Reviewerly/exHarmony huggingface dataset:
Predicting Geographic Energy Production for Tandem PV Designs Using a Compact Set of Spectra Correlated by Irradiance

We provide 20 direct spectra that capture the variation of the solar spectrum composition with intensity. We correlate the value of the air mass, aerosol optical depth at 500nm, precipitable water, and ozone with the cumulative irradiance for direct sunlight with the use of National Solar Radiation Database (NSRDB). We use the values of these atmospheric parameters to generate spectra that represent their corresponding cumulative irradiance levels with the use of SMARTS multiple scattering and transmission model. By simulation of the performance of a solar cell design under these 20 spectra and combination of the intensity-specific performance with the relative frequency of each irradiance level at a particular location from the NSRDB, we can predict tandem cell energy production across the United States. Through comparison of the energy production of ideal tandem cells with two to ten subcells as predicted by our model to energy production integrated over one year's worth of simulated spectra at nine locations across the United States as well as measured spectral irradiance from NREL's solar observatory, we find the error in our energy production estimate to be under 5% for ten subcells and under 3% for up to five subcells. We demonstrate the utility of the approach with a selection of prospective and ideal multijunction bandgap combinations.
A new Deep learning model for Sequential Pattern Mining using Ensemble learning and Models selection Taking Mobile Activity Recognition as a case

Sequential pattern mining has gained popularity in Data Mining and pattern recognition. Most sequential pattern mining algorithms are influenced by noisy variables, parameters tuning, bias-variance dilemma and learning instability. This paper presents a new deep learning model for sequential pattern mining, by using ensemble learning and models selection. Experimental studies on mobile activity recognition showed that our deep learning model, which is named Deep Sequential Pattern Mining (abbreviated as DeepSPM), obtained an enhanced generalization in comparison with Long Short Term Memory (LSTM), Bidirectional Associative Memory (BAM) and Hopfield. We provide a comparative performance analysis of pattern recognition. The advantages and the drawbacks of the benchmarking models are critically discussed.
A Digital Image Processing Pipeline for Modelling of Realistic Noise in Synthetic Images

The evaluation of computer vision methods on synthetic images offers control over scene, object, and camera properties. The disadvantage is that synthetic data usually lack many of the effects of real cameras that pose the actual challenge to the methods under investigation. Among those, noise is one of the effects more difficult to simulate as it changes the signal at an early stage and is strongly influenced by the camera's internal processing chain. The resulting noise is highly complex, intensity dependent, as well as spatially and spectrally correlated. We propose to transform synthetic images into the raw format of digital cameras, alter them with a physically motivated noise model, and then apply a processing chain that resembles a digital camera. Experiments show that the resulting noise exhibits a strong similarity to noise in real digital images, which further decreases the gap between synthesized images and real photographs.
Erasure machine: Inverse Ising inference from reweighting of observation frequencies

Maximum likelihood estimation (MLE) is fundamental to system inference for stochastic systems. In some generality, MLE will converge to the correct model in the infinite data limit. In the context of physical approaches to system inference, such as Boltzmann machines, MLE requires the arduous computation of partition functions summing over all configurations, both observed and unobserved. We present a conceptually transparent data-driven inference computation based on a reweighting of observed configuration frequencies that allows us to recast the inference problem as a simpler calculation. Modeling our approach on the high-temperature limit of statistical physics, we reweight the frequencies of observed configurations by multiplying with reciprocals of Boltzmann weights and update the Boltzmann weights iteratively to make these products close to the high-temperature limit of the Boltzmann weights. This converts the required partition function computation in the reweighted MLE to a tractable leading-order high-temperature term. We show that this is a convex optimization at each step. Then, for systems with a large number of degrees of freedom where other approaches are intractable, we demonstrate that this data-driven algorithm gives accurate inference with both synthetic data and two real-world examples.
A Mixed Reality Application for Linked Data in Engineering and Production

During the engineering and production process, numerous data and models are generated. As Digital Twin they form a virtual representation of the physical objects with their different views and characteristics. The contents spread over various disciplines, stakeholders and life cycle phases; they form a huge space of linked data and resources.
Reducing Noise in Label Annotation: A Lane Change Prediction Case Study

