Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditioning Neural Networks: A Case Study of Electricity Load Forecasting

Machine learning tasks typically involve minimizing a loss function that measures the distance of the model output and the ground-truth. In some applications, in addition to the usual loss function, the output must also satisfy certain requirements for further processing. We call such requirements model conditioning. We investigate cases where the conditioner is not differentiable or cannot be expressed in closed form and, hence, cannot be directly included in the loss function of the machine learning model. We propose to replace the conditioner with a learned dummy model which is applied on the output of the main model. The entire model, composed of the main and dummy models, is trained end-to-end. Throughout training, the dummy model learns to approximate the conditioner and, thus, forces the main model to generate outputs that satisfy the specified requirements. We demonstrate our approach on a use-case of demand charge-aware electricity load forecasting. We show that jointly minimizing the error in forecast load and its demand charge threshold results in significant improvement to existing load forecast methods.
Biped Robot Gait Control Based on Enhanced Capture Point

In this paper, we propose the concept of enhanced capture point. Then we develop the enhanced CP controller with two enhanced CP control methods eCPS, eCPT. Their stability and disturbance rejection ability are analyzed. In addition, we introduce the concept of controllable region of CP and compare the enhanced CP control with CP control on the disturbance rejection performance. The simulation results of the linear inverted pendulum model verify the effectiveness of the proposed method, and the use of eCPT to achieve a more stable gait of the robot.
Analysis of User Dwell Time on Non-News Pages

There is dwell time as one of the indicators of user’s behavior, and this indicates how long a user looked at a page. Dwell time is especially useful in fields where user ratings are important, such as search engines, recommender systems, and advertisements are important. Despite the importance of this index, however, its characteristics are not well known. In this paper, we analyze the dwell times of various websites by desktop and mobile devices using data of one year. Our aim is to clarify the characteristics of dwell time on non-news websites in order to discover which features are effective for predicting the dwell time. In this analysis, we focus on device types, access times, behavior on the website, and scroll depth. The results indicated that the number of sessions decreased as the dwell time increased, for both desktop and mobile devices. We also found that hour and month greatly affected the dwell time, but day of the week had little effect. Moreover, we discovered that inside and click users tended to have longer dwell times than outside and non-click users. However, we can not find a relationship between dwell time and scroll depth. This is because even if a user browsed the bottom of the page, the user might not necessarily have read the entire page.
Obstructive Sleep Apnea Detection Using Lomb-Scargle Periodogram Method

In this study, the power spectral density of the heart rate signal was investigated by the Lomb-Scargle Periodogram method for obstructive sleep apnea (OSA) estimation. Sleep apnea, which occurs as a result of respiratory failure during sleep, is interpreted by scoring polysomnographic signals. One of these signals, Electrocardiogram (ECG), contains noise in its structure and exhibits irregular alteration. In this study, the power spectrum of the heart rate variability (HRV) was obtained based on filtered ECG signals. Analyzes in the presence of OSA and in the absence of OSA have shown that deviations occur in the high and low frequency components of the HRV.
Dynamic Partition Forest: An Efficient and Distributed Indexing Scheme for Similarity Search based on Hashing

The similarity search over large-scale feature-rich data(e.g. image, video or text) is a fundamental problem and has become increasingly important in data mining research. Hashing based methods, especially Locality Sensitive Hashing(LSH), have been widely used for fast Approximate Nearest Neighbor search(ANNs). However, there are still two flaws in existing methods: (1) The state-of-the-art distribution scheme sacrificed too much accuracy for speeding up the query in practice. (2) Most LSH-based index approaches directly used the static number of compound hash values without considering the data distribution, resulting in system performance degradation. In this paper, a new index structure called Dynamic Partition Forest(DPF) is designed to hierarchically divide the high collision areas with dynamic hashing, which leads itself to auto-adapt various data distributions. A multiple-step search strategy is integrated with DPF to mitigate the accuracy loss with distributed scheme. The experiment results show that DPF increases the accuracy by 3% to 5% within the same timeframe compared to DPF without multiple-step search. Additionally, DPF with our partition scheme is 1.4 times faster than DPF without partition, which demonstrates the efficiency of our content-based distributed scheme. Experimental comparisons with other two state-of-the-art methods on three popular datasets show that DPF is 3.2 to 9 times faster to achieve the same accuracy with 17% to 78% decrease of index space.
Does music help to be more attentive while performing a task? A brain activity analysis.

