Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of Mindfulness-Based Cognitive Therapy (MBCT) on cognitive skills in young soccer players

The aim of this study was to examine the effect of Mindfulness-Based Cognitive Therapy training on mental skills of young soccer players. For this purpose, 30 soccer players were assigned to an experimental group of 15 players (intervention based on Mindfulness) and control group of 15 players that participations’ age ranged from 17 to 20 years old in provincial competitions in the year 2017-18. Athletes in the experimental group participated in MBCT course over eight weekly sessions of 2 h each. The control group did not receive any intervention. Before and after interventions Ottawa mental skills assessment tool questionnaire (Omsat-3) were used to collect data. The results of MANOVA showed that significant improvement in all sub-scales scores; Focusing (P=0.001), Refocusing (P=0.001), Imagery (P=0.001), Mental practice (P=0.001), and Competition planning (P=0.001) in the experimental group than control group. According to these findings, we concluded that Mindfulness-Based Cognitive Therapy training is appropriate to improve the level of cognitive skills of soccer players.
Wide-Area Control Schemes to Improve Small Signal Stability in Power Systems

One of the main concerns for the secure and reliable operation of power systems is the small signal stability problem. In the complex and highly interconnected structure of future power systems, relying solely on operator responses and conventional controls cannot assure reliability. Therefore, there is a need for advanced Wide-Area Control Schemes (WACS) that can automatically respond to degradation of reliability in the system. The main objective of this dissertation is to address two key challenges regarding the design and implementation of wide-area control schemes for damping inter-area oscillations. First is the high communication cost associated with optimal centralized control approaches. As power networks are large-scale systems, both the synthesis and the implementation of centralized controllers suggested by most of the previous studies are often impossible in practice. Second is the difficulty of obtaining accurate system-wide dynamic models for initiating and updating the control design. In this research, we introduced wide-area damping control strategies that not only ensure the small signal stability with the desired performance but also consider communication and model information limitations in the design. A state feedback formulation is proposed that aims to simultaneously optimize a standard Linear Quadratic Regulator (LQR) cost criterion and induce a pre-defined communication structure. We solved the proposed problem with three different objectives to target a specific wide-area damping control design challenge in each setting. First, the communication structure is enforced as a constraint in the optimization and solved for a large idealized power network with information symmetry. Second, to make the method suitable for systems with arbitrary structures and information patterns, we proposed a group-sparse regularization to be added to the optimization cost function. Applications of the method for inducing the desired communication network and
Bayesian Inference in MANTID – An Update

In the context of neutron science, Bayesian inference methods have been recently implemented within the MANTID framework [Monserrat D et al. 2015 J. Phys. Conf. Ser. 663 012009 (2015)]. In this contribution, we highlight the advantages of this software package for robust data analysis and subsequent model selection. To this end, we use the celebrated Rosenbrock function to illustrate its merits and strengths relative to classical fitting algorithms. We also introduce the latest additions implemented in MANTID, with a view to increasing its user friendliness as well as stimulating wider use. These include simulated-annealing schemes to reduce the need for initial guesses, as well as new options for multidimensional fitting.
Finite element characterisation of graphene-silicon hybrid waveguides

Recent years have witnessed remarkable progress in nanoscience and nonlinear optics research, paving entry of 2D materials such as graphene, phosphorene, silicene, and so on. Graphene has extraordinary optical, electrical, mechanical and thermal properties. The uniqueness of graphene lies in its modulation depth (> 90%) and signal attenuation (< 5%), achieved by controlling its Fermi level through gate voltage. It is envisioned that graphene-based modulators would be the ultimate high-speed modulator of the future, performing at speeds up to 10 times faster than existing ones. The accuracy of such solutions lies in determining the permittivity (er) of materials used. Graphene possesses complex conductivity (Ϭ(ω) = Ϭ1 + jϬ2) and permittivity (e(ω) = e1 + je2). Using Kubo formalism, we derived an analytical method for finding the conductivity of graphene (Ϭg,interband +Ϭg,intraband) and then the complex permittivity (eg). The values of e(ω) are plotted as a function of chemical potential (μ, eV), wavelength (λ, nm) and thickness of graphene layers (t(g)). Benchmarking was carried out using two solvers viz., complex and perturbation to ascertain the suitability of the method. Effective mode index (n(eff)) and absorption (α) are calculated for quasi-TE and quasi-TM guided modes of the waveguide. We found that the waveguide performance parameters are highly influenced by the position of graphene layers in the waveguide and the thickness and type of dielectric material that encapsulate the graphene layers. Two positions viz., graphene-as-top layer and graphene-as-slot layer were analysed. Three dielectric materials, hBN, Al2O3 and HfO2, classified as low-, high-and very-high index, respectively, are chosen. For operation wavelength range (1.3{1.7μm) and for varying dielectric layer thickness from 5 to 70 nm, the plots for neff and α (dB/μm) are obtained. Performance parameters such as extinction ratio (ER) and insertion loss (IL) were calculated for varying dielectric thickness (5-70 nm). ER and IL are achieved within the ranges 20-70 and 3-4 dB/μm, respectively. We inferred that to enhance the modal properties (n(eff) and α), graphene-as top layer waveguide can have a combination of very high-index (er = 25) dielectric material (thickness > 15 - 20 nm) encapsulating graphene whereas a very high-index dielectric with reduced thickness (< 5 - 10 nm) encapsulating graphene is suitable for graphene-as-slot layer waveguide.
Research on dynamic location model and algorithm of logistics distribution centers

