Reviewerly/exHarmony huggingface dataset:
Slow Degradation Fault Detection in a Harsh Environment

The ever increasing challenges posed by the science projects in astronomy have skyrocketed the complexity of the new generation telescopes. Due to the climate and sky requirements, these high-precision instruments are generally located in remote areas, suffering from the harsh environments around it. These modern telescopes not only produce massive amounts of scientific data, but they also generate an enormous amount of operational information. The Atacama Large Millimeter/submillimeter Array (ALMA) is one of these unique instruments, generating more than 50 Gb of operational data every day while functioning in conditions of extreme dryness and altitude. To maintain the array working under extreme conditions, the engineering teams must check over 130,000 monitoring points, combing through the massive datasets produced every day. To make this possible, predictive tools are needed to identify, hopefully beforehand, the occurrence of failures in all the different subsystems. This work presents a novel fault detection scheme for one of these subsystems, the Intermediate Frequency Processors (IFP). This subsystem is critical to process the information gathered by each antenna and communicate it, reliably, to the correlator for processing. Our approach is based on echo state networks, a configuration of artificial neural networks, used to learn and predict the signal patterns. These patterns are later compared to the actual signal, to identify failure modes. Additional preprocessing techniques were also added since the signal-to-noise ratio of the data used was very low. The proposed scheme was tested in over seven years of data from 132 IFPs at ALMA, showing an accuracy of over 70%. Furthermore, the detection was done several months earlier, on average, when compared to what human operators did. These results help the maintenance procedures, increasing reliability while reducing humans' exposure to the harsh environment where the antennas are. Although applied to a specific fault, this technique is broad enough to be applied to other types of faults and settings.
Exploring Neuromodulation for Dynamic Learning

A continual learning system requires the ability to dynamically adapt and generalize to new tasks with access to only a few samples. In the central nervous system, across species, it is observed that continual and dynamic behavior in learning is an active result of a mechanism known as neuromodulation. Therefore, in this work, neuromodulatory plasticity is embedded with dynamic learning architectures as a first step toward realizing power and area efficient few shot learning systems. An inbuilt modulatory unit regulates learning based on the context and internal state of the system. This renders the system an ability to self modify its weights. In one of the proposed architectures, ModNet, a modulatory layer is introduced in a random projection framework. ModNet's learning capabilities are enhanced by integrating attention along with compartmentalized plasticity mechanisms. Moreover, to explore modulatory mechanisms in conjunction with backpropagation in deeper networks, a modulatory trace learning rule is introduced. The proposed learning rule, uses a time dependent trace to modify the synaptic connections as a function of ongoing states and activations. The trace itself is updated via simple plasticity rules thus reducing the demand on resources. The proposed ModNet and learning rules demonstrate the ability to learn from few samples, train quickly, and perform few-shot image classification in a computationally efficient manner. The simple ModNet and the compartmentalized ModNet architecture learn benchmark image classification tasks in just 2 epochs. The network with modulatory trace achieves an average accuracy of 98.8%±1.16 on the omniglot dataset for five-way one-shot image classification task while requiring 20x fewer trainable parameters in comparison to other state of the art models.
Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach

Touchless interaction with electronic devices using gestures is gaining popularity and along with speech-based communication offers their users natural and intuitive control methods. Now, these interaction modes go beyond the entertainment industry and are successfully applied in real-life scenarios such as a car interior. In the paper, we analyse the potential of hand gesture interaction in the vehicle environment by physically challenged drivers. A survey conducted with potential users shows that the knowledge of gesture-based interaction and its practical use by people with disabilities is low. Based on these results we proposed a gesture-based system for vehicle on-board system. It has been developed on the available state-of-the-art solutions and investigated in terms of usability on a group of people with different physical limitations who drive a car on daily basis mostly using steering aid tools. The obtained results are compared with the performance of users without any disabilities.
Sub-Sahara Africa’s Higher Education: Financing, Growth, and Em-ployment

