Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Approach for Detecting Unemployment Using the Smart Metering Infrastructure

Technological advancements in the field of electrical energy distribution and utilization are revolutionizing the way consumers and utility providers interact. In addition to allowing utility companies to monitor the status of their network independently in autonomous fashion, data collected by smart meters as part of the wider advanced metering infrastructure, can be valuable for third parties, such as government authorities. The availability of the information, the granularity of the data, and the real-time nature of the smart meter, means that predictive analytics can be employed to profile consumers with high accuracy and approximate, for example, the number of individuals living in a house, the type of appliances being used, or the duration of occupancy, to name but a few applications. This paper presents a machine learning model comparison for unemployment prediction of single household occupants, based on features extracted from smart meter electricity readings. A number of nonlinear classifiers are compared, and benchmarked against a generalized linear model, and the results presented. To ensure the robustness of the classifiers, we use repeated cross validation. The results revealed that it is possible to predict employability status with Area Under Curve (AUC) = 74%, Sensitivity (SE) = 54% and Specificity (SP) = 83%, using a multilayer perceptron neural network with dropout, closely followed by the results produced by a distance weighted discrimination with polynomial kernel model. This shows the potential of using the smart metering infrastructure to provide additional autonomous services, such as unemployment detection, for governments using data collected from an advanced and distributed Internet of Things (IoT) sensor network.
BioRel: A Large-Scale Dataset for Biomedical Relation Extraction

Valuable biomedical knowledge usually exists in the form of electronic publications and literature, which is growing at an enormous rate. Relation extraction plays a critical role in discovering such knowledge and transform them into structural form. Previous relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature. In this paper, we present BioRel, a large-scale dataset constructed by using Unified Medical Language System (UMLS) as knowledge base and Medline as corpus. Entities in sentences of Medline are identified and linked to UMLS by Metamap. Relation label for each sentence is recognized using distant supervision. We adapt both state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on BioRel. Experimental results show that BioRel is suitable for training and evaluating relation extraction models for both deep learning and statistical methods by providing both reasonable baseline performance and many remaining challenges.
Vehicle Automation–Other Road User Communication and Coordination: Theory and Mechanisms

When automobiles were first introduced in the early 1900s, poor communication and unsafe interactions between drivers and other road users generated resistance. This created a need for new infrastructure, vehicle design, and social norms to mitigate their negative effects on society. Vehicle automation may lead to similar challenges as drivers are supplanted by machines, potentially eliminating social behaviors that serve to smooth on-road communication and coordination. Through a review of communication, robotics, and traffic engineering literature, we explore the mechanisms that allow people to communicate on the road. We show the sensitivity of road users to signals that are sent through vehicle motion, suggesting a need to design vehicle automation kinematics for communication and not just external lighting signals. The framework further points to interdependence in communication where road users modulate their behaviors concurrently to exchange information and develop common ground. Designing automation to support common ground may smooth negotiations by generating interpretable signals in ambiguous situations. We propose a process to make automation observable and directable for other road users by considering vehicle motion during development of algorithms, interfaces, and interactions. Road users will be incidental users of vehicle automation—users whose goals are not directly supported by the technology—and poor communication with them may undermine the safety and acceptance of vehicle automation. As the reach of automation grows, communication among humans and machines may fundamentally change social interactions, requiring a framework to guide the process of making automation interactions smooth and natural.
Classifiers in IRIS Biometrics for Personal Authentication

Machine learning is that which provides the systems the ability to improve on experience without being programmed. Higher accuracy rate is a challenging problem with Iris biometrics. In this paper the best performance based on the classifiers for iris biometric is identified. The normalized iris images are downloaded from IIT Delhi database. The features are extracted from normalized iris images using the techniques such as histogram and wavelet transform. The extracted features are then classified using Neural Networks and Support Vector Machine. The study shows that support vector machine has far better recognition rate than back propagation neural network. The proposed technique provides accuracy at the rate of 96.7% than the neural network.
A Superpixel-Based Coastline Extraction Algorithm for Single-Polarized ENVISAT and ERS Imagery

