the data revealed insight on the solar variation and potential of the catchment and is expected to contribute significantly to further decision-making by governmental and non-governmental agencies, investors, consultants, and project developers. It is also expected to help for future research and solar project implementation directions across similar catchments.
SCB: Flexible and Efficient Asymmetric Computations Utilizing Symmetric Cryptosystems Implemented with Intel SGX

The wide spread of cloud computing and the rapid growth of online activities raise the demand for privacy enhancing cryptography, such as identity-based encryption (IBE), attribute-based encryption (ABE), and group/ring signature, which are mostly asymmetric. However, implementing a practical asymmetric cryptosystem with privacy enhancing functionalities faces the big challenge of large computing overhead that stems from the hard mathematical problems they are based on, which in reality hinders the further deployment of such cryptosystems.In this paper, we propose "SGX Cipher Box" (SCB), a method of constructing flexible and efficient asymmetric computations by encapsulating symmetric algorithms into Intel SGX enclaves. Two types of enclaves are designed for serving computations with the public key and the private key, respectively. The security assumptions of asymmetric algorithms can be achieved in SCB based on guarantees offered by SGX. SCB is of great flexibility that it facilitates implementing various asymmetric computations with limited enclaves. We present four SCB-based cryptosystems, provisioning asymmetric primitives, IBE, ABE, and ring signature, respectively. The four prototype cryptosystems are implemented and benchmarked in terms of computing efficiency. Experimental results show that SCB surpasses conventional asymmetric cryptosystem implementations in performance.
Imagination-enabled Robot Perception

Many of today’s robot perception systems aim at accomplishing perception tasks that are too simplistic and too hard. They are too simplistic because they do not require the perception systems to provide all the information needed to accomplish manipulation tasks. Typically the perception results do not include information about the part structure of objects, articulation mechanisms and other attributes needed for adapting manipulation behavior. On the other hand, the perception problems stated are also too hard because — unlike humans— the perception systems cannot leverage the expectations about what they will see to their full potential. Therefore, we investigate a variation of robot perception tasks suitable for robots accomplishing everyday manipulation tasks, such as household robots or a robot in a retail store. In such settings it is reasonable to assume that robots know most objects and have detailed models of them. We propose a perception system that maintains its beliefs about its environment as a scene graph with physics simulation and visual rendering. When detecting objects, the perception system retrieves the model of the object and places it at the corresponding place in a VR-based environment model. The physics simulation ensures that object detections that are physically not possible are rejected and scenes can be rendered to generate expectations at the image level. The result is a perception system that can provide useful information for manipulation tasks.
Learning to Detect Monoclonal Protein in Electrophoresis Images

Monoclonal protein (M-protein) detection with elec-trophoresis is of vital importance for the diagnosis of lympho-proliferative processes and monoclonal gammopathies (MGs). Although identifying M-proteins are key for the diagnosis and monitoring of these disorders, it requires specialized knowledge and is time consuming and labor intensive. Despite existing powerful machine learning methods, it often requires to obtain large number of labeled data for training, which is difficult to obtain. Besides, electrophoresis image quality could vary dramatically, affecting the proper identification of M-protein. To address these challenges, we propose to represent electrophoresis images using Gaussian Mixture Model (GMM) and leverage peak detection method to identify visual features for M-protein detection. Utilizing random forest classifier, our method can work with a small amount of labeled data to train the model and is not sensitive to samples of varying quality. Furthermore, with extracted image features, it is possible for specially trained technologists and pathologists to understand and check the decision process of the learned model. Extensive experiments indicate our proposed method achieves satisfactory results on test data, demonstrating the effectiveness and robustness of the proposed model for M-protein detection.
Diagnostic analysis for outlier detection in big data analytics

Recently, Big Data analytics has been one of the most emerging topics in the business field. Data is collected, processed and analyzed to gain useful insight for their organization. Big Data analytics has the potential to improve the quality of life and help to achieve Sustainable Development Goals (SDG). To ensure that SDG goals are achieved, we must utilize existing data to meet those targets and ensure accountability. However, data quality is often left out when dealing with data. Any types of errors presented in the dataset should be properly addressed to ensure the analysis provided is accurate and truthful. In this paper, we have addressed the concept of data quality diagnosis to identify the outlier presented in the dataset. The cause of the outlier is further discussed to identify potential improvements that can be done to the dataset. In addition, recommendations to improve the quality of data and data collection systems are provided.
Interactive documentary and the reinvention of digital journalism, 2015–2020

