Miracle-dz/UniFAR-FTUs huggingface dataset:
Day Ahead Bi-Level Direct Load Control in Smart Residential Apartment Building: A Stackelberg Game Approach

This paper elucidates a day ahead direct load control strategy for a smart residential apartment building (SRAB) comprising of multiple dwelling units (DUs) of non-identical functionalities. The objective of the current article is to design a distributed load management strategy for SRAB in multi-objective optimization platform by utilizing a virtual energy trading software platform between building load master controller (BLMC) and the DUs. The propounded optimization portfolio is developed as a multi-objective optimization problem where maximization of consumed power utility is simultaneously considered with the energy cost minimization objective. Interaction between BLMC and DUs is modelled as one-leader, N-followers Stackelberg Game, where the outcome of the game is proved to be at Stackelberg Equilibrium point. Stringent simulation is carried out on a real world data of four DUs to show efficacy of the proposed load control process.
Analysis of Users' Behaviour and Adoption Trends of Social Media Payment Platforms

The recent proliferation of Electronic Commerce (E-commerce) has been further escalated by multifaceted emerging payment solutions such as cryptocurrencies, mobile, peer-to-peer (P2P) and social media payment platforms. While these technological advancements are gaining tremendous popularity, mostly for their ease of use, various impediments such as security and privacy concerns, societal and cultural norms etc. forbear the users' adoption trends to some extents. This article examines the current status of the social media payment platforms as well as the projection of future adoption trends. Our research underlines the motivations and obstacles to the adoption of social media platforms.
Mixture of Activation Functions With Extended Min-Max Normalization for Forex Market Prediction

An accurate exchange rate forecasting and its decision-making to buy or sell are critical issues in the Forex market. Short-term currency rate forecasting is a challenging task due to its inherent characteristics, which include high volatility, trend, noise, and market shocks. We propose a novel deep learning architecture consisting of an adaptive activation function selection mechanism to achieve higher predictive accuracy. The proposed architecture is composed of seven neural networks that have different activation functions as well as softmax layer and multiplication layer with a skip connection, which are used to generate the dynamic importance weights that decide which activation function is preferred. In addition, we introduce an extended Min-Max smoothing technique to further normalize financial time series that have non-stationary properties. In our experimental evaluation, the results showed that our proposed model not only outperforms deep neural network baselines but also other classic machine learning approaches. The extended Min-Max smoothing technique is step towards forecasting non-stationary financial time series with deep neural networks.
Support vector machine-based active fault-tolerant control for wind turbine

This paper presents an active fault-tolerant control method (FTC) for compensating pitch actuator faults in the hydraulic pitch system of variable speed wind turbine (WT). The proposed control strategy incorporates an unknown input observer (UIO) to estimate the state of the hydraulic pitch system, a support vector machine (SVM) based fault estimation block to provide active information on the state of the actuator fault, and an SVM-based reconfigurable controller to compensate for pitch actuator faults in order to recover the pitch dynamics. the effectiveness of the considered methods is validated using 4.8MW wind turbine.
SP0070 Pain management in rheumatology – lessons from a pain in motion: From the joint to the brain, from analgesics to biologics

