Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model

Real-time quantification of the patient's consciousness level during anesthesia is an important issue to avoid intraoperative awareness and post-operative side effects. A depth-of-anesthesia (DoA) monitoring method called Bispectral Index (BIS) is generally used for this purpose. However, BIS is known to be inaccurate at the transitory state, and also shows a critical time delay in quantifying the patient's consciousness level. This paper introduces a novel method to reduce the response time in the quantification process. This thesis develops a new index called HDoA by analyzing EEG using Hidden Markov Model. The proposed approach is composed by two steps, training and testing. In the training step, two HMM, awakened and anesthetized model are learned based on each training set. In the testing step, by evaluating the probability of producing the testing EEG from two models respectively, the index HDoA is derived. Since the evaluation of DoA using HMM is training based method, it have better performance with more training process. Experiments show that HDoA has a high correlation with BIS at a steady state, and outperforms BIS in two ways: (1) shorter delay time in transition state, and (2) higher Fisher Score. The validity of HDoA has been tested by 8 real clinical data.
A GMM and SVM Combined Approach for Automatically Software Fault Localization

To improve the efficiency and accuracy of automatic fault localization. We propose an approach to direct fault localization by applying Gaussian Mixture Model (GMM) and Support Vector Machine (SVM), which are two mathematical models with excellent classification and prediction abilities. We first preprocess the training data using GMM-based clustering algorithm. Then the constant penalty factor of SVM is replaced with two adjustable ones. After that, we find out the mapping relationships between the coverage information and the execution result of each test case by virtue of the robust learning ability of modified SVM. An efficiency comparison between our technique and others on Siemens Suite is carried out afterwards. The experiment result indicates that our localization approach achieves a better accuracy in single and multiple faults localization without increasing testing cost.
Combined Research on Thermoelectricity to Enhance the Capacity of Renewable Energy

Using renewable energy is the trend of world energy development. The volatility of renewable energy is increasing along with its installed capacity magnifying, resulting in abandoned wind, light and other issues, impacting on renewable energy consumption seriously. In order to solve this problem, trading the abandoned wind and light and using them supply heat based on power market means under the existing scheduling system can effectively promote the elimination of renewable energy, reduce the consumption of fossil fuels such as coal, and protect the environment. The coordinated optimization of combined electro-thermal system containing renewable energy for heating with controllable electric heating devices is researched in this paper to improve the consumption of renewable energy. The specific research contents are as follows: Firstly, a method for decomposing the monthly trading power of the combined electro-thermal system is put forward based on the monthly trading of abandoned wind and light. Then, arranging the day-ahead schedule plan according to the decomposition of the monthly transaction volume. The electric heating device is taken as the main control object in the day-ahead dispatching, and the double-layer optimal dispatching and control model of the combined electro-thermal system is established. Finally, the online rolling optimization scheduling model is established based on the above researches of the grid was analyzed.
Service Mesh: Challenges, State of the Art, and Future Research Opportunities

While the technology development towards microservices can significantly improve the speed and agility of software service delivery, it also raises the operational complexity associated with modern applications. This has led to the emergence of Service Mesh, a promising approach to mitigate this situation by introducing a dedicated infrastructure layer over microservices without imposing modification on the service implementations. Aiming to inspire more practical research work in this exploited area, we in this paper present a comprehensive review on the state of the art of Service Mesh and discuss the related challenges and its adoption. Finally, we highlight the opportunities for future research in this subject.
Scientific Software Development: Qualitative background for a model

In spite of different scientific conferences and journals report software like research outcomes, til de moment, there are few antecedents about software life cycles for supporting scientific software development. As far as we have looked for, the software development methodologies in this domain are coarse grain ones, and there is no empirical research supporting them. In this paper, We propose a software lifecycle for the development of scientific software. To obtain this model we have made a fusion of two existing coarse grain proposals and, after that, We have conducted a case study research, on a bibliometric research project, which has allowed to get a refined software process for scientific software.
Caching Salon: From Classical to Learning-Based Approaches

