Miracle-dz/UniFAR-FTUs huggingface dataset:
Broad Learning-based Intervertebral Discs Localization and Segmentation

Magnetic resonance image (MRI) can detect a variety of conditions of the spine, including problems with the bones, soft tissues, and disks. Accurate localization and segmentation of intervertebral disc is crucial for the assessment of spine disease diagnosis. In this paper, we present a broad learning-based intervertebral discs localization and segmentation algorithm. Firstly, intervertebral discs localization system based on broad learning is designed. Secondly, based on the localization result, the shape prior of intervertebral discs is established. Finally, a Chan-Vese model method with shape prior is proposed to obtain the segmentation result of intervertebral discs. Several baselines, with different segmentation algorithms, were used to demonstrate the effectiveness of the proposed architecture.
Logical topology of networks implementing IPv6 addressing

The massive growth of the global routing tables is one of the biggest problems that still face internet nowadays. This problem is mainly caused by the random distribution of IPv4 addresses. With the immigration to IPv6 and the large ranges of addresses provided by this protocol, it is crucial to wisely manage the assignment of IPv6 prefixes. In this paper, we propose a process to generate a logical topology of IPv6 networks. This topology uses perfectly the summarization technique and consists in representing the summary routes in hierarchical manner such that large range of addresses represents several smaller ranges. The proposed aggregation process optimizes and divides up the routing tables which may help resolve the problem of the explosive growth of internet routing tables. Furthermore, the logical topology can be easly customized to fit the features of the routers that are used in the network.
Binders by day, labels by night: effect instances via lexically scoped handlers

Handlers of algebraic effects aspire to be a practical and robust programming construct that allows one to define, use, and combine different computational effects. Interestingly, a critical problem that still bars the way to their popular adoption is how to combine different uses of the same effect in a program, particularly in a language with a static type-and-effect system. For example, it is rudimentary to define the “mutable memory cell” effect as a pair of operations, put and get, together with a handler, but it is far from obvious how to use this effect a number of times to operate a number of memory cells in a single context. In this paper, we propose a solution based on lexically scoped effects in which each use (an “instance”) of an effect can be singled out by name, bound by an enclosing handler and tracked in the type of the expression. Such a setting proves to be delicate with respect to the choice of semantics, as it depends on the explosive mixture of effects, polymorphism, and reduction under binders. Hence, we devise a novel approach to Kripke-style logical relations that can deal with open terms, which allows us to prove the desired properties of our calculus. We formalise our core results in Coq, and introduce an experimental surface-level programming language to show that our approach is applicable in practice.
GISKOP: A Modified Key Scheduling Operation of International Data Encryption Algorithm using Serpent Key Scheduling

Cryptography is a method of storing and transmitting data in a particular form. Only those for whom it is intended can read, use it, and return it back to the original data by using various techniques. The International Data Encryption Algorithm "IDEA" is a block cipher that works with 64-bit plaintext block and ciphertext blocks and it has a 128-bit input key. This paper describe the designing and implementation of a modified key schedule operation of IDEA called GISKOP. It uses the same number of rounds and output transformation that operates using 128 bit user input plaintext and a modified way of key scheduling operation of 256 bit keys. The modified algorithm uses Serpent key scheduling operation to derive the different sub keys to be used in each rounds. The algorithm was implemented to provide better security on user's password within the Document Management System to protect user's data within the cloud database. It has gone through initial testing and evaluations with very encouraging results.
POST: A Machine Learning Based Paper Organization and Scheduling Tool

Organizing and assigning papers into sessions within a large conference is a formidable challenge. Some conference organizers, who are typically volunteers, have utilized event planning software to ensure simple constraints, such as two people can not be scheduled to talk at the same time. In this work, we proposed utilizing natural language processing to find the topics within a corpus of conference submissions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare session assignments from previous conferences to ones generated with our proposed techniques.
Logarithmic Communication for Distributed Optimization in Multi-Agent Systems

Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case.
Hybrid force-vision control law based on convolutional neural networks

The vision and force seems to be useful since it have the ability to make an intelligent interaction in some service tasks, the reaching of the robot effector' with accuracy need the vision. In other hand, the interacting of the robot safely requires the controlling of the contact force of. In this paper, we propose a new control structure based on three control laws. In the first, the robot uses the vision controller based Convolutional Neural Networks (CNN); in the reaching phase. Next, the force of contact controlled using Proportional Integral (PI) control law. Another low level compensator is used which is based on computed torque technique. So as to show the performance of the proposed control law, we have tested the proposed control law by a series of tests under different constraints. The obtained results allowed us to validate the performance and robustness of the controller.
Characterization of Codon Usage Patterns and Evolutionary Relationships in Partitiviruses

