Miracle-dz/UniFAR-FTUs huggingface dataset:
New microarray image segmentation using Segmentation Based Contours method

The goal o f the research developed in this dissertation is to develop a more accurate segmentation method for Affymetrix microarray images. The Affymetrix microarray biotechnologies have become increasingly important in the biomedical research field. Affymetrix microarray images are widely used in disease diagnostics and disease control. They are capable o f monitoring the expression levels o f thousands of genes simultaneously. Hence, scientists can get a deep understanding on genomic regulation, interaction and expression by using such tools. We also introduce a novel Affymetrix microarray image simulation model and how the Affymetrix microarray image is simulated by using this model. This simulation model embraces all realistic biological characteristics and experimental preparation characteristics, which could have different impacts on the quality o f microarray image during the real microarray experiment. The most important aspect is that this model could provide the "ground true information,” which allows us to have a deep understanding on different segmentation algorithms performance. After the simulation, the new proposed segmentation algorithm Segmentation Based Contours (SBC) method is presented as well as the modifications o f the Active Contours Without the Edges (ACWE) method. By modifying the ACW E method with higher order finite difference scheme and fast scheme, we establish the new segmentation algorithm Segmentation Based Contours method. In the end, we compare the gene
Detection of Glottal Closure Instants from Raw Speech Using Convolutional Neural Networks

Glottal Closure Instants (GCIs) correspond to the temporal locations of significant excitation to the vocal tract occurring during the production of voiced speech. GCI detection from speech signals is a well-studied problem given its importance in speech processing. Most of the existing approaches for GCI detection adopt a two-stage approach (i) Transformation of speech signal into a representative signal where GCIs are localized better, (ii) extraction of GCIs using the representative signal obtained in first stage. The former stage is accomplished using signal processing techniques based on the principles of speech production and the latter with heuristic-algorithms such as dynamic-programming and peak-picking. These methods are thus task-specific and rely on the methods used for representative signal extraction. However, in this paper, we formulate the GCI detection problem from a representation learning perspective where appropriate representation is implicitly learned from the raw-speech data samples. Specifically, GCI detection is cast as a supervised multi-task learning problem solved using a deep convolutional neural network jointly optimizing a classification and regression cost. The learning capability is demonstrated with several experiments on standard datasets. The results compare well with the state-of-the-art algorithms while performing better in the case of presence of real-world non-stationary noise.
BotChase: Graph-Based Bot Detection Using Machine Learning

Bot detection using machine learning (ML), with network flow-level features, has been extensively studied in the literature. However, existing flow-based approaches typically incur a high computational overhead and do not completely capture the network communication patterns, which can expose additional aspects of malicious hosts. Recently, bot detection systems that leverage communication graph analysis using ML have gained attention to overcome these limitations. A graph-based approach is rather intuitive, as graphs are true representation of network communications. In this paper, we propose BotChase, a two-phased graph-based bot detection system that leverages both unsupervised and supervised ML. The first phase prunes presumable benign hosts, while the second phase achieves bot detection with high precision. Our prototype implementation of BotChase detects multiple types of bots and exhibits robustness to zero-day attacks. It also accommodates different network topologies and is suitable for large-scale data. Compared to the state-of-the-art, BotChase outperforms an end-to-end system that employs flow-based features and performs particularly well in an online setting.
Ray Tracing Generalized Tube Primitives: Method and Applications

We present a general high‐performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed‐ and varying‐radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high‐quality rendering, with low memory overhead.
A Reconfigurable Gripper for Dexterous Manipulation in Flexible Assembly

In this paper, a high-speed multi-fingered reconfigurable gripper is presented. The aim is to create a robotic end effector that is capable of handling parts of different geometries and weight. It consists of three fingers, accounting for a total of eight Degrees of Freedom (DoFs). The paper discusses the design, control, and reconfiguration aspects of the gripper, and demonstrates its applicability to different manipulation tasks. The gripper has the ability of high-force movement and its high number of DoFs enables it to grasp a large variety of geometries, while retaining the design simplicity. The preliminary grasping experiments highlight its potential in robotic handling applications in both research and production environments.
An efficient multi-resolution grid for global models and coupled systems

