Miracle-dz/UniFAR-FTUs huggingface dataset:
Relay Node Placement in Wireless Sensor Networks With Respect to Delay and Reliability Requirements

Wireless sensor networks are gradually employed in many applications that require reliable and real-time data transmission. As hop count is an important factor affecting end-to-end delay and reliability, we investigate the hop constrained relay node placement (HCRNP) problem in this paper. First, to achieve connectivity requirement, we study the connected HCRNP problem. Then, to design survivable network topologies against node failures, we study the 2-connected HCRNP problem. Correspondingly, two polynomial-time algorithms: cover-based 1-connected node placement (C1NP) and cover-based 2-connected node placement (C2NP) are proposed, respectively, to address the above two problems. Through rigorous analysis, we show that 1) C1NP has an approximation ratio better than existing algorithms for the connected HCRNP problem (i.e., O(1) for special settings and O(<inline-formula><tex-math notation="LaTeX">$\ln n$</tex-math></inline-formula>) for arbitrary settings, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of SNs) and 2) C2NP is the first algorithm that can provide an explicit performance guarantee for the 2-connected HCRNP problem, i.e., whenever C2NP finds a feasible solution, the ratio of this solution to the optimal solution is guaranteed to be O(<inline-formula><tex-math notation="LaTeX">$\ln n$</tex-math></inline-formula>). Finally, we verify the effectiveness of the proposed algorithms through extensive simulations.
Memory-based reinforcement learning algorithm for autonomous exploration in unknown environment

In the near future, robots would be seen in almost every area of our life, in different shapes and with different objectives such as entertainment, surveillance, rescue, and navigation. In any shape and with any objective, it is necessary for them to be capable of successful exploration. They should be able to explore efficiently and be able to adapt themselves with changes in their environment. For successful navigation, it is necessary to recognize the difference between similar places of an environment. In order to achieve this goal without increasing the capability of sensors, having a memory is crucial. In this article, an algorithm for autonomous exploration and obstacle avoidance in an unknown environment is proposed. In order to make our self-learner algorithm, a memory-based reinforcement learning method using multilayer neural network is used with the aim of creating an agent having an efficient exploration and obstacle avoidance policy. Furthermore, this agent can automatically adapt itself to the changes of its environment. Finally, in order to test the capability of our algorithm, we have implemented it in a robot similar to a real model, simulated in the robust physics engine simulator of Gazebo.
A STOCHASTIC STUDY OF THE LIFE TABLE AND ITS APPLICATIONS: I. PROBABILITY DISTRIBUTIONS OF THE BIOMETRIC FUNCTIONS'

The life table is one of the oldest, most useful, and best-known topics in the field of statistics. It has many applications in various areas of research where birth, death, and illness may take place. The earliest life tables date as far back as the seventeenth century; Halley's famous table for the City of Breslau, published in the year 1693 [9], already contained most of the columns in use today. The subject matter, however, is by no means limited to human beings. Zoologists, biologists, physicists, manufacturers, and investigators in other fields have found the life table a valuable means of presenting their data. In spite of its popularity in many research areas, the life table as a subject has yet to be systematically explored from a statistical point of view. There are two forms of the life table in general use: the cohort (or generation) life table and the current life table. In its strictest form a cohort life table records the actual mortality experience of a given group of individuals over a period of time extending from birth until the death of the last member of the group. A current life table, on the other hand, considers the mortality experience of an entire population at one point in time. The purpose of this investigation is to present a stochastic view of the subject, taking random variation into consideration and treating all the biometric functions as random variables. The results of our study will be given in a series of papers. In the first paper probability distributions of the main biometric functions are presented and formulas are derived for the corresponding mathematical expectations, variances, and covariances. Some of the findings are by no means original, but they are included for the sake of completeness.
Control Methods on Three-phase Power Converters in Photovoltaic Systems

In this paper, a three-phase load connected to a NPC three-level inverter is presented. To generate gate signals for the multilevel inverter, two commands are developed and compared: the phase disposition pulse width modulation (PDPWM) and the space vector pulse width modulation (SVPWM). DC supply is provided by photovoltaic cells. Boost converter controls the power transfer from photovoltaic generator. Due to nonlinear I-V characteristics of photovoltaic cells, a maximum power point tracking algorithm is adopted to maximize the output power, the nonlinear controller (sliding mode) is developed and simulated. To verify the effectivnesse of the introdueced controller, it is compared with the fuzzy logic controller. Matlab-simulink is used for simulation, analysis and interpretation the results of these controllers.
PROBABILISTIC TOPIC MODELING AND ITS VARIANTS – A SURVEY

