Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Introduction to MPEG-7 : Multimedia Content Description Interface

MPEG is intimately connected to digital audio and video. However, when MPEG made its first breath, bits were already abundant, but they were kind of ‘heavy’ bits. They were part of Pulse Code Modulation (PCM) samples of music stored on compact discs. No one thought of storing or moving a song around in digital form when this meant to store or move 50 MB, unless this was done in a special environment like a studio. The only known way of moving audio and video was in the shape of analog waveforms.
Elevational Effects on the Growth and Development of Tadpoles of Sauter ’ s Frog Rana sauteri Boulenger in Taiwan

The patterns of growth and development of tadpoles of Rana sauteri at Chitou (1100 m) and Mienyueh (2350 m) were investigat ed. At Chit ou, tadpoles of different developmental stages could be found from December to March, indicating that the larval period is prolonged, and the growth and development of tadpoles are not synchronized. At Mienyueh, the majority of tadpoles ceased to develop after reaching phase 2 in August even though they did not stop growing until December. Tadpoles resumed growth and development the following spring, and underwent metamorp hosis by June. It appears t hat tadpoles ceased to develop early enough so that they could channel a greater proportion of energy into growth to obt ain a larger size to better survive the cold winter. In addition, t adpoles at M ienyueh ceased to develop early enough so that they could overwinter in phase 2, a simpler type, to reduce the risk of limb fros tbite and larval mortality during the extreme cold temperatures bet ween December and February. Our results suggest that the different patterns of larval growth and development in these two R. sauteri populations are adaptive strategies for t he survival of tadpoles in the mountains of Taiwan.
Development and Field Evaluation of a Multiple Slit Nozzle-Based High Volume PM

This study presents the development and lab performance of a high volume (Q = 950 LPM), multiple slit nozzle-based PM2.5 (particle aerodynamic diameter < 2.5 μm) inertial impactor and the performance of the same was compared with an existing single stage low flow rate (Q = 15 LPM) PM2.5 impactor. Lab experiments were performed on various slit-based nozzle impactors using polydisperse dolomite powder as test aerosol. After carrying out rigorous parametric evaluation, the optimum slit nozzle-based impactor configuration selected had cutoff size of 2.51 μm (aerodynamic diameter) at an operating flow rate of 215 LPM (medium flow) with a pressure drop of 0.35 kPa across the impactor stage. The length of the slit of this optimum medium flow impactor was extrapolated to a flow rate of 950 LPM to obtain the high volume multiple slit nozzle-based PM2.5 inertial impactor assembly. This novel impactor assembly was fabricated from brass and chrome-plated and then retrofitted in a high volume dust sampler (Model AAS 217NL, Ecotech Instruments, India) downstream of the PM10 cyclone separator. High vacuum silicone grease was used as the impaction substrate. A field study was performed with co-located novel high volume impactor assembly (HVIA) and single stage low flow rate PM2.5 impactor, to not only compare the PM2.5 mass concentrations but elemental, anion and water soluble organic carbon (WSOC)/water soluble inorganic carbon (WSIC) concentrations as well in order to validate the HVIA developed in the present study.
Attention Based Hybrid i-Vector BLSTM Model for Language Recognition

In this paper, a hybrid i-vector neural network framework (iBLSTM) which models the sequence information present in a series of short segment i-vectors for the task of spoken language recognition (LRE) is proposed. A sequence of short segment i-vectors are extracted for every speech utterance and are then modeled using a bidirectional long short-term memory (BLSTM) recurrent neural network (RNN). Attention mechanism inside the neural network relevantly weights segments of the speech utterance and the model learns to give higher weights to parts of speech data which are more helpful to the classification task. The proposed framework performs better in short duration and noisy environments when compared with the conventional i-vector system. Experiments are performed on clean, noisy and multi-speaker speech data from NIST LRE 2017 and RATS language recognition corpus. In these experiments, the proposed approach yields significant improvements (relative improvements of 7.6 13% in terms of accuracy for noisy conditions) over the conventional i-vector based language recognition approach and also over an end-to-end LSTM-RNN based approach.
One-Shot Voice Conversion with Global Speaker Embeddings

Building a voice conversion (VC) system for a new target speaker typically requires a large amount of speech data from the target speaker. This paper investigates a method to build a VC system for arbitrary target speaker using one given utterance without any adaptation training process. Inspired by global style tokens (GSTs), which recently has been shown to be effective in controlling the style of synthetic speech, we propose the use of global speaker embeddings (GSEs) to control the conversion target of the VC system. Speaker-independent phonetic posteriorgrams (PPGs) are employed as the local condition input to a conditional WaveNet synthesizer for waveform generation of the target speaker. Meanwhile, spectrograms are extracted from the given utterance and fed into a reference encoder, the generated reference embedding is then employed as attention query to the GSEs to produce the speaker embedding, which is employed as the global condition input to the WaveNet synthesizer to control the generated waveform’s speaker identity. In experiments, when compared with an adaptation training based any-to-any VC system, the proposed GSEs based VC approach performs equally well or better in both speech naturalness and speaker similarity, with apparently higher flexibility to the comparison.
A Survey of Various Frameworks and Solutions in all Branches of Digital Forensics with a Focus on Cloud Forensics

Digital forensics is a class of forensic science interested with the use of digital information produced, stored and transmitted by various digital devices as source of evidence in investigations and legal proceedings. Digital forensics can be split up to several classes such as computer forensics, network forensics, mobile forensics, cloud computing forensics, and IoT forensics. In recent years, cloud computing has emerged as a popular computing model in various areas of human life. However, cloud computing systems lack support for computer forensic investigations. The main goal of digital forensics is to prove the presence of a particular document in a given digital device. This paper presents a comprehensive survey of various frameworks and solutions in all classes of digital forensics with a focus on cloud forensics. We start by discussing different forensics classes, their frameworks, limitations and solutions. Then we focus on the methodological aspect and existing challenges of cloud forensics. Moreover, the detailed comparison discusses drawbacks, differences and similarities of several suggested cloud computing frameworks providing future research directions. Keywords—Digital forensics; cloud forensics; investigation process; IoT forensics; examination stage; evidence
Screening of hepatocellular carcinoma hub genes and construction of a control network

Background: Hepatocellular carcinoma (HCC) is a malignant tumor that threatens human life and health. However, the molecular mechanisms of hepatocarcinogenesis remain unclear. Aim: The current study aimed to explore the molecular mechanisms of HCC through screening and functional analysis of differentially-expressed genes of HCC, providing a new method for diagnosis and treatment of HCC. Methods: Gene expression arrays of 14 pairs of HCC tissues and corresponding non-cancerous tissues were obtained from the GEO database. Next, this study identified differentially-expressed genes using the R program. The gene function and protein-protein interaction (PPI) network of DEGs was analyzed using the DAVID website and STRING database. Visualization was conducted with Cytoscape 3.7. 0 software. An MCC algorithm was used to screen high connectivity hub genes. At the same time, a hub gene-miRNA regulatory network was constructed. Finally, verification of the hub genes was conducted using Kaplan-Meier and UALCAN websites. Results: A total of 285 DEGs were identified to be significantly associated with HCC tissues, including 56 upregulated and 229 downregulated genes. Gene ontology analysis revealed that downregulated genes were mainly involved in Redox and metabolic processes. Furthermore, 20 hub genes were screened. Five genes, including RRM2, CCNB1, CDK1, NCAPG, and KIF20A, were found to be highly regulated by miRNAs. Validation results showed that expression of 20 hub genes was significantly higher in HCC tissues and closely related to prognosis of HCC patients. Conclusion: Through bioinformatics analysis, the roles of DEGs in HCC were examined. Thus study may provide a new molecular target for diagnosis and treatment of HCC, providing a theoretical basis for further study of the molecular mechanisms of HCC.
Nonparametric Quantile Regression for Prediction of Parking Data

