Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of overweight on the risk of developing common chronic diseases during a 10-year period.

BACKGROUND Overweight adults are at an increased risk of developing numerous chronic diseases. METHODS Ten-year follow-up (1986-1996) of middle-aged women in the Nurses' Health Study and men in the Health Professionals Follow-up Study to assess the health risks associated with overweight. RESULTS The risk of developing diabetes, gallstones, hypertension, heart disease, and stroke increased with severity of overweight among both women and men. Compared with their same-sex peers with a body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) between 18.5 and 24.9, those with BMI of 35.0 or more were approximately 20 times more likely to develop diabetes (relative risk [RR], 17.0; 95% confidence interval [CI], 14.2-20.5 for women; RR, 23.4; 95% CI, 19.4-33.2 for men). Women who were overweight but not obese (ie, BMI between 25.0 and 29.9) were also significantly more likely than their leaner peers to develop gallstones (RR, 1.9), hypertension (RR, 1.7), high cholesterol level (RR, 1.1), and heart disease (RR, 1.4). The results were similar in men. CONCLUSIONS During 10 years of follow-up, the incidence of diabetes, gallstones, hypertension, heart disease, colon cancer, and stroke (men only) increased with degree of overweight in both men and women. Adults who were overweight but not obese (ie, 25.0 < or = BMI < or = 29.9) were at significantly increased risk of developing numerous health conditions. Moreover, the dose-response relationship between BMI and the risk of developing chronic diseases was evident even among adults in the upper half of the healthy weight range (ie, BMI of 22.0-24.9), suggesting that adults should try to maintain a BMI between 18.5 and 21.9 to minimize their risk of disease.
Simulation and Optimization Performance of GaAs Homojunction Solar Cell with BSF Layer

our work is devoted to the study of the optimization of a GaAs solar cell (homojunction) of n+pp+ type of 1cm2 surface. It consists, mainly, of an emitting layer, a base of p-type and a heavily doped p+ type BSF layer and a double antireflection layer (MgF2/ZnS). Among the factors that influence the energy efficiency of solar cells are the technological parameters (Doping concentration, thickness) and for this purpose we try to find in this work the influence of these technological parameters (doping, thickness) of the solar cell on the performance of this cell. The optimum configuration of the device shows an efficiency of 26.80% under the AM1.5G spectrum and one sun.
DDoS detection and discrimination from flash events: a compendious review

Although there has been an immense advancement in the robustness of IT infrastructure, the organizations still face downtimes, lasting for short periods and even continuing for days. This could be due to malicious requests, deliberately sent to disrupt the services or the volume of legitimate users trying to access the website. Detection of these network traffic anomalies and then categorizing them as DDoS Attack or Flash event, is a challenging task in the front of network security researchers. This paper reviews and summarizes the details and scenarios of these anomalies. It highlights the various features, parameters and metrics used in research till date to detect these anomalies and distinguish the legitimate request from the malicious one. The intent of paper is to guide researchers in selecting efficient parameters in order to obtain accurate results with low false positives. It highlights the list of characteristics to discriminate the two traffic patterns has been proposed for better understanding of the problem. Thus, the paper is expected to help academicians and researchers find effective solution for the above-mentioned anomalous behavior of the network traffic.
The Design of the POSTGRES Storage System

Abstract : This paper presents the design of the storage system for the POSTGRES data base system under construction at Berkeley. It is novel in several ways. First, the storage manager supports transaction management but does so without using a conventional write ahead log (WAL). In fact, there is no code to run at recovery time, and consequently recovery from crashes is essentially instantaneous. Second, the storage manager allows a user to optionally keep the entire past history of data base objects by closely integrating an archival storage system to which historical records are spooled. Lastly, the storage manager is consciously constructed as a collection of asynchronous processes. Hence, a large monolithic body of code is avoided and opportunities for parallelism can be exploited. The paper concludes with a analysis of the storage system which suggests that it is performance competitive with WAL systems in many situations.
Understanding, Measuring, and Enhancing Individual Creative Problem-Solving Efforts