– Topic modeling is one of the fast-growing research areas as there is a huge increase in internet users. These users are the main source of large volumes of electronic data in terms of documents, tweets, or messages and so on. Collecting, organizing, storing and retrieving the data in text format is becoming more and more typical. The topic model is one research area which focuses on classifying the textual data into groups. In this study, we are presenting a survey on the advanced algorithms that are used in topic modeling. The main purpose of this survey is to provide a brief overview of the current topic models that motivate the budding researchers to select the best suitable algorithm for their work.
Recognition of an Emotion using Principle Component Analysis of Hand Gesture of a Human

: This paper explores recognition of human emotion through the hand gestures generated for particular emotion. In non-verbal reflection of an emotion hand generates various gestures using its most flexible parts viz. Fingers, Palm and Arm. In recognition process, an image or sequence of images of a hand gesture is first normalized before extracting the features from them. Thereafter, as a feature of these normalized gesture images, the Principle Components (PCs) of images are analysed. Furthermore, Neural Networks (NN), using Back Propagation Algorithm classifies the obtained PCs and finally the input gesture of an emotion is matched into the classes of pre stored trained hand gesture image sets of seven basic emotions Neutral, Happy, Sad, Fear, Anger, Disgust and Surprise.
The Successful Virtual Classroom: How to Design and Facilitate Interactive and Engaging Live Online Learning

Virtual training gives learning professionals unprecedented flexibility, making it possible to reach participants globally and unite physically dispersed teams. But designers and facilitators must adapt their programs and delivery styles in order to be effective in this new medium. The Successful Virtual Classroom goes beyond introducing the technology, to offer trainers proven techniques tailored specifically to engage live online audiences. Packed with easy-to-use tools, checklists, and worksheets—as well as case studies from Oracle, UPS, and more—the book introduces the PREP model for planning, rehearsing, executing, and then conducting a post mortem following the training event. Readers will learn how to: Make the most of virtual classroom features such as content and screen sharing,annotation tools, polls, and breakout rooms Weave chat responses into the discussion Compensate for the absence of body language Monitor feedback Engage individuals with different learning styles Encourage audience contribution Meet the unique needs of global participants And more Featuring icebreakers and interactive exercises designed for an online environment, this book helps readers create programs that truly drive learner engagement.
Design and Implementation of Digital Signal Transmission Performance Analyzer Based on FPGA

Digital signal transmission analyzer is mainly used for testing the transmission quality of digital communications signal and it usually composed of digital signal generator, pseudo-random signal generator, low- pass filter and digital signal analysis circuit. This study adopts FPGA to implement the digital signal generator and pseudo-random signal generator, as well as to implement the shift register to generate the required M sequence. By changing the low-pass filter cutoff frequency and the amplitude of the pseudo-random signal to analog transmission environment and signal analysis portion consists of the digital signal analysis circuit. The digital signal analysis module extracts the sync signal and produces synchronization scan signal by using the synchronous clock, as the outside trigger signal of oscilloscope to display the eye diagram of the digital signal stably. To analyze the degree of interference and the strength of inter-symbol interference by observing the eye diagram, thus this study implements the performance test for digital signal transmission.
Measurement and animation of the eye region of the human face in reduced coordinates

The goal of this dissertation is to develop methods to measure, model, and animate facial tissues of the region around the eyes, referred to as the eye region. First, we measure the subtle movements of the soft tissues of the eye region using a monocular RGB-D camera setup, and second, we model and animate these movements using parameterized motion models. The muscles and skin of the eye region are very thin and sheetlike. By representing these tissues as thin elastic sheets in reduced coordinates, we have shown how we can measure and animate these tissues efficiently. To measure tissue movements, we optically track both eye and skin motions using monocular video sequences. The key idea here is to use a reduced coordinates framework to model thin sheet-like facial skin of the eye region. This framework implicitly constrains skin to conform to the shape of the underlying object when it slides. The skin configuration can then be efficiently reconstructed in 3D by tracking two dimensional skin features in video. This reduced coordinates model allows interactive real-time animation of the eye region in WebGL enabled devices using a small number of animation parameters, including gaze. Additionally, we have shown that the same reduced coordinates framework can also be used for physics-
Priority-Based Real-Time Stream Coding under Burst Erasures

