Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiosensitization by gold nanoparticles: effective at megavoltage energies and potential role of oxidative stress

The multidisciplinary field of nanotechnology has the potential to deliver many novel applications in the biomedical field including improved strategies for the detection, diagnosis and treatment of cancer. In the radiation research arena, gold nanoparticles (GNPs) have demonstrated strong potential as diagnostic imaging agents, drug delivery platforms and radiation sensitizers due to their attractive physico-chemical characteristics. In the pursuit of dose modifiers to improve the therapeutic index of radiotherapy, GNPs have attracted much research interest due to the high atomic number (Z) of gold which results in significantly improved contrast compared to soft tissue. In this review, we consider the physical properties of GNPs which make them widely utilizable in the field of radiation research as image contrast agents, drug delivery vehicles and radiation sensitizers. In particular, we focus on the growing amount of preclinical evidence which demonstrates GNPs as radiation sensitizers and highlight the disparity between observed experimental findings and predictions based on mass attenuation, GNP concentration and beam energy. Considering the large amount of studies performed using a wide range of GNPs, emerging evidence suggests oxidative stress as a central mechanism of radiobiological response.
Target tracking algorithm of mobile sensor networks based on flocking control

In the mobile sensor networks(MSN),the existing target tracking control strategies have shortcomings in preserving topology connectivity and decreasing power consumption.Therefore,a target tracking algorithm based on the flocking-based mobility control algorithm is proposed.Firstly,the target is assumed to be observed by a small fraction of mobile sensors in the networks,which takes important role on reducing power consumption.Then,the connectivitypreserving and collision-avoiding flocking algorithm enables all mobile sensors to track the target utilizing the states of target which are estimated by the small percentage of mobile sensors using Kalman-consensus filtering.Finally,simulation results show the effectiveness of the proposed control scheme.
Learning Connotation over the Word+Sense Network

We introduce ConnotationWordNet, a connotation lexicon over the network of words in conjunction with senses. We formulate the lexicon induction problem as collective inference over pairwise-Markov Random Fields, and present a loopy belief propagation algorithm for inference. The key aspect of our method is that it is the first unified approach that assigns the polarity of both word- and sense-level connotations, exploiting the innate bipartite graph structure encoded in WordNet. We present comprehensive evaluation to demonstrate the quality and utility of the resulting lexicon in comparison to existing connotation and sentiment lexicons.
Clustering Algorithm Based on VQ and LBG in Mobile Ad Hoc Network

This paper designs a new clustering algorithm aiming at high stability in cluster structure and good performance in load balancing of cluster head.It applies the theory of vector quantization and LBG algorithm in the information theory and data compress,establishes a one by one relationship between the cluster and cell,the code vector and cluster head,the vector and node,and changes the process of clustering into the process of cell segmenting.Through the simulation,this algorithm proves that it can cluster rapidly and the structure of the cluster has high stability.
3D Imaging for Mapping and Inspection Applications in Outdoor Environments

This chapter is aimed at introducing the fundamentals of three-dimensional (3D) imaging to scientists, students, and practitioners while also documenting recent developments in the ability to rapidly digitize real-world environments. We begin with a survey of popular 3D sensing options and list factors that challenge 3D imaging in outdoor environments. The survey guides the reader towards the choice of a 3D sensor for his or her application of interest. Then, we describe 3D data acquisition strategies and integration methodologies for multi-view range data from laser scanners, multi-view image data from cameras mounted on a mobile platform and multi-sensor localization based 3D mapping. We explain the steps involved in creating 3D models from raw sensor data for each of these data acquisition strategies. Finally, we document research results obtained in the Imaging, Robotics and Intelligent Systems Laboratory at the University of Tennessee, Knoxville from 3D imaging prototypes developed for automated pavement runway inspection and urban mapping.
A Very Robust Dedicated and Verified Technique by Applying the Hybridity of Cloud for Deduplication of Data

