Miracle-dz/UniFAR-FTUs huggingface dataset:
WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics - Concept, Literature, and Future

This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.
A Deep Learning Study of Aspect Similarity Recognition

Aspect Similarity Recognition (ASR) is to identify whether two sentences express one or some aspects in common. This task is useful in review summarization where a summarized review needs to cover all aspects as well as avoid redundancy. To facilitate the application of supervised learning models for this task, we construct a dataset ASRCorpus containing two domains (i.e., LAPTOP and RESTAURANT). The four models i.e., Word Average, CNN, LSTM and BiLSTM) are employed to evaluate the performances of machine learning methods. According to the experimental results, the recurrent neural networks are the most efficient methods. We also analysis some typical samples to identify the task’s challenges.
LQG Control Design for a Coupled Ballbot Dynamical System

Ballbot is an under-actuated mobile robot which possesses an omnidirectional mobility. It can maintain the self-balancing and transfer on a spherical ball. The position and orientation of the robot in a space can be determined by two sensors, an encoder and inertia measurement unit. The measurement and fusion data processes from sensors are inevitably effected noise. The noise profile presented in the system is modeled as an additive white gaussian noise (AWGN). In this paper, a linear quadratic gaussian (LQG) controller is developed for a 3D dynamics of a ballbot system. The LQG control design is a combination of linear optimal state-feedback controller and an optimal estimator. The optimal estimator is applied to estimate the outputs of the system which is assumed to be affected by AWGN. Numerical simulations are implemented to demonstrate the effectiveness of our approach for a coupled ballbot dynamics.
Rotor Thermal Model for Real-time Protection in Adjustable Induction Motor Drives

The protection of the adjustable speed induction motor drives is usually implemented using single-mass thermal model. For more precise estimation of the thermal condition of electrical machine it is advisable to use more complex several-mass models. In this study it was investigated that for frequency regulated machines their rotor can be represented as a single thermal mass. That research allows to simplify the thermal model and to recommend three-mass model for implementation to protect the machine from overheat by means of control system. The recommended masses are rotor, stator windings, and stator iron. The experiments were conducted using 5.5 kW squirrel cage induction machine and proved that its rotor has a single time constant purely visible in the temperature transient.
A Mobile Game to Teach AVL Trees

Data structures and algorithms represent an important area of computer science. Sometimes, however, they are too abstract for students to understand them only from the lecture. Therefore we were looking for a way how to explain at least some of them to students. In recent years, the use of digital games at all levels of education has come to the focus of researchers and educators. The advantage of this approach is, among others, learning in a natural and interesting way. Since there are still very few good-quality educational games, we have decided to design, implement and test our own mobile game for teaching the algorithm of inserting new nodes to an AVL tree. In this paper, we describe designing of the game as a learning tool and introduce the first results from testing the game in a university course.
Evidence for the Scarr-Rowe effect on genetic expressivity in a large US sample

Using the Continuous Parameter Estimation Method (CPEM), a large genotyped sample of the population of Wisconsin, USA (the Wisconsin Longitudinal Study, N=8,509) is examined for evidence of the Scarr-Rowe effect, an adverse gene x environment (GxE) interaction that reduces the heritability of IQ among those with low socioeconomic status. This method allows the differential expressivity of polygenic scores predictive of both educational attainment and IQ (EA3) on the phenotype of IQ to be operationalized throughout the full range of these variables. Utilizing a parental SES factor-weighted composite as a measure of childhood SES, evidence for the Scarr-Rowe effect was found, i.e. the genetic expressivity of EA3 on IQ increased with increasing parental SES (β=.064, p=3.82×10−9, df=8508). The effect was found for both the male and female samples separately (β(males)=. 051, p=. 002, df=4062; β(females)=. 076, p=1.76×10−7, df=4445) — there were no significant sex differences in the effect magnitudes, however. The effects were furthermore robust to removing outlying values of parental SES.
Quantum-Mechanical Study of Nanocomposites with Low and Ultra-Low Interface Energies