Accumulated theories and research findings about the nature of the creative problem-solving (CPS) process, the cognitive and personality traits of persons who do CPS well, the measurement and evaluation of CPS, and the enhancement of CPS skills by training are reviewed with the aim of increasing understanding of and ability to measure and enhance individual CPS efforts. Given this review and analysis, a complete CPS process is understood to entail considerable convergent and divergent thought in continuing alternation. Other conclusions about the nature of CPS and the persons who do it well are set forth and discussed, and these conclusions are incorporated into a "matching theory" about CPS—which observes that creatively solvable problems vary in complexity, knowledge needs, and amount of divergent and convergent thought needed—and it predicts that problem solvers with different preferences and abilities, knowledge, and work plans will best match the needs of particular problems. Limitations in current c.
A Simplified Deep Residual Network for Citywide Crowd Flows Prediction

Crowd flows prediction is an important problem of urban computing. The existing best-known method adopts deep residual networks to model spatio-temporal properties and often achieves good prediction performance. However, since three separated network structures are used to model the properties, the time cost is often expensive for the best-known method. In this paper, we propose an improved method to reduce the running time of the best-known method by simplifying its architecture. Compared with the best-known method, the training time and predicting time of our method can be reduced dramatically. Moreover, the improved method can achieve similar prediction performance with the best-known method. Extensive experiments on the real-world datasets were conducted to show the efficiency of our proposed method.
Feature Extraction and Classification of Movie Reviews

Sentiment analysis identifies a user’s attitude towards a service, a topic or an event and it is very useful for companies that receive many written reviews of their services. We investigate the effect of feature extraction techniques on supervised machine learning classifiers using four different performance metrics using a publicly available movie review dataset. Our objective is to explore different classification algorithms as well as utilizing diverse feature extractors and compare outcomes and finally select the trio of feature extraction technique, classification algorithm and performance metric with the best result for the movie review classification use case.
Adolescent Filial Piety as a Moderator between Perceived Maternal Control and Mother-Adolescent Relationship Quality in Hong Kong.

This study examined the perceived parental styles of maternal warmth and control, as well as adolescent filial piety, in relation to parent–child relationship quality, in 308 Hong Kong Chinese adolescents. The three mother–child relationship qualities measured were perceived maternal support, conflicts, and relationship depth. Adolescents' attitudes toward filial piety explained unique variance in all three of these parent–child relational qualities, over the constructs of maternal warmth and control. In addition, the interaction of filial piety with maternal control explained unique variance in perceived maternal support and mother–child conflicts, but not relationship depth. Results established the potential unique importance of adolescent filial piety and confirmed our hypothesis that the more adolescents emphasize filial piety, the more parental control exerts a positive effect on mother–child relationship quality in Hong Kong families.
Energy Efficient Data Access in Mobile Ad Hoc Networks

In a mobile ad hoc network (MANET), mobile hosts basically run with battery and their battery capacity is limited. Data transmissions are one of the main factors of the power consumption of mobile hosts. Therefore energy efficient data access is one of the most important research issues in MANETs. In this paper, we introduce our recent work addressing issues of energy efficient data access in MANETs. We first present data access and replication methods that take into account the remaining amount of the battery of each host. We then present a message processing method for top-k queries in MANETs, which is effective to reduce the amount of data volume transmitted.
Le travail émotionnel du client: La structure séquentielle des émotions dans les usages problématiques d’un serveur vocal

Recent developments in the service sector have led sociologists to suggest that customers are increasingly encouraged to ‘work’ for service organizations. This article develops the hypothesis that the clients who work in this service sector may experience an emotional dimension to their work. In support of this hypothesis, a revision of the notion of ‘emotion work’ is proposed on the basis of a study on the problematic usages of a phone interface service supplied by a telecommunications operator. The study made use of a group of genuine calls that were transcribed and coded; utterances were described objectively regarding pitch and intensity; finally, a statistical method for detecting sequential patterns was applied. The results suggest that clients respond emotionally to usability problems, and that their emotions unfold according to a recurrent sequential structure. Goffman’s ‘remedial interchange’ provides the point of reference for the interpretation, which leads to the suggestion that the emotional patterns revealed are of a social origin, though not socially situated.
Time Series Forecasting Using Sequence-to-Sequence Deep Learning Framework

Time series forecasting has been regarded as a key research problem in various fields. such as financial forecasting, traffic flow forecasting, medical monitoring, intrusion detection, anomaly detection, and air quality forecasting etc. In this paper, we propose a sequence-to-sequence deep learning framework for multivariate time series forecasting, which addresses the dynamic, spatial-temporal and nonlinear characteristics of multivariate time series data by LSTM based encoder-decoder architecture. Through the air quality multivariate time series forecasting experiments, we show that the proposed model has better forecasting performance than classic shallow learning and baseline deep learning models. And the predicted PM2.5 value can be well matched with the ground truth value under single timestep and multi-timestep forward forecasting conditions. The experiment results show that our model is capable of dealing with multivariate time series forecasting with satisfied accuracy.
Toward More User-Centric OD