In this paper we will explore the approach taken by Juban, Ohlson, Maasoumy, Poirier, and Kolter in their paper, “A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014” [1] in detail as well as discuss general nonparametric quantile regression methods. Moreover, we have implemented the algorithms in [1] and created a scikit learn style python package1 to allow for the methods to be applied to generic data sets. We will present and discuss the results of using our implementation of the approach on parking occupancy data from the city of Seattle, WA.
PC-BASED OPEN ARCHITECTURE SERVO CONTROLLER FOR CNC MACHINING

There is an ever-increasing demand from the Industry for a flexible, modular and a cost-effective CNC machine servo controller. The primary aim of this research is to develop a real-time PC-based servomotor control system. Such a machine controller is not only required to perform control functions but also do other functions like database maintenance, tool path planning and optimization, and operator interface among many other useful functions. In realizing this objective, the environment is not only designed keeping in view the Open Architecture Controller (OAC) specifications but also to launch a platform for total factory automation. Good fault-tolerance, factory floor networking and flexibility in terms of software are considered in designing the system. RT-Linux has been chosen as the suitable real-time platform with Windows NT as the front-end for the system. RT-Linux is very reliable and gives a good performance with a worst case scheduling latency of 12 m sec and a worst case interrupt latency of 10 m sec on a 650MHz Pentium III processor. The rationale behind choosing various other components of the system that makes it possible to meet the OAC specifications and the functional requirements is elaborated in this paper. In addition, system working environment and some of its good features will be discussed. This open source based CNC servo control platform is a promising technology for the future of factory automation.
Intelligent Custom Garments Project ( ICGP ) : Body Parameters Measuring Via Mobile Phone Photos

Online clothing purchase is gaining in popularity today. The most common problem in purchasing clothes online is the clothes are not fit properly. To solve this problem, we introduce an innovative project: Intelligent Custom Garments Project (ICGP). ICGP provides a new way of online clothing purchase: measuring customers’ body parameters via mobile phone photos and then customizing formfitting clothes online. In ICGP, a mobile phone application is designed for users to take and upload two specific photos; a program is developed for intelligently locating key points in the photos; “transfer functions” and “measure functions” are explored for measuring users’ body parameters according to key points and clothes are customized by garments factories according to the body parameters. 32 subjects are tested and the result shows that ICGP is successful.
Yap 1 Phosphorylation by c-Abl Is a Critical Step in Selective Activation of Proapoptotic Genes in Response to DNA Damage

Cells undergo apoptosis upon exposure to severe DNA damage stress. Under this condition, p73 is phosphorylated and activated by c-Abl. The transcription coactivator Yap1 binds p73 to generate a complex that escapes p73 proteasomal degradation and recruits p300 to support transcription of proapoptotic genes. However, the mechanism of selective activation of proapoptotic genes by Yap1 remained unclear. In this study, we show that c-Abl directly phosphorylates Yap1 at position Y357 in response to DNA damage. Tyrosine-phosphorylated Yap1 is a more stable protein that displays higher affinity to p73 and selectively coactivates p73 proapoptotic target genes. Furthermore, we show that Yap1 switches between p73-mediated proapoptotic and growth arrest target genes based on its phosphorylation state. Thus, our data demonstrate that modification of a transcription coactivator, namely the DNA damage-induced phosphorylation of Yap1 by c-Abl, influences the specificity of target gene activation.
Who Are We ? Social Identity and Psychological Well-Being

Social identification – the perception of the self as an exemplar of a social category – forms an essential part of an individual’s identity. Previous studies have found a relationship between various forms of social identity (e.g., ethnic, religious, national) and psychological well-being. The current research examines whether strength of social identification, regardless of specific identity and pattern of acquisition, correlates with life satisfaction, self-esteem, depression, anxiety, and stress. One hundred and two participants completed an internet survey including the MultiComponent Social Identity Scale and multiple measures of psychological well-being. Results indicated that the strength of social identification predicted life satisfaction and self-esteem, controlling for demographic and clinical (i.e., depression, anxiety, and stress) variables. Possessing a strong social identity of any type appears to be an important correlate of life satisfaction and selfesteem. Potential implications for clinical work are discussed.
On evaluation of Network Intrusion Detection Systems: Statistical analysis of CIDDS-001 dataset using Machine Learning Techniques