A major challenge in automated driving is to understand what the perception system detects with its various sensors and map it to meaningful behavior. In situation interpretation one of the tasks is to capture patterns in the motion of surrounding vehicles to estimate their future behavior for safely master driving. One basic approach to estimate this future behavior is to predict whether a vehicle is going to perform a lane change or not. Different methods exist to predict lane change maneuvers. Here, the focus is on the domain of machine learning, such as neural networks or support vector machines. Since the systems are trained in a supervised fashion, ground truth information for the learning algorithm is needed. While manual labeling by human experts is time-consuming, expensive, and subjective, an automatic generation of the labels is desired. This paper investigates different methods of providing labels for the lane change prediction task automatically. Further, the focus is on optimizing the labeling strategy to prevent contradicting labels. Additionally, the interactions between different labeling methods and the imbalanced learning issue is discussed based on experimental results.
Multi-modal Associative Storage and Retrieval Using Hopfield Auto-associative Memory Network

Recently we presented text storage and retrieval in an auto-associative memory framework using the Hopfield neural-network. This realized the ideal functionality of Hopfield network as a content-addressable information retrieval system. In this paper, we extend this result to multi-modal patterns, namely, images with text captions and show that the Hopfield network indeed can store and retrieve such multi-modal patterns even in an auto-associative setting. Within this framework, we examine two central issues such as (i) performance characterization to show that the O(N) capacity of the Hopfield network for a network of size N neurons under the Pseudo-inverse learning rule is still retained in the multi-modal case, and (ii) the retrieval dynamics of the multi-modal pattern (i.e., image and caption together) under various types of queries such as image $$+$$ caption, image only and caption only, in line with a typical multi-modal retrieval system where the entire multi-modal pattern is expected to be retrieved even with a partial query pattern from any of the modalities. We present results related to these two issues on a large database of 7000 $$+$$ captioned-images and establish the practical scalability of both the storage capacity and the retrieval robustness of the Hopfield network for content-addressable retrieval of multi-modal patterns. We point to the potential of this work to extend to a more wider definition of multi-modality as in multi-media content, with various modalities such as video (image sequence) synchronized with sub-title text, speech, music and non-speech.
Development of a «smart» home system based on the modular structure and architectural data flow pattern Redux

Article presents the model and principles of the «smart» home system development using the modular structure and architectural pattern of the data stream Redux. Combination of these principles allow to develop «smart» house system rapidly, taking into account the variety of usage specifics and their scaling, thanks to the combination of pre-designed software and hardware modules.
Continuous Blood Pressure Estimation Through Optimized Echo State Networks

Technology is pushing cardiology towards non-invasive recording of continuous blood pressure, with methods that do not require the insertion of a pressure transducer in the Aorta. Although novel analyses based on the Electrocardiogram (ECG) and Photoplethysmography (PPG) provided an elegant model of the interaction between the heart and blood vessels necessary estimate systolic/diastolic points, these methods lack long-term stability and require intermittent re-calibrations. On the other hand, time-series centric algorithms are potentially able to map this coupling in a continuous way. Recurrent Neural Networks (RNN) in particular shown high accuracy and stability. We propose here a system that automatically optimizes, among other hyper-parameters, the input scaling of ECG and PPG signals individually, exploiting an efficient Bayesian Optimization process, on a Echo State Networks (ESN), to quantify continuous blood pressure. Compared to other RNN architectures, the ESN provide a faster training time and lower computational constraints that may allow the deployment on an embedded monitoring device. Preliminary results showed an accuracy of 80% and 98% in terms of Normalized Root Mean Squared Error and Median Symmetric Accuracy, respectively. Considering peaks' estimation, the system achieves grade A (British Hypertension Society) for both Systolic and Diastolic points, making it comparable with clinical recommended devices.
Winning Rate Prediction Model Based on Monte Carlo Tree Search for Computer <i>Dou Dizhu</i>

Poker is the typical game of incomplete information, and remains a longstanding challenge problem in artificial intelligence (AI). The poker game of Dou Dizhu has been viewed as a thorny topic in AI because of its own characteristics. This article introduces a developed Monte Carlo tree search (MCTS) method for Dou Dizhu to solve the decision making effectively. We built the winning rate prediction model (WRPM) to predict the winning rate of moves as the initial situation estimation and improve the model to be more applicable to different player roles. Then, the WRPM is embedded as the core algorithm into MCTS for extension and simulation and named it WRPM-MCTS. In addition, we also train a card distribution prediction model to predict the holding cards of opponents for further improving the performance of WRPM-MCTS on the agent of Dou Dizhu. Experiments show that the WRPM-MCTS has a statistically significant performance better than the pure MCTS and the pure WRPM. In the game with human players from an online game platform, the WRPM-MCTS-based agent had the winning rate of 52.86% in 4 000 000 games and ranked in top 1.22% among 500 000 human players, indicating that this agent had reached the expert level of humans.
Active Learning for Image Recognition Using a Visualization-Based User Interface

