Miracle-dz/UniFAR-FTUs huggingface dataset:
From Pixels to Object Sequences: Recurrent Semantic Instance Segmentation

We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end from an input image to a sequence of labeled masks and, compared to methods relying on object proposals, does not require post-processing steps on its output. We study the suitability of our recurrent model on three different instance segmentation benchmarks, namely Pascal VOC 2012, CVPPP Plant Leaf Segmentation and Cityscapes. Further, we analyze the object sorting patterns generated by our model and observe that it learns to follow a consistent pattern, which correlates with the activations learned in the encoder part of our network.
Secrecy performance evaluation of TAS protocol exploiting fountain codes and cooperative jamming under impact of hardware impairments

In this paper, we propose a transmit antenna selection (TAS) protocol exploiting Fountain codes for secure communication. In the proposed protocol, a base station (BS) selects its best antenna to transmit Fountain packets to a legitimate user (US). Moreover, a cooperative jammer (JA) is used to generate artificial noises to an eavesdropper (EA). The transmission between BS and US will terminate as soon as US can receive a sufficient number of the encoded packets for decoding the original message. To obtain a secure communication, US must receive enough the number of the packets before EA can. Under impact of hardware noises at all of the nodes, we derive expressions of probability of successful and secure communication (SSC), intercept probability (IP) and average number of the packets transmitted by BS over Rayleigh fading channel. Monte Carlo simulations are presented to verify the theoretical results.
Web 2.0 online collaboration tools as environments for task-based writing instruction

The paper presents an overview of a pilot study aiming at developing university students’ writing competence with the use of a selected Web 2.0 collaboration tool as authoring environment for Task-Based Language Teaching. Online word processors were used to design a pre-task, task and post-task sequence which was implemented in varied modes of work when teaching English to students at the Department of German Studies, Maria CurieSklodowska University in Lublin, Poland. The specific aim of the study was to investigate the applicability of one particular online collaborative environment, namely an online word processor such as Google Docs (http://docs.google.com), to implement form-focused language teaching at a tertiary level. The study was conducted in an action research fashion, with two parallel groups of students using two different collaborative environments (wikis and online word processors). As it was found out, the idea of learning writing in collaboration mediated by online resources was new to students and the awareness of how to make the best use of peer editing and peer feedback had to be established among students first. Once it was done, however, collaborative learning online proved to be a powerful vehicle for language instruction. The emergence of new Web 2.0 tools facilitating collaboration and publishing such as online word processors has helped to redefine the process of teaching language skills, and specifically writing, in the TBLT framework.
The Impact of Machine Translation and Computer-aided Translation on Translators

Under the context of globalization, communications between countries and cultures are becoming increasingly frequent, which make it imperative to use some techniques to help translate. This paper is to explore the influence of computer-aided translation on translators, which is derived from the field of the computer-aided translation (CAT) and machine translation (MT). Followed by an introduction to the development of machine and computer-aided translation, it then depicts the technologies practicable to translators, which are trying to analyze the demand of designing the computer-aided translation so far in translation practice, and optimize the designation of computer-aided translation techniques, and analyze its operability in translation. The findings underline the advantages and disadvantages of MT and CAT tools, and the serviceability and future development of MT and CAT technologies. Finally, this thesis probes into the impact of these new technologies on translators in hope that more translators and translation researchers can learn to use such tools to improve their productivity.
Robust and automatic remote photoplethysmography

This chapter introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the working principles of remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG algorithms for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method where a projection Plane Orthogonal to the Skin-tone (POS) is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model. This chapter is based on W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, “Algorithmic principles of remotePPG,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1479–1491, July 2017.
Three papers on decision theoretic agent-based modelling in demography