Pain in rheumatology represents a frequent situation. For many years it has been considered as the prototypical nociceptive pain condition but recent findings may modify pathophysiological and treatment approaches. 1. Recent findings on pain mechanisms in rheumatic disorders: – in the periphery, bone has appeared as an important component of pain: imaging has demonstrated good correlations between bone abnormalities and pain intensity. – in the brain, arthritis pain induce changes and sensitization, with dysfunctional changes misdiagnosed as a neuropathic pain component. – arthritis pain is not a stable condition: exacerbations related to movement and at night could be related to different pathophysiological mechanisms. 2. Consequently, new therapeutic strategies have emerged, to be accepted by rheumatologists: – antidepressants may be effective in chronic joint pain, even without mood disorders. – NSAIDs have been limited in their use but the real place of old and recent opioids in this condition remains challenged. – the place of opioids has increased, even if efficacy of new opioids in rheumatological conditions is still controversial, while safety issues have been raised. 3. Biologic agents will be developed for joint pain: – New biological agents acting on NGF have demonstrated significant effect, but safety issues may limit their development. – Biologic agents developed in inflammatory rheumatisms like anti-TNF or anti-IL6 agents may exhibit peripheral but also central action. 4. Finally, adapted from RA treatment strategies, we may propose a new classification of analgesics: – Antinociceptive analgesics for acute flares, and changes related to motion or inflammation – -Pain Modifying Analgesics Agents (PMADs) for long term treatment, on chronic component. In conclusion, pain management remains a challenge in rheumatology. We aim to demonstrate that all rheumatologists should be aware of recent findings in pain, both in pathophysiology and treatment. This may lead to new concepts of pain management in rheumatological conditions, for the benefit of our patients. Disclosure of Interest S. Perrot Grant/Research support from: BMS, Consultant for: Grunenthal, Sanofi, Mundipharma, Pfizer, Lilly
Teaching on Demand: an HPC Experience

In this work we present the experience of the course "Build your own supercomputer with Raspberry Pi", offered as a non-mandatory workshop with the purpose of bringing High Performance Computing (HPC) closer to bachelor students of Universitat Jaume I (UJI, Spain). The intention of the course is twofold; on the one hand, we target towards increasing the knowledge of Computer Science and Engineering students about the labor performed by the HPC community; on the other hand, we aim to create a personalized experience for each student by fulﬁlling their curiosity about the topics presented and discussed in the class. In order to evaluate the impact and learning, we analyze two surveys ﬁlled out by the students respectively before and after the course, where HPC interest and knowledge are exposed.
Neural Network Analysis of Stock Price Based on News Corpus Impact

Investors are more concerned about the stock prices they care about. The trend of stock price is related to many factors, such as the operation status of the enterprise, the heat of the exchange market and the reaction of all aspects of society to the operation of the enterprise, and the analysis and grasp of the enterprise information by individual investors. For the latter two items, the main impact comes from the individual's initiative to explore the trading information of the company's stock situation, which results in the purchase intention. This paper mainly considers the influence of news corpus. The impact of news corpus on the stock price of enterprises is a method that needs quantitative analysis. In this paper, we adopt the independent and identical distribution model of the positive (positive) and negative (negative) attributes of lexical frequency classification and corpus information based on word bag and the influence of social attributes on stock price, and use the artificial neural network to calculate stock price parameters. The experimental results show that the influence of news corpus on stock trend is affirmative, and the method of adding news corpus influence effectively improves the accuracy of stock price prediction.
Jazz Music Generation Based on Grammar and LSTM

There are many features that make music composition a challenging task for computer science. many people are trying to generate music in different methods which generated music do not match music rules. This paper uses music theory grammar combined with LSTM neural network to generate jazz music. We use the interval, duration, and note category information as the input data of LSTM model what parsed from the midi files. The neural network generates the sequence of notes according to the transition probability, then parse it according to the music grammar. Through improve the music grammar in the aspect of parse note sequence our system can generate music that matches music rules. we compared the effect of generated music with some popular models. Experiment results shows that the LSTM model generated music is better, Improved music grammar can generate better music than original music grammar.
Study on Text Classification of MOOC Course Comments Based on Chinese Character-level Convolutional Networks

With the rise of the educational big data and the development of MOOCs, a wide range of comments have come up. Now most of the existing MOOCs text classification models are for small scale comments, and fail to extract high-level abstract information among characters. In this paper, crawler technology is used to crawl the comment data in MOOCs forums comprehensively, and a character table containing 5500 comment data is constructed then. In terms of word embedding operation, a text classification model of MOOCs comment data based on Chinese character-level convolutional neural network is proposed to avoid the pre-training process of word vector. The experiments show that our model could improve the classification accuracy comparing with traditional methods.
Application of Iris Images in Racial Classifications Based on Dilate Convolution and Residual Network