In single-polarization synthetic aperture radar imagery, coastline extraction is a challenging task due to complex coastal region condition, low incidence angles (<30°), VV or HH polarization, speckle and sea wind, which have to be handled by visual inspection and manual operation for coastline extraction. To solve this problem, this article proposes a coastline extraction algorithm by utilizing superpixel as the basic unit. An improved line finder is presented and involved in the simple linear iterative clustering to obtain better results around the dam. Then, Gabor feature is extracted to characterize the superpixels. Finally, the hidden Markov random field model is utilized to classify superpixels. A coarse to fine strategy is utilized to extract coastline for the experimental imagery. Four VV polarization ENVISAT and ERS images with incidence angle (30.9°−36.6°, 17°−42°, 17°−42°, 19°−26°) are used as the experimental imagery. Four sites in each imagery are chosen to evaluate the performance of proposed superpixel-based algorithm under the condition of complex coastal region such as a thin dam, sharp corner, weak boundary, together with low-to-moderate sea wind. By using the manually traced coastlines as the standards in the qualitative evaluations with the extracted results, with mean offset (MO) in pixels, root-mean-square error (RMSE) in pixels, proportion of overlapped pixels (OLP) in percentage, distance within one pixel (WOP) in percentage, and distance within two pixels (WTP) in percentage as performance comparison, the accuracy improvements in detecting coastline along the dam are at least about 0.08, 0.19, 2.07% in MO, RMSE, and OLP. The accuracy improvements of the sharp corner are at least about 0.07, 0.09, 2.44%, 2.69%, and 2.77%. The accuracy improvements of the weak boundary are about 2.63, 2.99, 5.26%, 11.46%, and 13.96%. While the proposed algorithm shows weaknesses with the comparison algorithms in WOP, WTP for the dam, OLP, WOP, and WTP for sea wind. In addition, the processing time of the proposed algorithm is a burden to obtain better accuracy performance. Experimental results show that the proposed coastline extraction algorithm is more effective and suitable for coastline extraction in comparison with other existing algorithms in the dam, weak boundary, and sharp corner.
Learning Deep Binaural Representations With Deep Convolutional Neural Networks for Spontaneous Speech Emotion Recognition

Spontaneous speech emotion recognition is a new and challenging research topic. In this paper, we propose a new method of spontaneous speech emotion recognition on the basis of binaural representations and deep convolutional neural networks (CNNs). The proposed method initially employs multiple CNNs to learn deep segment-level binaural representations such as Left-Right and Mid-Side pairs from the extracted image-like Mel-spectrograms. These CNNs are fine-tuned on target emotional speech datasets from a pre-trained image CNN model. Then, a new feature pooling strategy, called block-based temporal feature pooling, is proposed to aggregate the learned segment-level features for producing fixed-length utterance-level features. Based on the utterance-level features, linear support vector machines (SVM) is adopted for emotion classification. Finally, a two-stage score-level fusion strategy is used to integrate the obtained results from Left-Right and Mid-Side pairs. Extensive experiments on two challenging spontaneous emotional speech datasets, including the AFEW5.0 and BAUM-1s databases, demonstrate the effectiveness of our proposed method.
Simulation-Based Performance Assessment of Production Planning Models With Safety Stock and Forecast Evolution in Semiconductor Wafer Fabrication

We study the performance of production planning models for wafer fabs in a rolling horizon setting under stochastic demand modeled using the Martingale Model of Forecast Evolution (MMFE). The models differ in how they incorporate lead times and safety stocks. The number of frozen periods is considered as an experimental factor to study the nervousness of the resulting plans. Our simulation experiments show that chance-constrained production planning models that integrate safety stock and production planning decisions outperform others. The formulations with workload-dependent lead times also outperform those with fixed lead times that are an integer multiple of the period length.
ROS-based Integration of Smart Space and a Mobile Robot as the Internet of Robotic Things

The Internet of Robotic Things is a concept that is rapidly gaining traction in the robotics industry. As the field of robotics advances, one of the obstacles to its widespread adoption remains the high cost of purchasing and maintaining robot. Although the gap is continuously closing, robots these days cannot make decisions with the efficiency that humans can. People are spatially aware, and we can perceive and understand changes in the environment that robots are not capable of. This paper attempts to propose how a smart space can be implemented to increase the spatial awareness of a robot by providing more data to make better informed decisions. We focus on using the Robot Operating System (ROS) as a framework to integrate Smart Space and a mobile robot to expand the robots sensory information and make collision avoidance decisions.
Pyramidal Temporal Pooling With Discriminative Mapping for Audio Classification

Audio signals are temporally-structured data, and learning their discriminative representations containing temporal information is crucial for the audio classification. In this article, we propose an audio representation learning method with a hierarchical pyramid structure called pyramidal temporal pooling (PTP) which aims to capture the temporal information of an entire audio sample. By stacking a global temporal pooling layer on multiple local temporal pooling layers, the PTP can capture the high-level temporal dynamics of the input feature sequence in an unsupervised way. Furthermore, in the top global temporal pooling layer, we jointly optimize a learnable discriminative mapping (DM) and a softmax classifier. Such that, a joint learning method for the discriminative audio representations and the classifier called DM-PTP is also presented. By treating the temporal encoding as a low-level constraint of a bi-level optimization problem, the DM-PTP can produce the discriminative representation while maintaining the temporal information of the whole sequence. For an audio sample with an arbitrary time duration, both our PTP and DM-PTP can encode the input feature sequence with arbitrary length into a fixed-length representation. Without using any data augmentation and ensemble learning methods, both PTP and DM-PTP outperform the state-of-the-art CNNs on the audio event recognition (AER) dataset, and can achieve comparable performance on the DCASE 2018 acoustic scene classification (ASC) dataset compared with other best models in the challenge.
Usefulness of ultrasound in the assessment of bone mineralization in newborns