This thesis consists of three papers, which address Agent-Based Modelling (AM) as a methodology in demography, focusing on the modelling of decision making processes. The discussion begins by assessing the utility of AM as a methodology and some of the issues peculiar to it, and argues that the modelling of choice is of special significance in the demographic context. Of the three papers, the first one outlines an approach to model development combining aspects of game theory, and decision theory. It then contrasts the effect of four choice models on the behaviour of a simulation based on qualitative accounts of the disclosure behaviours surrounding alcohol misuse in pregnancy. The second paper applies this approach to help-seeking in older adult care, drawing on survey data to parameterise and validate the model. Simulation results, and variance-based sensitivity analysis indicate that a model of decision making which incorporates a representation of the interactions between agents are necessary to reproduce observed rates of caregiving. The third paper reports experiments designed to validate the choice behaviour of agents in the older adult care model. I examine human decision making about paired gambles from experience, where the pair has some features common to both choices. I report results for eight decision problems undertaken by 20 participants, and contrast the predictive ability of four models of decision making. I then estimate parameters to maximise the fit where possible, and find that while the best performance is offered by decision models with a representation of the problem, they do not offer a significant advantage over heuristic methods. I discuss the implications of this, in the context of the original agent-based model, and for agent-based modelling more generally.
Jordanian EFL Teachers’ Perceptions of Communicative Language Evaluation

The purpose of this study was to investigate EFL teachers’ perceptions of communicative language evaluation. The study aimed basically to answer the following questions: 1. What are Jordanian EFL teachers’ perceptions of communicative language evaluation? 2. Are there any statistically significant differences at (α= 0.05) in the EFL teachers’ perceptions of communicative language evaluation due to gender? 3. Are there any statistically significant differences at (α= 0.05) in the EFL teachers’ perceptions of communicative language evaluation due to qualification? 4. Are there any statistically significant differences at (α= 0.05) in the EFL teachers’ perceptions of communicative language evaluation due to experience? The sample of the study consisted of 76 Jordanian EFL teachers, 25 teachers responded to an open questionnaire, which was intended to help develop the closed questionnaire, the main research tool, 51 teachers responded to the latter questionnaire. The analysis of data collected from the second questionnaire showed that EFL teachers’ perceptions of communicative language evaluation were to some extent positive. Further, there were no statistically significant differences at (α= 0.05) in the EFL teachers’ perceptions of communicative language evaluation that could be ascribed to gender, qualification, or experience. In light of the findings, it was recommended that further research studies should be conducted on the actual communicative language assessment practices and on the congruence between teaching and assessment of English. It was also recommended that awareness of communicative language assessment practices, especially alternative, non-testing ones should be raised among EFL teachers.
An Educational Technology Tool That Developed in the Natural Flow of Life among Students: WhatsApp.

This study was carried out to identify the benefits and drawbacks of using mobile social network application WhatsApp in the education of Secondary Education students. In this research, survey model was used and open-ended question form to 145 students together with semi-structured interview technique to 6 students were employed and answer to the same research question was sought by using data of different nature.The data were analysed by content analysis and phenomenologic analysis methods and some of the screenshots of students’ posts are given as they are when necessary, which made possible the inspection, comparison and verification of each data one another. The benefits and drawbacks of using WhatsApp for educational purposes, which students normally use for communication purposes, are listed under the subheadings of technique, education and academic. Results indicate that WhatsApp has the potential to provide a natural and unstructured learning environment. Accordingly, by taking the benefits and drawbacks of WhatsApp and the like into account, it is advised to support their educational use.
Privacy-Preserving Reputation Management for Edge Computing Enhanced Mobile Crowdsensing

Mobile crowdsensing (MCS) has gained popularity for its potential to leverage individual mobile devices to sense, collect, and analyze data instead of deploying sensors. As the sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with the edge computing paradigm to reduce time delays and high bandwidth costs. The sensing data may reveal personal information, and thus it is of great significance to preserve the privacy of the participants. However, preserving privacy may hinder the process of handling malicious participants. In this paper, we propose two privacy preserving reputation management schemes for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. In the basic scheme, a novel reputation value updating method is designed based on the deviations of the encrypted sensing data from the final aggregating result. The basic scheme is efficient at the expense of revealing the deviation value of each participant to the reputation manager. To conquer this drawback, we propose an advanced scheme by updating the reputation values utilizing the rank of deviations. Extensive experiments demonstrate that both these two schemes have high cost efficiency and are effective to deal with malicious participants.
Random Linear Network Coding Schemes for Reduced Zero-Padding Overhead: Complexity and Overhead Analysis