Abstract. The latitude-longitude (lat-lon) grid is the most widely used global coordinate system for various purposes but its singularity at the Pole and the vector polar problems associated with its converging meridians hinder its applications. Well from the very start of numerical modelling history, quite a few grids have been attempted to tackle these problems and reduced grid is the simplest one among other grids. However, the reduced grid is almost abandoned by modern numerical modellers due to its unsatisfactory results for dynamical models in the polar region. Spherical multiple-cell (SMC) grid is similar to the reduced grid apparently but uses the unstructured technique for efficiency. It merges longitudinal cells at high latitudes like the reduced grid to overcome the CFL restriction and introduces a polar cell to remove the polar singularity. It also supports quad-tree-like mesh refinement to form a multi-resolution grid. To tackle the vector polar problem associated with the increased curvature at high latitudes, the SMC grid uses a new fixed reference direction to define vector components near the poles for improved polar performance. Global transportation is quite efficient on the SMC grid with optional second or third order transportation scheme. Present applications of the SMC grid, particularly in ocean surface wave models, are presented and possible future usage in global models and coupled systems are proposed.
Storage Allocation Plan for Routing Nodes in Delay Tolerant Network

Received: 25 October 2018 Accepted: 7 January 2019 During data storage and forwarding, the storage resources in the delay tolerant network (DTN) are often allocated unfairly between the routing nodes. To improve the fairness and the routing performance in the DTN, this paper puts forward an optimal storage allocation plan for routing nodes based on the weighted max-min fairness. Unlike the existing storage allocation plan, which adopts the first-come, first-served mode, the weighted max-min fairness principle can allocate data transmission opportunities fairly to the data nodes, while giving more resources to key tasks and reducing service waiting time. The simulation experiment shows that the DTN routing algorithm after the optimization of storage resources achieved better transmission success rate and mean network delay than unoptimized routing algorithm.
THE IMPACT OF MULTIMEDIA TECHNOLOGY ON BOOSTING STUDENT TEACHERS’ PEDAGOGICAL CONTENT KNOWLEDGE COMPARED TO TRADITIONAL TEACHING

Objectives: The purpose of this research is to find out the role of multimedia technology with multimedia screens in terms of boosting student teachers’ pedagogical content knowledge compared to traditional teaching which includes the curriculum of course books and worksheets. Methods: The triangulation research design was used in this study because there were both qualitative and quantitative analysis of the study. The data collection tools for this study were a smart board, two projectors, academic achievement tests, interviews, observations and questionnaires. Results: This research which reflects the positive perspectives about the use of the multimedia technology with multi-screens compared to the traditional usage of course books. Originality: Since multimedia technology will be very effective in developing pre-service teachers' thinking, analyzing, reasoning and self-awareness skills, it will be the basis for many future studies.
Out-of-Band Power Suppression for Cyclic SLM with Delayed Correlation and Matched Filter

Orthogonal frequency-division multiplexing (OFDM) has been used in many communication systems. However, OFDM suffers from undesirable high peak-to-average power ratio (PAPR) as it uses inverse discrete Fourier transform (IDFT) operation at the transmitter. One of the simplest schemes of the PAPR reduction is the cyclic-selective mapping (SLM). This paper presents cyclic-SLM without side information (SI) which employs delayed correlation with a matched filter (DC-MF) at the receiver. The cyclic-SLM combines the original OFDM signal and its cyclically shifted ones. In the previous conventional cyclic-SLM, the cyclically shifted signal samples are multiplied by a phase sequence which is drawn from Barker code sequence. However, this scheme results in higher out-of-band (OOB) power leakage. In this paper, we propose to use different phase sequence for each OFDM symbol in one OFDM frame to overcome the OOB problem. As a consequence, multiple matched filters are used at the receiver. Simulation results show that the proposed method has the same OOB as the conventional OFDM. In addition, we also analyze the performance of the proposed scheme in terms of the PAPR reduction and bit error rate (BER).
Synchronization of Non-linear Oscillators for Neurobiologically Inspired Control on a Bionic Parallel Waist of Legged Robot