This paper presents a review study on the data deduplication or single instancing originally refers to the removal of needless data. In this process, duplicate data is expunged, leaving only one copy means single instance of the data to be accumulated. Though, indexing of each and every data is still maintained. Data deduplication is an approach for minimizing the part of storage space an organization required to retain its data. In most of the company, the storage systems carry identical copies of numerous pieces of data. Deduplication terminates these additional copies by saving just one copy of the data and exchanging the other copies with pointers that assist back to the primary copy. To ignore this duplication of the data and to preserve the confidentiality in the cloud here we are applying the concept of hybrid nature of cloud. A hybrid cloud is a fusion of minimally one public and private cloud. To secure the confidentiality of sensitive data while encouraging deduplication, the convergent encryption technique must be used to encrypt the data before expanding. For better shielding the data security, this paper makes an attempt to formally address the problem of data deduplication.
The Moderating Factors and Their Effect in Late Second Language Learners’ Syntactic Processing

Recent brain cognition research mainly based on ERP found that second language proficiency,cross-linguistic similarity and working memory-centered individual cognitive ability are independent but influence each other,which leads to the theoretical discrepancy between Shallow Structure Hypothesis indicating there is fundamental difference in L2 syntactic processing between late L2 learners and native speakers and Unified Competition Model which is on the opposite and proposes hat that there is no fundamental Differences. L2 processing as compared to the L1(or to native speakers of the L2) is generally associated with constructions that were crosslinguistically dissimilar or unique to the L2.The difference between the two claims lies in whether late L2 learner can achieve native-like L2 syntactic processing. Accordingly,cross-linguistic studies on the interactive role of these three factors should be conducted in the future.
Recent Trends Towards Improved Image Enhancement Techniques

Image enhancement process includes a couple of techniques that seek to improve the visual appearance of a graphic or to convert the image to an application better fitted to analysis by an individual or machine. The fundamental reason of image enhancement is to bring out detail that is hidden in an image that is covered up in a picture or to expand contrast in a low contrast picture . This paper presents a literature review on some of the image Enhancement techniques for improving images like CLAHE, DBLA, Mix-CLAHE, Histogram Equalization, Adaptive Histogram Equalization etc. This paper ends up with the suitable future directions to enhance the DBLA algorithm further.
Humanizing advanced communication online writing instruction: Developing social presence to communicate, collaborate, and connect

Online writing instruction comes with its own peculiar set of affordances and constraints. One affordance is the flexible nature of learning “anytime, anywhere” while an important constraint (that affects both instructors and students) is transactional distance—the geographical, psychological and emotional distance that occurs when students learn in online environments (Garrison, Anderson, Archer, 1999; Moore, 2013). Prior researchers have responded to transactional distance and its influence on student learning and satisfaction by developing the Community of Inquiry Framework (Garrison, Anderson, Archer, 1999; Garrison and Arbaugh, 2007). This instructional design model addresses the non-geographical distances that affect the communication of both instructors and students by establishing three “presences” in the online learning environment (OLE): cognitive presence, teaching presence, and social presence. This research project looks at just one of these—social presence—in the advanced communication online writing course to determine how it influences instructors’ and students’ abilities to construct knowledge and to connect within the advanced communication course, and as Johnson-Eilola (1998, 2005) alludes, to a larger network beyond the course itself. In the context of this project, social presence is defined as the “the ability of participants to identify with the community (e.g. the course of study), and communicate purposefully in a trusting environment, and develop interpersonal relationships by way of projecting their individual personalities” (Garrison 2010). Social presence is most closely connected to an individual’s ability to form and maintain effective individual and team relationships, both of which are necessary components to
Improving the throughput time and answer quality of a technical support help desk with a knowledge management system

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Computer Science & Information Technology