Interactive documentary (i-docs), an innovative hybrid form at the intersection of film, journalism, and digital games, has matured beyond its first wave of experimentation, gaining distinction among the most highly evolved immersive media of the twenty-first century. The latest generation of i-docs is currently winning accolades at both major film festivals and game design summits. This study charts the evolutionary trajectory of North America’s most recent and influential wave of i-docs in works mostly appearing since 2015. It culturally situates i-docs as immersive media that extend experimentation with narrative journalism into the realm of fine art and social activism. Building on the foundation of activist, highly empathic news experiences established in the early 2010s, the most recent advances in i-docs range from live action VR to animated digital games. Such works include the Canadian National Film Board’s 2018 AR (augmented reality) experience East of the Rockies, Occupied’s 2019 Cannes entry The Holy City VR, Roger Ross Williams’ 2019 Tribeca debut Traveling While Black, and iNK Stories Verité VR Series’ 2017 Blindfold and Hero, winner of the prestigious Storyscapes Award at Tribeca in 2018. The vanguard of i-docs has expanded collaboration between film, news, and digital game industries to provide new forms of citizen engagement through advocacy journalism aimed at social and political change. Through the use of John Pavlik’s (2019) critical framework for understanding immersive journalism, this article examines the texts, producers, and industrial contexts of the most recent and influential North American i-docs, as one branch of the form defined by Gaudenzi, Aston, and Rose. Principles of transparency, social responsibility, and a commitment to veracity in i-docs epitomize the esthetic and political potential of digital journalism as an empathic alternative to traditional news coverage.
Interoperability and Integration: An Updated Approach to Linked Data Publication at the Dutch Land Registry

Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster’s larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets.
Transfer Learning and Curriculum Learning in Sokoban

Transfer learning can speed up training in machine learning, and is regularly used in classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new tasks. In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much planning. Sokoban is a challenging puzzle game. It has been used widely as a benchmark in planning-based reinforcement learning. In this paper, we show how prior knowledge improves learning in Sokoban tasks. We find that reusing feature representations learned previously can accelerate learning new, more complex, instances. In effect, we show how curriculum learning, from simple to complex tasks, works in Sokoban. Furthermore, feature representations learned in simpler instances are more general, and thus lead to positive transfers towards more complex tasks, but not vice versa. We have also studied which part of the knowledge is most important for transfer to succeed, and identify which layers should be used for pre-training (Codes we used for this work can be found at https://github.com/yangzhao-666/TLCLS ).
Understanding the state of the Art in Animal detection and classification using computer vision technologies

This work presents the results of a survey through the analysis of studies published between January 2017 and May 2021, aiming to compose a broader view of the state of the art in the field of animal detection and classification using computer vision technologies in urban environments, and also the majors researches gaps available to address. We conducted an automatic search through two digital knowledge bases identifying 146 studies in the subject, among them 20 were selected for our analysis and data extraction. Further, the 20 studies were classified into 6 categories: (i) studies using SVM, (ii) studies using HOG, (iii) studies using SIFT, (iv) studies using PCA, (v) studies using CNN, and (vi) DFDL. As a result, it can be noted that the use of CNN is predominant concerning other approaches and that there are also combinations to improve the accuracy of classification models. In conclusion, it is possible to observe that the state-of-the-art approaches have been used in different situations, however, in the context of animal detection and classification in intelligent urban environments, there is still a lack of specific architectures to improve results.
Detection of GAN-Synthesized street videos

Research on the detection of AI-generated videos has focused almost exclusively on face videos, usually referred to as deepfakes. Manipulations like face swapping, face reenactment and expression manipulation have been the subject of an intense research with the development of a number of efficient tools to distinguish artificial videos from genuine ones. Much less attention has been paid to the detection of artificial non-facial videos. Yet, new tools for the generation of such kind of videos are being developed at a fast pace and will soon reach the quality level of deepfake videos. The goal of this paper is to investigate the detectability of a new kind of AI-generated videos framing driving street sequences (here referred to as DeepStreets videos), which, by their nature, can not be analysed with the same tools used for facial deepfakes. Specifically, we present a simple frame-based detector, achieving very good performance on state-of-the-art DeepStreets videos generated by the Vid2vid architecture. Noticeably, the detector retains very good performance on compressed videos, even when the compression level used during training does not match that used for the test videos.
Better Scrum through Essence

Abstract We live at an exciting time where software has become a dominant aspect of our everyday life. Although software provides opportunities for improving various aspects of our society, it also presents many challenges. One of them is development, deployment, and sustaining of high quality software on a broad scale. While agile methods (Scrum being one of the most prominent examples) ease the process, their popularity deteriorates the clarity and simplicity they were once meant to bring into software development. This article explores the synergy of Scrum and Essence, a domain model of software engineering processes, intending to become a common ground for software development methods, bringing clarity into the composition of methods from individual practices. This short communication motivates the interplay of Scrum and Essence, being accompanied with a set of videotutorials and 21 Scrum Essential cards to further guide more effective team's way of working.
LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification

In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance.Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised learning.This type of learning extracts the decision rules of classification by utilizing both labeled and unlabeled data.Regarding multi-label data, however, current semi-supervised learning methods are unable to classify them accurately.Therefore, with the goal of generalizing the state-of-the-art semi-supervised approaches to multilabel data, this paper proposes a novel two-stage method for multi-label semi-supervised classification.The first stage determines the label(s) of the unlabeled training data by means of a smooth graph constructed using the manifold regularization.In the second stage, thanks to the capability of the twin support vector machine to relax the requirement that hyperplanes should be parallel in classical SVM, we employ it to establish a multilabel classifier called LP-MLTSVM.In the experiments, this classifier is applied on benchmark datasets.The simulation results substantiate that compared to the existing multi-label classification algorithms, LP-MLTSVM shows superior performance in terms of the Hamming loss, average precision, coverage, ranking loss, and one-error metrics.
Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.
Learning-based Detection of Stealthy False Data Injection Attack Applied to Cooperative Localization Problem

Security of autonomous, connected UAVs plays an essential role in their far-reaching capability and acceptance. A single adversarial UAV injected with false bearing measurements in the cooperative localization setting can disrupt other UAVs sharing bearing information with the adversarial UAV. This paper focuses on detecting the stealthy false data injection (not detected by statistical tests) attacks on UAVs in the cooperative localization environment. Our approach is decentralized and requires only local bearing measurements of the UAVs. To detect the attacks, we have explored both machine learning and deep learning methods, such as Dynamic Time Wrapping-Nearest Neighbor (DTW-NN) classifier, Fully Connected Deep Neural Network (FCDNN), and Convolutional Neural Network (CNN). We have shown that pre-processing techniques like Gramian Angular Fields (GAF) and Markov Transition fields improve the performance of deep CNN in detecting attacks. Also, three decentralized detection strategies, Unique Neural Network (UNN), General Neural Network (GNN), and One Shot learning, are studied. The results show that we can achieve up to 98$\%$ accuracy with the One-Shot learning approach. The problem can be generalized to $n$ number of UAVs in the environment with various sensing topologies. Finally, the results also show the performance of the strategies and algorithms when the attacker has complete and incomplete information about the system.
GA-Net: A Guidance Aware Network for Skeleton-Based Early Activity Recognition

Early activity prediction, which aims to recognize class labels before actions are fully performed, is a very challenging task since partially observed action sequences contain insufficient class-discrimination information, and thus, many partial action sequences belonging to different categories may look very similar. Therefore, in this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guidance aware network (GA-Net)</i> to boost the ability to distinguish different activities in diversified partially observed action sequences via metric learning. To mitigate the similarity problem of action segments at very early stages, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guided metric learning module (GMLM)</i> is able to encourage the feature extractor to mine class-discriminative information given partially observed sequences. Specifically, the GMLM is able to minimize the intraclass distance with a full-length guided direction approach and maximize the difference between interclass categories with different observation ratios. To enhance the similarities between the partial- and full-length sequences in the same action categories, we further introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution alignment module (DAM)</i> that employs full-length guidance to pull the partially observed features closer to the global features. We evaluate our proposed method on three public human activity datasets and achieve competitive results compared with the state-of-the-art approaches.
Content-Based Image Retrieval Using Angles Across Scales

This letter proposes a content-based image retrieval technique using novel dense angle descriptor and dictionary learning (DL). The histogram of oriented gradients (HOG) descriptor fails to obtain rotation invariance and well-defined rotation behavior, and therefore, a dense angle-based HOG descriptor has been presented to address the image rotation invariance. The technique computes angles across multiple scales and uses bag-of-visual features at different scales for DL. Experiments conducted on building and remote sensing datasets show that the proposed technique achieves high retrieval performance.