This article argues for a major shift in focus from the strong management orientation of organization development (OD) to a more “user-centric” OD, one that seeks to mobilize and privilege change on behalf of the consumers or users of an organization’s product or service, involving them at every stage of the design process, from problem diagnosis to solution generation and implementation. This reconceptualization of OD draws its inspiration from the rapidly expanding field of experience-based design (EBD), a subfield of the design sciences whose distinctive features are direct user participation in the design process and a focus on designing experiences as opposed to systems or processes. The article reports on an original EBD intervention methodology designed and tested by the authors and colleagues in a cancer clinic within the National Health Service, which following successful “proof of concept,” offers OD some promising new directions for the future.
A Robust Second-Order Sliding Mode Control for Single-Phase Photovoltaic Grid-Connected Voltage Source Inverter

In this paper, a super-twisting algorithm (STA) second-order sliding mode control (SOSMC) is proposed for single-phase photovoltaic grid-connected voltage source inverters. The SOSMC approach is aimed to inject sinusoidal current to grid with low total harmonic distortion (THD), strong robustness to parameter variations, and fast dynamic response to solar irradiance changes. As the discontinuous sign function in the traditional sliding mode control (SMC) is replaced by the continuous super-twisting function, the chattering problem is eliminated. Furthermore, a robust sliding mode differentiator (SMD) is proposed to acquire the derivative of current reference which is difficult to obtain in practice. The stability of the system under the proposed method is proved through the Lyapunov theory. The experimental results validate that the proposed method has satisfactory performance in both dynamic and steady-state conditions, as well as disturbance rejection.
Item-Based Collaborative Filtering in Movie Recommendation in Real time

Collaborative filtering is one of the most effective and adequate technique used in recommendation. The fundamental aim of the recommendation is to provide prediction of the different items in which a user would be interested in based on their preferences. Recommendation systems based on collaborative filtering techniques are able to provide approximately accurate prediction when there is enough data. User based collaborative filtering taechniques have been very powerful and success in the past to recommend the items based on user’s preferences. But, there are also some certain challenges such as scalability and sparsity of data which increases as the number of users and items increases. In a large website, it is difficult to find the interested information in a certain time. But the recommendation system filter out information and items that are best suitable for us. Although there are different recommendation approaches, yet collaborative filtering technique is very popular because of the effectiveness. In this work, movie recommender system has been described, which basically uses item-based technique of collaborative filtering to provide the recommendations of items, which is dynamic and will learn from the positive feedback.
How to Go Beyond the Frontiers of Haptic Interaction ?

This paper discusses the issue of using haptic devices with limited workspaces in large immersive virtual environments. In other words, we try to answer to the following question: how to go beyond the physical limitations of the force-feedback device and reach distant virtual objects that are located outside the mechanical workspace of the device? We describe in this paper two interaction techniques that were both designed to cope with this issue: the Bubble technique for translation motions, and the Haptic Hybrid Rotations for rotation motions. These two interaction techniques use a hybrid position/rate control of the objects. The force-feedback of the device is used to simulate the use of an elastic device when in the rate-control mode. These two techniques were evaluated in several experiments that showed both their efficient usability and positive acceptance during assembly or painting tasks.
Stock Market Prediction Using Machine Learning

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume.
Modeling Influenza Transmission Using Environmental Parameters

Influenza is an acute viral respiratory disease that has significant mortality, morbidity and economic burden worldwide. It infects approximately 5-15% of the world population, and causes 250,000 – 500,000 deaths each year. The role of environments on influenza is often drawn upon the latitude variability of influenza seasonality pattern. In regions with temperate climate, influenza epidemics exhibit clear seasonal pattern that peak during winter months, but it is not as evident in the tropics. Toward this end, we developed mathematical model and forecasting capabilities for influenza in regions characterized by warm climate – Hong Kong (China) and Maricopa County (Arizona, USA). The best model for Hong Kong uses Land Surface Temperature (LST), precipitation and relative humidity as its covariates. Whereas for Maricopa County, we found that weekly influenza cases can be best modelled using mean air temperature as its covariates. Our forecasts can further guides public health organizations in targeting influenza prevention and control measures such as vaccination. * Corresponding author.
Improved Direct Torque Control for Induction Motor in Electric Vehicle Application

