Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection of Lung Nodules in CT Images with 3D Convolutional Neural Networks

Accurate lung nodule detection in computed tomography (CT) images is a critical step in diagnosing lung cancer. This paper proposes an efficient model to build a Computer-Aided Detection (CADe) system for lung nodule detection, which points out a way to enhance a patient's chance of survival. We first introduce a specially designed three-dimension convolutional neural networks (3D CNN) for candidate detection that takes into account of nodules with various sizes. Then, another 3D CNN is presented for the subsequent false positive reduction. Experimental results of the Tianchi Medical AI Challenge demonstrate the superior detection performance of the proposed approach.
Preparation and application of α-Fe2O3@MIL-101(Cr)@TiO2 based on metal–organic framework for photocatalytic degradation of paraquat

In this study, a new magnetic α-Fe2O3@MIL-101(Cr)@TiO2 photocatalyst was successfully synthesized. The material synthesized had been fully characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, vibrating sample magnetometry, transmission electron microscopy, and Brunauer–Emmett–Teller isotherm methods. The X-ray diffraction analysis corroborates that nanoparticles are polycrystalline with rhombohedral and tetragonal crystal structures for Fe2O3 and TiO2, respectively. In addition, the photocatalytic degradation of the herbicide paraquat in the presence of α-Fe2O3@MIL-101(Cr)@TiO2 under ultraviolet (UV) irradiation was studied. The effect of experimental parameters such as the initial concentration of catalyst, the pH, and the initial paraquat was investigated. The optimal conditions were achieved for concentration of catalyst 0.2 g L−1, pH 7, and concentration of paraquat 20 mg L−1. The photocatalytic degradation efficiency was 88.39% after 15 min with α-Fe2O3@MIL-101(Cr)@TiO2 under UV irradiation. The pseudo-second-order kinetic model for photocatalytic degradation of paraquat was obtained. The catalysts could be recovered and reused without any loss of efficiency for five times in the consequent reactions. To the best of our knowledge, this is the first report on the photocatalytic degradation of paraquat using new α-Fe2O3@MIL-101(Cr)@TiO2 photocatalyst under UV irradiation condition.
I-SPARK: IoT Based Smart Parking System

TRAFFIC a direct or an indirect outcome of many minor issues has been creating havoc for an individual especially in urban areas. This paper proposes an architecture that introduces an efficient and eminent; rather a smart way to resolve the small module that actually counts i.e. Parking Area. We design a system so as to eliminate the time wastage and irrelevant frustration faced by the drivers based on IoT for real time monitoring of the empty slots for car parking from anywhere using a webpage or a mobile app; IoT the emerging research domain is the heart of the proposed system.
Colour Image Segmentation of Fundus Blood Vessels for the Detection of Hypertensive Retinopathy

Retina is the major part of the human eye. Most of the diabetic and hypertensive problems will initially affect the retinal blood vessels of the eye. Using normal segmentation techniques, we can identify only the blood vessels. Therefore, color image segmentation is the best way to identify the retinal problems, because using color image segmentation we can differentiate the retinal veins and arteries. We have used masking techniques to segment the images of arteries and vein. The proposed technique is evaluated with data from ten patients diagnosed with hypertensive retinopathy. For this process, histogram equalization is performed on the complemented green plane image. From the result, the optic region is removed by morphological operation, after which it is converted into binary image. This output is multiplied with R, G, & B channels of input data and combined to get required segmented region of retinal blood vessels. All this vessels extraction has been done by using image processing techniques.
Artificial Neural Networks for Communication Wireless Networks: a Synthetic Study

The dynamic behavior of nodes makes management and administration in MANET networks more difficult. To overcome these problems, the researchers have tried to improve the used routing protocol. in this article, we have studied the impact of ANN on quality and performance evaluation in MANET networks from a set of approaches and techniques proposed by researchers in this field and we have tried to capitalize this study in a summary table to deduce possible remarks and improvements for these approaches.
Privacy Preserving Profile Matching for Social Networks

User privacy is one important issue in online social networks. The rapid development in recent anonymous social networks shows that most users may not trust the service provider. Existing anonymizing techniques cannot support profile matching, making it difficult for a user to connect with his friends. This paper proposes a novel privacy preserving profile matching method based on the attribute based encryption, and applies this method to an anonymous and private social network. We evaluate the runtime performance of our proposed method on both laptop and mobile devices, and our experimental results show that this method can be adapted to real user applications.
Multi-Task Handwritten Document Layout Analysis

Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.
TL-FCMA: Indoor Localization by Integrating Fuzzy Clustering with Transfer Learning