As a key technology in content delivery, content caching can not only reduce the load on the origin server to save the traffic costs for content providers, but also can enhance users' Quality-of-Experience (QoE) by placing popular objects in close proximity. In reality, Content Delivery Network (CDN) providers always try to design their caching strategies carefully to improve the hit ratio of requests. With the development of learning-based methods and their great successes in many fields, researchers are also trying to explore learning-based caching approaches in recent years. In this paper, we first review the classical caching approaches, most of which are based on recency, frequency and content size. Then we summarize the recent learning-based methods in content caching field. Finally, we discuss the potential content caching strategies in the future, and point out some open issues. Through the studies in this article, we wish it can be used as a reference for interested parties.
Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval

This paper presents a simple modification to previous work on learning cross-lingual, grounded word representations from image-word pairs that, unlike previous work, is robust across different parts of speech, e.g., able to find the translation of the adjective 'social' relying only on image features associated with its translation candidates. Our method does not rely on black-box image search engines or any direct cross-lingual supervision. We evaluate our approach on English-German and English-Japanese word alignment, as well as on existing English-German bilingual dictionary induction datasets.
3-D Printed High-Gain Wideband Waveguide Fed Horn Antenna Arrays for Millimeter-Wave Applications

3-D printed air-filled waveguide fed horn antenna arrays are investigated in the Ka-band. Pyramidal E-plane horn antennas with a small size are introduced as the radiating element to improve the bandwidth and radiation characteristics of the array. Commercial direct metal laser sintering technology is applied to print the antenna structure in a whole piece. Excellent operating performance including wide impedance bandwidths of 25.8% and 23.8% for $\vert \text{S}_{11}\vert < -10$ dB, gains up to 27.5 and 33.8 dBi with a variation of less than 1.5 dB over the operating band and radiation efficiencies of around 90% and 80% is obtained experimentally by two fabricated prototypes consisting of 64 and 256 radiating elements, respectively. Stable radiation patterns with low cross polarization are confirmed as well. The array designs and results obtained in this paper verify the feasibility of utilizing advanced a 3-D printing technique to realize millimeter-wave large-size antenna arrays with complicated multilayered configurations, which is of value for various wideband millimeter-wave wireless applications.
Image Shaking Calibration Algorithm for The Vehicle Image-based Surveillance System

In this paper, we proposed an image shaking calibration algorithm for the vehicle image based surveillance system. The proposed algorithm uses Vertical Edge Detection to select the confidence region of extracted image and Lucas-Canada Optical Flow technique to estimate motion. And we implemented the image correction algorithm for the selected region in the difference image between two frames in the digital image. In order to verify the efficiency of the proposed algorithm, the extracted video image was divided into $8\times 6$ format so as to have a size of 160 pixels in width × height in 1280 × 960 size. As to the binarization result, the white boundary frequency was obtained for each area in the state of the monochrome image. Thereafter, the simulation was performed by selecting the region as the confidence region when it is above a certain threshold value. As a result, a similar restoration angle of about 85% was obtained in the frame between the original image and the difference image.
Brake Detection for Electric Bicycles using Inertial Measurement Units

The traffic situation of today’s streets is changing through an increase in light electrical vehicular transportation systems. One of those systems is the electronically power assisted cycle (EPAC). As this vehicle group is regarded as strongly exposed, the improvement of cycling safety and the analysis of the rider-behavior is gaining importance. By integrating inertial measurement based brake detection systems, both goals are simultaneously addressed. The approach presented in this paper estimates the brake magnitude of electric bicycles by combining the state-of-the-art brake detection methods and a model-based longitudinal dynamics system to achieve not only a fast response time, but also a reliable detection of persistent braking situations. The experimental results show good accuracy and the influence of further pre-estimated parameters is evaluated through a sensitivity analysis.
NSC Data Detection Scheme in NR-based Communications System

The 3rd Generation Partnership Project (3GPP) has recently developed new radio (NR) for the 5G introduction and commercialization. The requirements of the NR service are significantly higher than those of existing 4G. The maximum transmission rate of the downlink is 20Gbps, which is 20 times higher than that of the existing 4G. In addition, communications should be performed with an extremely low error probability within a time delay of several to several tens of ms. In this paper, 256QAM modulation scheme and low density parity check (LDPC) codes are applied to satisfy 5G requirements of high data rate and high reliability. In addition, we propose a new data detection technique called novel suppression and cancellation (NSC) which combines IRC and SIC, a data detection technique. Simulation results show that the proposed scheme improves the data rate and reliability compared to the existing scheme.
The Riemann Pump: a Technique For Carrier-Aggregation Radio-Frequency Transceivers

