Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting movie demand using total and split exponential smoothing

In the motion picture industry, the movie market players always rely on accurate demand forecasts. Distributors require the demand forecasts to decide the best release date of the new movies that arguably the most difficult decision. Thus, forecasting methods which are able to capture historical patterns can be relied on to produce an accurate prediction. Exponential smoothing methods are the common methods, but there is limited study using this technique in movie demand forecasting. In this paper, we study the performance of a newly proposed seasonal exponential smoothing method that previously has been considered for forecasting daily supermarket sales. It is known as total and split exponential smoothing, and apply it to daily box office from the United States market. The resulting forecasts are compared against other exponential smoothing methods, seasonal adjustment, non-seasonal, and seasonal exponential smoothing methods. Overall, total and split exponential smoothing with optimised parameters separately for each lead time is performing good, followed by seasonal (damped trend) exponential smoothing method (DA-A). The identification of the best performing method assists distributors to make a decision on the best release date for their new movies earlier than the competitors.
Security and Trust in the DEECo Component Model

DEECo represents an example of a Cyber-Physical System (CPS) consisting of potentially vast number of components able to share data with each other. So far, access to data was not restricted, thus enabling components to exploit sensitive data owned by other components. The goal of this work is to analyze security threats in distributed environments such as DEECo and propose a security solution which would provide both physical security of component data and introduce an access control mechanism. However, while confidentiality may be critical to certain applications, data integrity is crucial to almost every one. This work therefore also proposes a trust model, which prevents components operating with defective or malicious data. Both proposed models are realized in jDEECo, a Java implementation of DEECo.
School-Based Intervention to Promote a Healthy Lifestyle and Obesity Prevention Among Fifth- and Sixth-Grade Children

ABSTRACT Background: Children’s overweight and obesity have increased in Western societies, including Israel. Childhood is an important time for shaping healthy habits. Purpose: The purpose of this study was to evaluate the effectiveness of a healthy lifestyle intervention on health knowledge, behavior, and anthropometric measurements. Methods: A total of 396 fifth- and sixth-grade children, from 2 religious and 2 secular schools, participated in a nonrandomized controlled trial during the 2011–2012 school year. Intervention group were given an extensive educational program focusing on eating habits and physical activity. Results: Overweight and obesity decreased significantly within the intervention group (from 25% to 17.9%, P = .04), without a significant change in the control group (from 20.5% to 17.6%, P = .12). Religious children had double the risk of being overweight or obese (odds ratio [OR] = 2.10; 95% confidence interval [CI], 1.15–3.73; P = .02). Knowledge was improved in both study groups (P < .01), with no difference in health behavior scores at the beginning (P = .59) or end (P = .36) of the year. Discussion: Health Education during fifth and sixth grades improved children’s weight and health knowledge but not healthy behavior. Translation to Health Education Practice: School-based healthy lifestyle educational programs are important for primary prevention and weight management. A AJHE Self-Study quiz is online for this article via the SHAPE America Online Institute (SAOI) http://portal.shapeamerica. org/trn-Webinars
Emergent optimization: design and applications in telecommunications and bioinformatics

In this PhD Thesis, we are interested in designing new Particle Swarm Optimization (PSO) proposals that solve or mitigate the main disadvantages present in this algorithm. To this point, we have used a series of methodologies to analyze the internal behavior of this algorithm and to identify the main problems or improvement opportunities appearing in existing PSO versions. In order to assess the effectiveness of the new proposals, we have performed comparative studies from two main points of view: solution quality and scalability in terms of the problem size (decision variables). For this task we have followed specific experimental procedures of standard benchmark test suites (CEC05, SOCO10, DTLZ, etc.), and we have compared against the most prominent metaheuristics in current state of the art (G-CMA-ES, MSGA-II, OMOPSO, DE, MOS-DE, and MTS). In this context we have proposed a series of new algorithmic approaches: DEPSO, RPSO-vm, PSO6, SMPSO, PSO6-Mtsls, that have been validated and located at the top level outperforming algorithms in the state of the art. Second, we are aimed at solving real world complex problems with PSO based algorithms to determine the adaptability of this algorithm to different representations and scenario conditions, within limited computational time, and requiring huge data base management. In concrete, we have focused in this thesis on three NP-Hard real applications trying to cover quite different industry fields: Gene Selection in DNA Microarrays (Bioinformatics), Communication Protocol Tuning in VANETs (Telecommunications), and Signal Lights Timing in traffic management (Urban Mobility).
Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements. This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation. This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms.
An investigation into the use of constraint modelling techniques in the design of packaging machinery motion

The motion of packaging machinery is subject to a wide variety of constraints. These have to be taken into account during the conceptual stages of the design process in order to create a machine that can behave correctly. The constraints apply to both the motion of the machine’s end-effector and its joints. In this thesis the motion constraints that are typically imposed on continuous-processing packaging machinery have been examined, and the critical ones have been identified. It has been shown that they can be grouped into the following categories: task-related constraints, clearance constraints, quality of motion, and commercial interests. A methodology has been proposed to assist in the design of machinery motion. This involves identifying constraints that apply to a particular problem. The approach has been successfully applied in relation to a number of case studies. A crucial part of the methodology is the ability to identify and manipulate important motion constraints. To this end the idea of parametric timing diagrams has been introduced and demonstrated with the case studies. The thought processes required to produce the diagrams can help to identify gaps in an engineer’s appreciation of the design problem, or to highlight significant features in the required motion. The former is important in order to produce acceptable designs. The latter encourages investigation of the range of performance that can be achieved by a particular design concept under different sets of constraints. Acknowledgement I would like to express my thanks to my supervisor Dr Glen Mullineux, and Professor Tony Medland for the tremendous help and advice they have provided whilst I have carried out this work. The task of completing a thesis also requires having a group of friends and colleagues with whom one can discuss the work, and who can provide general encouragement. The following people fall into one or other or both of these categories and I would like to thank them all very much for their help: Dr John McGarva, Andrew Rentoul, Dr Laurence Kenney, Brian Apps, Dr Richard Hall, Drs Gerry & Helen Agnew and all my colleagues at MDI Ltd. The support of the DTI/EPSRC LINK programme in the Design of High Speed Machinery, together with the collaborating companies Unilever and CMB Technology is also gratefully acknowledged.
Application of Artificial Intelligence Methods for the Semantic Web

A web based Artificial Intelligent (AI) system is proposed for building semantic representations in a Question and Answer (Q&A) framework. The system is built into a software tool for semantic Q&A using the PHP/MySQL language which is the proven tool for the web. The tool supports semantic Q&A data gathering from a front-end interface. The data is then used in an AI system trained by the Long Short-Term Memory (LSTM) algorithm. Simulation results show that the system can indeed perform as expected. It further shows that increasing the hidden layer size leads to improved perplexity or negative-log likelihood estimates with values as low as 1.1 and 3.5 respectively. Keywords: Artificial Intelligence, LSTM, Q&A, Semantic Web
Estimating Pilots’ Cognitive Load From Ocular Parameters Through Simulation and In-Flight Studies

Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. This paper investigated use of eye gaze trackers in military aviation environment to automatically estimate pilot’s cognitive load from ocular parameters. In the first study, we used a fixed base variable stability flight simulator with longitudinal tracking task and collected data from 14 military pilots. In a second study, we undertook four test flights with BAES Hawk Trainer and Jaguar aircrafts doing air to ground attack training missions and constant G level turn maneuvers up to +5G. Our study found that ocular parameters like rate of fixation is significantly different in different flying conditions. It also significantly correlated with rate of descent during air to ground dive training task, normal load factor (G) of the aircraft during constant G level turn maneuvers and pilot’s control inceptor and tracking error in simulation tasks. Results from our studies can be used for real time estimation of pilots’ cognitive load, providing suitable warnings and alerts to the pilot in cockpit and training of military pilots on cognitive load management during operational missions.
Fast Algorithm for Selective Image Segmentation Model

Selective image segmentation model aims to separate a specific object from its surroundings. To solve the model, the common practice to deal with its non-differentiable term is to approximate the original functional. While this approach yields to successful segmentation result, however the segmentation process can be slow. In this paper, we showed how to solve the model without approximation using Chambolle’s projection algorithm. Numerical tests show that good visual quality of segmentation is obtained in a fast-computational time.
Understanding the future of Deep Reinforcement Learning from the perspective of Game Theory

This paper is to discuss the development of Deep Reinforcement Learning and the future of it from the perspective of Game Theory. The relationship and potential interaction between these two areas are also introduced, especially the optimization method. This paper discusses about the situations both under non-cooperative and cooperative game. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have grasp sufficient attention from various research areas. Deep Reinforcement Learning, as one of the most promising ML methods, enlighten more researchers to devote themselves in this area. However, even such accomplishment could not belie that, for most kinds of real-life problems, DRL is still unable to provide with an optimal strategy. Because unlike the well-adjusted environment in laboratory, real life problems are not always able to be converted into mathematical problems. Under such circumstances, most of real-life problems have no nominal “optimal solution”. Game Theory provides potential solutions to covert “real issues” into “mathematical problems”, then it is easier for researchers to handle.
T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities

Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system.
Evolution of Blockchain Technology in Business Applications