Aiming at the problem of all possible states and the inventory holding cost not completely considered in general dy-namic facility location model,this paper developed a new model. Firstly,obtained the formula of inventory cost in per period with storage and traffic capacity constraints through two steps approximately method. Then,gave the formulas of opening,operation,closing and reopening cost in planning horizon,and developed a new dynamic facility location model. Finally,solved the model by genetic algorithm,clone selection algorithm,particle swarm optimization respectively,and compared the capacities of finding optimal solution,stability,counting speed and astringency between these algorithms. The results of numerical example show that the model is effective and the genetic algorithm is the most suitable for the problem.
IOT based Automated Intrusion Detection System

The significance of home security framework can't be denied in the present crime-ridden society. Home security is getting to be noticeably important these days as the potential outcomes of interruption are expanding day by day. Protection from burglary, leaking of crude gas and fire are the most vital prerequisites of home security systems for individuals. The most common system used for such purpose is CCTV camera. However, it is expensive, uses excessive space for keeping records and furthermore expects manpower to supervise the unapproved action. In this paper, we proposed a system which is cost effective with better resolution and can detect intrusion single-handedly. Here, PIR motion sensor, along with the Raspberry Pi, is used to detect motion and once it is activated, a video is triggered via Pi camera module. IOT based applications can be used remotely to view image and get email on intrusion detection.
A cognitive model interfacing eye movements with parsing

• Rereading from disambiguating region entraron/entraran occurs more often in the high-attachment condition due to repair of initial low attachment. • According to the good-enough account, h igh-WMC participants attach more often than low-WMC and thus have to reanalyse more often in the high-attachment condition. • For comparison: The alternative account of an unrestricted race model (Traxler et al . , 1 998) wrongly predicts differences beteen b. and c. Ambiguity advantage
Combining Singular Value Decomposition and Neighbor-based Method in Collaborative Filtering

Collaborative filtering is becoming a popular technique for reducing information overload.However,it has three major limitations,accuracy,data sparsity and scalability.We propose a new collaborative filtering algorithm to solve the problem of data sparsity.We utilize the results of singular value decomposition for neighbors selecting,then use the neighborhood-based method to produce the prediction of unrated items.Our experimental results on EachMovie dataset show that the algorithm outperforms the conventional neighborhood-based method and SVD method when the available ratings are sparse.
Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression

One of the major challenges in current Computer-Aided Detection (CADe) of polyps in CT Colonography (CTC) is to improve the specificity without sacrificing the sensitivity. If a large number of False Positive (FP) detections of polyps are produced by the scheme, radiologists might lose their confidence in the use of CADe. In this chapter, the authors used a nonlinear regression model operating on image voxels and a nonlinear classification model with extracted image features based on Support Vector Machines (SVMs). They investigated the feasibility of a Support Vector Regression (SVR) in the massive-training framework, and the authors developed a Massive-Training SVR (MTSVR) in order to reduce the long training time associated with the Massive-Training Artificial Neural Network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, the authors proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. They compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks’ lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks’ lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, the authors compared it with the original MTANN in the distinction between actual polyps and various types of FPs
Pansharpening via Detail Injection Based Convolutional Neural Networks

Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains. In this paper, we design a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injection based CNN (DiCNN1) mines MS details through the PAN image and the MS image, and the second one (DiCNN2) utilizes only the PAN image. The main advantage of the proposed DiCNNs is that they provide explicit physical interpretations and can achieve fast convergence while achieving high pansharpening quality. Furthermore, the effectiveness of the proposed approaches is also analyzed from a relatively theoretical point of view. Our methods are evaluated via experiments on real MS image datasets, achieving excellent performance when compared to other state-of-the-art methods.
The Urban Food Question in the Context of Inequality and Dietary Change: A Study of Schoolchildren in Accra