With the rapid development of sensing, computation, wireless networking and communication technologies, many indoor location-based services have emerged, which promote technology of precise indoor localization in Internet of Things (IoT). Fingerprinting localization has become attractive because of its open access and low cost. However, wireless signals are easily affected by environmental conditions, which leads to localization models built from previous radio maps inaccurate. Reconstructing radio maps and updating them with fewer labeled new data while maintaining high-accuracy positions is a key but difficult problem. In this paper, an indoor localization algorithm integrating fuzzy clustering with transfer learning(TL-FCMA) is proposed. Fuzzy C-means clustering aims at minimizing the effect of regional environmental changes in the location area. With the result of fuzzy clustering, the radio map is reconstructed and the positioning is conducted with manifold alignment based transfer learning. Experimental results show that TL-FCMA has lower location errors than that of those compared algorithms.
A Compact 39-GHz 17.2-dBm Power Amplifier for 5G Communication in 65-nm CMOS

This paper presents design of a 39-GHz power amplifier for fifth-generation (5G) mobile communication in millimeter-wave. The power amplifier consists of two differential capacitive-neutralized common-source stages. Low-loss transformers are employed for matching network in each stage. With 1.1-V supply, the power amplifier achieves a small-signal gain of 17 dB, saturated output power (PSAT) of 17.2 dBm, and 1-dB compression point (P1dB) of 15.5 dBm at 39 GHz. The amplifier has a maximum power-added efficiency (PAEMAX) of 31.2% and 30.2% at P1dB. The amplifier has an average output power of 9.0 dBm and 10.0% PAE with −25 dBc EVM. The amplifier has been fabricated in standard 65-nm CMOS and occupies an area of 0.081 mm2.
Problematic Gaming and Internet Use but Not Gambling May Be Overrepresented in Sexual Minorities – A Pilot Population Web Survey Study

Background: Substance-related addictive disorders are known to be overrepresented in non-heterosexual individuals, but it is largely unknown whether this is also the case for behavioral addictions such as problem gaming and gambling. This study aimed, in a pilot web survey design, to assess whether problematic gambling, gaming and internet use may be more common in individuals with a non-heterosexual orientation. Methods: An online survey was distributed through media and social media, and answered by 605 individuals (51% women and 11% non-heterosexual). Problem gambling, problem gaming and problematic internet use were measured through structured screening instruments (the CLiP, the GAS and the PRIUSS, respectively). Results: Problem gaming and problematic internet use were significantly more prevalent in non-heterosexual subjects. Instead, problem gambling did not differ between heterosexual and non-heterosexual respondents. Psychological distress and social media use for more than 3 h daily were significantly more common in non-heterosexual respondents. In the overall sample, gaming and gambling were associated statistically. Conclusion: Based on the present pilot online survey, problematic gaming and internet use, but not problem gambling, may be more common in non-heterosexual populations. This area merits more and larger studies, and potentially preventive efforts aimed for non-heterosexual individuals in the population. Possible explanations and study limitations are discussed in the paper.
Hardening the Virtual Password Authentication Scheme

Although Internet has become one of most important parts and mostly needed by societies, that does not mean Internet is a safe place to share sensitive data. One of many unsolved Internet attacks is key-logger which is used to steal victim's data such as passwords. Researchers have done a lot of research to overcome these attacks. However, the authentication system still lacks password complexities which can be compromised with short cracking time and limited generated passwords. Therefore, we proposed a virtual password method that has the following rules: i) has minimum and maximum limit of password and ii) the generated passwords are not limited only to letters, but also numbers and symbols. With those rules, by using Kaspersky Lab secure password measurement, the cracking time can be significantly increased to 9 centuries on the conficker botnet with 10 million cores of processors. Moreover, by using Password Meter the proposed method gets score of 171.
Link Prediction Method Using Topological Features and Ensemble Model

Online social networking has progressively been the new interdisciplinary research area, especially for developing new strategies of investigating these informal networks containing billions of users. However, such networks might not represent real-world connections among people either due to imperfect procurement forms or not yet reflected on the online platform like friends in real-world might not connect with each other online. To predict these unknown connections in the online community is still an open-ended problem. In this paper, a novel link prediction method is proposed to find the missing connections in the social network graphs. The proposed method extracts topological features from the network graph which are used to train an ensemble learning model i.e., random forest classifier. The trained model is used to predict the missing connections. The experimental evaluation is conducted on two networking dataset namely; ‘Facebook networking dataset’ and the ‘Flickr following dataset’ publicly available on Stanford Network Analysis Project (SNAP) and Koblenz Network Collection (KONECT) respectively. The comparison is done with the prediction results on the same features by the state-of-the-art learning models namely; linear support vector machine (LSVM), K-Nearest Neighbours (KNN), AdaBoost, and Gradient Boost. The performance of the considered methods is defined in terms of accuracy, precision, recall, F1-measure, and AUC value. Additionally, the efficiency of the proposed method is validated against the existing link prediction method. The experimental results conclude that the proposed method is accurate than the compared methods in uncovering the hidden links of a social network.
Comparison of Different Deep-Learning Methods for Image Classification