The real-time streams play an increasingly important role in both traditional and emerging networks. Whereas, transmitting such streams requires vast channel resources, posing a huge challenge to the networks. Motivated by this problem, this paper considers a real-time stream system, where real-time messages with different importance should be transmitted through a burst erasure channel, and be decoded by the receiver within a fixed delay. We introduce the concepts of Symmetric Real-time (SR) stream and Asymmetric Real-time (AR) stream according to the stream characteristics. Different from the classical Shannon capacity, the channel transmission capacities are derived for SR and AR streams respectively, which can be used to guide the design of efficient real-time stream coding approaches. The capacity for SR streams is shown asymptotically achievable with a Proportional Time-invariant Intra-session Code (PTIC). Moreover, a Maximum Time-invariant Intra-session Code (MTIC) is presented for AR streams, which can also asymptotically achieve the capacity. Finally, the performances of PTIC and MTIC are verified by simulation experiments.
Constructive neural network in model-based control of a biotechnological process

In the present work, a constructive learning algorithm is employed to design an optimal one-hidden layer neural network structure that best approximates a given mapping. The method determines not only the optimal number of hidden neurons but also the best activation function for each node. Here, the projection pursuit technique is applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is optimally defined for every approximation problem, better rates of convergence are achieved. The proposed constructive learning algorithm was successfully applied to identify a large-scale multivariate process, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model is then considered as part of a model-based predictive control strategy, with high-quality performance in closed-loop experiments.
A Review of Histogram Equalization Techniques in Image Enhancement Application

Image enhancement can be considered as one of the fundamental processes in image analysis. The goal of contrast enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. In this paper, an image enhancement methods based on Histogram Equalization (HE) was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.
Speech intelligibility enhancement for Thai-speaking cochlear implant listeners

Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noise and babble noise at SNR levels of 0, 5, and 10 dB. Then the noisy words were enhanced using SE algorithms. The enhanced words were fed into the CI system to synthesize vocoded speech. The vocoded speech was presented to twenty normal-hearing listeners. The results indicated that speech intelligibility was marginally improved by the MBSS algorithm and significantly improved by the WF algorithm in some conditions. The enhanced bisyllabic words showed a noticeably higher intelligibility improvement than the enhanced monosyllabic words in all conditions, particularly in speech-shaped noise. Such outcomes may be beneficial to Thai-speaking CI listeners.
Influence of infrastructure and info structure, curriculum, pedagogy and content in teaching and learning through MOOCs

In Malaysia, Massive Open Online Course (MOOC) is an emerging approach in the higher education teaching and learning which takes the new role of technology to the next level. However, the successful implementation of MOOC requires effective strategic planning and understanding of the whole process involved. This study investigates the influence of infrastructure and info structure, curriculum, pedagogy, and content towards enhancement in teaching and learning in Massive Open Online Courses (MOOCs) in one of the common course offered in Malaysian Public Universities. This study examines the pilot data from a bigger dataset collected from an online survey. Collected data was analyzed using Structural Equation Modeling (SEM), to propose a model of enhancement in teaching and learning through MOOCs. Preliminary analysis shows that infrastructure and info structure, curriculum, pedagogy, content, and enhancement in teaching and learning were significantly correlated to each other. The overall contribution of this model which to enhancement in teaching and learning was 13.0%. The model summarized that only curriculum construct influence the enhancement in teaching and learning (β=0.244; p<0.01) make it as the sole contributor for this model.
Holistic development of eHealth technology

In this chapter, the CeHRes Roadmap – a framework for eHealth development, implementation and evaluation – is explained. The Roadmap consists of six main phases: the contextual inquiry, value specification, design, operationalization, summative evaluation and formative evaluation. In this chapter, the rationale behind the Roadmap is provided. After that, each phase is explained, its objectives are stated, relevant concepts, methods and activities are explained, and the types of outcomes of the phase are given. Furthermore, cases are provided to illustrate certain abstract concepts, and references to other chapters that provide more in-depth information are included.
Power systems voltage emergency control approach using trajectory sensitivities