This study investigated the overall sequence characteristics and codon usage patterns of Partitiviridae viruses and applied them to viruses that are part of the family Partitiviridae but are unassigned to a genus. In addition, phylogenetic analysis was performed to investigate the evolutionary relationships of viruses belonging to Partitiviridae in relation to their fungal hosts. The sequence data of the CDS region were downloaded from GenBank, including that of the conserved RdRP, capsid protein. The results showed that Botryotinia fuckeliana partitivirus 1, Beauveria bassiana partitivirus 1, Beauveria bassiana partitivirus 2, Penicillium marneffei partitivirus-1, and Aspergillus fumigatus partitivirus 2 had evolutionary relationships with Gammapartitivirus. Among them, Aspergillus fumigatus partitivirus 2 and Penicillium marneffei partitivirus-1 preferred U-ended codons similar to Gammapartitivirus in terms of codon usage patterns. In addition, Botryosphaeria dothidea virus 1 showed a different pattern from the viruses belonging to Partitiviridae in all the analysis results. This virus showed a strong bias towards the C-ended codons and the lowest ENC value among the analyzed viruses, forming a separate cluster in the phylogenetic tree. These results may help to understand the genetic diversity and evolutionary relationships of the members and tentative members of the family Partitiviridae.
Labelling Stomach Anatomical Locations In Upper Gastrointestinal Endoscopic Images Using a CNN

In this paper, we aim to develop a diagnostic assistant system for labelling the stomach anatomical locations in upper GastroIntestinal Endoscopy (UGIE) examination. To address this task, we construct an appropriate manner which utilizes both ability of a convolutional neural network (CNN) and supportive interactions between machine and doctors. We solve the problem by a two-phase scheme. The first is a coarse-phase to classify seven major anatomical locations including cardiac orifice, gastric body, fundic, antrum, pyloric ring, lesser curvature and greater curvature using advances of a CNN. The constructed CNN network is compact with high performance and appropriate integration into a Graphic User Interface (GUI). In order to classify with 13 further detailed positions, a GUI is developed so that the endoscopists can conveniently specify the anatomical locations from results of the coarse-phase. In this the fine-phase, the doctors will prune the automatic results as well as specify the more detailed positions of each major location. In the experimental results, the developed application is shown as an efficient way in an UGIE diagnosis. It reduces a significant time from averagely 13:03 minutes in a manual procedure to 4:35 minutes by using the developed system when comparing with trainee endoscopists. The results of specifying anatomical locations satisfied accuracy requirements and showed promising research trend for future application as a computer-aided GIE diagnostic system.
An Efficient Method for Discovering Variable-length Motifs in Time Series based on Suffix Array

Repeated patterns in time series, also called motifs, are approximately repeated subsequences embedded within a longer time series data. There have been some popular and effective algorithms for discovering motifs in time series. Nevertheless, these algorithms still have some weaknesses such as users have to choose an appropriate value of the motif length or suffer high computational cost. In this paper, we propose an efficient method for discovering motifs based on suffix array. This method consists of transforming a time series into a symbolic string and then finding repeated substring in the symbolic string based on the suffix array. Besides, we also speed up the execution time of the method by applying multi-core parallelism. Experimental results reveal that our proposed method can discover variable-length motifs and perform very fast on large time series datasets while providing high accuracy.
Effective Representation of Three-Dimension Nodules for False-Positive Reduction in Pulmonary Nodule Detection