Confusion matrix is a useful and comprehensive presentation of the classifier performance. It is commonly used in the evaluation of multi-class, single-label classification models, where each data instance can belong to just one class at any given point in time. However, the real world is rarely unambiguous and hard classification of data instance to a single class, i.e. defining its properties with single distinctive feature, is not always possible. For example, an image can contain multiple objects and regions which makes multi-class classification inappropriate to describe its content. Proposed solutions to this set of problems are based on multi-label classification model where each data instance is assigned one or more labels describing its features. While most of the evaluation measures used to evaluate single-label classifier can be adapted to a multi-label classification model, presentation and evaluation of the obtained results using standard confusion matrices cannot be expanded to this case. In this paper we propose a novel method for the computation of a confusion matrix for multi-label classification. The proposed algorithm overcomes the limitations of the existing approaches in modeling relations between the classifier output and the Ground Truth (i.e. hand-labeled) classification, and due to its versatility can be used in many different research fields.
Footprint-Based Personal Recognition using Scanning Technique

Objectives: Human footprint is emerging as a latest biometric trait for biometric authentication that could be used to detect an individual’s identity and to compute parameters such as foot length, foot category, height, weight, and body mass index. Methods: Static footprint images of 94 individuals (57 males and 37 females) of different regions of north India between age group 15-25 years have been acquired using Scanning technique. Matlab functions have been used for database acquisition, enhancement and feature extraction of the footprint images and for computation of performance measurements. Findings: The performance parameters computed for scanning techniques show a better agreement with experimental results and could be used for biometric authentication. A correlation analysis has also been performed and a strong correlation is observed between actual height and toes, actual height and foot length, height and weight. Applications: This study could be of potential use for forensic and non-forensic purposes and researchers working in foot biometrics.
Wavelet transformation and its application in image processing

The paper introduces the two-dimension wavelet transformation and inverse transformation algorith and explains its application in image processing by example. The results show, much image processing work such as image compression, image, de-noise, image. engeancement, image merging, can be attained by wavelet transformation efficiently. 
Minimizing makespan in a two-stage flow shop with parallel batch-processing machines and re-entrant jobs

ABSTRACT Against a background of heat-treatment operations in mould manufacturing, a two-stage flow-shop scheduling problem is described for minimizing makespan with parallel batch-processing machines and re-entrant jobs. The weights and release dates of jobs are non-identical, but job processing times are equal. A mixed-integer linear programming model is developed and tested with small-scale scenarios. Given that the problem is NP hard, three heuristic construction methods with polynomial complexity are proposed. The worst case of the new constructive heuristic is analysed in detail. A method for computing lower bounds is proposed to test heuristic performance. Heuristic efficiency is tested with sets of scenarios. Compared with the two improved heuristics, the performance of the new constructive heuristic is superior.
Impact factor modification based on the journal quality of citations

The impact factor of an academic journal has long been based on the arithmetic mean of citation counts,regardless of the quality of citations. However,the validity of the current algorithm is still open to question. We propose an alternative way of calculating the impact factor by the weighted mean,and the weights are based on the journal quality evaluation of citations. The simulation study indicates that the modified formula evaluates the academic journals more appropriately,and it can be used to build up a more justified system for scientific quality evaluation.
Problemas e Dificuldades no Ensino de Programação: Um Mapeamento Sistemático

The programming education is a complex task. Several studies point to problems ranging from the difficulty of the students in understanding the programming concepts to the lack of motivation of them in performing the programming activity. The objective of this paper is to present the results of a literature review conducted to collect and evaluate evidence-problems and difficulties in teaching and learning of programming. The idea is to provide a comprehensive summary of these problems and the solutions proposed to minimize them. To achieve this goal, a systematic mapping (MS) was performed, since he is a research method that establishes procedures for identifying studies related to a research question. We reviewed 519 articles and identified 70 identified as relevant in relation to the objectives of MS. The results point to trends and research needs in teaching and learning of programming and provides educators with a catalog of works that propose solutions to the problems faced in real scenarios of teaching and learning. In general, mapping results suggest a trend of research in methods and visualization tools to help students understand the concepts of programming.
Get Out of the Way – Obstacle Avoidance and Learning by Demonstration for Manipulation