We applied first-principles electronic structure calculations to study structural, thermodynamic and elastic properties of nanocomposites exhibiting nearly perfect match of constituting phases. In particular, two combinations of transition-metal disilicides and one pair of magnetic phases containing the Fe and Al atoms with different atomic ordering were considered. Regarding the disilicides, nanocomposites MoSi2/WSi2 with constituents crystallizing in the tetragonal C11b structure and TaSi2/NbSi2 with individual phases crystallizing in the hexagonal C40 structure were simulated. Constituents within each pair of materials exhibit very similar structural and elastic properties and for their nanocomposites we obtained ultra-low (nearly zero) interface energy (within the error bar of our calculations, i.e., about 0.005 J/m2). The interface energy was found to be nearly independent on the width of individual constituents within the nanocomposites and/or crystallographic orientation of the interfaces. As far as the nanocomposites containing Fe and Al were concerned, we simulated coherent superlattices formed by an ordered Fe3Al intermetallic compound and a disordered Fe-Al phase with 18.75 at. % Al, the α-phase. Both phases were structurally and elastically quite similar but the disordered α-phase lacked a long-range periodicity. To determine the interface energy in these nanocomposites, we simulated seven different distributions of atoms in the α-phase interfacing the Fe3Al intermetallic compound. The resulting interface energies ranged from ultra low to low values, i.e., from 0.005 to 0.139 J/m2. The impact of atomic distribution on the elastic properties was found insignificant but local magnetic moments of the iron atoms depend sensitively on the type and distribution of surrounding atoms.
Wideband Printed Ridge Gap Semi-Log Periodic Structure Antenna for Millimeter Wave Applications

A wideband semi-log periodic antenna design based on the log periodic antenna is presented. The proposed antenna is fed using a printed ridge gap technology which results in improvement of the efficiency. The proposed design has a matching level less than −10 dB with a fractional bandwidth of 49.37% at 36 GHz. The proposed design has an average realized gain of 6.5 dB over the whole band with stable radiation pattern.
Voltage-Stacked GPUs: A Control Theory Driven Cross-Layer Solution for Practical Voltage Stacking in GPUs

More than 20% of the available energy is lost in "the last centimeter" from the PCB board to the microprocessor chip due to inherent inefficiencies of power delivery subsystems (PDSs) in today's computing systems. By series-stacking multiple voltage domains to eliminate explicit voltage conversion and reduce loss along the power delivery path, voltage stacking (VS) is a novel configuration that can improve power delivery efficiency (PDE). However, VS suffers from aggravated levels of supply noise caused by current imbalance between the stacking layers, preventing its practical adoption in mainstream computing systems. Throughput-centric manycore architectures such as GPUs intrinsically exhibit more balanced workloads, yet suffer from lower PDE, making them ideal platforms to implement voltage stacking. In this paper, we present a cross-layer approach to practical voltage stacking implementation in GPUs. It combines circuit-level voltage regulation using distributed charge-recycling integrated voltage regulators (CR-IVRs) with architecture-level voltage smoothing guided by control theory. Our proposed voltage-stacked GPUs can eliminate 61.5% of total PDS energy loss and achieve 92.3% system-level power delivery efficiency, a 12.3% improvement over the conventional single-layer based PDS. Compared to the circuit-only solution, the cross-layer approach significantly reduces the implementation cost of voltage stacking (88% reduction in area overhead) without compromising supply reliability under worst-case scenarios and across a wide range of real-world benchmarks. In addition, we demonstrate that the cross-layer solution not only complements on-chip CR-IVRs to transparently manage current imbalance and restore stable layer voltages, but also serves as a seamless interface to accommodate higher-level power optimization techniques, traditionally thought to be incompatible with a VS configuration.
Online Single Homogeneous Source Transfer Learning Based on AdaBoost

Transfer learning has made great achievements in many fields and many excellent algorithms have been proposed. In recent years, many scholars have focused on a new research area called online transfer learning, which is different from general transfer learning. Online transfer learning concentrates on how to build a good classifier on the target domain when the training data arrive in an online/sequential manner. This paper focuses on online transfer learning problem based on a single source domain under homogeneous space. The existing algorithms HomOTL-I and HomOTL-II simply ensemble the classifiers on the source and target domains directly. When the distribution difference between the source domain and the target domain is large, it will not result in a good transfer effect. We are inspired by the idea of the boosting algorithm, that is we could form a strong classification model by a combination of multiple weak classifications. We train multiple classifiers on the source domain in an offline manner using AdaBoost algorithm, combine these classifiers on source domain with the classifier trained in an online manner on the target domain to form multiple weak combination in an ensemble manner. Based on the above ideas, we propose two algorithms AB-HomOTL-I and AB-HomOTLII, which have different ways to adjust the weights. We tested our algorithms on sentiment analysis dataset and 20newsgroup dataset. The results show that our algorithms are superior to other baseline algorithms
On the Combined Inverse-Dynamics/Passivity-Based Control of Elastic-Joint Robots