The human brain is one of the most complex physiological systems, involving billions of interacting physiological and chemical processes giving rise to the experimentally observed neuroelectric activity, called electroencephalographic (EEG) signals. A biomedical signal, electroencephalography (EEG), reflects the state of mind and is often used to verify the influence of music on human brain activity. In this paper we present a brief study of various effects of music on Attention and Concentration state of the brain using the Neurosky Mindwave Mobile Headset and EEG Analyser application. The aim of the study is to identify which type of music can help the brain activities to be more attentive while performing a cognitive task (playing a game). In contrast the effects of music in the Concentration state is also studied in order to understand how the music can help the brain activities to be more focused. Although the number of participants was reduced, the results were interesting. The music acts in an individualized way in the performance of each participant. It was found that for certain tasks there is a strong correlation between Attention and Concentration levels. In addition, a distinction between genders of participants can be made according to the results.
Antenna Design for Magnetic Energy Transmission to Low Frequency RFID Tags in Small Aquaculture Tanks

The main objective of the thesis is to develop a radio frequency identification (RFID) system to transmit energy between a tag located on a free-swimming fish in an aquaculture tank and a base station located outside the tank. The RFID system operates at the frequency of 134.2 kHz. In particular, in this thesis, the ad-hoc antenna design and transmission stage of an RFID system operating, optimized for distance and energy efficiency, is designed and developed.
A Formal Methodology for Notational Analysis and Real-Time Decision Support in Sport Environment

Sport analytics is the process of searching, interpreting and processing information related to the sports performance of a team or a single athlete, using information systems and mathematical methods of data evaluation. The aim is to encourage improvement of sports performance. In this paper we propose a formal methodology aimed to design an expert system to support real-time decisions for notational analysis in sport environment exploiting big data obtained from several sources. The aim is to improve and predict individual and collective performances during a match.
Investment Portfolio Trading based on Markov Chain and Fuzzy Logic

In the present paper, a trading strategy is proposed for a portfolio composed of shares in the stock exchange. The proposed strategy is based mainly on three blocks: 1) a K-means clustering algorithm is used to determine and learn the internal hidden patterns in the time series of stock market prices, 2) a pattern predictor is performed based on a simple Markov chain, and 3) a fuzzy inference system take the decision to trade based on the estimation. The fuzzy inference system is composed by the rules provided by a expert trader. The performance of the trading algorithm is validated through simulations using real prices of the Mexican stock exchange.
Pulsar Signal Denoising Method Based on Empirical Mode Decomposition and Independent Component Analysis

This paper introduces a novel EMD-ICA denoising method, where relevant modes are selected using a hausdorff distance (HD) measure between the probability density function (pdf) of the input and that of each mode. Then, the first layer intrinsic modal function (IMF) is used to construct multidimensional input vectors. These multidimensional input vectors are used as the input of ICA to realize the filtering of each layer of IMF with aliasing. Numerical simulation was carried out to evaluate the effectiveness of the proposed method. Simulation and test results show its superior performance over other traditional denoising methods in whole.
A Framework Based on Compiler Design Techniques for Programming Learning Environments

It has been observed that the studies for starting programming learning at an early age have increased in recent years. Programming that enhances the high level cognitive skills of individuals is seen as a competency that must be acquired for everyone nowadays. In current programming learning environments, programming is usually done using visual components. However, it is emphasized that there is little contribution to the transition to actual programming languages by programming with visual components in the studies carried out. For this reason, a new programming learning environment has been developed in this study in which everyone can learn programming. To facilitate programming learning and contribute to the transition of actual programming languages, a new programming language is described which is simple syntax and similar to real programming languages. This language is controlled in terms of meaning and grammar with lexical and syntax analysis steps. The specified compiler design techniques are implemented using finite state machines. Analysis of the created programming language is performed by regular expressions. Instead of providing a free workspace for the user, an environment with quests covering programming concepts is presented. This quest based environment aims at the successful learning process by guiding to the user through feedbacks. The programming learning environment has been developed as an open source software by using computer science and engineering techniques and it is a framework for researchers seeking to develop a similar environment.
A Path Planning Method for a Four-Wheeled Robot Based on an Intelligent Algorithm

An intelligent based local path planning algorithm is proposed enabling a four-wheeled differential mobile robot to avoid both static and moving obstacles. The developed intelligent method named as feedback compensation neural network (FCNN) refers to improving traditional back propagation neural network (BPNN). With respect to local path planning, the structure of FCNN is composed of two BPNNs. The first BPNN (main NN) is properly trained, playing a dominant role in dynamic obstacle avoidance. Subsequently, the second BPNN (compensation NN) is trained online in order to obtain the compensation for the output. Moreover, the FCNN can predict the motion of dynamic obstacles according to the previous obstacle avoidance situation, even if the obstacle velocity continuously changes. In such a way, the robot collision avoidance performance is highly enhanced, especially in the presence of moving obstacles changing their velocities and heading. The effectiveness of the presented algorithm is illustrated by simulation results.
Tracking Control of An Autonomous Underwater Vehicle under Time Delay