The zero-padding overhead created when performing Random Linear Network Coding (RLNC) on unequal-sized packets can curb its promising benefits since it can be as high as the data to convey. The concept of macro-symbol coding was introduced recently in order to reduce the zero-padding overhead that RLNC has brought. Macro-symbols are subsets of the packets, i.e. concatenated bytes. They allow performing coding mainly on the payload and they proved to be efficient against the naive padding. This paper studies the properties of macro-symbols and provides a characterization of their impact on the computational complexity as well as the overall overhead. Furthermore, we provide a theoretical framework for the encoding and decoding complexity for the state-of-the-art schemes for unequal-sized packets. Our simulations results performed on a series of benchmark video traces show that small macro-symbols guarantee a dramatic reduction of padding overhead, whilst it results on a higher decoding complexity on the other hand compared with RLNC in the worst case.
The Effect of Google Earth and Wiki Models on Oral Presentation Skills of University EFL Learners.

This article reports the results of an experimental study that investigated the effectiveness of Google Earth and Wiki tools in improving the oral presentation skills of English as a Foreign Language (EFL) learners and boosting their motivation for learning. The participants (n =81) are enrolled in writing classes at two English-medium institutions. The study employed the factorial mixed methods pre-test/post-test control group experimental design. The experimental conditions included the experimental group’s use of Google Earth and Wiki dynamics in conducting research and delivering oral presentations whereas participants in the control group were given the regular oral presentation and the research paper instruction. The findings of the study underscored the effectiveness of the integration of Google Earth and Wiki into classroom practices. Google Earth and Wiki allowed active learning with information. As such, Google Earth and Wiki improved learners’ oral presentation skills and perceptions. Wiki visualization devices and structure could increase scaffolding and collaboration. Google Earth could facilitate critical thinking and true spatial analytical operations. The integration of Google Earth and Wiki could promote student-centered learning and motivation.
The Bilingual Vocabulary Size Test for Vietnamese Learners: Reliability and Use in Placement Testing

Abstract:To be useful, placement tests need to be both reliable and practical. Placement tests must be reliable in order to have confidence in the results. They must be practical in terms of expense, in the time required to administer and mark the test, and in the ease with which the results are interpreted (Nation & Macalister, 2010, p. 120). This article reports on the use of a freely available test, the Bilingual Vocabulary Size Test for Vietnamese Learners (BVSTV), and its use and modification as a reliable and practical placement test. The study involved 147 Vietnamese university graduates of various academic backgrounds. The participants sat the BVSTV as part of placement testing prior to participating in an intensive four-week, 100-hour English program. The results of the study indicate that a shortened version of the BVSTV containing the words from the first eight frequency levels could be used for vocabulary assessment in placement testing. It is reliable and the first eight levels are sufficient for use of partial knowledge at high-frequency word levels while avoiding random guessing at low-frequency word levels.
Initial Node Exchange and Convergence of SOM Learning

Self Organizing Map (SOM) is a kind of neural networks, that learns the feature of input data thorough unsupervised and competitive neighbourhood learning. In SOM learning algorithm, every connection weights in SOM feature map are initialized to random values to covers whole space of input data, however, this is also set nodes to random point of SOM feature map independently with data space. The move distance of output nodes increases and learning convergence becomes slow for this. To improve SOM learning convergence, here I proposed a new method, node exchange of initial SOM feature map, and a new measure of convergence, the average of the move distance of all output nodes. As a result of experiments, the average of the move distance of all nodes was drastically shortened to about 73% by proposed method, and there is effect sufficient by performing exchange process by about 5% of the whole learning data.
Evaluation of binary decision diagrams for redundancy, shadowing, generalisation and correlation in an Information sharing model.

This paper defines a structured methodology which is based on the foundational work of Al-Shaer et al. in [1] and that of Hamed and Al-Shaer in [2]. It defines a methodology for the declaration of policy field elements, through to the syntax, ontology and functional verification stages. In their works of [1] and [2] the authors concentrated on developing formal definitions of possible anomalies between rules in a network firewall rule set. Their work is considered as the foundation for further works on anomaly detection, including those of Fitzgerald et al. [3], Chen et al. [4], Hu et al. [5], among others. This paper extends this work by applying the methods to information sharing policies, and outlines the evaluation related to these.
A Robust Approach of Embedding Watermark Blocks In Digital Video for Copyright Protection Using Zero Padding Algorithm