Abstract Diets are changing globally, as agricultural and food systems have become globalised. Understanding how patterns of globalisation affect welfare is a key development question, but we know little about the way that the globalisation of food systems impacts different groups. This study explores food security and consumption among schoolchildren in Accra. We use a novel approach based on triangulation of primary data on food consumption and a synthesis of secondary literature on food trade, policy and urban food environment. Thus, we bridge a divide between micro-level analyses of food consumption and macro-level studies of food systems. We find that socio-economic status is a critical dimension, with poorer children more vulnerable to food insecurity and narrow dietary diversity. However, the consumption of packaged and processed foods, often sugar-rich and nutrient-poor, cuts across wealth groups. We argue that the urban food question today is defined by two intersecting phenomena: inequality and dietary change. The urban poor continue to face the fundamental challenge of adequate food access amidst a food environment that provides consumers with unhealthy and cheap food options. Therefore, food policy needs to regulate imports of cheap, unhealthy and enticing food.
How inference isn’t blind: Self-conscious inference and its role in doxastic agency

2 Acknowledgments . 4 Introduction . 9 Summary of chapters . 13 1. Inference, self-consciousness and an ontological quandary . 17 1.1. What is inference? 17 1.2. Inference as a conscious act . 18 1.3. Inference and belief fixation . 20 1.4. Inference and judgment . 23 1.5. The possibility of doxastic agency . 27 1.6. The self-consciousness of inference as a constraint in what follows 33 1.7. Overintellectualization . 34 1.8. An ontological quandary . 35 1.9. Theoretical and practical inference . 40 1.10. Conclusion . 41 2. Causalist accounts of inference . 43 2.1. The causalist conception of inference . 44 2.2. The causalist conception’s motivation.45 2.3. The Taking Condition . 49 2.4. Two versions of causalism . 54 2.5. The case for the Taking Condition . 55 2.6. The case against the Taking Condition: overintellectualization . 59 2.7. The case against the Taking Condition: Carroll’s regress . 60 2.8. Trouble for the causalist: inferences beyond one’s ken . 64 2.9. Causalism’s motivation undermined . 67
Single-signal entity approach for sung word recognition with artificial neural network and time–frequency audio features

Singing voice recognition is very different from speech recognition or automatic speech recognition because there are distinct differences between speaking and singing voices. The problem is complex because music audio signals with their background instrumental accompaniments are regarded as noise sources that degrade the performance of the recognition system. This study proposes a statistical learning method to recognise words in a vocal audio signal with background music and to classify the region of a singing voice in a polyphonic audio signal. The goal of this study is to solve the problem of recognising words from sung input without using any method to separate instrumental from the background. This study also applies a concept from image recognition by using a spectrogram feature as an image to solve the problem. An audio signal with accompanying music was analysed and transformed into a spectrogram feature. To recognise it, the entire spectrogram feature was sliced, forming a feature vector for a feed-forward neural network classifier. Several classification functions were compared, including K-Nearest Neighbour, Fisher Linear Classifier, Linear Bayes Normal Classifier, Naive Bayes Classifier, Parzen Classifier and Decision Tree. The results show that using a feed-forward neural network can effectively recognise sung words at an accuracy rate of more than 93.0%. In particular, this system can recognise cross-language music data.
UPAYA MELESTARIKAN BUDAYA INDONESIA DI ERA GLOBALISASI

The era of globalization can lead to changes in the lifestyle of a more modern society. As a result, people tend to choose a new culture that is considered more practical and simpler than the local culture. One of the factors that cause local culture to be forgotten in the present is; lack of future generations who have an interest in learning and inheriting their own culture. Therefore, this article aims to describe the efforts to maintain Indonesia culture in the era of globalization. According to Malinowski, higher and more active cultures will influence lower and passive cultures through culture contact.
BTSM protocol monitoring scheme in GSM user perception system

The BTSM protocol monitoring is the foundation of GSM user perception system.Based on the user perception system,the paper proposes a kind of scheme on realizing BTSM protocol monitoring.Firstly,the paper introduces the user perception system and Abis protocol stack,then focuses on the decoding scheme and CDR synthesis algorithm of BTSM protocol.Using methods encapsulation and hash algorithm the authors implemented the detailed decoding,simple decoding as well as CDR process synthesis according to the programming code.Finally,the scheme was applied to the user perception system for testing.According to the test of network data,it is verified that the test result is correct,and the effect is good.
ANALISIS METODE ALTMAN Z-SCORE SEBAGAI ALAT PREDIKSI KEBANGKRUTAN DAN PENGARUHNYA TERHADAP HARGA SAHAM PADA PERUSAHAAN TEKSTIL DAN GARMEN YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2014-2016

ABSTRACT The purpose of this study was to determine the predicted outcome of bankruptcy and also to test there is influence between independent variable is the value of the model financial ratios Altman Z-score as a predictor of bankruptcy with the dependent variable is price stock.this research take secondary data through the financial statements of each company in Indonesia Stock Exchange website. The sample used in this study were fifteen industrial company textile and garment sector on Indonesia Stock Exchange. using normality test and simple linear regression. The results showed that from 2014 to 2016 the majority of the industrial company textile and garment sector on Indonesia Stock Exchange in the category of companies that grey area and bankcrupt. hypothesis testing results of this study showed insignificant relationship between predicted bankruptcy by using the value of financial ratios Altman Z-score and the stock price at the company industry textile and garment sector on Indonesia Stock Exchange. Keywords: Altman Z-score, prediction of bankrupty, the stock price
Real Time Routing in Wireless Sensor Networks

- As the sensor nodes have limited energy resources, the protocol designed for the wireless sensor networks should be energy efficient and provide low latency. Conventional routing protocols such as LEACH, PEGASIS, CCS and TSC use fixed deployment of nodes. Here we compare adaptive CCS and adaptive track sector clustering scheme when the nodes are deployed randomly in the network and the cluster head (CH) are chosen with respect to minimum distance from the base station (BS). The simulation results show that adaptive TSC performs better compared to other protocols when the nodes are deployed randomly
TECHNOLOGY IMPLEMENTATION OF DFA PARSER FOR MANUFACTURING QUERY LANGUAGE TOKENS

Installing a manufacturing method might be very expensive and time consuming project. Organization should examine and decide on how best to make this decision of selecting appropriate process meeting their requirements. In order to improve the manufacturing cycle more than 110 manufacturing processes have been proposed. The objectives aimed at and the functions focused on by these processes vary. The process should be flexible enough to accommodate reasonable changes in design. This poses a great challenge to a manager in selection of effective and economical manufacturing process. Different organizations have different objectives and based on their specific requirement they deploy suitable process conforming to their objective. Today's business scenario is highly competitive, complex and dynamic in nature which demands strategic planning meeting the challenges of changing time. Recently, we have developed a tool to enable the end user a quick selection of appropriate manufacturing method based on multiple objectives. The information pertaining to the method selection is stored in a persistent Relational DataBase Management System (RDBMS) which can be manipulated by the end user as the organizational objectives and the market needs change. The end user instead of querying the database directly will use the natural language, termed as Manufacturing Query Language (MQL) designed by us, which is interfaced with RDBMS using prolog. To implement MQL, we have defined a finite set of symbols, words and language rules, MQL grammar. In this paper we present a deterministic finite automata (DFA) parser developed by us for parsing MQL tokens. The state table and state diagrams are developed for different tokens of MQL identified by us. State information is stored in a persistent database management system as a measure towards improving efficiency and extensibility. Currently, MQL consists of only few commands and more commands will be added to MQL in near future.
Building Distributed Systems with Informational Flow Control

class Servlet { // allows servlets to specify // a default action protected Action{req} defaultAction(Request req); // allows servlets to create a // servlet-specific SessionState object protected SessionState createSessionState(); public void setReturnPage{*:req.session}( Request{*:req.session} req, label out, label in, Node[out,in]{*in} page) where {*out;*in} <= {*:req.session}; } abstract class Action { public abstract void invoke{*lbl}(label{*lbl} lbl, Request{*lbl} req) where caller(req.session); }class Action { public abstract void invoke{*lbl}(label{*lbl} lbl, Request{*lbl} req) where caller(req.session); } // base class of HTML elements abstract class Node[label Out, label In] { } Figure 2.2: Jif signatures for the SIF Servlet class Servlet framework. Figures 2.2 and 2.3 show a simplified Jif signature for the Servlet class, as well as other key classes of SIF. The important aspects of these signatures are explained as they arise, but because of space limitations, the syntax of Jif methods and fields are not fully explained. Web clients establish sessions with the servlet; sessions are tracked by the servlet container, as in the Java Servlet specification. The SIF framework creates a session principal for each session, which can be thought of as corresponding to the session key shared between the client and server [37], if such a key exists. The application would typically define its own user principals, which can delegate to the session principal. Step 2: HTTP request wrapped in a Request object. The class Request is a SIF wrapper class for an HTTP request, providing restricted access to informa-