Many studies explore the tracking control of autonomous underwater vehicle (AUV). Unfortunately, the inevitable time-varying delay makes it difficult to achieve such a tracking task. This paper is concerned with position tracking problem under time-varying delay. Based on the complex dynamics model of AUV, model-free proportional-derivative (PD) control law with time-varying delay is designed to enforce the final state synchronization of AUV and the desired target. In order to improve the dynamic performance of position tracking, Takagi-Sugeno(T-S) fuzzy rules are adopted to adjust the proportional and derivative gains of the proposed controller. Finally, simulations and experiments are performed to verify the validity of the proposed control scheme.
Pole Rebalancing Methods for Pole-to-Ground Faults in Symmetrical Monopolar HVDC Grids

Pole rebalancing in symmetrical monopolar high-voltage direct current (HVDC) grids is necessary to remove pole imbalances resulting from pole-to-ground faults. For selective protection employing DC circuit breakers, the interaction between DC circuit breakers and pole rebalancing methods have not been studied. This paper proposes new strategies for pole rebalancing methods to deal with DC circuit breaker operation in HVDC grids. A complete analysis of pole rebalancing using equipment at DC or AC side is performed for all stages of the fault clearing process. Based on the analysis, new control strategies are proposed to optimize the use of the pole rebalancing equipment. The proposed control methods are shown to enable the pole rebalancing equipment to meet the required high-protection speed and low losses. Both DC and AC side equipment such as dynamic braking systems and AC groundings are investigated and proven to be applicable for pole rebalancing in selective protection strategies. The impact of the breaker technology on the interaction between DC circuit breaker requirements and pole rebalancing needs is investigated in detail. The conclusions are validated using electromagnetic transients program (EMTP) simulation on a four terminal test grid.
Topology optimization of truncated cone insulator with graded permittivity using variable density method

Dielectrics with functionally graded material (d-FGM), which have spatially nonuniform dielectric properties, is effective to improve the insulation performance without complicating the structure. In the application of d-FGM, searching the optimal distribution of dielectric properties (permittivity or conductivity) is the most important goal, which however is difficult for conventional insulation design approaches. In this paper, the topology optimization technique is introduced to design a truncated cone insulator with permittivity FGM, which is to relieve local electric field (E-field) enhancement at the triple junctions. Firstly, a multi-objective topology optimization model is proposed using the variable density method. Secondly, the optimal values of several model parameters are determined based on the analysis of E-field uniformity. Finally, finite elements calculation demonstrates that the E-field uniformity is significantly improved for the topology-optimized permittivity FGM, and the E-field enhancement at triple junctions is strongly inhibited. It is thus expected that by applying the topology-optimized permittivity FGM insulator, the breakdown strength in gas or vacuum would be effectively improved.
Attention-Based Multi-Task Learning in Pharmacovigilance

Pharmacovigilance (PV) is the process of monitoring and assessing adverse events (AEs). Currently, the primary method of analysis in PV is manual inspection by individual case managers. However, this is largely unsustainable due to the increased volume of cases over the past few years. Since AE processing involves several sub-tasks, such as annotation and classification, our paper explores a novel solution to PV. In this paper, we propose a multi-task learning (MTL) model, where the hidden layers are shared, to jointly learn the tasks (Named Entity Recognition (NER), Classification). The results of our paper demonstrate that MTL is able to outperform our baseline classification model and equal the baseline model for annotation/NER.11Disclaimer: This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors.
Development of Diagnostic Method and Algorithm for Skin Type Based on Consumer Language and Sentiment

As the global cosmetics market is steadily growing, Korea's cosmetics market is also growing. In spite of the growth of the cosmetics industry, most of the recommendation methods based on the information received by the consumers without clearly knowing their skin type are based on the recommendation methods of existing cosmetics. Also, it is not easy to find recommendation methods based on accurate and objective indicators of the consumer's skin type. As for the customized cosmetics that have received much attention in recent years, the recommendation method is based on the self-questionnaire of the consumer or the skin type of the skin which is known to the user. However, the conventional skin type survey is often complicated and requires expertise to proceed on its own. In this process, the actual type of skin and the type of skin it is recognizing may be different, and the deviation from the desired ideal skin may be larger. Therefore, it is necessary to develop research method and analysis algorithm for diagnosis of accurate skin type and language which is easy for consumers to understand. In this study, we are going to make a diagnosis about current skin condition quantitatively through analyzing the current skin problem, distress, cosmetic use purpose and pursuing emotional distinction based on consumer language and emotion. The results of this study can be used as a guide for making customized cosmetics according to consumers' skin type and skin condition. Furthermore, if we combine cosmetics-related information and consumer reactions on various webs and apps, we expect it to be used as an index for recommending customized cosmetics.
A BCHC genetic algorithm model of cotemporal hierarchical Arabidopsis thaliana gene interactions