The iris is an area of the approximation of the ring between the pupil and the sclera of the human eye, which contains a large number of texture features. Due to the stability and specificity of the iris texture features, the iris can solve problems such as racial classification. The existing method of racial classification by iris image mainly adopts manual extraction of features and classification research, which has certain limitations. We proposed a method based on improved residual network for iris image race classification. In order to more fully extract the iris image features, we divide the network into two parts. We take apart in the first part of the network to the channel, for each channel for unused convolution kernels is utilized to extract features, and then connect the second part the back-end network residual, need special pointed out that in order to increase the receptive field. We use dilate convolution in each convolution layer, and we also use CAIAS and UBIRIS public data sets to verify the effectiveness of our method, the classification accuracy is 96.71%, and F1-score is 0.97. It is a good way to realize the basic classification of species and subspecies, improves the precision and feasibility of racial classifications, makes the racial classification theory more perfect.
The Effect of Curcumin on Cardiovascular Health in Obese Men

OF DISSERTATION THE EFFECT OF CURCUMIN ON CARDIOVASCULAR HEALTH IN OBESE MEN Cardiovascular disease is the leading cause of death in the United States, and conventional treatment often emphasizes pharmaceutical approaches. Research has recently started exploring non-pharmaceutical approaches, including nutritional interventions. This research study was conducted to test the effectiveness of a novel nutritional approach, curcumin, on the improvement of cardiovascular health in young, obese males (BMI≥30 kg/m2). This study included 22 men, matched based on BMI and randomly assigned to the intervention (n=11) or placebo group (n=11). The intervention consisted of 12 weeks of curcumin supplementation (1.0 g/day) with fenugreek added to enhance the curcumin bioavailability; the placebo consisted of 12 weeks of equal parts fenugreek to that found in the intervention. To determine cardiovascular improvements, arterial stiffness via gold-standard carotid-femoral pulse wave velocity (cfPWV), endothelial dysfunction via reactive hyperemia index (RHI), and inflammation via plasma cytokine concentrations were measured. There were no overall differences in cfPWV (p=0.428) or RHI (p=0.951) between groups following the 12 weeks of intervention. However, some individuals did respond to the curcumin treatment with reductions in cfPWV, while others did not. Subjects who did respond to the curcumin treatment (n=6) entered the study with higher baseline values of cfPWV than those that did not respond (n=5) (6.81 m/s v. 5.84 m/s, p = 0.045). This suggests a potential role for curcumin to improve arterial stiffness in individuals with stiffer arteries at baseline. A possible mechanism to explain the difference in responsiveness is a trending increase in IL-13 (p=0.052), an anti-inflammatory cytokine that has been associated with amelioration of collagen content in the arteries. Also, 12 weeks of curcumin intervention resulted in reductions in brachial pulse pressure (p<0.05), a surrogate marker of arterial stiffness. This change in brachial pulse pressure in the curcumin group could be explained by an increased trend in anti-inflammatory cytokine IL-10 (p=0.071), but further studies are required to confirm this finding. Based on the findings of this study, curcumin might serve as a non-pharmaceutical intervention to improve vascular health in young obese men, especially when arteries are stiffer than age-matched counterparts.
Data Mining for Speed Bump Detection from Car Wheels and GPS Sensors using Random Forest

Wireless Sensor Network is several sensor nodes that form a network capable of communicating with each other wirelessly. Data mining is useful for finding a pattern from a large amount of data, for driving condition monitoring can be obtained through Controller Area Network (CAN)-bus data. Vehicle sensors for this research provide signal data, including wheel speed and GPS data. Random Forest is used for this project because of it easy to use and understand. Random Forest Classification is one of the popular methods of data mining. Sensors, applications, and users who want access the vehicle information are integrated to communicate with each other using Mobius IoT platform. The accuracy of Random Forest Classifier obtained through this research is 80.9%.
Leaky-Wave Antenna for a Military Vehicle-to-Satellite Link