In this paper, we present a novel global tracking control approach for elastic-joint robots that can be efficiently computed and is robust against model uncertainties and input disturbances. Elastic-joint robots provide enhanced safety and resiliency for interaction with the environment and humans. On the other hand, the joint elasticity complicates the motion-control problem especially when robust and precise trajectory tracking is required. Our proposed control approach allows us to merge the main benefits of the two well-known control schemes: inverse-dynamics (ID) control, which can be efficiently computed thanks to modern recursive algorithms, and passivity-based (PB) tracking control, which provides enhanced robustness to model uncertainty and external disturbances. As an extension of our previous work, we present a detailed robustness analysis of our combined ID/PB controller, a new variant of the original scheme that shows practically relevant implications, and finally, experimental results that verify the effectiveness of the approach.
Long-Term Recurrent Merge Network Model for Image Captioning

Language models based on Recurrent Neural Networks, e.g. Long Short Term Memory Network (LSTM), have shown strong ability in generating captions from image. However, in previous LSTM-based image captioning models, the image information is input to LSTM at 0th time step, and the network gradually forgets the image information, and only uses the language model to generate a simple description, leaving the potential in generating a better description. To address this challenge, in this paper, a Long-term Recurrent Merge Network (LRMN) model is proposed to merge the image feature at each step via a language model, which not only can improve the accuracy of image captioning, but also can describe the image better. Experimental results show that the proposed LRMN model has a promising improvement in image captioning.
Development of a New Low-Cost EMG Monitoring System for the Classification of Finger Movement

Electromyography (EMG) signals are commonly used by researchers to study kinesiology, which can then be used to control prosthetic arms, hands and limbs [1]. Additionally, EMG signals are used in the field of Bio Robotics for gesture control applications. The aim of this work is to implement a set of affordable active electrodes for the classification of finger movement via time-domain analysis. The electromyography signals are received through the Ag-AgCI electrodes, which are conductive materials used for gathering and transferring the muscle activation potential. The proposed paper focuses on presenting a new low-cost based system for the classification of human finger movement using auto-gain adjustment active electrodes. Different people will have different EMG amplitudes; therefore, it is difficult to determine the gain required prior performing further signal processing. In this work the implementation of an auto-adjustable gain amplifier was used. This circuit monitors the maximum EMG signal amplitude and adjusts the gain stage accordingly, without any user interaction. This ensures that the gain is always automatically adjusted to get the most effective performance from the data acquisition or analogue to digital converter (ADC) module since the signal will be neither too low in amplitude to cause inefficient use of the ADC resolution, not too high to cause saturation of the signal. Additionally, the instrumentation amplifier is directly interfaced near the Ag-AgCI electrodes. Through extensive experiments, our sEMG sensor outperforms a widely used commercially available product and when tested under ten different persons our low cost EMG data acquisition system achieves reproducible and repeatable results for the detection and classification of finger movements.
Genre Classification using Feature Extraction and Deep Learning Techniques

Music genre refers to categorisation of music on the basis of interaction between artists, market forces, and culture. It helps to organize music into collections by indicating similarities between compositions or musicians. Automatic genre classification is non-trivial as it is difficult to distinguish between different genres. Many times the boundaries are not clearly defined and genres are overlapping. In this paper we present a novel approach to classify a list of songs present on Spotify into mainly four genres-Christian, Metal, Country, Rap. Two different kinds of data - lyrics and album artwork are used for the classification process. Two Natural Language Processing techniques namely bag-Of-Words and Term Frequency-Inverse Document Frequency (TFIDF) are used to process the lyrical data. We apply machine learning algorithms like Random Forest, Support Vector Machine (SVM), Naive Bayes, Linear Support Vector Classifier (Linear SVC) and eXtreme Gradient Boosting (XGBoost) on lyrical data and Deep Convolutional Neural Network (CNN) on the album artwork to predict the genre. On application of machine learning algorithms on lyrical data obtained from bag-Of-Words a mean precision of 75.96% and a mean f-score of 75.92% is achieved. On application of the same set of algorithms on lyrical data obtained from TFIDF a mean precision of 76.85% and a mean f-score of 77.38% is achieved. In both cases XGBoost outperforms all the other algorithms giving a maximum precision of 79.30% and 80.16% and a maximum f-score of 79.6% and 84.09% for bag-Of-Words and TFIDF respectively. On application of deep neural network on album artwork, a precision of 82.46% and a f-score of 81.84% is achieved.
Recognition of Single-Land Countries on Outline Images by Using BAS Feature