This paper introduces a novel approach for querying samples to be labeled in active learning for image recognition. The user is able to efficiently label images with a visualization for training a classifier. This visualization is achieved by using dimension reduction techniques to create a 2D feature embedding from high-dimensional features. This is made possible by a querying strategy specifically designed for the visualization, seeking optimized bounding-box views for subsequent labeling. The approach is implemented in a web-based prototype. It is compared in-depth to other active learning querying strategies within a user study we conducted with 31 participants on a challenging data set. While using our approach, the participants could train a more accurate classifier than with the other approaches. Additionally, we demonstrate that due to the visualization, the number of labeled samples increases and also the label quality improves.
Visual large-scale industrial interaction processing

In this work we investigate the coordination of human-machine interactions from a bird's-eye view using a single panoramic color camera. Our approach replaces conventional physical hardware sensors, such as light barriers and switches, by location-aware virtual regions. We employ recent methods from the field of pose estimation to detect human and robot joint configurations. By fusing 2D human and robot pose information with prior scene knowledge, we can lift these perceptions to a 3D metric space. In this way, our system can initiate environmental reactions induced by geometric events among humans, robots and virtual regions. We demonstrate the diverse application possibilities and robustness of our system in three use cases.
The Impact of Blockchain Technologies on Recruitment Influencing the Employee Lifecycle

The aim of this research proposal is to investigate the impact of blockchain technologies on the employee lifecycle, especially the recruitment process. Blockchain technology provides many benefits, such as decentralised transactions, transparency, immutable records and security. The employee lifecycle’s stages of recruitment, on-boarding, employability, retention and off-boarding provide the basis for further investigation of recruitment in relation to blockchain technologies. Blockchain platforms are disruptive technologies not only in the financial sector but also in several non-financial fields, such as healthcare, automotive, real estate, e-commerce and electronic voting. Presently, many start-ups and researchers face the challenges of combining blockchain technologies with other businesses, processes and applications. Current business applications in various industries are introduced, and their results are discussed.
Comparison of Distance Methods in K-Means Algorithm for Determining Village Status in Bekasi District

The Bekasi regency government reveals that there are around 21 slummy villages that are spread across seven sub-districts in Bekasi Regency. This indicates the need for funding and development assistance. Regarding to the village development, the government and regional government must provide information about which villages should be prioritized for development. The Village Potential or “Potensi Desa” statistics dataset in 2014 (Podes 2014) at Bekasi regency are released by the Central Bureau of Statistics in the form of unsupervised data consisting of 182 villages and 41 indicators. The Podes 2014 data is collected based on village specific levels in Indonesia by making the village a unit of analysis. By using the k-means algorithm, village status can be determined in Bekasi Regency. The data clustering using k-means is done by calculating the closest distance from a data to a centroid point. This study comparison of distance calculation methods on k-means using Manhattan, Euclidean and Chebychev will be made. Tests will be carried out using Davies Bouldin index and execution time. Based on the tests result, Euclidean metric has most optimum value of Davies Index and efficient execution time compared to Manhattan and Chebyshev metrics.
A General Robotic Framework for Automated Cloth Assembly

In the pipeline of garment manufacturing, the assembly of cloth pieces using fixtures is a widely used technique to reduce the reliance on skilled workers and to improve the sewing quality. The assembly is achieved by aligning the holes on the cloth pieces with a set of vertical locating pins. Such a task, however, is difficult to be automatized by robots due to the complex dynamics of the highly deformable cloth material. In this paper, we present a general visual-based approach to automatically align cloth pieces with target pins without any prior knowledge. Given a piece of cloth, the system will detect and track the holes on it and compute the position of each hole in the 3D space. After exploring the local features of the cloth piece by moving the robotic arm several times, the system can learn the deformation model of the cloth piece and then generate an appropriate control policy which maps perception feedbacks to the control velocity of the robotic end-effectors. We demonstrate the performance of our system on an IRB 14000 YuMi robot with an Intel Realsense RGB-D camera mounted. Experiments show that our system can robustly accomplish the assembly task for cloth pieces with different materials and different patterns of locating holes.
Designing navigation aides for wildland firefighters