Abstract Humans acquire manipulation skills by trial and error within a few trials, whereas programming a robot to perform the same task requires robotic expertise and effort. This paper presents a robot which learns a movement from demonstrations with the ability to generalize the movement to new goal poses and avoid the collision with obstacles in the workspace. The general movement is represented by dynamic movement primitives (DMP) augmented by potential fields in order to modulate the motion in the presence of obstacles. The approach is validated in experiments with a robotic arm in which dynamic obstacles partially blocking the movement are detected by a Photonic-Mixer-Devices (PMD) camera.
Complete spectrum of the stochastic master equation for random walks on treelike fractals

We study random walks on a family of treelike regular fractals with a trap fixed on a central node. We obtain all the eigenvalues and their corresponding multiplicities for the associated stochastic master equation, with the eigenvalues being provided through an explicit recursive relation. We also evaluate the smallest eigenvalue and show that its reciprocal is approximately equal to the mean trapping time. We expect that our technique can also be adapted to other regular fractals with treelike structures.
Implementing Query Operations over Encrypted Sensitive Data

Due to the emergence of cloud environment, the data are being provided as a service in the cloud. To maintain the confidentiality of the sensitive attributes, the data are encrypted and are stored at the service provider. When the encrypted data is queried, complete record has to be decrypted to fetch the actual results. This paper is mainly concen- trating on improving the performance of mainly three types of query operations on encrypted data i.e. non-aggregate, aggregate and user-defined query operations over the encrypted data stored at the server and subsequently its performance is compared with that of the earlier methods, in terms of the execution time.
A no-reference video quality assessment method based on digital watermark

Video quality assessment in real time is a critical requirement for channel evaluation and codec optimization in mobile multimedia system. However, most proposed approaches for video quality assessment need reference sequences, which is quite impossible in real time multimedia communications especially in wireless and IP video services. This paper proposes a novel no-reference video quality assessment method. By comparison of the extracted watermark embedded in transmitted video with the copy of original watermark in the destination, we can assess all reconstructed video quality without any original reference video sequences. Simulation results conclusively demonstrate that our method offers no-reference video quality assessment with little channel overhead and can be applied to real time multimedia systems.
On English Vocabulary Teaching

Vocabulary is generally perceived as the most difficult but important aspect in English teaching. For years, many ELT researchers and instructors have worked hard to develop an effectively way of teaching and learning. Enlarge learners' vocabulary has become an urgent task, because a large vocabulary is the prerequisite for language learning. In this paper, the author puts forward her suggestions on how to improve vocabulary teaching.
Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

ABSTRACT Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.
Multi-classification of audio signal based on modified SVM

As one of the important multimedia information carrier, audio signal effectively enriches and satisfies people's apperception and acquirement of the information; in order to improve the accuracy of audio classification, we adopt the modified SVM that is based on hierarchical clustering analysis and binary decision tree to classify the seven types of audio signal in this paper, a number of the samples are used for training of each audio signal so as to obtain the excellent training templates, and then to test the audio signal. Experimental results show that the method has a good classification performance, compared with the traditional one-to-one and other algorithms, our algorithm not only reduces the training and testing time, but also further improves the accuracy rate, up to over 90%.
Guided Image Filter Using Multi-Exposure Image Fusion: A Patch-Wise Approach

A lot of research has been contributed in the area of optimization of design. MOST applications in computer vision and computer graphics involve image filtering to suppress and/or extract content in images. We propose a patch-wise approach for multi-exposure image fusion (MEF) using guided image filter. A key step in our approach is to decompose each colour image patch into three conceptually independent components: signal strength, signal structure and mean intensity. Upon processing the three components separately based on patch strength and exposedness measures, we uniquely reconstruct a colour image patch and place it back into the fused image. Unlike most pixel-wise MEF methods in the literature, the proposed algorithm does not require significant pre/post processing steps to improve visual quality or to reduce spatial artefacts. Moreover, the novel patch decomposition allows us to handle RGB colour channels jointly and thus produces fused images with more vivid colour appearances using guided image filter. Extensive experiments demonstrate the superiority of the proposed algorithm both qualitatively and quantitatively with guided filter. Hybrid thresholding is applied on remaining sub bands (LH, HL and HH). The proposed work will be implemented using MATLAB R2015a.