Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional Assessment of Female Yoga Practitioners with Different Levels of Experience

Yoga includes many components for a healthy lifestyle such as physical exercises, abstaining from tobacco, stress management and a low-fat diet. The aim of this study was to assess the nutrition of yoga practitioners and to compare it with international guidelines for a healthy diet. This study comprised 89 female yoga practitioners from Bulgaria. The nutrient intake was assessed by using a food frequency questionnaire based on the USDA National Nutrient Database. The relative intake of proteins, carbohydrates, fats, energy contribution of each nutrient, and the total daily energy intake and relative energy intake were calculated. The BMI of the yoga practitioners was near the lower normal limit (18.5 kg/m2). Only 3 practitioners were overweight. The yoga practitioners consumed small amounts of pork and beef and ~60% did not consume any meat at all. They consumed an average of 600 g of fruits and vegetables per day, which complies with the 400 g recommended by the WHO. Practising yoga helps to maintain normal weight which is one of the prerequisites for a healthy lifestyle. The nutritional assessment of the yoga participants corresponded with the recommendations of the WHO and the American Cancer Society Guidelines for a healthy diet.
Impact of quality attributes and marketing factors on prices for indigenous pork in Vietnam to promote sustainable utilization of local genetic resources

Within the food system transformation in developing countries, demand for high quality products increases. In Vietnam pork demand is rapidly growing, leading to supply shortages of high quality pork.The major objective of this study is identification of important factors affecting indigenous pork prices. Meat from Ban pigs – an indigenous pig breed in Vietnam – is mainly sold through specialized restaurants.Therefore 1944 detailed weekly transaction data for one year period from October 2010 to September 2011 from 33 restaurants in four provinces in the north of Vietnam were analyzed by using a hedonic price model to identify factors influencing Ban pig prices. The empirical results indicate that marketing factors and quality attributes of Ban pig have significant effects on Ban pig prices. An effective method to ensure benefits of increasing Ban prices to smallholders can be seen in the formation and development of a professional breed and marketingorganization that link public and private institutions with an organized farmers association. The latter would safeguard appropriate benefits of the rural poor from a systematic and sustainable marketing of quality-controlled pork of regional origin. At the same time, this would contribute to the sustainable utilization of valuable local genetic resources.
Influence of the value of the pH on the preparation of nano hydroxyapatite – poly vinyl alcohol composites

Many investigators have reported the incorporation of PVA with HA via in situ formation of HA crystals in a PVA solution which leads to the formation of a biodegradable and biocompatible nanocomposite. However a detailed study of the effect of the value of the pH of the reactive solution on the synthesis of HA/PVA composites had not yet been made. Using extensive FTIR and XRD analyses, the aim of the present study is to investigate the affection of the value of the pH on the formation and interaction of organic and inorganic compartments of the composite.
Cluster generators for large high-dimensional data sets with large numbers of clusters

Here, the use of a distribution % & serves to encourage the production of elongated clusters. Data generation is based on a simple trial-and-error scheme. Clusters are iteratively constructed, and a simple heuristic is used to detect overlap between them. Overlapping clusters are rejected and regenerated, until a valid set of clusters has been found. Using this method, 80 different data sets were generated, as described in Table 1. In low dimensions, the clusters generated are frequently elongated and of arbitrary orientation (see Figure 1). However, in higher dimensions (that is, more than 10), the shape of a cluster becomes more (hyper)spherical and more axisaligned: the former because a high variance in one dimension hardly affects the Euclidean distance of points when there are very many dimensions; the latter because with higher dimensions the non-diagonal entries in the covariance matrix are forced to be relatively smaller compared with those on the diagonal. Because of the lack of generality of spherical clusters, we have developed a second Table 1: Gaussian data sets of low dimension. For each of the 8 combinations of cluster number and dimension, 10 different instances were generated, giving 80 data sets in all.
UNSUPERVISED ANOMALOUS SOUND DETECTION USING SELF-SUPERVISED CLASSIFICATION AND GROUP MASKED AUTOENCODER FOR DENSITY ESTIMATION Technical

This technical report outlines our solutions to Task 2 of the DCASE 2020 challenge, Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring . The objective is to detect audio recordings containing anomalous machine sounds in a test set, when the training dataset itself does not contain any examples of anomalies. Our approaches are based on an ensemble of a novel density estimation based anomaly detector (Group Masked Autoencoder for Density Estimation (GMADE)) and self-supervised classiﬁcation based anomaly detector.
ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) IN BIOMETRIC DATA FUSION

