Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirements for future digital visitor flow management

Purpose The purpose of this paper is to examine visitor management in the German-Swiss border area of the Lake Constance region. Taking a customer perspective, it determines the requirements for an application with the ability to optimize personal mobility. Design/methodology/approach A quantitative study and a survey of focus groups were conducted to identify movement patterns of different types of visitors and their requirements concerning the development of a visitor management application. Findings Visitors want an application that provides real-time forecasts of issues such as traffic, parking and queues and, at the same time, enables them to create a personal activity schedule based on this information. Research limitations/implications Not every subsample reached a sufficient number of cases to yield representative results. Practical implications The results may lead to an optimization and management separation of mobility flows in the research area and be helpful to municipal planners, destination marketing organizations and visitors. Originality/value The German border cities of Konstanz, Radolfzell and Singen in the Lake Constance region need improved visitor management, mainly because of a high level of shopping tourism by Swiss visitors to Germany. In the Summer months, Lake Constance is also a popular destination for leisure tourists, which causes overtourism. For the first time, the results of this research presented here offer possible solutions, in particular by showing how a mobile application for visitors could defuse the situation.
A framework to automate time series data collection using mobile phone for deep learning training and deployment

Followed by area of machine learning and deep learning, gesture or pattern recognition then became one of the most active subject matter for deep learning. The problem is that data collection process in gesture recognition research is time consuming when it comes to labelling the data. Thus, a framework which facilitates a time series data collection process through dynamic labelling which is useful for deep learning training and deployment is proposed. The framework can be demonstrated through a simple mobile application with the framework implemented within it. First, the Android mobile application collects the time series data with embedded sensors in smartphone and sends the data off to one of the storage service, S3 which is handled by the AWS cloud. On the EC2 handled by AWS, an instance which acts as a web server with the Flask backend is going to receive a HTTP request from the mobile application with the aid of OKHTTPClient, which is a HTTP client that allows HTTP request transmission. After the web server receives the request, it proceeds to build and train the deep learning model based on the data stored in S3. The deep learning model that is going to be used in the project is LSTM model, which is a variant of RNN model. Finally, the web server is going to return the training output to the cloud storage which can be accessed through the mobile application.
Deciphering mitochondrial and molecular pathology in Type 2 Diabetes Mellitus

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.
Student-Assisted Teaching: A Guide to Faculty-Student Teamwork.

For faculty, faculty developers, and administrators wanting to make learning more student-centered, more effective, and more productive, here is an innovative handbook that provides a range of models for student-assisted partnerships. Each of the 31 models is supported by practical details and each focuses on four main aspects of a specific peer-assisted learning environment: implementation, evidence of effectiveness and learning benefits, analysis of time and cost expenditures, and suggestions for replication. Extensive appendices aid implementation by providing concrete examples of evaluation methods, training syllabi, teaching materials, and hiring documents.
Mechanisms of epidermal growth factor receptor signalling in primary rat hepatocytes

s: 1. Luo, Y. , White, PJ. , Dixon, CJ. , Boarder, MR., (2006). “Akt inhibitor A-443654 shows Akt is necessary for epidermal growth factor stimulation of hepatocyte proliferation. ” 75 th Anniversary Meeting Abstracts; British Pharmacological Society. 2. Luo, Y. , Hall, JF. , Dixon, CJ. , Taylor, E. , Boarder, MR., (2008). “Epidermal growth factor receptors in the hepatocyte nucleus. ” August 2008-Vol.22 Abstracts of the EPHAR 2008 Congress, Manchester, UK 3. Luo, Y. , Zhong, C. , Dixon, CJ. , Hall, JF. , Boarder, MR., (2008). “Endosomal signalling from internalised epidermal growth factor receptors in hepatocytes. ” August 2008-Vol.22 Abstracts of the EPHAR 2008 Congress, Manchester, UK. Mechanisms of Epidermal Growth Factor Receptor Signalling in Primary Rat Hepatocytes De Montfort University Yi Luo VI Table of contents ACKNOWLEDGEMENTS III ABSTRACT IV IV PUBLICATION ARISING FROM THIS THESIS V LIST OF FIGURES XIII LIST OF TABLES XVII LIST OF ABBREVIATIONS XVIII CHAPTER
Robust Multiple Model Adaptive Control for Dynamic Positioning of Quadrotor Helicopter System

