Miracle-dz/UniFAR-FTUs huggingface dataset:
A display module

The embodiment according to the invention provides a display module that can be used as a part of a display assembly. The display module comprises a box wherein a display matrix unit, a driver arrangement, a control unit, and a memory unit are located. The display matrix unit provides a front face of the box. The box has at least three edges, each of them comprising a mechanical lock part and a communication interface.
Comparative Study between Big Data Analysis Techniques in Intrusion Detection

Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed IDS that uses Apache Spark Structured Streaming and its Machine Learning library (MLlib) to detect intrusions in real-time. To demonstrate the efficacy and effectivity of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities. A decision tree algorithm is used to predict the nature of incoming data. For this task, the use of the MAWILab dataset as a data source will give better insights about the system capabilities against cyber-attacks. The experimental results showed a 99.95% accuracy and more than 55,175 events per second were processed by the proposed system on a small cluster.
Interference Control of LTE-LAA using Q-learning with HARQ

LTE-LAA (LTE-Licensed Assisted Access) is the LTE (Long Term Evolution) to use an unlicensed band. However, in addition to the currently used license band, to use the unlicensed band, interference control with Wi-Fi that also uses the unlicensed bandwidth is required. A technique called LBT (Listen Before Talk) is considered as a technique of LTE-LAA interference control. However, since the channel occupation time (TXOP) of LTE-LAA is larger than that of Wi-Fi, there is a problem that Wi-Fi channel access is hindered. As a solution to this problem, one way is to increase the chances of channel access to Wi-Fi by establishing a waiting time of Tmute in LTE-LAA to reduce TXOP of LTE-LAA and postpone transmission. In this paper we propose an interference control method for LTE-LAA using Q-learning, which is one of the reinforcement learning techniques. In the proposed method, we select TXOP and Tmute using Q-learning. In the proposed method, the number of ACKs and that of NACKs in HARQ (Hybrid Automatic Repeat Request) are used as reward. In LTE-A the user terminal generates ACK / NACK according to the received radio frame and transmits it to the BS (base station). BS retransmits according to the number of ACKs / NACKs. Learning values of TXOP, Tmute of the LTE-LAA BS can be obtained so as to increase the number of ACKs or NACKs. When the LTE-LAA BS learns to increase the number of ACKs, the throughput of LTE-LAA is controlled to be high and that of Wi-Fi is controlled to be low. On the other hand, when the LTE-LAA BS learns to increase the number of NACKs, the throughput of LTE-LAA is controlled to be low and that of Wi-Fi is controlled to be high. Simulation result shows the proposed method using LTE-LAA UE (User Equipment) and Wi-Fi UE.
METHOD OF DELIVERY AND DEVELOPMENTAL OUTCOME AT FIVE YEARS OF AGE

A controlled follow‐up study examined the impact of delivery method on developmental outcome of the child. The modes of delivery investigated were low forceps delivery (188 infants), midcavity forceps delivery (51 infants), forceps rotation with forceps delivery (57 infants), manual rotation with forceps delivery (67 infants), elective caesarean section (101 infants) and spontaneous delivery (control, 207 infants). Breech presentation (100 infants) was separately compared with the vertex presentation groups. Sample selection controlled for complications during pregnancy and low birthweight and was restricted to married English‐speaking mothers. The children were assessed at the age of five years on verbal and non‐verbal subtests of a standardized intelligence scale, tests of gross motor coordination, and auditory and visual tests. a full paediatric examination was also performed. Breech presentation children performed less well on tests of balance and fine motor coordination and on visual acuity and stereopsis testing than children who presented in the vertex position. No deleterious effect of delivery method was found. In the absence of other complicating events (like a poor antenatal history, prematurity, and a disorganized home environment) delivery complication constitutes an early risk factor which the growing child is able to overcome.
Strategies for the prevention of obesity in children

The prevalence of obesity is increasing at an unprecedented rate in the United Kingdom, in both adults and children. Obesity is a risk factor for many chronic diseases and may also lead to psychological disorders. The long-term successful treatment of obesity is a difficult goal to achieve. Preventing obesity, particularly in children, has become a public health objective. In this thesis, two strategies for the prevention of obesity in children are
Adaptive Motion Planning for 6-DOF Robots Based on Trigonometric Functions