ABSTRACT This State-of-the-Art Report focuses on recent advances in biometric recognition technologies and the application of artificial intelligence and machine learning to recognition tasks in multimodal identification systems. Multimodal systems use “feature-level” data fusion (e.g., periocular and gait recognition), which provides faster reference set retrieval across identity templates and significantly improves recognition accuracy over a unimodal system. Specifically, the recent field of biometric data fusion holds promise to deliver improved biometric data sample capture and analysis to the warfighter regardless of disguised, altered, or occluded facial characteristics. The use of convolutional neural networks, deep neural networks, and recurrent neural networks in biometric data fusion is also explained in this report. In addition, topics in leading-edge biometric recognition research are also presented.
DDOS attack detection and mitigation using statistical and machine learning methods in SDN

Software deﬁned networks is the future of networking as it decouples the data plane and control plane of the network devices to provide a centralized control over the network. SDN has abilities to provide superior management and security of a network and allows us to program the network for better ease of use and performance. However, SDN is vulnerable to attacks, DDOS attacks are the most dangerous and threatening attacks in a network, as it can ﬂood the network and block access to the server network with large counts of packets and make use of network resources to deny response for further requests incoming. In a cloud environment DDOS attacks are known only to increase. The method presented is to combine statistical and machine learning methods to eﬃciently detect and mitigate DDOS attacks in SDN. Implementation of this method is done using ryu controller and mininet network simulator with openﬂow SDN protocol, the machine learning algorithm implemented has achieved accuracy of 99.26% and a detection rate of 100% in detecting and mitigating DDOS attacks in a software deﬁned network.
A METHOD OF ANOMALOUS SOUND DETECTION WITH MULTI-DIMENSIONAL AUDIO FEATURE INPUTS

In this technical report, we describe the system we submitted to DCASE2020 task 2 in details, i.e., anomalous sound detection (ASD). The goal is to train a model which can distinguish normal sound and abnormal one when only normal sound samples are used as training data. To achieve this goal, we need to find out the characteristics of normal sound. Firstly, we adopt the preprocessing method to intensify the features of normal audio, secondly, we extract different types of features including artificial features and implicit features. Moreover we also use psychoacoustic features to assist to train the model. Finally, we achieve better performance than DCASE2020 baseline system.
The Impact of Terrorism on Tourism: Pilot Study 2007–2017: (Case Study of Jordan, Egypt, Spain and France)

This study discusses the impact of terrorism on tourism from a hypothesis that whenever certain indicators on the Global Scale of Terrorism increase, tourism rates decline. In other words, the escalation of terrorism in a country leads to a decline in its tourist numbers, financial revenue from tourism and the percentage of jobs provided by the tourism sector. The scientific significance of the study is in providing explanatory theorems about the relationship between terrorism and tourism on the one hand, as it endeavors to provide explanatory theorems about various methods, means and measures taken by the countries of the case study to avoid the potential impact of terrorism on their tourism industries, particularly as tourism is one of the most important sources of income for many countries, including the Arab countries. This study uses comparative methodology, case study methodology and quantitative description methodology. The study shows the validity of its premise by conducting a pilot test on the impact of terrorism on several countries in a case study: France, Spain, Egypt and Jordan. The study concludes that, despite the negative impact of terrorism on tourism in the countries in this case study, hidden variables were found that could reduce the negative impact of terrorism on tourism through governmental intervention, such as the programs of diplomatic attraction and promotion, as seen in some countries like France and Jordan, which contributed to the reduction of the negative impact of terrorism on tourism.
A Customizable Matrix Multiplication Framework for the Intel HARPv2 Platform

General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary. Wecompare our implementation to the latest NVIDIA Pascal GPU and evaluate the performance benefits provided by optimizations built into the hardware template. Using three neural networks (AlexNet, VGGNet and ResNet) we illustrate that reduced precision representations such as binary achieve the best performance, and that the HARPv2 enables fine-grained partitioning of computations over both the Xeon and FPGA. We observe up to 50x improvement in execution time compared to single precision floating point, and the runtime configuration options can improve the efficiency of certain layers in AlexNet up to 4x achieving an overall 1.3x improvement over the entire network.
ANOMALOUS SOUND DETECTION WITH LOOK, LISTEN, AND LEARN EMBEDDINGS Technical Report

The goal of anomalous sound detection is to unsupervisedly train a system to distinguish normal from anomalous sounds that substantially differ from the normal sounds used for training. In this paper, a system based on Look, Listen, and Learn embeddings, which participated in task 2 “Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring” of the DCASE challenge 2020, is presented. The experimental results show that the presented sys-tem signiﬁcantly outperforms the baseline system of the challenge both in detecting outliers and in recognizing the correct machine type or exact machine id. Moreover, it is shown that an ensemble consisting of the presented system and the baseline system performs even better than both of its components.
A new decade for social changes