In this paper, we showed how to solve exemplary image classification problem. The goal of image classification problem is to correctly classify the input image within the expected class/label. In our case, we focused on classifying the animal image into one of two categories: mammals or birds. That classification problem is not a trivial task using standard machine learning algorithms. The main reason for that is the fact that the algorithms are based on previously prepared features for classifying object. It is often done by hand by a researcher. Defining specific features for example how a beak looks, wing, tail, fur and etc. is natural for humans, but understanding it by computers is extremely difficult. However, nowadays deep learning algorithms managed to overcome some obstacles and work best for these types of problems. We can use Deep Neural Networks (DNN) in two ways - by developing it from scratch for the specific problem or using method calls transfer learning. The main goal of this paper is comparing the two methods by using them in a real-life example. We showed how to correctly prepare a dataset, create a DNN model from scratch and how to adjust it. We also showed how to use the transfer learning technique. All the steps we made are described in a way which allows for easy adaptation of these algorithms to similar problems.
Optimal Operation Region of LLC Resonant Converter for on-Board EV Battery Charger Applications

In this study, optimal operation region of a LLC resonant converter is discussed for on-board electrical vehicle (EV) battery charger applications. In the operation mode optimization, wide range output voltage regulation and wide range load condition, which are required lithium-ion battery cells are taken into consideration. The basic operation fundamentals of LLC resonant converter is presented and an analytical comparison for the operations above resonance, below resonance and above-below resonance is presented to evaluate output voltage regulation under very wide range load conditions. In addition, an analytical efficiency comparison is also presented to evaluate operation region effects. The operation and validity of obtained optimization region is tested by a simulation study which has 250–450 V/6 A output rates.
Uneven Illumination Compensation for Unconstrained Face Recognition Using LBP

Face recognition results are degraded when the test image is having nonuniform illumination variations. Due to the lighting variations, different expressions and occlusions the facial appearance of the face changes dramatically such that recognition by the methods is quite a difficult task. Out of which the problems due to illumination variations such as shadows, under lighting, over lighting in the face are the crucial problems which are to be overcome to achieve the satisfactory results for an automatic face recognition system. In this paper, we propose an efficient illumination compensation method based on frequency analysis and multi-resolution analysis. The effect of uneven lighting variations of the test image is efficiently and effectively removed by applying the LBP image to modify the magnitude information in the frequency domain. The magnitudes of the LBP image compensate the distorted magnitudes of the original image, caused by the light variations, as well as add some structural information to the restored image. To examine the efficacy of our developed method, histogram equalization based normalization and Fourier based normalization methods are compared with the proposed method. An extensive simulation study has been carried out to measure the effectiveness of our proposed method on the images containing extreme illumination variations. For this purpose, we have used the Extended Yale-B face database. From the results, it is found that our proposed method outperforms other methods and also it works better for extremely poor illuminated images.
Optimal Design of Uniform Circular Antenna Array in mmWave LOS MIMO Channel

The capacity of millimeter-wave (mmWave) line-of-sight (LOS) multiple-input multiple-output (MIMO) channel can be reduced due to the absence of rich scattering. However, the optimization of antenna array deployment can enable the maximum spatial multiplexing gain in the mmWave LOS MIMO channel, which can be applied to the long distance airborne data transmission. In this paper, we first establish the channel model of mmWave LOS MIMO based on a uniform circular antenna array (UCA) and derive the upper/lower bound of channel capacity. Aiming at achieving the maximum capacity of mmWave LOS MIMO channel, we propose the Newton-iterative method to obtain the optimal parameters of UCA. To simplify the optimization algorithm, the 2-D search can be transformed into 1-D search. Then, the analysis of bit error rate (BER) of the mmWave LOS MIMO system based on the UCA has been provided to quantify the mmWave LOS MIMO channel. Finally, numerical results demonstrate that the optimal parameters of UCA can be obtained by the Newton-iterative method and the BER of mmWave LOS MIMO communication system based on the corresponding optimized UCA can be minimized.
Scaling Embedded In-Situ Indexing with DeltaFS