 Abstract — Building an appropriate motion model is crucial for trajectory planning of robots and determines the operational quality directly. An adaptive acceleration and deceleration motion planning based on trigonometric functions for the end-effector of 6-DOF robots in Cartesian coordinate system is proposed in this paper. This method not only achieves the smooth translation motion and rotation motion by constructing a continuous jerk model, but also automatically adjusts the parameters of trigonometric functions according to the variable inputs and the kinematic constraints. The results of computer simulation show that this method is correct and effective to achieve the adaptive motion planning for linear trajectories.
Placement of Service Chain and Removal ofbottlenecks in Edge-Fog-Cloud computing

Data management nowadays is being focused a lot in many areas in the industry, the reason for this is attributed to the speed we are obtaining the data, the volume of it and the new technologies. So, when we transfer this data through the network, we obviously need the most efficient way to send and retrieve data in our network with less response time, also known as Edge-Fog-Cloud computing. In this work, a framework is implemented in such a way that it provides minimum latency to User devices at the Edge of the network based on the SLA agreement between a Service Provider and the Network Provider. It provides an algorithm for Service chain Placement and monitors the network for potential bottlenecks that may be created in future and removes it.
Timing Error Analysis in Digital-to-Analog Converter : Effects of Sampling Clock Jitter and Timing Skew(Glitch)

This paper describes two timing nonideality issues of Digital-to-Analog Converters (DACs); sampling clock jitter and clock skew effects. (i) A formula for the output error power due to sampling clock jitter is derived, and this has been validated by numerical simulation; spectrum characteristics of jitter-related noise are also examined. We have also found that when an analog lowpass filter follows the DAC and only the noise power inside the signal band is considered, increasing jitter and increasing input signal frequency degrade the DAC SNR. (ii) The clock timing skew inside the DAC causes glitch impulses. We try to characterize them by simulation and we have found the followings; as the input frequency increases, the effects of the glitch on the DAC SNR decrease. The effects of the glitch due to upper bits on the DAC SNR and SFDR are more significant than due to lower bits. Also glitch power is mainly located at the odd-multiple frequencies of the input signal.
An Examination of Distance Learning Faculty Satisfaction Levels and Self-Perceived Barriers

This action research project examined satisfaction levels and self-perceived barriers of distance learning faculty. It included methods of providing professional development opportunities for educators who teach online and hybrid courses. This solution-oriented investigation included problem identification, systematic data collection, reflection, analysis, data-driven action, and problem redefinition. The study focused on the factors that influence faculty satisfaction and dissatisfaction with distance learning. The research examined self-perceived barriers to faculty satisfaction and how distance learning could be improved. Various professional development options were offered for the participants as well as a discussion about the effects of these interventions. Selected key findings of the study indicated that there is a need for future research in the area of measuring and analyzing online faculty satisfaction levels and self-perceived barriers. Also, the findings demonstrated that there are distance learning faculty members who want more professional development options as well as clear guidelines for teaching distance learning courses. Furthermore, the findings suggested that higher education institutions may need to change their attitudes towards providing incentives for faculty who design and develop distance learning courses.
Spatio-Temporal Source Separation using Temporal Priors with Parameterized Uncertainties

The objective of this progressive research is to develop a spatio-temporal source separation scheme using temporal priors with parametric uncertainties. The method has potential applications in single and multi-modal data analysis. It is shown that the proposed scheme can be jointly used with classical source separation techniques based on generalized eigenvalue decomposition (GEVD) and joint approximate diagonalization of eigenmatrices (JADE). As a case study, the proposed method is evaluated over noninvasive fetal electrocardiogram signals.
A Trainable Information Distribution System to Support Crisis Management

Crisis response and management involve multiple collaborative actors who execute tasks in a dynamic setting. For the effectiveness of collaboration and crisis fighting it is essential that all actors have access to relevant information necessary for their tasks. Managing the information flow, i.e. presenting the right information to the right person at the right time, is of great importance. However, the complexity of a crisis event makes it very difficult to keep an overview of all ongoing activities and information flow within the entire crisis environment. In this paper we address the problem of selecting and distributing information to users as a function of their characteristics, tasks and the state of their workflows in a collaborative setting. In particular, we propose a trainable system for information distribution that will be able to support the dynamic nature of collaborative processes and provide users with task-relevant information. We expect that this will reduce problems due to information overload and will lead to more effective collaboration between all actors in the crisis management environment.
Computational Models for Morphology

In this introductory paper I will examine different computational models that can be used to solve morphological problems. The target problem is the inflection problem and its inverse, the stemming problem. The introduced methods can be used to inflect an arbitrary input word to one of its inflected forms, as well as determining the stem and affix list of an inflected input word. The examined computational methods include dictionary based systems, finite state transducers, rule based systems and applying classification methods. All of these models have advantages and disadvantages, but in most cases combining their strength can improve the results.
THE MANAGEMENT OF MALDESCENDED TESTIS

FEW matters in pasdiatrfc surgery have given r ise to more differences of opinion than has the subject of this paper. Much of this confusion has been due to differences in terminology rather than in principle, but certain problems in treatment may be the subject of useful discussion. In my view a maldescended testis can be defined as one which does not lie in the normal scrotal position. On closer examination cases fall into one of three groups: (i) retractile testes, comprising some two-thirds of cases of maldescended testes the gland lies high, but can be manipulated into the scrotum with proper care (this is a normal state for small boys and no treatment is necessary, the testis coming to lie normally as it enlarges); (ii) undescended testes, comprising one-third of cases (there is arrest in the line of normal descent, the gland is abnormal, and treatment is by operation, which is often difficult); (iii) ectopic testes, which are very rare (the testis has turned aside from the Iine of normal descent, the gland and cord are normal, and treatment is by operation, which is a straightforward procedure) . The technique of manipulation is to milk the testis down by means of a firm stroking movement of the hand in the line of the inguinal canal towards the scrotum. The testis in a retractile case can then be caught from below the pubic ramus with the other hand, and clearly enters the scrotum. Certain borderline cases are encountered in which a decision is not possible at the first examination, but these get fewer with increasing experience. However, it should always be remembered that although 1% of boys show cryptorchidism at one year of age, this figure drops to 0'25% in adult males, and hence if doubt exists operation should be delayed.
Blockchain-based authentication of IP addresses

En aquest projecte s'escolleix i estableix un sistema software capac d'assignar adreces IP amb una seguretat acceptable, comprovant quins problemes sorgeixen en la seguretat i quins son evitables partint d'eines actuals que desenvolupen la mateixa tasca. Per realitzar aquest treball utilitzarem IOTA.
Requirements Engineering for Embedded Systems)

In requirements engineering for embedded systems the fundamental functional and non-functional requirements for a system with an embedded software system are discussed, captured, analysed, validated, and documented. We suggest a stepwise proceeding. We advise to describe the requirements in terms of different system views obtained from an abstract logical architecture of an embedded system. By the logical architecture a system and its context are decomposed into four components. We discuss the nature of these components and the need and techniques to model their behaviours.
Lower BMI cut-off than the World Health Organization-based classification is appropriate for Nigerians

Universally, Body Mass Index (BMI) is used to diagnose obesity, and is associated with cardiovascular disease, as well as significant morbidity and mortality. Because the effect of obesity on health risk is influenced by ethnic background, age, and gender, using a universal BMI cut-off been questioned by some authorities or workers. Reports on appropriate BMI for Nigerians are scanty. Results of a cross-sectional study involving residents of Ado Ekiti, Nigeria, and neighboring towns and villages who participated in a health screening were analyzed. BMI cut-off which identifies the presence of hypertension and hypercholesterolemia was determined with Receiver Operating Characteristics curve. Five hundred and fifty-two (552) participants comprise of 230 (41.7%) men aged 38.8±15.6 years and 322 (58.3%) women aged 40.7±15.4 years. More women (27.7%) than men (8.8%) had obesity (p<0.001). Hypercholesterolemia was more prevalent in women (7.1%) than men (3.9%), p=0.014. Prevalence of hypertension was 24.8 and 22.0% in men and women, respectively. Overall, the prevalence of overweight/obesity and hypertension increases with age (p<0.001). The optimal BMI cut-off for men and women were 24.1kg/m2 (AUC= 0.619 [95% CI, 0.535 to 0.704], p= 0.007: SS, 70.2% and SP 56.6%) and 28.9 kg/m2 (AUC: 0.690 [95% CI, 0.548 to 0.831], p= 0.016: SS, 64.3% and SP 67.5%), respectively. Lower BMI cut-off than the WHO-based classification is appropriate for Nigerians. The BMI cut-off for identifying cardiovascular disease is different for men and women. Key words: Body mass index, cardiovascular disease, obesity, cut-off, Nigeria.
Using Technology Tools for Formative Assessments