Although higher education plays a vital role in the socio-economic development of Sub-Saharan Africa, enrollment in universities in the region is unexpectedly low compared to other regions. However, Sub-Saharan African countries have made strides in increasing access to higher education amidst constraints and challenges. The efforts have led to increases in enrollment and what many countries did not anticipate is the increase in unemployment from the greater output of students. In this study, we use panel data from eleven Sub-Saharan African countries for 2000-2018 to analyze the relationship between higher education and unemployment. A panel fixed effect model was estimated, and the results indicate that unemployment has a negative and significant effect on higher enrollment. Besides, higher education enrollment has a significant but negative effect on employment. Per capita income significantly affects enrollment into higher education and has the expected sign. The estimates further show that government expenditures on higher education play a significant role in the demand for places in higher education.
Who Does What? – Research Data Management at ETH Zurich

We present the approach to Research Data Management (RDM) support for researchers taken at ETH Zurich. Overall requirements are governed by institutional guidelines for Research Integrity, funders’ regulations, and legal obligations. The ETH approach is based on the distinction of three phases along the research data life-cycle: 1. Data Management Planning; 2. Active RDM; 3. Data Publication and Preservation. Two ETH units, namely the Scientific IT Services and the ETH Library, provide support for different aspects of these phases, building on their respective competencies. They jointly offer trainings, consulting, information, and materials for the first phase. The second phase deals with data which is in current use in active research projects. Scientific IT Services provide their own platform, <em>openBIS</em>, for keeping track of raw, processed and analysed data, in addition to organising samples, materials, and scientific procedures. ETH Library operates solutions for the third phase within the infrastructure of ETH Zurich’s central IT Services. <em>The Research Collection</em> is the institutional repository for research output including Research Data, Open Access publications, and ETH Zurich’s bibliography.
Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition

Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition. Our goal is to explore the best correlation and determine high-level semantic features. First, a multiscale dynamic aggregate GCN module is used to capture important semantic information and to establish dependence relationships for different bone nodes. Second, the higher level semantic feature is further refined, and the semantic relevance is emphasized through a dual attention guidance module. In addition, we exploit the relationship of joints hierarchically and the spatial temporal correlations through two modules. Experiments with the DAG-GCN method result in good performance on the NTU-60-RGB+D and NTU-120-RGB+D datasets. The accuracy is 95.76% and 90.01%, respectively, for the cross (X)-View and X-Subon the NTU60dataset.
Novel interacting multiple model filter for uncertain target tracking systems based on weighted Kullback–Leibler divergence

Interacting multiple model (IMM) filter is a classical method to track targets in hybrid situations. However, it can exhibit divergence when the models are correlated or the system suffers from uncertainties. The generalized covariance intersection method based on the weighted Kullback–Leibler (K–L) divergence can solve the divergence problem of correlated estimates. A novel interacting multiple model (NIMM) filter is presented that combines two different algorithms, the adaptive fading Kalman filter and the maximum correntropy Kalman filter, based on the model interacting with the weighted K-L divergence to address the uncertainty problems of the system. The NIMM filter algorithm is designed and the stability and accuracy are analyzed. The simulation results demonstrate that the proposed filter can effectively improve the accuracy under different uncertainty conditions for classical examples and ballistic trajectory tracking scenarios.
Trace metals dispersion from 1000 years of mining activity in the northern French Alps

This paper investigates the long-term dispersal of trace metals and their impact on the environment long after mine closure in the French Alps. Three statistical methods derived from modern mining (Exploratory Data Analysis, fractals and Sediment Quality Index) were applied to geochemical analyses of streambed sediments to produce mineral prospectivity maps. About 5% of the samples have a poor or marginal quality and may have an effect on biota. Positive anomalies are either related to the geological background (lithology, major tectonic contacts) or to ancient mining sites. The local impact of former mining was investigated on a medieval Pb–Ag district (Brandes; Pbmax ~8 000 mg kg−1), a small district mined during the 17th–19th c. (Peisey-Nancroix; Pbmax = 583 mg kg−1), and a major modern Pb–Ag mine (Macôt-La Plagne; Pbmax ~ 3 200 mg kg−1).The mining heritage is still traceable in the northern French Alps. The anomalies observed at local scale are related to the volume of extracted ore rather than to the timing and duration of mining, suggesting that old mining sites still release trace metals over centuries and up to a thousand years. The statistical methods used provide a strong tool for archaeological prospection and environmental studies.
Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine

The review of the users of one application is of great help to development in improving the quality of the application and may be the means for assessments that users feel satisfied or not. The study conducted a sentiment analysis of the Ruangguru application by testing the three classification models such as N aive B ayes , R andom F orest and Support V ectors M achine . The study has yielded results that from Random Forest classification model 97,16% by using Cross Validation and an AUC score of 0.996. Then accuracy with the model of Support V ector Machine classification support results in accuracy rate of 96.01% to an AUC value of 0.543 and accuracy in the testing of Naive Bayes classification model was 94,16% of AUC score 0,999. This study shows that an increase in accuracy from previous studies of 7.16% with Random Forest’s final cut as a Random Forest classification model with the best performance.
Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Heuristic search is a search process that uses domain knowledge in heuristic rules or procedures to direct the progress of a search algorithm.Hill climbing is a heuristic search technique for solving certain mathematical optimization problems in the field of artificial intelligence.In this technique, starting with a suboptimal solution is compared to starting from the base of the hill, and improving the solution is compared to walking up the hill.The optimal solution of the hill climbing technique can be achieved in polynomial time and is an NPcomplete problem in which the numbers of local maxima can lead to an exponential increase in computational time.To address these problems, the proposed hill climbing algorithm based on the local optimal solution is applied to the message passing interface, which is a library of routines that can be used to create parallel programs by using commonly available operating system services to create parallel processes and exchange information among these processes.Experimental results show that parallel hill climbing outperforms sequential methods.
Self-Sovereign Applications: return control of data back to people

This article presents new solutions for the difficult problem of data protection. The discussion in our approach will be based on the premise that data needs to be shared between the adequate number of entities under a set of strict rules. In this context, our proposal consists of a suite of privacy and data control focused approaches that become the building blocks for Self-Sovereign Applications (SSApps). SSApps are a new class of applications proposed for the first time in this paper and are aimed to ensure data ownership and proper data control for people and companies. The building blocks for SSApps consist of the insights that the role of blockchain should be reduced to ensure data anchoring of data shared between multiple participants. The blockchain ensures integrity and data provenance but should not be used for storing any private data. Therefore, the data is stored in off-chain storages that are structured in the form of a new approach called Data Sharing Units (DSU or Dossier). A method for performant implementation of SSApps, Dossiers and anchoring is proposed in this article.
Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images

The measurement of fetal head circumference (HC) is performed throughout the pregnancy to monitor fetus growth using ultrasound (US) images. Recently, methods that directly predict biometric from images, instead of resorting to segmentation, have emerged. In our previous work, we have proposed such method, based on a regression convolutional neural network (CNN). If deep learning methods are the gold standard in most image processing tasks, they are often considered as black boxes and fail to provide interpretable decisions. In this paper, we investigate various saliency maps methods, to leverage their ability at explaining the predicted value of the regression CNN. Since saliency maps methods have been developed for classification CNN mostly, we provide an interpretation for regression saliency maps, as well as an adaptation of a perturbation-based quantitative evaluation of explanation methods. Results obtained on a public dataset of ultrasound images show that some saliency maps indeed exhibit the head contour as the most relevant features to assess the head circumference and also that the map quality depends on the backbone architecture and whether the prediction error is low or high.
Machine Learning-Driven Event Characterization under Scarce Vehicular Sensing Data

Connected vehicle networks and future autonomous driving systems call for characterization of risky behavior to improve safety models and autonomous driving features. While risky behavior patterns entail potential safety issues on road networks, the advent of vehicular sensing and vehicular networks cannot guarantee accurate characterization of driving/movement behavior of vehicles. One of the roadblocks against robust event characterization systems in connected vehicles is the scarcity of anomalous data to enable training of event classification models. With this in mind, the contribution of this paper is two-fold: 1) a reliable methodology to generate representative data under the scarcity of diverse anomalous sensory data, 2) classification of mobility/driving events of vehicles with high accuracy. To this end, as a baseline method, an optimized deep recurrent neural network-based encoding model is introduced to extract the precise feature representation of the anomalous data. In addition to this, an event characterization pipeline is introduced that uses the representation provided by the encoder to generate reliable data, then train and classify future events. To improve the classification performance of the baseline method, a long short-term memory (LSTM)-based auto-encoder network is proposed. Through experimental results, it is shown that the LSTM-based auto-encoder network can achieve over 0.93 accuracy, which outperforms the baseline recurrent neural network model by 12%.
A Multi-Angle Hierarchical Differential Evolution Approach for Multimodal Optimization Problems

Multimodal optimization problem (MMOP) is one of the most common problems in engineering practices that requires multiple optimal solutions to be located simultaneously. An efficient algorithm for solving MMOPs should balance the diversity and convergence of the population, so that the global optimal solutions can be located as many as possible. However, most of existing algorithms are easy to be trapped into local peaks and cannot provide high-quality solutions. To better deal with MMOPs, considerations on the solution quality angle and the evolution stage angle are both taken into account in this paper and a multi-angle hierarchical differential evolution (MaHDE) algorithm is proposed. Firstly, a fitness hierarchical mutation (FHM) strategy is designed to balance the exploration and exploitation ability of different individuals. In the FHM strategy, the individuals are divided into two levels (i.e., low/high-level) according to their solution quality in the current niche. Then, the low/high-level individuals are applied to different guiding strategies. Secondly, a directed global search (DGS) strategy is introduced for the low-level individuals in the late evolution stage, which can improve the population diversity and provide these low-level individuals with the opportunity to re-search the global peaks. Thirdly, an elite local search (ELS) strategy is designed for the high-level individuals in the late evolution stage to refine their solution accuracy. Extensive experiments are developed to verify the performance of MaHDE on the widely used MMOPs test functions i.e., CEC'2013. Experimental results show that MaHDE generally outperforms the compared state-of-the-art multimodal algorithms.
Research and Practice in Smart Learning: A Literature Review

This paper provides a comprehensive review of smart learning research and practice published in the past decade (2010-2019). The review includes a total of 85 articles collected from three publication databases, namely Web of Science, Scopus and ProQuest. It covers the patterns of publications on smart learning, the research topics and research purposes, as well as the types of devices/tools and features involved in smart learning practices. Over the period analysed, the results showed an increasing number of publications from a wider range of countries/regions and sources. More than half of the research topics belonged to the categories of smart learning environment and smart classroom. The research purposes most frequently addressed the design and evaluation of smart learning systems/services. The smart learning practices commonly involved the use of mobile and smart devices (e.g. smartphones, tablet computers and sensors), and the features of collaboration, personalisation and intelligence. Based on the results, potential areas for future work are also discussed.
Temperature Compensation Method for Raster Projectors Used in 3D Structured Light Scanners

Raster projectors are commonly used in many various measurement applications where active lighting is required, such as in three-dimensional structured light scanners. The effect of temperature on the raster projector, in some conditions, can lead to significant deterioration of the measurements performed with such a scanner. In this paper, the outcomes of several experiments concerning the effects of temperature on raster projectors are presented. The described research is focused on the thermal deformations of projected images caused by common thermal effects observed in projectors: those caused by the warming-up process and changes in ambient environmental temperature. A software compensation method is also presented. It is suitable for implementation in any existing measurement method that uses raster projectors. The results of performed verification experiments show that the developed compensation method can decrease the thermal drift of the projected images by up to 14 times in the ambient temperature range 14–42 °C.
Real-Time Weather Analytics

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.
Centaur: Hybrid Processing in On/Off-chip Memory Architecture for Graph Analytics

The increased use of graph algorithms in diverse fields has highlighted their inefficiencies in current chip-multiprocessor (CMP) architectures, primarily due to their seemingly random-access patterns to off-chip memory. Recently, two families of solutions have been proposed: 1) solutions that offload operations generated by all vertices from the processor cores to off-chip memory; and 2) solutions that offload only operations generated by high-degree vertices to dedicated on-chip memory, while the cores continue to process the work related to the remaining vertices. Neither approach is optimal over the full range of vertex's degrees. Thus, in this work, we propose Centaur, a novel architecture that processes operations on vertex data in on- and off-chip memory. Centaur utilizes a vertex's degree as a proxy to determine whether to process related operations in on- or off-chip memory. Centaur manages to provide up to 4.0× improvement in performance and 3.8× in energy benefits, compared to a baseline CMP, and up to a 2.0× performance boost over state-of-the-art specialized solutions.