In the era of digital revolution, a huge amount of data is being generated from different networks on a daily basis. Security of this data is of utmost importance. Intrusion Detection Systems are found to be one the best solutions towards detecting intrusions. Network Intrusion Detection Systems are employed as a defence system to secure networks. Various techniques for the effective development of these defence systems have been proposed in the literature. However, the research on the development of datasets used for training and testing purpose of such defence systems is equally concerned. Better datasets improve the online and offline intrusion detection capability of detection model. Benchmark datasets like KDD 99 and NSL-KDD cup 99 obsolete and do not contain network traces of modern attacks like Denial of Service, hence are unsuitable for the evaluation purpose. In this work, a detailed analysis of CIDDS-001 dataset has been done and presented. We have used different well-known machine learning techniques for analysing the complexity of the dataset. Eminent evaluation metrics including Detection Rate, Accuracy, False Positive Rate, Kappa statistics, Root mean squared error have been used to show the performance of employed machine learning techniques.
Tied Mixture of Factor Analyzers Layer to Combine Frame Level Representations in Neural Speaker Embeddings

In this paper, a novel neural network layer is proposed to combine frame-level into utterance-level representations for speaker modeling. We followed the assumption that the frame-level outputs of the speaker embedding (a.k.a x-vector) encoder are multi-modal. Therefore, we modeled the frame-level information as a mixture of factor analyzers with latent variable (utterance embedding) tied across frames and mixture components, in as similar way as in the i-vector approach. We denote this layer as Tied Mixture of Factor Analyzers (TMFA) layer. The optimal value of the embedding is obtained by minimizing the reconstruction error of the frame-level representations given the embedding and the TMFA model parameters. However, the TMFA layer parameters (factor loading matrices, means and precisions) were trained with cross-entropy loss as the rest of parameters of the network. We experimented on the Speaker Recognition Evaluation 2016 Cantonese as well as in the Speaker in the Wild datasets. The proposed pooling layer improved w.r. t. mean plus standard deviation pooling–standard in x-vector approach– in most of the conditions evaluated; and obtained competitive performance w.r. t. the recently proposed learnable dictionary encoding pooling method, which also assumes multi-modal frame-level representations.
An Exploratory Study of Functional Status in Post Cardiac Arrest Survivors Discharged to Home

Abstract : Cardiopulmonary arrest is a major health problem claiming 350,000 to 450,000 lives per year in the United States. Aims of the project were to describe perceived functional capacity, physical functional performance, mental health, symptom distress, and demographic factors in survivors of cardiopulmonary arrest after discharge to home and to examine the relationship among perceived functional capacity, mental health and physical functional performance in the survivor of cardiopulmonary arrest after discharge to home. As a comparison group, non-cardiac arrest post-myocardial infarction subjects actively engaged in cardiac rehabilitation were recruited. Overall significant findings suggested that in the cardiac arrest group, symptom distress had a negative influence in the ability to participate in social activities and roles at work or within family settings. Symptom distress also had an inverse relationship in the total number of daily steps taken. As symptom distress increased, daily average steps decreased suggesting that control of symptoms such as nausea and fatigue are important factors in functional status and mental health in this sample.
Atlas of small animal ultrasonography