Analysis of large-scale simulation output is a core element of scientific inquiry, but analysis queries may experience significant I/O overhead when the data is not structured for efficient retrieval. While in-situ processing allows for improved time-to-insight for many applications, scaling in-situ frameworks to hundreds of thousands of cores can be difficult in practice. The DeltaFS in-situ indexing is a new approach for in-situ processing of massive amounts of data to achieve efficient point and small-range queries. This paper describes the challenges and lessons learned when scaling this in-situ processing function to hundreds of thousands of cores. We propose techniques for scalable all-to-all communication that is memory and bandwidth efficient, concurrent indexing, and specialized LSM-Tree formats. Combining these techniques allows DeltaFS to control the cost of in-situ processing while maintaining 3 orders of magnitude query speedup when scaling alongside the popular VPIC particle-in-cell code to 131,072 cores.
Dynamic Clustering and GPU Based Parallel Processing Approach to Accelerate Circuit Simulation

In this digital era, electronic circuit is the key component and its design is tested and validated through simulator. Simulator uses mathematical model to replicate circuit behavior. All electronic designs rely truly on simulation software. But even though simulation is cost-effective; large circuit simulation is relatively time consuming. Also various iterations in transient analysis may make simulation slower. Fast simulator is the basic requirement for large circuit simulation. In this paper, we have addressed parallel computing approach using Graphics Processing Unit(GPU) to accelerate simulation. As GPU is many core processor, compute intensive functions are redesigned to execute on GPU. Matrix operations, linear-nonlinear equations, integration, differential equations, numerical methods are some of the very basic operations required in circuit analysis. Mathematical operations are redesigned to get clusters of sufficient size. Forming clusters of circuit components and mathematical procedures proves to be crucial, for reliable mapping to graphics processor. Loop replacement, data-code partitioning, parallel data mapping, reductions, fast memory access are the strategies adopted for parallel processing on GPU. More than 40% speed gain is achieved on circuit having at least four components and transient analysis for more than thousand iterations.
ReDecode Framework for Iterative Improvement in Paraphrase Generation

Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP. Automatically generating paraphrases has its utility in many NLP tasks like question answering, information retrieval, conversational systems to name a few. In this paper, we introduce iterative refinement of generated paraphrases within VAE based generation framework. Current sequence generation models lack the capability to (1) make improvements once the sentence is generated; (2) rectify errors made while decoding. We propose a technique to iteratively refine the output using multiple decoders, each one attending on the output sentence generated by the previous decoder. We improve current state of the art results significantly - with over 9% and 28% absolute increase in METEOR scores on Quora question pairs and MSCOCO datasets respectively. We also show qualitatively through examples that our re-decoding approach generates better paraphrases compared to a single decoder by rectifying errors and making improvements in paraphrase structure, inducing variations and introducing new but semantically coherent information.
Development and validation of reflective log for researching and supporting teachers' relational agency for change

This paper presents the development and validation of a reflective log designed for researching and supporting teachers’ relational agency for change. Drawing on theories of teacher agency and inclusive pedagogy, relational agency is defined as teachers working inclusively and collaboratively with families, colleagues and other professionals to address barriers to learning experienced by some students, while avoiding their marginalisation. The log is designed collaboratively with practitioners to analyse these ways of working in relation to particular purposes of change that matter to them, including but not limited to enhancing student achievement. Following responses from 24 teachers and 22 student teachers about the purposes and nature of their collaborative practices within and beyond school, a draft log has been adjusted for uses in future research and professional reflection. Background and objective Traditional preparation for teaching as an isolated teacher-classroom activity is increasingly challenged, and teacher collaboration is promoted as beneficial for student outcomes and innovation (Daly et al., 2010; Grangeat & Gray, 2008; Louis et al., 1996; Moolenaar, 2012). How teachers interact with each other and with other staff is critical for taking a collective responsibility for student learning, particularly for students from migrant and low socioeconomic backgrounds (Goddard et al., 2007; Louis et al., 1996; Moolenaar et al., 2012). Impactful teacher collaboration, e.g. in the form of ‘communities of practice’ (Wenger, 1998) is characterised by shared sense of purpose and focus on students learning (see Vescio et al., 2008 for review). However, existing studies have been criticised for trivialising teachers’ practices in a simplistic assumption that teacher collaboration leads to improved student achievement (Riveros et al., 2012). Moreover, the very notion of community is subverted if members are not seen to exercise much choice over the purposes of their collaboration (Roth & Lee, 2006). What is also overlooked is that schools do not necessary comprise a single community given a variety of purposes that teachers, students and their families, may pursue. Contexts of contemporary schooling involve many co-existing, sometimes competing demands on teachers and schools that involve diverse perspectives and understandings of their roles and priorities. For example, schools increasingly have roles in collaborating with external agencies to protect children who are at risk of social exclusion, as well as underachievement