The Riemann Pump is an integrated CMOS wideband Arbitrary Waveform Generator (AWG) for Carrier Aggregation. It is designed to target the sub-6 GHz 5G standard and is implemented in 65nm CMOS technology from TSMC. It is based on a piecewise linear approximation of a signal, using a dedicated algorithm. Its principle is about integrating constant current steps into a capacitive load. The ability to generate multicarrier signal is demonstrated with measurements of 5G schemes with SNDR on multi-carrier signal confirming the theory and to convert very large bandwidths from analog-to-digital. EVM measurements are detailed in a large range of frequencies up to 3GHz. The Riemann Pump consumes less than 1mW for a very reduced die area and thus, is an excellent candidate for any Radio-Frequency Transceivers architecture.
Conditioning Circuit for Simultaneous Sensing and Actuation in Piezoelectric MEMS Resonators

This paper presents a conditioning circuit able to simultaneously actuate and measure the output of an integrated resonator with embedded aluminum nitride (AlN) layer. The basic idea is to use this active layer as actuation transducer and as sensor to detect the resonant frequency of a MEMS oscillator. In this context a suitable conditioning system has been developed and the working principle has been demonstrated with a MEMS cantilever fabricated in PiezoMUMPs technology, used as a mass sensor. The sensor was characterized by means of known masses of Al2O3 nano-particles, deposited at the free end of the beam. The proposed architecture has been studied and modeled, and a prototype of the MEMS has been characterized demonstrating the validity of the proposed technique.
Use of Bayesian Network Characteristics to Link Project Management Maturity and Risk of Project Overcost

The project management field has the imperative to increase the project probability of success. Experts have developed several project management maturity models to assets and improve the project outcome. However, the current literature lacks of models allowing correlating the measured maturity and the expected probability of success. This paper uses the characteristics of Bayesian networks to formalize experts' knowledge and to extract knowledge from a project overcost database. It develops a method to estimate the impact of project management maturity on the risk of project overcost. A general framework is presented. An industrial case is used to illustrate the application of the method.
Cross-Domain Recommendation Method in Tourism

In recent years, online travel service has become increasingly popular and effective. Its development has become a hotspot of tourism. Considering the particularity of tourism products, this paper aimed at hotel recommendation service. However, the sparsity of tourism data is an unavoidable problem in recommender systems. In order to solve this problem, we introduce the data from air ticket area, and then builds the cross-domain user profile. In this way, hotel recommendation problem turns to become user profile analysis problem. In addition, we proposed a recommendation method based on transformation matrix, on the one hand, it can solve the cold start problem, on the other hand, users with unsatisfactory results based on user profile recommendation method can be improved. Experiments show that the recommendation method based on cross-domain user portrait is much better than the traditional recommendation method based on popularity or price. Finally, we prove that the recommendation method based on transformation matrix can effectively improve the accuracy of the recommendation method based on user portrait.
A Trend-based Stock Index Forecasting Model with Gated Recurrent Neural Network

Prediction of stock index is seen as a challenging task of financial time series prediction. In this study, we proposed a movement trend-based data preparation method, preprocessing the trend indicator in two steps, and modeled stock index using Gated Recurrent Unit (GRU). In the two-stage preprocessing, the trend indicators were extracted from five aspects and then were discretized based on the dynamic relationship. And three Recurrent Neural Networks were applied to forecast financial time series. Compared with random tri-prediction method, our model improved the stock index movement trend prediction from 33% to 68%.
A Framework for Development of Serious Game for Learning Musical Instrument

Serious Game (SG) has been used in many area of education and training. It has been used to help teacher or educator to teach students so that they can increase their ability to understand the subject using a realistic simulations of real-life situations. On the other hand, music has become a big element in the world of entertainment. Thus, this paper propose a new framework for the software developer to create new Serious Game for learning musical instrument. This paper discusses the notion of a SG schema and how pre-existing SG for musical practice can be used to provide design guidelines by developing a framework. Thus, SG developer for musical instrument game can easily map the learning aspects, describe software design, and describe ways to control them with new interfaces for musical expression. The framework is developed from analysis of several previous researches and associated to the game design process component of musical instrument. The framework has been implemented using three case studies of musical instrument (saron, kendhang, and angklung). Each components of the framework are mapped to the new case studies which produce three software game applications. Each application has been evaluated by 20 users. The result shows that the framework has been able to effectively and efficiently help game developer to create musical instrument game.
Marrying women 15 years younger maximized men's evolutionary fitness in historical Sami