This paper addresses an original problem. It presents an approach for the recognition of single-land countries on outline satellite images by using Beam Angle Statistics (BAS). For this purpose, we have created an image dataset. After converting the RGB input images to binary and applying the necessary preprocessing steps, a single contour is extracted from each image by Canny Edge detector. In order to represent each county as a one-dimensional vector, BAS feature is extracted from contour point vector. Thus, each country has its unique feature vector and is independent from rotation, scale and is also robust to image deformation. For comparison of the one-dimensional vectors, Dynamic Time Warping (DTW) is used. In experiments, we had Additive White Noise (AWN) added to test images which are scaled with factors 1.5, 0.5, 0.25 and are also iteratively rotated by 45°. Benchmarking has been conducted between the proposed algorithm, Scale Invariant Feature Transform (SIFT) and Oriented Fast Rotated Brief (ORB) features. Results show that BAS feature is the most robust feature against rotation followed by ORB and then SIFT. In case of upscale, BAS outperforms the others. In downscale versions, SIFT outperforms others and ORB comes as last.
Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm

This paper presents a scheme based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.
Cooperation of Offshore Wind Farm with Battery Storage in Multiple Electricity Markets

Wind power generation plays a critical role in meeting the energy demand in Denmark. Due to increasing penetration of renewable energy, and in particular wind power, the frequency deviation has become a national problem that needs to be solved urgently to continue the positive development of renewables. In this paper, a co-optimization method of an offshore wind farm with the battery energy storage system (BESS) is proposed which enable the wind farm to participate in both days ahead (DA) electricity market and primary frequency control reserve market. The optimization problem involves the hourly bidding energy, charging and discharging status which creates a mixed integer nonlinear programming (MINLP) problem. The bilinear term is linearized using big M method in this work and thus the facilitate the conversion from a MINLP to a MILP problem. The results show that the wind farm owner can obtain more profits if they want to provide ancillary service using the proposed bidding strategy. It is expected that such proposition could ensure market and political support for continuous wind power installations in Denmark.
Lung Function in Young Adults and Risk of Cardiovascular Events Over 29 Years: The CARDIA Study

Background Diminished peak lung function in young adulthood is a risk factor for future chronic obstructive pulmonary disease. The association between lung disease and cardiovascular disease later in life is well documented. Whether peak lung function measured in young adulthood is associated with risk of future cardiovascular events is unknown. Methods and Results CARDIA (The Coronary Artery Risk Development in Young Adults) study is a prospective, multicenter, community‐based, longitudinal cohort study including 4761 participants aged 18 to 30 years with lung function testing we investigated the association between lung health in young adulthood and risk of subsequent cardiovascular events. We performed Cox proportional hazards regression to test the association between baseline and years 10 and 20 pulmonary function with incident cardiovascular events. Linear and logistic regression was performed to explore the associations of lung function with development of risk factors for cardiovascular disease as well as carotid intima‐media thickness and coronary artery calcified plaque. At baseline, mean age (±SD) was 24.9±3.6 years. Baseline forced expiratory volume in 1 second (hazard ratio) per −10‐unit decrement in percent predicted forced expiratory volume in 1 second (hazard ratio, 1.18; 95% CI, 1.06–1.31 [P=0.002]) and FVC per −10‐unit decrement in percent predicted FVC (hazard ratio, 1.19; 95% CI, 1.06–1.33 [P=0.003]) were associated with future cardiovascular events independent of traditional cardiovascular risk factors. Baseline lung function was associated with heart failure and cerebrovascular events but not coronary artery disease events. Conclusions Lung function in young adulthood is independently associated with cardiovascular events into middle age. This association appears to be driven by heart failure and cerebrovascular events rather than coronary heart disease. Clinical Trial Registration URL: https://www.clinicaltrials. gov. Unique identifier: NCT00005130.