In recent years, online teaching has become extremely popular. Most institutions of higher learning are offering online courses in almost every field of study. Teaching any course online is challenging, but teaching quantitative courses, such as operations management, management science, statistics, and others, have added a more challenging dimension to online teaching. Publishers have been assisting professors of quantitative methods courses by developing various teaching and evaluation tools. This study explores one such publisher’s tool, Quiz Me Mastery Points, of Pearson’s MyOmLab. The performance of students on their examinations and the Mastery Points they earned through the Quiz Me feature were compared, and it was determined that there was a significant correlation between the two.
RSS-based Device-free Passive Detection and Localization using Home Automation Network Radio Frequencies

This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of the system deployment in a simple room with no furniture produced detection rates up to PD = 100% and false positive rates as low as PF = 1.6% for baseline threshold detection along the line of sight (LOS) in a simple tripwire setup. Signal attenuation of foam blocks at different distances along this LOS ranged from 2.2-4.4 dB. Cell-based fingerprinting for localization using multiple nodes in this room achieved accuracy only as high as P A = 5.4% and false positives only as low as PF = 88.3%. A context-based localization method was developed in response and was able to achieve P A = 28.3% and PF = 40.0%. The system was then deployed in a similar room containing several metal objects and achieved P A = 42.2% and PF = 0.0%. Deployment in a similar room with RF absorbent objects achieved P A = 23.3% and PF = 53.3%. Feasibility of exploiting RF of a home automation network for DfP indoor detection and localization was demonstrated. Despite not achieving optimal localization performance, the results showed promise for future DfP system deployment on top of home automation RF devices.
Scalable Performance Analysis of Epidemic Routing Considering Skewed Location Visiting Preferences

This paper investigates the performance of epidemic routing, in mobile social networks (MSNs), which makes use of the store-carry-forward paradigm for communication. Real-life mobility traces show that people have skewed location visiting preferences, with some places visited frequently and some others infrequently. In order to model epidemic routing in MSNs, we first analyze the time taken for a node to meet the first node belonging to a set of nodes restricted to move in a specific subarea. Afterwards, a monolithic stochastic reward net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This monolithic model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic SRN and an approximate folded SRN is proposed to evaluate the performance of epidemic routing. Discrete-event simulation is applied to cross-validate the proposed models. Results indicate that the monolithic model has higher accuracy in predicting the performance of epidemic routing. The approximate folded model also achieves a good accuracy and can be solved for a network with a large number of nodes/frequently visited locations. This model is more accurate than the ordinary differential equation approach.
A Voxel-based Deep Learning Approach for Point Cloud Semantic Segmentation

Semantic segmentation has been a research topic in computer vision for decades. This task has become a crucial challenge nowadays due to emergence of new technologies such as autonomous driving. Nonetheless, most existing segmentation methods are not designed for handling the unstructured and irregular nature of 3D point clouds. We propose a voxel-based technique for 3D point cloud data semantic segmentation using 3D convolutional neural networks. It uses local voxelizations for learning spatial patterns
THE IRRITABLE COLON AND RIGHT ILIAC FOSSA PAIN

It has been pointed out that the irritable colon, especially in association with sigmoid spasm, Is an important cause of abdominal pain, particularly in areas remote from the left iliac fossa. Pain in the right iliac fossa is not infrequently part of the syndrome. The diagnosis should be made with care. Detailed investigations in the first instance may be unwarranted. From a personal series of 163 such patients it was noted that pain on the right side of the abdomen had often been treated by operation, but without success. It behoves us to look at this subject more closely and, by a better understanding of the serological factors involved, perhaps we will reduce the number of unnecessary operations performed on these patients.
Evaluation of CNN Models with Fashion MNIST Data

The work seeks to evaluate the performance of four CNNs with respect to Fashion MNIST data set. Fashion MNIST is a dataset of images consisting of 70000 28× 28 grayscale images, associated with a label from 10 classes. In this report, the accuracy of four popular CNN models that are LeNet-5, AlexNet, VGG-16 and ResNet for classifying MNIST-fashion data revealed that ResNet was the best suited for the selected dataset. The training process has been coded with Tensorflow. After the result accuracy improving, we could use the new model to the fashion company that can help the fashion company more accurately classify clothing. Moreover you could build your own closet online for your fashion.