Most men marry younger women. This has been attributed to men selecting young women due to their high reproductive value and women preferring older men due to their wealth and high social status. Such mate preferences have been suggested to be adaptive, but despite a flourishing number of studies on the mate selection patterns themselves, little is still known of their actual fitness consequences. We examined how the age difference between spouses who married only once affected their lifetime reproductive success in historical monogamous Sami populations. We found that men maximized their fitness by marrying women approximately 15 years younger and vice versa. However, most couples failed to marry optimally. Only 10% of marriages fell within the optimal parental age difference, suggesting that cultural and ecological constraints for maximizing fitness were considerable. Those who succeeded in marrying optimally were the most preferred partners: young women and old men. Our findings indicate that, in Sami, parental age difference was under natural and sexual selection, as suggested by evolutionary theory.
Ethnicity, Gender, and Global Risk Indicators in the Lives of Status Offenders Coming to the Attention of the Juvenile Court

The present study examined the risks and needs of status offenders in comparison to an at-large juvenile court sample, with specific attention paid to factors related to gender and ethnicity. Status offenders displayed significantly higher risk scores than the comparison sample in domains associated with both family and parenting concerns and educational concerns. In addition, females displayed higher risk levels than males and White youth displayed significantly higher risk levels than minority youth across a wide variety of risk domains. Further analyses were conducted regarding the participation of African American males and females in a program designed as an alternative to detention. For both groups, successful program completion was accompanied by little or no further court involvement, whereas failure to complete the program was related to a variety of negative outcomes. However, African American females and males successfully completing programrequirements displayed different risks and needs at the outset of their participation.
A L1-constrained DNN Acceleration Algorithm with ADMM

When facing high dimension and low sample sized data, such as image classification problem using manually labeled realistic images, deep learning network suffers from overfitting and may fail to fit additional data or predict future observations. L1-constrained Deep Neural Network (DNN) is widely used to avoid overfitting problem and has achieved breakthroughs in applications. However, the non-linear and non-convex characters of L1-constrained DNN are obstacles to rapidly converge to global optimum. In this paper, a non-gradient-based approach, called B-ADMM, is proposed to train L1-constrained DNN. It decomposes the objective function into a bunch of mini-steps and updates parameters alternatively. To avoid local minima, B-ADMM solves each sub-step seperately and achieves global optimal solution. Experiments show that the convergence of B-ADMM is faster than SGD, Adam and RMSprop while accuracy maintains to be the same. With a restrained L1-regularizer, the network also achieves a sparse architecture.
LoRa-based Visual Monitoring Scheme for Agriculture IoT

LoRa is a low-power wide-range wireless networking technology suitable for low-rate long-range applications in the Internet of Things (IoT). For example in the agriculture industry, LoRa-based environmental sensing system enables farmers to remotely monitor the status of a large farm in near real-time. However, there had been only a few explorations to transfer multimedia data such as images or video using LoRa because of its low data rate and restricted bandwidth. To this end, we introduce a novel system to transmit continuous images taken from a camera on a static environment through LoRa. The key challenge is to reduce the amount of transmitted data while preserving the image quality and the quality of service delivered to the application. We develop a technique that splits image to grid patches, and transmits only the modified area of an image based on their dissimilarity measure. We implement and evaluate our scheme on a real LoRa device to show its performance and image quality.
The Physical Channels Alternative Constructions for Wireless Systems and Network

A variant of code-time-division multiple access is considered. The emitted radio signal is a structured relatively short code constructions that performing a direct extension of the carrier frequency spectrum, immediately consecutive and modulating it. Herewith, the uniform amplitude envelope (modulus of the complex envelope) of the resultant group signal emitted into the radio channel is retained, if the influence of low-pass filters that limit out-of-band emissions is not included. The structure of the codes allows to implement modulation methods of the carrier frequency, similar to QAM, but more efficient. All structures include fragments of arbitrarily long code that couple them and is designed for solving a wide range of tasks related to the temporal structuring of the communication channel, synchronization, processing of multipath signals in the Rake-receiver, positioning, etc.