The growing field of shipping and the changes in the global military strategy of large countries which of course indirectly affect how well a country acts. As an archipelagic nation, Indonesia has the right to conceptualize a Traffic Separation Scheme to improve navigation safety in the Islands Sea Channel, based on Article 53 (6) of the 1982 United Nations Convention on the Law of the Sea (UNCLOS) 1982 and also based on the IMO General Provisions for Adoption, Appointment and Replacement of the Islands Sea Plate. Traffic Separation Scheme (TSS) is a dividing line between the navigation boundaries of trade ships and the territorial waters of national jurisdictions. The Traffic Separation Scheme (TSS) Scheme is a traffic management route system governed by the International Maritime Organization or IMO. Then we can know that Indonesia has the right and obligation to regulate its own sea. Departing from this, the concept of the Traffic Separation Scheme (TSS) and Particularly Sensitive Sea Area (PSSA) was coined, as an effort of embodiment, a derivation of "lex specialis" International provisions adopted by all sea users in the world. This study uses a qualitative method with an explanatory approach. The procedure used to obtain this information is through literature review. Data analysis in this study uses SWOT analysis (strength, weakness, opportunity and threat) and POAC (planning, organizing, actuating and controlling). The result for this case
Language Teacher Wellbeing in the Workplace: Balancing Needs

Teachers who experience high wellbeing in their workplace teach more effectively, have better relationships with learners, and high attainment among their learners (Mason, 2017). To understand what contributes to language teacher wellbeing, we examined the three pillars of positive psychology (Seligman, 2011) and drew in particular on work in Positive Organizational Scholarship (Cameron & Spreitzer, 2012) to explore institutional and personal factors which teachers perceived as influential for their wellbeing. The paper reports on insights from 15 language teachers in 13 different countries. This sampling technique ensured a diverse set of perspectives on this topic. Data were gathered through in-depth, semi-structured interviews which were analyzed using Thematic Analysis (Braun & Clarke, 2006). The analysis revealed five main themes the teachers perceived as relevant for their wellbeing including workplace culture, social relationships, sense of meaning and purpose, language teacher status, and physical wellbeing. The findings highlight that wellbeing is not just a personal and subjective phenomenon, but it is also collectively and socially determined. The study concludes with a reflection on implications for practice, policy makers, and school leaders as well as a consideration of issues of individuality to address in future research.
On the Role of Wireless Sensor Networks in Subsurface Exploration: An Overview

Wireless Sensor Networks (WSN) play a noteworthy role in a wide arena of daily life, like environmental pollution control, habitat monitoring, natural disaster monitoring etc. Despite this fact, a little has been done towards utilizing this technology for the Subsurface Exploration. Subsurface Exploration basically refers to the extraction of hydrocarbons from the oil field. In fact, subsurface exploration is a very established field which has been practiced for a very long time now. It can be observed from the statistics that with the increasing rate of consumption of extracted hydrocarbons there has been no significant increase in the quantity and quality of natural resources extracted. In view of this the oil companies are not only constantly looking towards new sources of oil but also to improve the production of existing wells. This paper presents an overview of research that has been carried out in this direction and also the issues that still need to be debated upon.
Performance Management in Czech Construction: Public Investors’ Perspective

Performance management belongs to crucial managerial activities. This study aims to address how performance management in construction is applied among Czech public organizations on the project level. In order to address this issue, qualitative data has been collected by semi-structured interviews of experienced experts representing organizations owning/operating important facilities and infrastructure (road and rail infrastructure, water and sewage systems, education facilities and collectors). Findings revealed that the level of performance management adoption is rather low, organizations mostly focus on supplier performance evaluation as a response to the ISO 9001 requirements. The practical use of BIM or life-cycle costing is rather in the reflection and preparation phase, on the other hand, a progressive approach is applied in terms of deployment of modern equipment for monitoring of structural defects or the use of robots for maintenance and repairs.
INVESTIGATING OCCUPATIONAL FACTORS LINKED TO BACK PAIN: REPETITIVE LIFTING STRATEGIES AND A METHOD FOR EXAMINING EFFECTS OF VIBRATION

Repetitive lifting is a common occurrence in the occupational setting that has been linked to back injury; therefore, it is important to investigate techniques that might reduce injury. Maduri et al. examined the use of a lumbar-flexion envelope to normalize the lumbar posture to a subject’s range of motion limits as a function of trunk lumbar flexion to examine lumbar motion in lifting and found that subjects often approached the limits of their envelope during the extension phase of lifting (Maduri et al., 2008). The current study hypothesized that, while novice lifters would approach these limits, experienced lifters would maintain a more neutral posture. Further, this study examined whether both novice and experienced lifters could be trained to different coordination strategies using a biofeedback program, specifically one maintaining near the center of the ROM envelope and another highly kyphotic strategy approaching the limits of the ROM envelope. The results show a significant difference between the lifting coordination strategies where the experienced lifters maintained a more neutral coordination even during trained strategies. Novice lifters self-selected a coordination pattern approaching the limits of lumbar ROM and were able to adjust and perform the neutral coordination pattern with the biofeedback training. The experienced lifters self-selected a more neutral coordination pattern, maintaining near the middle of their range of motion, yet they were able to adjust and perform a highly kyphotic coordination pattern with biofeedback training. Future research should examine whether the biofeedback training can influence the self-selected coordination following training.
AUDIO CAPTIONING WITH MESHED-MEMORY TRANSFORMER Technical Report

Automated audio captioning is the task of describing the audio content of a given audio signal in natural language. Through advancing for years, transformer-based language models are widely used in audio captioning. However, most architectures based on transformer, cannot learn prior knowledge between samples well, leading to worse text decoding. For better acoustic event and language modeling, a sequence-to-sequence model is proposed which consists of a CNN-based encoder, a memory-augmented reﬁner and a meshed decoder. The proposed architecture reﬁnes a multi-level representation of the relationships between audio features integrating learned a priori knowledge. At decoding stage, it exploit low-and high-level features with a mesh-like connectivity. Experiments show that the proposed model can achieve a SPIDEr score of 0.2645 on Clotho V2 dataset.
Power Management of a Grid Forming Enabled Hybrid Energy System

2 Acknowledgement: . 3 Table of Content: . 4 List of Figures: . 6 List of Tables: . 8 Chapter 1: Introduction . 9 1.1 Background and Motivation: . 9 1.1.1 Future Trend of Renewable Power Generation: . 10 1.1.2 Wind Energy Conversion System: . 11 1.2 Challenges of renewable energy penetration: . 13 1.2.1 Energy storage technologies as a solution: . 13 1.2.2 Hydrogen Energy Storage: Prospects. 14 1.3 Problem statement: . 15 1.4 Research objectives: . 16 1.5 Research methodology: . 17 1.6 Thesis outline: . 17 Chapter 2: Theoretical Modelling and Analysis of the System Components . 18 2.1 System description: . 18 2.2 WECS modelling: . 18 2.2.1 Pitch angle control: . 22 2.2.2 Drive Train Modelling: . 22 2.2.3. PMSG Modelling: . 24 2.3. Power Converter System: . 26 2.3. 1 Machine side converter (MSC) control: . 27 2.3. 2 Grid Side Converter (GSC) control: . 28 2.4 DC Link Modelling (Dynamics): . 31 2.5 Energy storage modeling: . 32 2.5. 1 Battery Storage modeling: . 32
An introduction to innovative design - Elements and applications of C-K theory

While innovation has always been the lifeblood of any economic system, the forms this notion takes today may seem confusing. These new design regimes require to collectively support creative reasoning which strongly destabilize the traditional representations we have of known objects and organizations. C-K theory was born in Management Science Lab of Mines ParisTech in a modeling effort the activities undertaken in innovative design processes. The prospect of a better understanding of the characteristics of design led to the development of formal and practical aspects of C-K theory. If the theory is now established in the academic world and applied in a variety of industrial contexts, it had not yet been presented to a wider audience. This book presents briefly some elements of theory illustrated by examples as well as tools and methods that have been developed jointly by researchers and their industrial partners.
The complete mitochondrial genome of marine gastropod Sydaphera spengleriana (Deshayes, 1830) (Neogastropoda: Cancellariidae)

Abstract The complete mitochondrial genome of Sydaphera spengleriana was characterized for the first time. The total length of the mitogenome was 16,336 bp including a standard set of 13 protein-coding (PCGs), 22 transfer RNA (tRNA), and 2 ribosomal RNA (rRNA) genes. The overall nucleotides base composition of the heavy strand is A (26.44%), G (19.89%), C (13.25%), and T (40.41%), with an A + T bias (66.85%). With the exception of eight tRNA genes, all other mitochondrial genes are encoded on the heavy strand. The mitogenomic phylogenetic relationships indicated that S. spengleriana was clustered with Bivetiella cancellata within the family Cancellariidae clade with a high bootstrap value.