In this paper, a CRLH (Composite Right and Left Handed) leaky-wave antenna is proposed for V2X communication. The unit cell is composed of series chip capacitors and via for metamaterial properties. Also, the ground length is varied to attach on the automobiles. The size of the unit cell is 20 × 40 mm2 and the EM Simulation was used to see the results.
An Analysis of the Effects of Physical Abilities on RTS Game Outcome Using Machine Learning Approach

Physical ability in games represents how many actions or multitasks a gamer can do or how fast the gamer can react. In this paper, we characterize physical ability features from Starcraft II data. The proposed win prediction model based on machine learning algorithms evaluates how much features affect a game outcome. Even if features are used only related to physical ability, prediction result shows higher accuracy on a small scale than one with score-related features. Finally, we analyze those features and conclude that although matchmaking rating(MMR) plays primary role to lead a game win, actions per minute and others are also crucial factor to win.
Deployment and Evaluation of Azalea multi-kernel for manycore

In a manycore environment, the monolithic kernel has problems in terms of scalability. To solve this problem, we propose an Azalea multi-kernel. The Azalea multi-kernel takes all the advantages of multi-kernel and unikernel. First, intensive I / O offloads to the full-weight kernel to improve the locality of the kernel service. Second, application performance is enhanced by removing kernel noise. In this paper, we describe the characteristics of the Azalea multi-kernel and demonstrate its feasibility in terms of performance and scalability with existing Linux through the experiment of deploying a full-weight kernel(Linux) and three unikernels(azalea unikernel) in a single node.
The fusion analysis between tool failure mechanism and process signal

The cutting tool condition during machining is closely related to the accuracy and roughness of the workpiece. The realization of the important aspects in intelligent manufacturing, such as perception, analysis and decision-making relies on the condition monitoring technology. As an important concern during processing, a number of studies are being done by many researchers for tool reliability based on condition monitoring technology. This paper will review the fusion analysis between tool failure mechanism and process signal. The methods and objects in this area will be teased out. And an up-to-date comprehensive survey of the cutting process signals, failure mechanism analysis for signal selection and multi-sensor information fusion will be provided. Finally, the future challenges and trends will also be presented.
Speech Emotion Recognition Based on Residual Neural Network with Different Classifiers

In this paper, a framework based on residual neural network with different classifiers is proposed for speech emotion recognition. We firstly train residual neural network to extract robust features, then replace origin fully connection layer with classifiers including SVM, GMM and KNN to observe their performance. The corpus applied in this work is EMODB. Finally we find ResNet with SVM outperforms other proposed architectures with an average accuracy of 0.900. or Math in Paper Title or Abstract.
ISP Network Bandwidth Management: Using Blockchain and SDN

In general shared internet service, the ISP shares a static bandwidth resource pool to multiple customers in the same neighborhood area. The users’ internet speed wavers when the number of customers accessing the same resource is increasing. The issue becomes problematic during peak hours. Many customers are expected to use the limited bandwidth simultaneously, thus deteriorating the overall user experiences. The ISP is subject to blame when such incidents happen; the customers claim the ISP breaks the Service Level Agreement by providing lower internet speed than the one the ISP marketed. It is a dilemma for the ISP, increasing the bandwidth resource pool may solve the problem. However, when the peak hours period is over, the additional bandwidth becomes useless, thereby generating the over-allocation problem. In this paper, we propose the use of blockchain and SDN to provide agile network bandwidth management for the ISP in the form of three use cases: Bandwidth on Demand, Selling Bandwidth, and Real-Time Pricing. We argue that using these proposed use cases, the ISP can serve the customer fairly, while also generating more appealing economics to compete in the modern market.
Vehicle Interaction Engine Adaptive to Driver Characteristics and Driving Situations

