Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving protein structure prediction using amino acid contact & distance prediction

With more and more protein sequences generated, one of the most pressing tasks in bioinformatics has become to interpret these data. This thesis concerns how to predict the 3D structure of a protein relying on its sequence only, which is a long-standing problem in computational biology. A commonly adopted intermediate step for this task is to predict pairwise amino acid contacts based on the query sequence. Due to the simplicity of the current algorithms, which include statistical models and machine learning techniques, the accuracy of contact prediction is still low for many proteins. Also, these available algorithms are unable to predict amino acid distances (distance longer than contact). Thus, the lack of high quality and enough geometry constraints make it difficult for 3D structure prediction for many proteins. To deal with the current limitations of amino acid constraint and structure prediction, a state-of-the-art deep neural network based amino acid contact & distance prediction algorithm, DeepCDpred, is proposed in this thesis. For a given query protein sequence, the geometry constraints predicted by DeepCDpred are fed into a Rosetta ab initio modelling protocol for protein structure prediction. In addition, a neural network-based method is proposed to evaluate the quality of predicted structures. The accuracies of amino acid contact and distance predictions, the quality of structure predictions and the accuracy of confidence score predictions were evaluated by a test set of 108 protein chains whose experimental structures are known. Any sequence in the test set shares no greater than 25% sequence identity with any sequence in the training set, which was used to train DeepCDpred. The accuracy of amino acid contact predictions of DeepCDpred is just slightly worse than a newly published method, RaptorX; but exceeds all others mentioned in this thesis. Thanks to the predicted extra distance constraints and the Rosetta ab initio modelling protocol, the structure prediction quality based on the algorithms proposed in this study is better than that from the RaptorX server. A blind test, which was done with a yet to be released protein, was also used to validate the effectiveness of DeepCDpred. The protein classes of structures predicted with amino acid contact constraints from MetaPSICOV (the amino acid contact predictor, which DeepCDpred is most often compared within this thesis), are analysed and compared to the predictions based on contact constraints from DeepCDpred, and also to the predictions based on both contact and distance constraints from DeepCDpred. An online server, http://proteincoevolution.bham. ac.uk, is programmed and released to make the proposed methods for amino acid contact and distance predictions, structure prediction and structure confidence prediction accessible to average users, and it is expected beneficial to the research community.
Automatic Taxonomy Induction and Expansion

The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called Smart SpreadSheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction and expansion features of KGIS. A screencast video demonstrating the system is available in https://ibm.box. com/v/emnlp-2019-demo .
Similar imageretrieval based on texture feature vector using Local Octal and Local Hexadecimal Pattern and comparison with Local Binary Pattern

Local binary patterns (LBP) is a very powerful texture feature of an image. Many variants of LBP models are available and almost all of the derived models are based on the idea to calculate the difference of each central pixel in the 3×3 neighborhood matrix. Based on this difference is positive or negative, we replace neighborhood pixel intensity with 1 or 0 respectively and then convert obtained 0 and 1 pattern into a decimal value. In this paper, we propose modification of this idea, instead of using local binary pattern, local octal and local hexadecimal pattern is used. Local octal pattern (LOP) and the local hexadecimal pattern(LHP) is further tested on two different datasets of 100 images each of sizes 150 x 150 and the obtained results are compared with the state-of-art local binary pattern. For similarity measure, Euclidian distance and Manhattan distance is used. Results show that local octal pattern is superior over local hexadecimal pattern and the local binary pattern is superior over both local octal pattern and local hexadecimal pattern.
Compressed Sensing MRI with Joint Image-Level and Patch-Level Priors

We develop a novel compressed sensing magnetic resonance imaging (CSMRI) algorithm with joint image and patch priors, where the total variation (TV) is adopted for image-level sparse prior and the expected patch log likelihood (EPLL) is used for patch-level sparse prior. The proposed joint priors capture global and local sparse nature of the MR image to promote image structures and suppress artifacts or noise, which can alleviate the limitations of previous CSMRI methods. And we derive an appropriate cost function which can be addressed by an efficient optimization scheme that iteratively alternates among l1 norm approximation, latent patch reconstruction and ideal image reconstruction. Final experiments are provided to show the satisfactory performance of the proposed method in MRI reconstruction, which outperforms other competitive CSMRI approaches in both subjective results and objective assessments.
Toward Robust Fully 3D Filopodium Segmentation and Tracking in Time-Lapse Fluorescence Microscopy

Development, parameter tuning, and objective benchmarking of bioimage analysis workflows heavily rely on the availability of diverse bioimage datasets accompanied by reference annotations. In this paper, we present a new benchmark dataset, FiloData3D, designed for in-depth performance assessments of fully 3D filopodium segmentation and tracking algorithms that emerged recently in the field. It consists of 180 synthetic, fully annotated, 3D time-lapse sequences of single lung cancer cells, combining different cell shapes, signal-to-noise ratios, and anisotropy ratios, which are the well-known factors that influence the quality of segmentation and tracking results. Using FiloData3D, we show that the number of filopodia and their lengths extracted are significantly underestimated in the case of traditional 2D protocols that prevail in daily practice compared to fully 3D measurements, calling for a procedural change in filopodial analyses of 3D+t bioimage data.
Network Analysis and Synthesis

This book has been designed as a basic text for undergraduate students of electrical, electronics and communication and computer engineering. The book explains both fundamental concepts such as circuit elements, Kirchhoff's laws, network equations and resonance, and relatively advanced topics, namely modern filters, state variable analysis, active RC filters and sensitivity considerations. The book is laid out in a systematic and user-friendly way, consisting of 16 chapters, each with solved examples and practice problems to immediately test the reader's understanding of the subject. There are also over 500 multiple choice questions at the end of the book for the reader to dip into and further assess his grasp of the book.In particular, Prof. Wadhwa deals with the theory and application of Fourier and Laplace transforms, classical and modern filter theory, z-transform for discrete systems and analogous systems, SPICE, and both Foster and Cauer realization. This is the third edition of a successful text book suitable for courses in electrical and computer engineering and also relevant to postgraduates and professional engineers.
Optimal power flow based on fuzzy linear programming and modified Jaya algorithms

Optimal power flow (OPF) solution is a crucial tool in electric power networks operation. Recently, several factors, including the deregulated electricity markets, electricity consumption growth, the growing role of decision makers, and the increasing exploitation of distributed generation (DG), affect the operation strategies of many meshed power networks. These new conditions have raised the intricacy of OPF problems and necessitate a reliable optimization algorithm that can tackle economic and security concerns. A set of modified and novel optimization algorithms are proposed in this thesis to deal with different single and multi-objective OPF problems. A new formulation for the multi-objective optimal power flow (MOOPF) problem that considers DG is introduced. The proposed algorithms have been examined and validated using the IEEE 30-bus and IEEE 118-bus test systems. The first proposed approach is a multi-objective fuzzy linear programming optimization (MFLP) algorithm to solve the MOOPF problem. The results indicate that a unique and optimum solution with an excellent satisfaction for the extreme targets can be achieved. Secondly, the application and modification of a Jaya algorithm to deal with different OPF problems is presented. The main advantage of this algorithm is that no algorithm-particular controlling parameters are required for this algorithm. Two versions of the Jaya algorithm namely, the basic Jaya algorithm and novel quasi-oppositional Jaya (QOJaya) algorithm are proposed to solve different single objective OPF problems. In the proposed novel QOJaya algorithm, an intelligence strategy, namely, quasi-oppositional based learning (QOBL) is incorporated into the basic Jaya algorithm to enhance its convergence speed and solution optimality. For each considered case, results demonstrate that Jaya algorithm can produce a global optimum solution with rapid convergence. Meanwhile, the proposed QOJaya algorithm produces better results than the basic Jaya method in terms of solution optimality and convergence speed. In addition, two novel Jaya-based methods namely, the modified Jaya (MJaya) algorithm and quasi-oppositional modified Jaya (QOMJaya) algorithm are proposed to solve different MOOPF problems. In this work, a considerable contribution has been made in terms of modifying Jaya algorithm for handling MOOPF problems. Results show the applicability, potential, and efficacy of the proposed MJaya and QOMJaya algorithms in solving MOOPF problems. Finally, two novel hybrid optimization algorithms namely, FLP-QOJaya algorithm for single objective OPF problems and MFLP-QOMJaya algorithm for MOOPF problems are proposed. For all single objective OPF cases, results demonstrate that the FLP-QOJaya algorithm outperforms the proposed Jaya and QOJaya algorithms in terms of solution quality, convergence speed and execution time. For multi-objective OPF problems, results show the supremacy of the proposed MFLP-QOMJaya over the proposed MJaya and QOMJaya algorithms in terms of producing superior Pareto optimal solutions and finer best compromise solutions.
Classification of Structural MRI Images in Adhd Using 3D Fractal Dimension Complexity Map

Attention deficit hyperactivity disorder (ADHD) is a common mental-health disorder in adolescent groups. Successful automatic diagnosis of ADHD based on features extracted from magnetic resonance imaging (MRI) data, would provide reference information for treating. Previous researches have shown gray matter (GM) of some anatomical brain structures will increase in ADHD subjects. Fractal analysis has been widely used in texture image processing and fractal dimension is capable of representing intrinsic structural information of images. With large-scale MRI data becoming publicly available, deep-learning methods for ADHD diagnosis become feasible. This paper proposes a novel classification approach using 3D fractal dimension complexity map (FDCM) for ADHD automatic diagnosis. We calculate the Hausdorff fractal dimension of GM density data extracted from structural MRI data. Subsequently, we design a 3 dimensional convolutional neural network (3D-CNN) for extracting features from FDCM then judging ADHD and TDC. Our model is evaluated on the hold-out testing data of the ADHD-200 global competition and performance outperforms previous approaches based on structural MRI data.
Linear‐Time Korean Morphological Analysis Using an Action‐based Local Monotonic Attention Mechanism

For Korean language processing, morphological analysis is a critical component that requires extensive work. This morphological analysis can be conducted in an end‐to‐end manner without requiring a complicated feature design using a sequence‐to‐sequence model. However, the sequence‐to‐sequence model has a time complexity of O(n2) for an input length n when using the attention mechanism technique for high performance. In this study, we propose a linear‐time Korean morphological analysis model using a local monotonic attention mechanism relying on monotonic alignment, which is a characteristic of Korean morphological analysis. The proposed model indicates an extreme improvement in a single threaded environment and a high morphometric F1‐measure even for a hard attention model with the elimination of the attention mechanism formula.
Skills and knowledge management in higher education: how service learning can contribute to social entrepreneurial competence development

The purpose of this paper is to elaborate on how service learning approaches are able to foster social entrepreneurship competences. The aim of the paper is to formulate a framework of key competences for social entrepreneurship and to give first insights in how service learning actually has an impact on change in students’ set of competences. ,This paper uses a mixed-methods approach combining qualitative data collectionmethods of learning diaries of the students and semi-structured interviews, including 40 master’s students studying at a German university in interdisciplinary learning settings and five instructors from the same universities. Analysis was carried out by means of qualitative content analysis. ,This paper provides empirical insights about the competences that are being fostered by service learning. From these, a framework for social entrepreneurship competences is being derived. ,The set of competences should be further investigated, as it was derived out of a small data set. Therefore, researchers are encouraged to use the set of competences for social entrepreneurship as a basis for future research and on a longer-term perspective, which lead to substantial implications for educational practice. ,This paper includes implications for new perspectives on service learning in the light of the development of a relevant framework for social entrepreneurship competence, having significant implications for educational practice in social entrepreneurship education. ,With this paper, the authors fulfill the need of a framework of social entrepreneurship competences that serves as a foundation for educational practice and further research in the context of service learning and beyond.
A Scalable and Error-Tolerant Solution for Traffic Matrix Assessment in Hybrid IP/SDN Networks

The advent of the Software Defined Networking (SDN) paradigm represents a great opportunity for the definition of new network management solutions. In this work, we focus on the definition and implementation of a novel technique to solve the Traffic Matrix Assessment (TMA) problem from the perspective of an Internet Service Provider. Since the migration from legacy IP networks to fully-deployed SDN ones needs to be incremental due to budget and technical constraints, this paper proposes a mixed measurement and estimation scalable solution for hybrid IP/SDN networks to accurately solve the TMA problem by exploiting the availability of flow rule counters in SDN switches. The performance evaluation shows that our error-tolerant solution is able to assess the TM with a negligible estimation error by only measuring a small percentage of traffic flows, overcoming other state-of-the-art algorithms proposed in the literature. Moreover, the performance analysis of the proposed implementation using the OpenDaylight controller over an emulated network environment, shows that a trade-off between the quality of the assessed TM and its impact on the network in terms of control messages can be found by properly tuning the number of measured flows.
Service quality in early child care center. Does parents satisfied

This study investigates the effect of customer satisfaction on service quality in child care center especially in Pusat Perkembangan Kanak-kanak Tunku Puteri Intan Safinaz (PPKKTPIS) located in University Utara Malaysia, Sintok, Kedah and Pewaris Generasi UniMAP located in Kangar, Perlis in order to identify whether parents who are send their kids to both child care center are satisfied with services provided or not. The objectives of this study is to identify the relationship between service quality (reliability quality, assurance quality, tangible quality, empathy quality and responsiveness quality) and user satisfaction of services offered in both child care center as appear in the framework of study. This study was conducted using a quantitative approach where the questionnaire question survey method had been used and were distributed among entire parents which are send their child to both child care center. It was applied by adopting the SERVQUAL instruments by Parasuraman et al., (1990) which are reliability quality, assurance quality, tangible quality, empathy quality and responsiveness quality while customer satisfaction adopting from Best, 1977: 174). Next, the data were analyzed using SPSS software. From the findings of study, it was concluded that empathy quality applied by Pusat Perkembangan Kanak kanak Tunku Puteri Intan Safinaz and Pewaris Generasi UniMAP can be considered as effective and satisfactory by parents from difference perspectives other than other four dimensions. In addition, parents are more attracted to the fees that are much cheaper compared to fees to the other child care center and a distance to workplaces is much closer and convenience. These factors are the reasons for parents to sending their kids to both child care center rather than service quality in SERVQUAL instruments
Indoor Path Loss Variations with Frequency and Visibility Conditions at 3.5 GHz Band

Studying the radio wave propagation within indoor environment is necessary previously to deploy wireless networks. Thus, and next to launching 5G systems at 3.5 GHz band, some insights on the behavior of the channel are required. This contribution describes the results of a measurement campaign in three different indoor scenarios, which are representative for a collection of similar environments. A simple path loss exponential decay model, adjusted by the measurement outcomes, indicates the evolution of radio waves as a function of the distance to the transmitter. In line of sight conditions, path loss seems to be just a bit stronger than in open space situations (the exponent is between 2 and 2.5, depending on the frequency, compared to the standard value 2 of open spaces). However, obstructed line of sight condition strengthens this decay rhythm, being over 4.
Offline Contextual Bayesian Optimization

In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration. In many practical problems of interest, one would like to optimize several systems, or ``tasks'', simultaneously; however, in most of these scenarios the current task is determined by nature. In this work, we explore the ``offline'' case in which one is able to bypass nature and choose the next task to evaluate (e.g. via a simulator). Because some tasks may be easier to optimize and others may be more critical, it is crucial to leverage algorithms that not only consider which configurations to try next, but also which tasks to make evaluations for. In this work, we describe a theoretically grounded Bayesian optimization method to tackle this problem. We also demonstrate that if the model of the reward structure does a poor job of capturing variation in difficulty between tasks, then algorithms that actively pick tasks for evaluation may end up doing more harm than good. Following this, we show how our approach can be used for real world applications in science and engineering, including optimizing tokamak controls for nuclear fusion.
Object Detection and Classification using Convolutional Neural Network

Owing to the close relationship with the detection of objects in video learning and image recognition, many are attracted. Recent research has focused on traditional defining objects the methods are built on features of handmade trains and shallow architectures. The performance stalls easily making complex sets that combine multiple low image levels with high-level context from detectors and object views classifications with the rapid development of in-depth study, more powerful tools, we can learn semantics architecture in learning the physical things to detect. In this paper, deeper features are introduced to address the problems that exist in the area of physical object detection and classification against traditional architecture. The results show that our proposed model outperform
A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks

In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from electrocardiogram (ECG) based biometrics, a classification task which aims to identify a subject from a given time-series sequential data. Despite having a major issue in traditional RNN networks which they learn representations from previous time sequences, bidirectional is designed to learn the representations from future time steps which enables for better understanding of context, and eliminate ambiguity. Moreover, GRU cell in RNNs deploys an update gate and a reset gate in a hidden state layer which is computationally efficient than a usual LSTM network due to the reduction of gates. The experimental results suggest that our proposed BGRU model, the combination of RNN with GRU cell unit in bidirectional manner, achieved a high classification accuracy of 98.55%. Various neural network architectures with different parameters are also evaluated for different approaches, including one-dimensional Convolutional Neural Network (1D-CNN), and traditional RNNs with LSTM and GRU for non-fiducial approach. The proposed models were evaluated with two publicly available datasets: ECG-ID Database (ECGID) and MIT-BIH Arrhythmia Database (MITDB). This paper is expected to demonstrate the feasibility and effectiveness of applying various deep learning approaches to biometric identification and also evaluate the effect of network performance on classification accuracy according to the changes in percentage of training dataset.