This paper proposed an improved Direct Torque Controlled (DTC) scheme for driving induction motor (IM) for wide speed range. It proposes decoupling of the components to improve the dynamics and transient response. A proportional integral (PI) controller is used to control the torque while flux is controlled by a derivative controller. A wide variation in speed range is obtained for the application of electric vehicles. The studied model is simulated in the MTALAB to implement the proposed DTC scheme to analyze the effectiveness and improved behavior of the system. The proposed DTC method is validated in different operating conditions of the electric vehicle and compared with the conventional DTC control to justify the improved transient and dynamic behavior of the IM.
ACC: Anonymous Cooperative Caching in Wireless Ad Hoc Networks

Cooperative caching is an effective approach to share cache copies among network nodes and then reduce data access cost in ad hoc networks. Since cached data may be privacy sensitive, privacy preservation should be considered. In this paper, we study this problem by achieving anonymity in cooperative caching. Our anonymous caching protocol borrows idea from anonymous routing protocols. Data query and access are realized based on anonymous hop-by-hop broadcasting. Pseudonyms are used in communications so as to protect the real identity. Security analysis confirms that our protocol can achieve sender anonymity, receiver anonymity, and communication anonymity. We also conduct simulations to evaluate the effect of anonymity mechanism on system performance. Different variants of anonymous caching algorithm and non-anonymous algorithms are simulated for comparisons. The results show that, anonymity will cause additional message cost and time cost, but the cost is reasonable. To the best of our knowledge, this is the first work on anonymity in cooperative caching.
Multimodal Neural Machine Translation With Weakly Labeled Images

Machine translation refers to a fully automated process that translates a user’s input text into a target language. To improve the accuracy of machine translation, studies usually exploit not only the input text itself but also various background knowledge related to the text, such as visual information or prior knowledge. Herein, in this paper, we propose a multimodal neural machine translation system that uses both texts and their related images to translate Korean image captions into English. The data in the experiment is a set of unlabeled images only containing bilingual captions. To train the system with a supervised learning approach, we propose a weak-labeling method that selects a keyword from an image caption using feature selection methods. The keywords are used to roughly determine an image label. We also introduce an improved feature selection method using sentence clustering to select keywords that reflect the characteristics of the image captions more accurately. We found that our multimodal system achieves an improved performance compared to a text-only neural machine translation system (baseline). Furthermore, the additional images have positive impacts on addressing the issue of under-translation, where some words in a source sentence are falsely translated or not translated at all.
Removing Ambiguity Problem Based on Clustering in a Web Search

Detecting meaning phrases out of the search string is a challenge and need to be tackled through the existing or modified approaches. Word sense is the big breakthrough regarding meaningful information extraction from the user query. Another problem presented within the user query and requires attentionisambiguity. This problem causes highe rexecution time in result fetching during the processing of a query string. This paper proposed an efficient mechanism to detect and eliminate ambiguity and extract meaningful sentences from user query. To detect the ambiguity, clustering mechanism with most probable values (MPV) is used. Comparison of results is presented to prove the betterment in results. Results are presented in terms of execution time and classification accuracy.
Deep Learning Algorithm Applied to Daily Solar Irradiation Estimations

Deep learning is applied in many research areas, and in many of them remarkable outcomes are attained compared to conventional methods. There are quite a number of studies also in the estimation of solar irradiation. Estimation of solar irradiation is vitally important for the design of solar energy systems. In this work, multi-layer perceptron (MLPs) method of deep learning is used to develop an estimation method for calculating horizontal daily solar irradiation and the results are compared with classical approaches. The results indicate that deep learning with little inputs can be used to estimate daily solar irradiation with a decent accuracy that is comparable to classical approaches.
A Fully Integrated 5.8 GHz BiCMOS SiGe:C tunable active phase shifter for Beamforming

This paper presents the design and the implementation of a fully integrated active phase shifter for beamforming at 5.8 GHz in a 0.25 µm BiCMOS SiGe:C process. The proposed circuit is able to provide a 360° continuous phase shift range, allowing to control the radiation pattern of a phased antenna array. The required phase shift is synthesized using an Injection-Locked Oscillator (ILO) for fine tuning followed by an in-phase/quadrature (IQ) modulator for coarse tuning. The total current consumption of the circuit is 33.7 mA from a 2.5 V supply voltage. The core size including all the pads is 1.324*1.086mm2.