Contributors vii Preface ix About the CompanionWebsite xi 1. Practical Physical Concepts and Artifacts 1 Marc-Andre d Anjou and Dominique Penninck 2. Eye and Orbit 19 Stefano Pizzirani, Dominique Penninck and Kathy Spaulding 3. Neck 55 Allison Zwingenberger and Olivier Taeymans 4. Thorax 81 Silke Hecht and Dominique Penninck 5. Heart 111 Donald Brown, Hugues Gaillot and Suzanne Cunningham 6. Liver 183 Marc-Andre d Anjou and Dominique Penninck 7. Spleen 239 Silke Hecht andWilfried Mai 8. Gastrointestinal Tract 259 Dominique Penninck and Marc-Andre d Anjou 9. Pancreas 309 Dominique Penninck and Marc-Andre d Anjou 10. Kidneys and Ureters 331 Marc-Andre d Anjou and Dominique Penninck 11. Bladder and Urethra 363 James Sutherland-Smith and Dominique Penninck 12. Adrenal Glands 387 Marc-Andre d Anjou and Dominique Penninck 13. Female Reproductive Tract 403 Rachel Pollard and Silke Hecht 14. Male Reproductive Tract 423 Silke Hecht and Rachel Pollard 15. Abdominal Cavity, Lymph Nodes, and Great Vessels 455 Marc-Andre d Anjou and Eric Norman Carmel 16. Clinical Applications of Contrast Ultrasound 481 Robert O Brien and Gabriela Seiler 17. Musculoskeletal System 495 Marc-Andre d Anjou and Laurent Blond 18. Spine and Peripheral Nerves 545 Judith Hudson and Marc-Andre d Anjou Index 563
Assimilation of AMSU-A Microwave Radiances with an Ensemble Kalman Filter (EnKF)

Introduction The ensemble Kalman filter (EnKF) is a 4D data assimilation method that uses a Monte-Carlo ensemble of short-range forecasts to estimate the covariances of the forecast error (Evensen 1994; Burgers et al. 1998; Houtekamer and Mitchell 1998). It is a close approximation to the standard Kalman filter. The approximation becomes more accurate as the ensemble size increases. The EnKF is conceptually simple. It does not depend strongly on the validity of hypotheses about the linearity of the model dynamics and requires neither a tangent linear model nor its adjoint. In addition, it parallelizes well. Like most modern data assimilation methods, the EnKF directly assimilates observed radiance data. This aspect of the EnKF, and in particular the assimilation of AMSU-A microwave radiances, is the focus of this presentation. First, the EnKF and the experimental environment are briefly described. Then we focus on how the EnKF assimilates the AMSU-A microwave radiances and show some results indicating their impact with the EnKF (including a comparison with similar results from a 3D-Var system). The present text ends with some concluding remarks and a brief outline of our future plans in this area.
A Study of Sojourn Time for Indoor LiFi Cellular Networks

Sojourn time is an important parameter in the analysis and design of mobile cellular networks. It shows the expected time that a user equipment (UE) remains connected to the serving access point (AP) while moving in the network, i.e. stays within one cell. Therefore, an accurate estimation of the sojourn time is an essential element for mobility management. In this study, an analysis of the sojourn time for indoor light-fidelity (LiFi) cellular networks is presented based on the random waypoint (RWP) mobility model. It is initially assumed that the UE is oriented vertically upward and closed-form analytical expressions are derived. Monte-Carlo simulations are also provided to validate the analytical derivations and to gain more insight into the performance with different orientations. It is shown that the sojourn time mainly varies depending on the speed of the user. However, other factors such as the device orientation and the coverage area of cells also play a role in determining the sojourn time. The results offer several insights and design guidelines in consideration of this parameter for indoor LiFi networks.
Linguistically Motivated Parallel Data Augmentation for Code-Switch Language Modeling

Code-switch language modeling is challenging due to data scarcity as well as expanded vocabulary that involves two languages. We present a novel computational method to generate synthetic code-switch data using the Matrix Language Frame theory to alleviate the issue of data scarcity. The proposed method makes use of augmented parallel data to supplement the real code-switch data. We use the synthetic data to pre-train the language model. We show that the pre-trained language model can match the performance of vanilla models when it is ﬁne-tuned with 2.5 times less real code-switch data. We also show that the perplexity of a RNN based language model pre-trained on synthetic code-switch data and ﬁne-tuned with real code-switch data is signiﬁcantly lower than that of the model trained on real code-switch data alone and the reduction in perplexity translates into 1.45% absolute reduction in WER in a speech recognition experiment.