In the previous research, we have extracted user requirements from the literature survey and generated system requirements for the driver-adaptive vehicle interaction system (DAVIS). And as a following research, we also have proposed the system architecture of DAVIS and explained the role of the composing modules. As a next step to these earlier studies, we implemented the core function of DAVIS, the vehicle interaction engine which provides the multimodal interaction mechanism adaptive to driver characteristics and driving situation. Regarding the sensory and cognitive characteristics of driver groups, our vehicle interaction engine sets the configuration of in-vehicle information devices to make the information easier to be sensed and recognized by the driver. Through these configured devices the vehicle interaction engine provides warning or guidance information according to the driving situations which the driver gets difficulty to cope with. We implemented the system and tested in vehicle simulator.
Deposited structures of reduced graphene oxide onto glass substrates influenced by solvents and cleaning reagents

In this paper, a study on the deposition formation of reduced graphene oxide (rGO) via drop casting onto glass substrates were carried out. Two different solvents (deionised water (DIW) and N-Methyl-2-Pyrrolidone (NMP)) and cleaning reagents (RCA-Standard Clean-1 (RCA-1) and isopropyl alcohol (IPA)) were used for the investigation. The deposition structures were analysed by using high-power and scanning electron microscopes. Ultraviolet-visible (UV-Vis) spectroscopy was used to validate the authenticity of the as-prepared rGO. Results indicated that the as-prepared rGO from DIW was more suitable to be deposited on the glass substrate cleaned with IPA compared to RCA-1, yielding better uniformity and a high density of rGO coverage. Meanwhile, the rGO suspension prepared from NMP and cleaned with RCA-1 was more suitable to be deposited on the glass substrate. The combination of solvents and cleaning reagents affected the glass substrates surfaces where a different in deposition structured were observed. Furthermore, a concentration of 1 mg/mL was considered as a very high concentration to prepare the rGO colloidal suspension where such a concentration results in a poor exfoliation.
An Iris based Smart System for Stress Identification

The critical stress problem is a crucial issue that needs considerations and requires a solution. A number of methods are used to identify and control the stress which includes different counseling programs and medication. But the diagnosis and identification of the stress and its levels is an important issue which does not have an on-time and accurate solution. Therefore, a non-invasive stress identification system is required that can identify stress and it's level. In this work, we have proposed and developed a non-invasive smart system for stress identification (SSSI). The SSSI takes human iris image and applies machine learning techniques to identify the level of stress based on the iridology map. While testing with 50 subjects having stress, the results confirm that the SSSI identifies the stress with an accuracy of 98%.
Design and Modeling of a 28/38/60/70/80 GHz Antenna for Fifth Generation (5G) Mobile and Millimeter Wave (mmW) Applications

This paper presents design of a 5th generation (5G) array antenna for future 28 GHz, 38 GHz and 60 GHz mobile applications. The individual elements of the array antenna consists of unique shape patches printed on top and bottom sides of a Rogers RT5880 substrate, having relative permittivity and thickness of 2.2 and 0.8 mm, respectively. The size of the single element of the 5G array antenna is 0.8λxλ, where λ is free-space wavelength at 60 GHz. With an interelement spacing of 0. 64λ the overall size of the proposed 1x8 element array is 0.8λx5λ. The antenna radiates in a wider frequency band (21.33-90 GHz), which covers the three 5G frequency bands of 28 GHz, 38 GHz and 60 GHz as well as the millimeter wave (mmW) bands of 70 GHz and 80 GHz. The 28 GHz, 38 GHz and 60 GHz (57–64 GHz), bands are recommended for future high-speed 5G applications. The antenna has decent gains (>13 dB) in the 5G bands and can be used in 5G mobile handsets and ultra-high speed wireless communications. The antenna also gives a decent gain (10.45~13.54 dB) in the mmWave bands of 70 GHz and 80 GHz. The modeling and simulation of the proposed antenna are performed in CST Microwave Studio.