The quadrotor control has been one of the benchmark control problems. It is considered as an under-actuated, multivariable and high nonlinear system due to its dynamics, having strong coupling between translation and angular motion and affected by external disturbances associated with flight environment. Therefore, there is a need to design a robust control that can keep up with sudden changes and find better tracking performance against modeling error and uncertainties. In this work, an adaptive state feedback control method denoted as Classical Multiple Model Adaptive Control (CMMAC) has been implemented. This method embodies in its structure a bank of filters. Kalman filter (KF) has been used where each filter has been designed for a specific value of an equilibrium point and set of controllers, which was provided by the LQ-servo design. Comparisons of the performance of a quadrotor system between control designs for single Kalman filter with CMMAC for the same value of uncertainty in terms of Root Mean Square Error (RMSE) have been presented. CMMAC meets better performance of tracking design for all variations; the performance of the controlled quadrotor has been improved for the linear and angular coordinates 100%, as compared to the performance when using one Kalman filter. Keywordsquadrotor, Kalman filter, Classical Multiple Model Adaptive control (CMMAC), LQ-servo control, tracking performance, performance indices. How to cite this article: S.M. Raafat, and Z.SH. Mahmoud, “Robust Multiple Model Adaptive Control for Dynamic Positioning of Quadrotor Helicopter System,” Engineering and Technology Journal, Vol. 36, Part A, No. 12, pp. 1249-1259, 2018.
Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art

This paper provides a brief survey of recent developments on the use of electroencephalogram (EEG) sensors for detecting mental fatigue (MF) in human operators during tasks involving human–machine interaction. This research topic has received much attention since there is a consensus among experts on the increasing relation between human failure and accidents in safety-critical tasks. MF is one of the most influential aspects leading to human failure and the most reliable way to assess it is using operator's physiological data, especially EEG. In the past few decades, hundreds of publications have explored the use of EEG alone or together with other objective and subjective measures for assessing MF, drowsiness, and tiredness in human operators. With recent improvements in data preprocessing, feature extraction, and classification algorithms, the monitoring and mitigation of MF in real time has become a reality. This trend is mainly due to the increasing use of machine learning techniques. This paper provides a comprehensive look at the current state of the art in the field of MF detection using EEG, identifying the currently used technique, algorithms, and methods and possible trends and promising areas for further research. The paper is concluded by suggesting a kernel partial least squares discrete-output linear regression based model as an all-around good option for an MF assessment system.
Mismatch receptive fields in mouse primary visual cortex

In primary visual cortex, a subset of neurons responds when a particular stimulus is encountered in a certain location in visual space. This activity can be modeled using a visual receptive field. In addition to visually driven activity, there are neurons in visual cortex that integrate visual and motor-related input to signal a mismatch between actual and predicted visual flow. Here we show that these mismatch neurons have receptive-fields and signal a local mismatch between actual and predicted visual flow in restricted regions of visual space. These mismatch receptive-fields are aligned to the retinotopic map of visual cortex and are similar in size to visual receptive-fields. Thus, neurons with mismatch receptive fields signal local deviations of actual visual flow from visual flow predicted based on self-motion and could therefore underlie the detection of objects moving relative to the visual flow caused by self-motion.
Machine Learning DDoS Detection Using Stochastic Gradient Boosting

— DDoS (Distributed Denial of service) attacks emerge as the most devastating attacks of all time for organizations and ISPs of all sizes. The increasing availability of DDoS-for-hire services and the proliferation of billions of Unsecured IoT devices and botnets contributed to a significant increase in DDoS attacks. These attacks continue to grow in magnitude, frequency, and sophistication. The legacy methods like signature-based detection and scrubbing are challenged, as attacks are growing smarter day by day and evading IDS. The next-generation security technologies also cannot keep pace with the scale of attacks targeting organizations. Even anomaly-based detection is suffering from many limitations with accuracy and false positives by demanding human intervention. This is our attempt to obviate manual analysis in anomaly-based DDoS detection by achieving perfect accuracy with zero misclassifications. In this paper, we demonstrated DDoS anomaly detection on the open CIC datasets using Stochastic Gradient Boosting (SGB) machine learning (ML) model. Using this ML model and by meticulously tuning hyperparameters, we achieved maximum accuracy and compared the results with other machine learning algorithms.
The impact of columnar file formats on SQL‐on‐hadoop engine performance: A study on ORC and Parquet

Columnar file formats provide an efficient way to store data to be queried by SQL‐on‐Hadoop engines. Related works consider the performance of processing engine and file format together, which makes it impossible to predict their individual impact. In this work, we propose an alternative approach: by executing each file format on the same processing engine, we compare the different file formats as well as their different parameter settings. We apply our strategy to two processing engines, Hive and SparkSQL, and evaluate the performance of two columnar file formats, ORC and Parquet. We use BigBench (TPCx‐BB), a standardized application‐level benchmark for Big Data scenarios. Our experiments confirm that the file format selection and its configuration significantly affect the overall performance. We show that ORC generally performs better on Hive, whereas Parquet achieves best performance with SparkSQL. Using ZLIB compression brings up to 60.2% improvement with ORC, while Parquet achieves up to 7% improvement with Snappy. Exceptions are the queries involving text processing, which do not benefit from using any compression.
Factors affecting voluntary nursing staff turnover in Mengo Hospital

The purpose of this study was to examine the relationship between perceived availability of opportunities for promotion, training, career progression, existing management style and voluntary staff turnover intention among the nurses working in Mengo Hospital. A quantitative, descriptive correlational design was used. Data collection was done using structured questionnaires. Full time staff nurses (N= 235) were surveyed. The findings revealed that all the four variables were significantly and negatively correlated to the intention to leave and predicted 16.8 percent of the variance in intention to leave scores. Management style was the strongest predictor of intent to leave (14.5%). Nurses perceiving their managers as participatory had lower intention to leave. To retain qualified personnel, hospital administrators should focus on participative management style and career development programs that address the needs of the staff and the hospital.
Determinants and Values of Willingness to Pay for Water Quality Improvement: Insights from Chia Lagoon, Malawi

Water resources in the Chia lagoon in Malawi experience a possible threat to sustainability. Communities are seeking alternatives to improve water quality in the lagoon. This study quantified the communities’ willingness-to-pay (WTP) and their influencing factors while using contingent valuation (CV) techniques. A wide range of data collection procedures, including focus group discussions, key informant interviews, field observation, and CV survey, were employed. A sample of 300 households was randomly selected. The CV results showed that 57.4% of the households were willing to pay. The monthly individual aggregate WTP amount ranged from MK696.83 (US$0.95) to MK81697 (US$111.38), and on average MK7870.45 (US$10.73), generating aggregate annual values ranging from MK6, 689,568 (US$9126.29) to MK784, 294,080 (US$1,069,978), and on average MK75,556,320 (US$103,078) (ceteris paribus). Logistic regression model demonstrated a significant (p < 0.01 or p < 0.05) relationship between demographic (gender, age, literacy level), social-economic (land ownership, main agriculture water source, and income), and institutional (civic education and social network, extension, institutional trust, household socio trust) factors and WTP. The findings from this study provide significant clues for further research and baseline information for local government and communities in the development of more effective and holistic approaches for improving water quality in natural ecosystems.
Anatomia da região periorbital

A careful study of the anatomy of the orbit is very important for dermatologists, even for those who do not perform major surgical procedures. This is due to the high complexity of the structures involved in the dermatological procedures performed in this region. A detailed knowledge of facial anatomy is what differentiates a qualified professional—whether in performing minimally invasive procedures (such as botulinum toxin and dermal fillings) or in conducting excisions of skin lesions—thereby avoiding complications and ensuring the best results, both aesthetically and correctively. The present review article focuses on the anatomy of the orbit and palpebral region and on the important structures related to the execution of dermatological procedures.
Molecular Communication With Anomalous Diffusion in Stochastic Nanonetworks

Molecular communication in nature can incorporate a large number of nano-things in nanonetworks as well as demonstrate how nano-things communicate. This paper presents molecular communication where transmit nanomachines deliver information molecules to a receive nanomachine over an anomalous diffusion channel. By considering a random molecule concentration in a space-time fractional diffusion channel, an analytical expression is derived for the first passage time (FPT) of the molecules. Then, the bit error rate of the <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>th nearest molecular communication with timing binary modulation is derived in terms of Fox’s <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-function. In the presence of interfering molecules, the mean and variance of the number of the arrived interfering molecules in a given time interval are presented. Using these statistics, a simple mitigation scheme for timing modulation is provided. The results in this paper provide the network performance on the error probability by averaging over a set of random distances between the communicating links as well as a set of random FPTs caused by the anomalous diffusion of molecules. This result will help in designing and developing molecular communication systems for various design purposes.
CNN-Based Intra-Prediction for Lossless HEVC

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNNs) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. Several contributions are proposed to improve neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques for lossless video coding applications.
Design, Control, and Optimization of Robots with Advanced Energy Regenerative Drive Systems

We investigate the control and optimization of robots with ultracapacitor based regenerative drive systems. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use ultracapacitors for energy storage. An electri­ cal interconnection known as the star configuration is considered for the regenerative drives that allows for direct electric energy redistribution among joints, and enables higher energy utilization efficiencies. A semi-active virtual control strategy is used to achieve control objectives. We find closed-form expressions for the optimal robot and actuator parameters (link lengths, gear ratios, etc. ) that maximize energy regeneration between any two times, given motion trajectories. In addition, we solve several tra jectory optimization problems for maximizing energy regeneration that admit closed-form solutions, given system parameters. Optimal solutions are shown to be global and unique. In addi­ tion, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. Numerical examples are provided in each case to demonstrate the results. For problems that don't admit analytical solutions, we formulate the general nonlinear optimal control problem, and solve it numerically, based on the direct collo­ cation method. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy and efficiency are evaluated. Exper­ imental results show that when following optimal trajectories, a reduction of about