BACKGROUND The study of bone mineralization in the neonatal period and of bone disease is receiving increasing interest. Osteosonography is a new method for measuring bone mineralization without exposing the patient to radiation. OBJECTIVE To determine the speed of ultrasound in healthy newborns, the possible variations in preterm infants, and their relationship with gestational age and anthropometry. METHODS One-hundred newborns (53 term infants [26 males, 27 females] and 47 preterm infants [27 males and 20 females]) were studied. The variables measured were gestational age, length, weight, circumferences (head and thoracic) and amplitude-dependent speed of sound (Ad-SOS). A prior study was carried out to select the area of measurement with ultrasound between both femora and humeri. RESULTS No significant differences in Ad-SOS were found between the right and left femora or humeri, but there were significant differences between femora and humeri (p 0.001). The mean Ad-SOS in the population was 1706.6 36.3 m/s. Ad-SOS was 1726 26.2 m/s in term infants and 1679 36.7 m/s in preterm infants; this difference was statistically significant. Correlations were also found between Ad-SOS and gestational age and anthropometric variables. CONCLUSIONS Ad-SOS is an effective method for bone mineralization screening in newborns and for detecting postnatal changes.
Classification of Locally Grown Apple Based On Its Decent Consuming Using Backpropagation Artificial Neural Network

Fruit is in the highest ranking together with protein as dietary food that containing high energy but low sugar. The risk of some chronic disease may be reduced by consuming fruit. One of the fruits chosen as a diet is apple because it has high phytochemical and anti-oxidant content. As one of the fruits most sought after by the public to maintain diet and competition with imported fruit, handling post-harvest apples is very important. Color is one of the indicators considered when consumers choose fruit. The classification method in sorting fruit automatically is based largely on the color of the fruit because in this method there is no physical contact with the fruit which can cause fruit damage. In this research, the classification system is developed for the Malang's local grown apple Manalagi based on the extraction of the average RGB color feature using the Backpropagation Artificial Neural Network algorithm. The purpose of the developed system is to distinguish the Manalagi, the famous Malang grown apple of worth consumption and not worth the consumption. The fruit classification is based on the average RGB color composition of the fruit skin. The developed system can classify Manalagi in worth consumption and not worth the consumption with an accuracy rate of 90%.
Correlation of heart function with plasma BNP and serum uric acid level in elderly heart failure patients

Objective To study the correlation of heart function with plasma BNP and serum uric acid(UA)levels in elderly heart failure patients.Methods One hundred and thirty-two elderly patients with chronic heart failure(CHF)served as a CHF group in this study.They were divided into classⅠgroup(n=26),classⅡ group(n=38),classⅢ group(n=40),and classⅣ group(n=28)according to the NYHA Classification.Fifty healthy subjects undergoing physical examination served as a control group.Their plasma BNP and erum UA levels were measured and compared.Results The plasma BNP and erum UA levels were significantly higher in CHF group than in control group(784.19±206.00ng/L vs 41.25±15.34ng/L,405.24±90.16μmol/L vs286.43±78.68μmol/L,P0.01)and were significantly different in classⅠ-Ⅳ groups(P0.05,P0.01). The plasma BNP level was positively correlated with the NYHA Classification,serum UA level and LVEDD,and negatively correlated with the LVEF in CHF patients(r=0.76,0.33,0.22,P0.05;r=-0.34,P0.0 5). The serum UA level was positively correlated with the NYHA Classification and LVEDD,and negatively correlated with the LVEF in CHF patients(r=0.41,0.23,P0.05;r=-0.24,P0.05). Conclusion Plasma BNP and serum UA levels are correlated with the cardiac function classification in CHF patients.
Improved Single Barrier Ozone Chamber