The convolutional neural networks (CNNs) can learn features representation from large amounts of training data, and it has achieved remarkable successes in image processing. However, due to expensive expert annotation and privacy issues, the lack of sufficient training data limits the application of CNNs, especially for 3D CNNs, in medical images. In this paper, we decomposed a 3D sample into a set of 2D images based on a series of sequential uniformly-distributed viewpoints and used some "effective" 2D images of them to train a 2D CNN for the false-positive reduction in pulmonary nodule detection with fewer model parameters and less computational burden. The 2D images from different representative viewpoints would provide more complete and independent information than simplifying a 3D nodule into several orthogonal planes. And the non-nodules appear clearly as non-circular linear structures in the "effective" 2D images, which enable us well to distinguish nodules from false positives. Our method was evaluated on 888 CT scans from the database of the LUNA16 challenge. Compared with other methods by using 2D CNNs, our proposed method achieves the highest competition performance metric (CPM) score in the false-positive reduction track. Compared to published work using 3D CNNs which needs significantly larger training data, our method achieves comparable performance by using only about 20% of training data. The "effective" 2D images from representative viewpoints augment the database for training a 2D CNN and provide more crucial information of 3D nodules and would improve the performance of false-positive reduction.
Applied Linear Algebra Methods for Data Science

In this paper, we discuss efficient algorithms for using eigenvalues and eigenvectors. The main algorithm is t Principal Component Analysis (PCA), a powerful method widely used for dimensionality reduction, image compression, and face recognition. We derive the optimal k of the number of principal components selected. The implementation of PCA application is demonstrated by an image compression example using the MATLAB programming language. We also discuss PageRank and Network Classification algorithms.
Wireless communication in internet of vehicles networks: DSRC-based Vs cellular-based

As part of smart cities, Internet of Vehicle (IoV) is an application of IoT. In such network, the wireless communication technologies to facilitate the vehicles communication with each other and with the other public networks include two main classes of communication standards are used: (i) short range communication such as DSRC (Dedicated Short Range Communications), and (ii) cellular long-range communication including LTE (Long Term Evolution). The vehicles communications require reliability and low latency with large volumes of data that can be transferred within IoV. In this paper, we design a new data real-time communication protocol in IoV that is based on both DSRC and LTE standards for short range and long-range communications, respectively. We called our protocol, ERTP-IoV for Efficient Real-Time protocol in IoV. Extensive simulations, in real urban environments, will be presented later to evaluate the performance of our protocol, as compared to other existing protocols, with 85% expected results.
Learning over categorical data using counting features: with an application on click-through rate estimation

Input data for many machine learning applications are often categorical and contain multiple fields. A common feature representation for such categorical data is one-hot encoding, which expresses data instances as high-dimensional sparse binary vectors. Given this encoding machine learning models, such as logistic regression or boosted trees, are trained. However, the following problems occur when dealing with large-scale data sets: (i) The binary feature space is sparse yet extremely large, which can require a large amount of computational resources. (ii) Models based on such a feature representation will typically need to be re-trained in order to keep up-to-date with any changes in the data distribution. (iii) The one-hot feature representation provides little generalisation ability. In this paper, we propose counting features, a novel statistics-based feature engineering paradigm, to address the above problems. Mathematically, we show a deterministic relationship between the optimal regression parameters of counting features and one-hot binary features. Then, in the context of click-through rate estimation in online advertising, we demonstrate that counting features indeed bring better generalisation ability. Our experiments on real-world large-scale datasets demonstrate that, despite their compressed nature, the proposed counting feature engineering outperforms the one-hot binary encoded features in various cases such as cold start training and cross-campaigns training.
Citizen empowerment in newly born smart cities in Mauritius

Advances in technology are quickly paving the way for smart cities. The Government of Mauritius has set up the Smart City Scheme to provide an enabling framework and a package of attractive fiscal and non-fiscal incentives to investors for the development of smart cities across the island. However, prior to the design and implementation of such technologies, it is important to predict the behavioural intention to use such technology so that smart city technologies effectively empower citizens and improve the quality of life of citizens. In this research work, it is proposed to use the TAM to effectively assess the perception and readiness and the perceived usefulness of certain smart city technologies such as for transportation as well as identifying key smart city applications for Mauritius. The aim of this research project is to evaluate and assess the different factors and condition that can have an impact on the perceived ease of use (PEOU), perceived usefulness (PU), behavioural intention (BI) to use and actual use (AU) of smart city technologies. For smart cities to become a reality in Mauritius, smart city applications and services should be citizen-centred and relevant for ordinary citizens in their everyday life. This research work allows assessing the readiness of Mauritian citizens for Smart city applications, as well as identifying applications which can empower citizen.
Context-awareness in the smart car: study and analysis