Digital copyright protection has become an effective way to prove the ownership and protect the multimedia contents from illegal use and unauthorized users. In order to prove the ownership of a video certain security program is embedded in a video and one of the ways of ensuring the ownership of a video is embedding the watermark in a video. In this paper a new approach of digital video copyright protection with Zero Padding Algorithm (ZPA) is proposed. With the help of this algorithm, it is hard to know the original pattern of watermark. This algorithm splits the watermark into small pieces and minimizes the perceptual degradation of watermarked video because of ZPA. In this paper Digital Wavelet transform (DWT) is used to embed the watermark in the LL sub-band, based on the energy of high frequency sub-band in an adaptive manner. The proposed algorithm has undergone various attacks, such as compression, uniform noise, Gaussian noise, frame repetition and frame averaging attacks. The proposed algorithm, sustain all the above attacks and offers improved performance compared with the other methods from the literature.
The Constructive Nature of Color Vision and Its Neural Basis

Our visual world is made up of colored surfaces. The color of a surface is physically determined by its reflectance, i.e., how much energy it reflects as a function of wavelength. Reflected light, however, provides only ambiguous information about the color of a surface as it depends on the spectral properties of both the surface and the illumination. Despite the confounding effects of illumination on the reflected light, the visual system is remarkably good at inferring the reflectance of a surface, enabling observers to perceive surface colors as stable across illumination changes. This capacity of the visual system is called color constancy and it highlights that color vision is a constructive process. The research presented here investigates the neural basis of some of the most relevant aspects of the constructive nature of human color vision using machine learning algorithms and functional neuroimaging. The experiments demonstrate that color-related prior knowledge influences neural signals already in the earliest area of visual processing in the cortex, area V1, whereas in object imagery, perceived color shared neural representations with the color of the imagined objects in human V4. A direct test for illumination-invariant surface color representation showed that neural coding in V1 as well as a region anterior to human V4 was robust against illumination changes. In sum, the present research shows how different aspects of the constructive nature of color vision can be mapped to different regions in the ventral visual pathway.
Support vector machine in gender recognition

In the paper, Support Vector Machine (SVM) methods are discussed. The SVM algorithm is a very strong classification tool. Its capability in gender recognition in comparison with the other methods is presented here. Different sets of face features derived from the frontal facial image such as eye corners, nostrils, mouth corners etc. are taken into account. The efficiency of different sets of facial features in gender recognition using SVM method is examined.
Statistical mining in data streams

Recent years have seen a steady rise of a new class of data management systems called Data Stream Management Systems (DSMS). These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent relations. Data streams are common to applications such as network traffic and transaction monitoring systems, click-stream processors, industrial process control, and sensor networks. A DSMS operates on these continuous and time-varying data streams to facilitate on-the-fly query answering, and to support data acquisition, monitoring and analysis. In this dissertation, we present statistical stream mining solutions for effective online processing of streaming data. We focus research issues related to adaptive stream resource conservation and online mining in a DSMS. We have developed statistical linear and non-linear filtering techniques based on the Kalman Filter to capture temporal correlations in the streaming data. Such correlations help in stream resource conservation. We also propose techniques that capture spatial correlations between the streaming sources that further helps improving resource conservation and facilitates answering group-queries in an efficient manner. In addition to resource management and query processing, a DSMS needs to address issues related to online stream mining. Once the data stream arrives at a central server, effective mining techniques are necessary for stream analysis, before the data can be discarded. Since a stream continuously evolves with time, stream mining techniques need to be adaptive and should operate under a given memory constraint. We propose adaptive clustering solutions that use the kernel trick to capture non-linear relations in the streaming data. We also present OCODDS, a change-detection approach that can track evolutionary changes in the stream in both linear and non-linear settings. Finally, we present our techniques for effective acquisition and processing of data streams common to video sensor networks.
Case Studies in Acceleration of Heston's Stochastic Volatility Financial Engineering Model: GPU, Cloud and FPGA Implementations

Here we present a comparative insight of the performance of the Heston stochastic volatility model on different acceleration platforms. This model was tested against a MacBook’s CPU, a Techila grid server hosted on Microsoft’s Azure cloud, a GPU node hosted by Boston Ltd, and an FPGA node hosted by Maxeler Technologies Ltd. Temporal data was collected and compared against the CPU baseline to provide quantified acceleration benefits from all platforms.