There have been many studies regarding ozone generation using Dielectric Barrier Discharge (DBD) method at atmospheric pressure in various conditions such as variations in gap distance, dielectric materials, electrode settings, gas flow rate, and perforated aluminum of configurations. However, the current study is based on our previous work. This has been intended to improve the ozone chamber design in order to reduce gas leakage as well as to enhance ozone concentration. Perforated aluminum electrodes were arranged on both the cathode and anode sides (double perforated) with an electrode area of 97.38%. The highest ozone concentration of 2002.37 ppm was obtained at 8 kVrms. The obtained ozone concentration in the current work is higher than the previous one, at 6 kVrms applied voltage. A 29.41% enhancement in ozone concentration was obtained. This showed that the improvement on chamber design and electrode configuration contributed to the ozone concentration. Based on these results, there is a possibility to increase the ozone concentration by raising the applied voltage as long as the stability can be achieved.
Does Inertia Effect E-Learning System Acceptance Among University Lecturers? Insights from Sriwijaya University

Sriwijaya University aims to utilize e-learning within lecture activities. However, there is grave lack of interest amongst lecturers in using e-learning. To achieve higher e-learning usage, it is imperative to understand the factors that inhibit interest among university lecturers in using e-learning. Such factors were analyzed using the SQB theory and the UTAUT models. The data were gathered using questionnaires and were analyzed statistically using PLS-SEM. The results showed that the lack of computer self-efficacy and lack of organizational support encourage inertia, while lack of individuals’ experience with computers and lack of resources were not significantly towards inertia. Inertia was shown to negatively affect behavioral beliefs, thus indirectly reducing the intention to adopt e-learning. Performance expectancy and effort expectancy proved to have a positive effect on user intentions to use e-learning.
Modeling and Predicting Protein-Protein Interactions of Type 2 Diabetes Mellitus Using Feedforward Neural Networks

Data of protein-protein interactions (PPIs) are still limited. More data of PPIs are required so one can find significant proteins representing a disease more accurately. Computational approach which can predict PPIs is one of alternatives to reduce time and cost that generally required by experimental work. This research focused on predicting PPIs of Type 2 Diabetes mellitus using feedforward neural network (FNN). Impact of different activation functions, number of units per hidden layers and number of hidden layers themselves to estimation error were observed. Rectifier activation function, seven hidden layers and 36 units per hidden layers gave smallest MSE separately. The model with those configurations predicted a PPI with predicted combined score of 0.922. FNN model had better prediction accuracy than random forest and support vector regression models.
Detecting In-Season Crop Nitrogen Stress of Corn for Field Trials Using UAV- and CubeSat-Based Multispectral Sensing

Nitrogen (N) fertilizer management is one of the main concerns for precision agriculture under corn production, which aims to not only maximize the profits, but also ensure environmental sustainability. Effective N fertilizer management can either avoid N stress or provide timely and accurate detection of in-season N stress for remedies. Traditional N trial experiments to evaluate different N management practices have to wait until harvest, and do not allow tracking of when and how N stress develops. Meanwhile, rapidly developed remote sensing technology offers new opportunities for in-season evaluation of N status and detection of N stress for crops, including both the unmanned aircraft vehicle (UAV)-based and satellite-based multispectral sensing. In this study, we collected weekly multispectral images of UAV and Planet Lab's CubeSat, as well as various other ground measurements for an experimental cornfield that included 28 N management treatments in Central Illinois, 2017. We found that both the UAV- and CubeSat-based multispectral sensors were able to detect N stress at vegetative stages before tasseling, and could detect changes in the level of N stress through derived chlorophyll index green (CIg) for different N management practices. The CubeSat-based CIg showed high consistency with the UAV-based CIg (correlation above 0.9), which indicated the potential of CubeSat-based CIg to be applied for N stress detection at a larger spatial scale. This study demonstrates that the UAV- and CubeSat-based multispectral sensing has the promising potential to monitor N stress of corn throughout the growing season, which may assist decision making of N management.
Analysis of Surface Passivation and Laser Firing via Light-Beam Induced Current Measurements

—Liquid phase crystallized silicon (LPC-Si) solar cells on glass have demonstrated 14.2 % efficiency using a heterojunction interdigitated back contact cell architecture and an absorber thickness of 13 µm. However, losses are incurred in charge collection under the majority carrier contact regions even after reducing their width to 60 −120 µm. Recently, we developed a new method to first passivate these contact fingers with a-Si:H(i) and then locally laser-fire them to maintain a low contact resistance. High resolution light beam induced current measurements were used to analyze these developments. Without a-Si:H(i), an effective minority carrier diffusion length around 19 µm under the majority carrier contacts lead to only 0.13 mA cm−2 being collected there. With the new method, the diffusion length was improved to 36 µm and up to 113.6 µm in the best case. The reduction of passivation in the laser-fired spots was limited to a diameter of 55 µm. Over-all, charge collection increased by 0.8 mA cm−2 under the electron contact. Spatial mapping of charge collection creates a clear outlook to achieve 15 % efficiency even with the current material quality by reducing the laser firing spot size and implementing the new approach on cells with larger and more uniform grains.