In this research, we explored the design of navigation technology for wildland firefighters. We worked within a set of empirically informed design constraints to create prototypes of a wearable system that provides peripheral navigation cues via visual and haptic feedback. We used physical and interactive prototypes of this system as technology probes to provoke discussions with wildland firefighters about their navigation and location technology needs. Our pilot study results indicate that our prototypes helped to uncover ideas for future technical work in the domain of wildland firefighting, as well as on mobile and wearable navigation systems, more broadly.
Knowledge Graph Embedding Based on Adaptive Negative Sampling

Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and real-valued vector space. Among various embedding models appeared in recent years, translation-based models such as TransE, TransH and TransR achieve state-of-the-art performance. However, in these models, negative triples used for training phase are generated by replacing each positive entity in positive triples with negative entities from the entity set with the same probability; as a result, a large number of invalid negative triples will be generated and used in the training process. In this paper, a method named adaptive negative sampling (ANS) is proposed to generate valid negative triples. In this method, it first divided all the entities into a number of groups which consist of similar entities by some clustering algorithms such as K-Means. Then, corresponding to each positive triple, the head entity was replaced by a negative entity from the cluster in which the head entity was located and the tail entity was replaced in a similar approach. As a result, it generated a set of high-quality negative triples which benefit for improving the effectiveness of embedding models. The ANS method was combined with the TransE model and the resulted model was named as TransE-ANS. Experimental results show that TransE-ANS achieves significant improvement in the link prediction task.
SInFo – Structure-Driven Incremental Forum Crawler That Optimizes User-Generated Content Retrieval

In this paper we present a Structure-driven Incremental Forum crawler (SInFo) that targets the latest content in crawling cycles. On a Web forum, user generated content is almost never changed or deleted, but it is constantly added. There is a wide spectrum of forum technologies that have different representations and navigational paths to lead the user to the latest content. Targeting the latest content is not a trivial task, since adding some new content to a forum often results in shifting the old content between pages. Ignoring the way forum content is distributed and sorted can lead to repetitive visits to the pages with the same data from previous crawls while incrementally crawling. The main goal of SInFo is to avoid transfer of duplicate content in forum incremental crawling, using the generic approach regardless of the forum technology. The problem is reduced to discovering and utilizing the following forum technology features: (1) forum index and thread page content and sort representation and, (2) available forum technology navigational options between pages. With the proposed methods and techniques, we show how to locate the target page by observing the URL signature format and minimize the number of required downloads to fetch the page containing the latest content. The experiments were conducted on custom technologies and also on a wide range of pre-built forum packages covering more than 80% of representative widely used software packages. SInFo showed high accuracy and low level of duplicates transmission by reaching the average of 92.6% for the new content in each recrawl cycle.
Topic Modeling to Discern Irregular Order Patterns in Unlabeled Electronic Health Records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool - LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.
Activity prediction for improving well-being of both the elderly and caregivers

The issue of ageing population is gaining significant attention across the world, while the caregivers' psychological burden caused by a variety of geriatric symptoms is often overlooked. Efficient collaboration between the elderly and caregivers has great potential to relieve the caregivers' psychological burden and improve the caregiving quality. For instance, activity prediction can provide a promising approach to cultivate this efficient collaboration. Given the ability to predict the elderly patients' activity and its timing, caregivers can provide timely and appropriate care, which not only can relieve caregiving stress for professional or family caregivers, but also can reduce the unwanted conflicts between both parties. In this paper, we train an activity predictor by integrating the activity temporal information into the Long Short-Term Memory (LSTM) networks. The approach leads to significant improvements in the prediction accuracy both in the next activity and its precise occurrence time.
The Potential and Capabilities of NoSQL Databases for ERP Systems

Today, nearly all possible business activities or information systems in enterprises such as sales, marketing, accounting, finance, customer relations, and manufacturing are carried out through traditional relational database management systems. However, technological, social, and competitive pressures in enterprises coming together with the rapid change in technology and then the problems arising from traditional databases force enterprises to adopt new database technologies. This chapter aims to highlight the main differences between traditional relational databases and NoSQL databases and to present an overview of the concepts, features, potential, problems, benefits, and limitations of NoSQL databases for enterprise information management systems, for especially enterprise Resource Planning (ERP) systems, which have a significant role in digital transformation of enterprises.