: Blockchain technology is an invention where digital transactions refer to distributed database without the need of third party intervention and authentication. In present time several organizations in various disciplines are adopting this technology which has transparency, integrity, efficiency in cost and time . Its unique technology is being adopted in various industries like banking, legal, healthcare, rentals, education and many more. The intention of this paper is to outline the Blockchain technology and its function in various domain.
A cross sectional study of contraceptive prevalence and factors responsible for usage of different contraceptive methods in an urban slum area

Background: According to WHO; contraceptive prevalence is the percentage of women who are currently using, or whose sexual partner is currently using, at least one method of contraception, regardless of method used. It is usually reported for married women aged 15-49. According NFHS-4 (2015-16) current use of any family planning method is 53.5%. The objectives of the study were to find out contraceptive prevalence and usage of different contraceptive methods among married women of reproductive age group and to study factors affecting contraception use among married women of reproductive age group.Methods: A community based cross sectional study was carried out in urban slum area catered under Urban Health Training Center of medical college. Sample size of 390 married women of reproductive age residing in urban slum area taken. By using systematic random sampling method data collected by house to house visits. Data was analyzed by using SPSS software version 24. Chi-square test used for showing association.Results: Contraceptive prevalence was found to be 53.84%. Use of tubal ligation was found maximum (12.05%) followed by condom, O.C. P. Contraceptive use was found to be significantly associated with age, number of living son, and number of living children of women.Conclusions: Counselling of women should be done for appropriate use of contraceptive method. Awareness regarding female education, birth spacing, advantage of small family, etc. should be done by I.E.C. activity.
Assessing Teachers’ Knowledge of Mathematical Modeling: Results from an Initial Scale Development

This pilot study underscores the claim that teachers’ mathematical content knowledge plays a significant role in their teaching profession. Specifically, this article reports on the initial development of a scale to empirically measure teachers’ knowledge of the nature of mathematical modeling from a Midwestern school district (n = 71). The phases used in this initial scale development included item generation, experts’ reviews, item analysis, and factor analysis. Reliability, factor analysis, and scaling work confirmed the scale is a reliable and valid measure to assess teachers’ knowledge of the nature of mathematical modeling. Findings from this study contribute to an important discussion about the need for further research and the development of quantitative tools for supporting instruction of mathematical modeling. Implications for teacher preparation and professional development are also discussed.
Consumption patterns of public water supply using linear programming techniques

This report presents the outcome of a survey of water use pattern in Kaduna metropolis, Kaduna State Nigeria. The water use pattern study extends to various water consumers such as residential, institutional, commercial and industrial. The study was conducted by distribution of questionnaires and oral interview. The questionnaires contain questions related to water demand and uses. The research work presents the outcome of a linear Programming Techniques Model and sampling techniques of water use pattern carried out. The study was conducted by the application of linear Programming Approach and sampling techniques. The sampling technique contains questions related to water demand and uses. The information obtained were analysed and the result show that the residential water consumption varies from 46l/c/d to 115l/c/d with lower values for high and medium density areas, while higher value is for low density areas because of high standard of living. The daily values for commercial, industries and Public facilities sampled within the metropolis were; 29,915.854, 366,666.666 and 29, 308.804 litres respectively. A Water consumption Consumption patterns of public water supply using linear programming techniques Ibrahim ABDULLAHI, Ismail ABUBAKAR, Iyadunni Ndububa OLUFUNMILAYO, Lawal Batagarawa RABI’A, Adebola Ajibike MA’ARUF and Tsoho USMAN 258 model was developed using Linear programming modelling technique to the allocation of 176,360,000 L of water produced daily by the State Water Corporation and supplied to the four sectors of the town. The result shows that the model gave an optimal solution of 275, 170, 410 L to the four sectors with an allocation of 150,441,410 L to residential sector, 101,325,000 L to commercial sector, 223,040,000 L to public sector and 1,100,000L to industrial sector. It is concluded that the water supply by the Water cooperation is less than the water demanded.
Intelligent Content-Aware Traffic Engineering for SDN: An AI-Driven Approach

TE is a critical and difficult problem that tries to map traffic with various requirements to paths in dynamic communication networks. The emerging SDN enables centralized TE optimization with a global view. From the architectural viewpoint, ICN facilitates TE from many aspects, such as in-network caching which can reduce redundant traffic and content-awareness which can extract prior knowledge of content type directly. Thus, we leverage ICN to optimize SDN TE. However, ICN brings more complexities and dynamics to the network environment, which makes model-based TE methods inefficient. Inspired by recent advances in applying artificial intelligence techniques to solve complex online control problems, we investigate deep learning for content-awareness and DRL for TE decision. In addition, we propose a parallel online learning mechanism to safely utilize DRL which has trial-and-error nature. Results show that our proposal significantly improves network performance in terms of total network throughput, bandwidth utilization, and load balance.
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.