Smart Car is an automobile with an Advanced Driver Assistance System (ADAS) that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a comparative and statistical study of the research published in the last three years on context-based driver assistance systems, depending on variant criteria such as: the context, the algorithms used, and the technologies adopted. We provide an overview of the results of this survey and the algorithms based on, the sensors used, and categories of context studied. We also propose a model that classifies the different contexts and their features based on the literature review.
Comparison of Dermatological Complications Associated with Repeated use of a High Anionic vs a Low Anionic Soap to Decontaminate Canines

ii DEDICATION . iii ACKNOWLEDGEMENTS . iv NOMENCLATURE . v TABLE OF CONTENTS . vi LIST OF FIGURES . viii LIST OF TABLES . ix INTRODUCTION . 1 LITERATURE REVIEW . 4 Introduction . 4 Search & Rescue Canines . 4 Exposures during Disasters for Search & Rescue Canines . 5 Canine External Decontamination . 6 Canine Decontamination Challenges . 7 Conclusion . 13 Objectives and Aims . 13 Hypothesis . 13 MATERIALS AND METHODS . 15 Sample Size . 15 Animals . 16 Washout Period . 19 Canine Decontamination Protocol . 20 Epidermal Evaluations . 22 Transepidermal Water Loss . 23 Statistical Analysis . 23 Canine Decontamination . 20 . 22
Model and Predictive Uncertainty: A Foundation for Smooth Ambiguity Preferences

Smooth ambiguity preferences (Klibanoff, Marinacci, and Mukerji (2005)) describe a decision maker who evaluates each act f according to the twofold expectation V ( f ) = ∫ P ϕ ( ∫ Ω u ( f ) d p ) d μ ( p ) defined by a utility function u, an ambiguity index ϕ, and a belief μ over a set P of probabilities. We provide an axiomatic foundation for the representation, taking as a primitive a preference over Anscombe–Aumann acts. We study a special case where P is a subjective statistical model that is point identified, that is, the decision maker believes that the true law p ∈ P can be recovered empirically. Our main axiom is a joint weakening of Savage's sure‐thing principle and Anscombe–Aumann's mixture independence. In addition, we show that the parameters of the representation can be uniquely recovered from preferences, thereby making operational the separation between ambiguity attitude and perception, a hallmark feature of the smooth ambiguity representation.
An Efficient and Flexible Metadata Management Layer for Local File Systems

The efficiency of metadata processing affects the file system performance significantly. There are two bottlenecks in metadata management in existing local file systems: 1) Path lookup is costly because it causes a lot of disk I/Os, which makes metadata operations inefficient. 2) Existing file systems have deep I/O stack in metadata management, resulting in additional processing overhead. To solve these two bottlenecks, we decoupled data and metadata management and proposed a metadata management layer for local file systems. First, we separated the metadata based on their locations in the namespace tree and aggregated the metadata into fixed-size metadata buckets (MDBs). This design fully utilizes the metadata locality and improves the efficiency of disk I/O in the path lookup. Second, we customized an efficient MDB storage system on the raw storage device. This design simplifies the file system I/O stack in the metadata management and allows metadata lookup to be completed with constant time complexity. Finally, this metadata management layer gives users the flexibility to choose metadata storage devices. We implemented a prototype called Otter. Our evaluation demonstrated that Otter outperforms native EXT4, XFS, Btrfs, BetrFS and TableFS in many metadata operations. For instance, Otter has 1.2 times to 9.6 times performance improvement over other tested file systems in file opening.
Developing an Indonesian Reading Proficiency Test for BIPA Learners

The use of Indonesian proficiency tests for non-native speakers of Bahasa Indonesia is still equated with tests for native speakers. This has become a point of debate for many teachers and experts of Indonesian for Foreigners (Bahasa Indonesia untuk Penutur Asing - BIPA). The crux of the debate focuses on whether the same proficiency test should be used for both native speakers (NS) and non-native speakers (NNS) alike, or whether separate tests should be developed. In accordance with the peculiarities of Bahasa Indonesia, Indonesian proficiency tests for NS and NNS should be differentiated. The underdevelopment of specialized proficiency tests for NNS can be explained by the fact that Bahasa Indonesia is not one of the dominant languages learned in the world today. This research aims to develop materials for an Indonesian proficiency test for NNS. The development of the test focuses on reading comprehension. To advance development of the test, discussions of the processes for defining the theoretical construct as well as empirical analysis of students' result were combined. The method used in this study involved expert review, text readability analysis, and item analysis. The findings show that the test items developed can be used to test students’ proficiency, particularly in reading comprehension.