Very stressed power systems often lack ability to withstand certain disturbances, what might result in serious consequences. Therefore, the tools mitigating such a situation have a crucial significance. This paper presents a method for real-time stability assessment and emergency control of power systems based on the trajectory sensitivities. Out of the present conditions a nominal trajectory and its sensitivities is precalculated. If an incipient instability is detected, necessary stabilizing control measures are determined. A short review of trajectory sensitivities is included. A principal description of the method together with a theoretical foundation and examples with simulation results is provided.
W.J. Mitchell — In Celebration of an Approach

Among the founding fathers of architectural computing, the late W.J. Mitchell was arguably the one who managed to capture the imagination of the younger generations through overviews and insights that described the backgrounds and scope of the area in a way that was suitable for novices as well as people outside the area. Calling such overviews successful popularization is only part of the truth. More importantly, they formulate a coherent and comprehensive approach that allowed Mitchell to move between eras and contexts without loosing focus or sacrificing fundamental priorities. It is an approach that remains valid and usable, a source of inspiration and understanding for future generations in the area.
Font Style Conversion Based on Deep Learning

In view of the cost of traditional design of new fonts, a method of combining deep learning for font style conversion is proposed. By using a U-Net type network structure combining the training method of generative adversarial network, supervised learning part of fonts, the font style conversion ability of the font generator network is constantly enhanced so that the fonts can be converted to another style through the generator network. The experimental results show that this method convert font structure generated clear and smooth, less noise, and the original character of the same font highly consistent in size, weight, style etc.
A short survey on image zero-watermarking techniques based on visual cryptography

The development of information technologies has increased tremendously, thus multimedia data as digital images require security measures when used on the internet. Image watermarking is a widely used technique for copyright protection and integrity control. To provide more security and to protect the host image quality, researchers have been using the visual cryptography concept combining with the watermarking technique. Visual cryptography is a technique in which secret data is decomposed into a number of shares and distributed to participants so that only participants can read that data. Following this concept, image watermarking based on visual cryptography allows a protection by constructing secret and public shares without altering the original image. Therefore, in this paper, we present a survey on various image watermarking techniques based on visual cryptography.
A Contention-Friendly, Non-Blocking Skip List

This paper presents a new non-blocking skip list algorithm. The algorithm alleviates contention by localizing synchronization at the least contended part of the structure without altering consistency of the implemented abstraction. The key idea lies in decoupling a modification to the structure into two stages: an eager abstract modification that returns quickly and whose update affects only the bottom of the structure, and a lazy selective adaptation updating potentially the entire structure but executed continuously in the background. As non-blocking skip lists are becoming appealing alternatives to latch-based trees in modern main-memory databases, we integrated it into a main-memory database benchmark, SPECjbb. On SPECjbb as well as on micro-benchmarks, we compared the performance of our new non-blocking skip list against the performance of the JDK non-blocking skip list. Results indicate that our implementation is up to 2:5 faster than the JDK skip list.
Moving Object Detection Based on Improved Codebook Model

Background modeling is a key technology in video surveillance field. Conventional methods used to detect the moving object have drawbacks in the aspects of accuracy and robustness. In order to address these problems, we change the RGB columnar structure in traditional codebook model and establish codebook background model in the YUV color space. We cluster every pixel on a timeline in an image and extract background templates. Then the current image and the background templates are compared. At the same time, the background templates are renewed. We integrate the frequency information to improve the process of symbol decision, deletion and matching. In the meantime, the spatial information of current pixel is added into the prediction of the foreground, making the results of foreground detection more reliable. The experimental results indicate that the proposed method can detect the moving objects effectively and correctly under various conditions. In addition, the proposed algorithm also has the good noise robustness. Keywords-background modeling; codebook; moving object detection; noise robustness
TEXT MINING BASED ONLINE NEWS ANALYSIS ABOUT SMART FACTORY

Continuous advancement in information and communication technologies leads to wide range of changes in manufacturing field, and they are represented by the term, smart factory. Many people have been interested in them, and they are covered in news articles. In this study, text mining based analysis was conducted to uncover the meaningful trends of topics and issues about smart factory within the online news articles. We used association rule mining of keywords, and topic modeling for Korean online news corpus from 2014 to April 2017. We can find that the number of articles on the technology-oriented topic is increasing and policy-oriented articles have shown a decreasing tendency.