Gene interaction network models from time course gene transcript abundance data are algorithmically created using a new aggressive genetic algorithm denoted by BCHC. The BCHC algorithm rigorously integrates probabilistic hierarchical likelihood and Bayesian methodology to produce accurate posterior probabilities of interactions between genes after observance of hierarchical gene transcript abundance data. Forbidden pairwise gene relationships are incorporated into the modeling process. This gene interaction model is compared to a previous gene interaction model utilizing the same data and Bayesian likelihood, however based upon an exponentially slower, less aggressive, and less adaptive Metropolis-Hasting search algorithm. In addition for a smaller data set, our gene interaction model is compared to less rigorous non-probabilistic Lasso estimated partial correlation models which do not fully incorporate the hierarchical structure. A comparison is also made between the smallest Bayesian model and tests for edges based on a restricted non-Bayesian hierarchical technique. The BCHC algorithm performs well when the number of genes is moderately increased, both in terms of execution time and model quality.
Finding Evidences by Crowdsourcing

Crowdsourcing is a promising tool involving multiple people in completing tasks that are difficult to complete by an individual, a small team or a computer. Ensuring the quality of the results is also one of the primary problems in crowdsourcing. One of the major approaches to improve the data quality to aggregate answers from more than one workers. This study explores a different approach - we ask workers to prove facts. We devise a general framework for collecting and ranking evidence-based proofs. The experiments results show that the proposed framework works and how diverse the collected proofs are. Our results clearly indicate that the crowd-based approach to prove facts is promising.
Suspicious Traffic Detection in SDN with Collaborative Techniques of Snort and Deep Neural Networks

Software Defined Networks (SDN) with OpenFlow (OF) based protocol tend to transform traditional network architecture to vendor independent architecture with data-plane and control-plane programmability. This programmability provides a rich functionality in central traffic management, switch configuration, Intrusion Detection System (IDS) integration and global view of entire deployed infrastructure. The SDN network comprises single point failure vulnerability mainly at central controller unit, the deployment of standalone legacy IDS sensors cannot guarantee for safeguard against intruders. Therefore, in the first stage of proposed work, a signature-based Snort IDS is implemented for malicious activity detection and traffic monitoring with traffic mirroring in Open vSwitch (OVS), then store in csv log file of Barnyard 2. In second stage, for the purpose of effective attack detection in the test-bed, a flowbased anomaly detection is deployed with Deep Neural Networks (DNN) to improve the signature-based IDS limitation with higher detection rate with low false-positive triggers. To assess the efficacy of our proposed collaborative detection technique, a testbed is developed to simulate malicious and benign traffic. From the simulation results, our collaborative detection mechanism achieved more than 90% true positive rate with less than 5% of false alarms for all TCP, UDP and ICMP attacks in general, demonstrating effective malicious traffic detection method as compared to conventional signature based methodologies.
Parallel Large-Scale Neural Network Training For Online Advertising

Neural networks have shown great successes in many fields. Due to the complexity of the training pipeline, however, using them in an industrial setting is challenging. In online advertising, the complexity arises from the immense size of the training data, and the dimensionality of the sparse feature space (both can be hundreds of billions). To tackle these challenges, we built TrainSparse (TS), a system that parallelizes the training of neural networks with a focus on efficiently handling large-scale sparse features. In this paper, we present the design and implementation of TS, and show the effectiveness of the system by applying it to predict the ad conversion rate (pCVR), one of the key problems in online advertising. We also compare several methods for dimensionality reduction on sparse features in the pCVR task. Experiments on real-world industry data show that TS achieves outstanding performance and scalability.
A Simple and Robust Sensorless Control Based on Stator Current Vector for PMSG Wind Power Systems

This paper proposes a novel sensorless control based on the stator current vector for permanent-magnet synchronous generators in variable-speed wind energy conversion systems. In the proposed method, the rotor angle and speed are estimated indirectly from the stator currents, where only the stator inductance is required regardless of the stator resistance. In addition, the proposed method uses one current control loop to regulate the stator current magnitude, rather than two current control loops. To eliminate the current measurement noise, the second-order generalized integrator (SOGI) with a frequency-locked loop (FLL) is utilized, which can improve the estimation performance. The proposed sensorless control method with the SOGI-FLL is simple and easy to implement while achieving a fast and accurate estimation performance of the rotor angle and speed. The feasibility of the proposed method has been verified by simulation and experimental results.