Synchronization of coupled non-linear oscillators inspired by a central pattern generator (CPG) can control the bionic robot and promote the coordination and diversity of locomotion. However, for a robot with a strong mutual coupled structure, such neurobiological control is still missing. In this contribution, we present a σ-Hopf harmonic oscillator with decoupled parameters to expand the solution space of the locomotion of the robot. Unlike the synchronization of original Hopf oscillators, which has been fully discussed, the asymmetric factor of σ-Hopf oscillator causes a deformation in oscillation waveform. Using the non-linear synchronization theory, we construct the transition state model of the synchronization process to analyze the asymmetrical distortion, period change and duty ratio inconsistency. Then a variable coupling strength is introduced to eliminate the waveform deformation and maintain the fast convergence rate. Finally, the approach is used for the locomotion control of a bionic parallel waist of legged robot, which is a highly coupled system. The effectiveness of the approach in both independent and synthesis behavior of four typical motion patterns are validated. The result proves the importance of controllability of the oscillation waveform and the instantaneous state of the synchronization, which benefits the transition and transformation of the locomotion and makes the coupling motion more flexible.
YouInteract : an application for gesture interaction with public displays

With the release of affordable gesture-tracking hardware and the launch on the market of cheaper large displays making it possible to install them in various locations, it is now interesting to create an interactive system to be installed in public spaces to allow users to interact with relevant information. This dissertation continues such a system project developed at Department of Electronics, Telecommunications and Informatics of University of Aveiro. The objective is to further improve and develop a system that allows the creation of additional content and its configuration through a website. Although the final application might be configured for different context, we developed this work in collaboration with Fábrica Centro Ciência Viva de Aveiro to perform two pilot studies, evaluating the designed solution and the acceptance of the new created applications. This dissertation presents the results of this work hoping to contribute to the use of gesture interaction with public displays.
Caries Management-Science and Clinical Practice

A comprehensive approach to modern caries management This systematic approach to modern caries management combines new, evidence-based treatment techniques with the scientific underpinnings of caries formation, providing an in-depth review for both clinicians in daily practice and students advancing in the field. Beginning with patho-anatomic changes in the dental hard tissues, Dental Caries: Science and Clinical Practice goes on to cover non-invasive, minimally invasive, and more aggressive interventions based on each stage of the disease. From microbiology and histology to visual, tactile and radiographic diagnosis, risk assessment, preventive measures, and tooth preservation and treatment strategies, the book is packed with valuable clinical information for all dental practitioners. Key Features: * Succinctly covers the science behind the disease, with recommendations for treatments based on assessment starting at the microscopic level * Written by a team of leading worldwide authorities on caries treatment and management-and utilizing the International Caries Detection and Assessment System (ICDAS) standard throughout* Covers the newest treatment techniques, including adhesion technology, fissure sealing and infiltration, caries removal, tooth-colored restorations, and more * Demonstrates step-by-step caries procedures in striking, full-color illustrations of adult and pediatric cases * Offers the newest thinking on early prevention and behavioral changes in oral health promotion, including the role of diet and nutrition, biofilm management, fluoride use, population-based approaches, and more Shifting to the new paradigm of "heal and seal" rather than the more invasive "drill and fill," this beautifully illustrated text puts scientific principles into clinical action for the best results. It is an essential resource for a complete, proactive approach to caries detection, assessment, treatment, management, and prevention in contemporary dental practice.
A review on sentiment discovery and analysis of educational big‐data

Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts.
Modifying ShortLoop Software to run in Higher Dimensions

Many problems in science and engineering involve determining geometrical and topological information about a structure based on a point cloud derived from the structure. One such problem involves computing a shortest basis for the first homology group H1(M) where M ⊂ R is a smooth manifold. Dey, Sun, and Wang have already come up with an algorithm to approximate H1(M) from a point cloud P derived from M [10]. This algorithm was implemented in C++ by the Jyamiti research group at The Ohio State University; however, it was only implemented to run on three dimensional surfaces or point clouds. Although many problems in science and engineering involve three dimensional complexes, there are certainly problems where finding a shortest basis for the first homology group of a higher dimensional complex may be useful or necessary. In this thesis, we will describe the method of updating the code to run on higher dimensional complexes. Additionally, we aim to provide some flexibility to how a potential user might use the code by updating the code to work for different types of input files.
New Metrics for Effective Detection of Shilling Attacks in Recommender Systems

Collaborative filtering techniques are successfully employed in recommender systems to assist users counter the information overload by making accurate personalized recommendations. However, such systems are shown to be at risk of attacks. Malicious users can deliberately insert biased profiles in favor/disfavor of chosen item(s). The presence of the biased profiles can violate the underlying principle of the recommender algorithm and affect the recommendations. This paper proposes two metrics namely, Rating Deviation from Mean Bias (RDMB) and Compromised Item Deviation Analysis (CIDA) for identification of malicious profiles and compromised items, respectively. A framework is developed for investigating the effectiveness of the proposed metrics. Extensive evaluation on benchmark datasets has shown that the metrics due to their high Information Gain lead to more accurate detection of shilling profiles compared to the other state of the art metrics.
Runtime Parallelization of Static and Dynamic Irregular Array of Array References

The advancement of computer systems such as multi-core and multiprocessor systems resulted in much faster computing than earlier. However, the efficient utilization of these rich computing resources is still an emerging area. For efficient utilization of computing resources, many optimization techniques have been developed, some techniques at compile time and some at runtime. When all the information required for parallel execution is known at compile time, then optimization compilers can reasonably parallelize a sequential program. However, optimization compiler fails when it encounters compile time unknowns in the program. A conventional solution for such problem can be performing parallelization at runtime. In this article, we propose three different solutions to parallelize a loop having an irregularity in the array of array references, with and without dependencies. More specifically, we introduce runtime check based parallelization technique for the static irregular references without dependency, Inspector-Executor based parallelization technique for static irregular references with dependencies, and finally Speculative parallelization technique (BitTLS) for dynamic irregular references with dependencies. For pro ling the runtime information, shared and private data structures are used. To detect the dependencies between footprints and for synchronization of threads at runtime, we use bit level operations. A window based scheduling policy is employed to schedule the iterations to the threads.
Video Logging Casing Damage Image Recognition Based on Improved Convolutional Neural Network

Oil casing damage detection is the key point to ensure the smooth production of oil fields. In recent years, the automatic image recognition technology based on deep learning has become a researchful hot topic. But the common deep learning models have some defects in identifying the target features of casing damage images in the complex environment. This paper proposes an oil casing damage image recognition model based on DS-CNN(deep and shallow convolutional neural network). Based on VGG19, this model integrates the shallow convolution neural network. It combines global features extracted by the shallow network and the local features extracted by the deep network to form the input of the fully connected layer. The joint training of the shallow network and the deep network enables the image to be expressed in multiple scales to improve the recognition accuracy of the entire model. The experimental data is obtained from the downhole casing image dataset of an oil field in Sichuan. Experimental result shows that the macro-average F1 scores of the DS-CNN are 4.41 and 5.74 percentage points higher than those of the VGG19 model and the GoogleNet model, indicating that this model improves the recognition accuracy of oil casing damage images.
Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations.