Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptographic Design of PriCloud, a Privacy-Preserving Decentralized Storage with Remuneration

Over the last years, demand for file hosting has sky-rocketed due to cost reductions and availability of services. However, centralized providers have a negative impact on the privacy of their users, since they are able to read and collect various data about their users and even link it to their identity via their payments. On the other hand, decentralized storage solutions like GNUnet suffer from a lack of participation by providers, since there is no feasible business model. We propose PriCloud, a decentralized storage system which allows users to pay their storage providers without sacrificing their privacy by employing anonymous storage smart contracts and private payments on a blockchain. We are able to provide privacy to the users and storage providers, and unlinkability between users and files. Our system offers decentralized file storage including strong privacy guarantees and built-in remuneration for storage providers.
AUTOMATED CHAIN FOR LARGE-SCALE 3D RECONSTRUCTION OF URBAN SCENES FROM SATELLITE IMAGES

Abstract. Automatic city modeling from satellite imagery is a popular yet challenging topic in remote sensing, driven by numerous applications such as telecommunications, defence and urban mamagement. In this paper, we present an automated chain for large-scale 3D reconstruction of urban scenes with a Level of Detail 1 from satellite images. The proposed framework relies on two key ingredient. First, from a stereo pair of images, we estimate a digital terrain model and a digital height model, by using a novel set of feature descriptors based on multiscale morphological analysis. Second, inspired by recent works in machine learning, we extract in an automatic way contour polygons of buildings, by adopting a fully convolutional network U-Net followed by a polygonization of the predicted mask of buildings. We demonstrate the potential of our chain by reconstructing in an automated way different areas of the world.
A Scalable Load Balancing Scheme for Software-Defined Datacenter Networks based on Fuzzy Logic

In order to solve the problem that traditional load balancing technology lacks sufficient scalability and flexibility, we propose a dynamic and scalable load balancing scheme based on fuzzy logic for datacenter networks using the SDN architecture characteristics of control and forwarding separation, named LBSFL. The main parameters affecting the server load performance are analyzed and used as the input variables of fuzzy logic system to evaluate the server load, and then SDN controller schedules current network requests to achieve server load balancing. Additionally, we take into account the issue of server activation and deactivation under various traffic loads in this scheme. The experimental results show that the response time of the system can be increased by about 15% compared with LBBSRT, and it can also maintain the system load between the lower (30%) and normal (50%) boundaries. The scheme can greatly improve the load balance of servers under the premise of guaranteeing the network performance
Automated Facial Age Estimation Using Deep Belief Network

Recently, automatic age estimation has gained an increasing interest from the research community because of its relevance to many applications in different fields such as law enforcement, security control, and human-computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem due to a set of challenges which includes incomplete aging patterns, multi-class nature, personalized aging patterns, and disturbance. We consider the problem of automatic age estimation from face images. Age estimation problem is usually formulated as a multi-class classification problem relating the facial features to a specific age group. In this work, an automatic facial age estimation system based on a deep belief network was proposed. The proposed approach includes four steps: image preprocessing, feature extraction, feature reduction, deep believe network classification into one of four age groups. The performance of the proposed approach is evaluated using a standard dataset and the experimental results are promising.
Unifying Question Answering, Text Classification, and Regression via Span Extraction

Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks, We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.
Review: A Genetic Algorithm Approach to the Iterated Prisoner’s Dilemma

Introduction: The use of machine learning tactics such as the Moran process and evolutionary finite state machines have the potential to outperform classic strategies for the iterated prisoner’s dilemma. Methods: Applying a genetic algorithm approach to the iterated prisoner’s dilemma while modeling strategies with finite state machines proved to be an efficient method in which the produced strategies were able to cooperate unilaterally with their opponents. Results: Varying parameters in the evolution process such as the amount of generations, population size and bottleneck size were shown to directly contribute to the success of the strategies produced. In comparing various optimization methods, the genetic algorithm utilizing finite state machines outperformed the Moran process with respect to the highest scoring strategies produced by each. Discussion: These results can be explained due to the benefits of genetic recombination that was made possible with the use of finite state machines, where crossing over of state / action pairs resembling choices to make depending on the state environment occurred from generation to generation. Due to natural selection and recombination, the genes of the strategies with the highest fitness levels were bred into the next generations. Population bottlenecks and gene mutation tactics were used to recreate the naturally occurring phenomenon of gene variation, resulting in the creation of new species (which are iterated prisoner’s dilemma finite state machine strategies) within the generations. Conclusion: Tangible tactics can be extracted from these strategies, which were evolved using standard genetic algorithm tactics utilizing gene crossover and mutation techniques.
The Influence of Sign Variations on Drivers’ Comprehension

Road Sign comprehension studies typically focus on differences among signs, demonstrating large variability in comprehension among different signs. Differences in features of sign design can be grouped into their shape, background color, and the symbol/icon in their center. This study demonstrated that specific sign messages can be presented with different sign features without detrimental effects on either comprehension level or response time. In particular, the choice of background color (yellow or white) appears to be inconsequential for comprehension. It seems that some sign characteristics are not critical to comprehension and consequently licensed drivers may even incorrectly identify a non-local sign as the standard sign that they actually encounter on the roads. However, other sign features – especially those relating to the icon/symbol - can be critical to comprehension when they violate the icon-concept compatibility, as it is represented in drivers' long-term memory.
Co-located Collaborative Block-Based Programming

With the increasing need to teach programming to novices using collaborative methods like pair programming, it is important to understand how different input devices can help support collaborative learning. In this paper we present Multi-Device Grace, the first application to explore block-based programming in a cross-device environment consisting of digital tabletops, mobile tablets, and laptops. We conducted a user study $(n =18)$ to explore how cross-device environments can support co-located collaborative block-based programming. The study used Tiled Grace, an existing block-based programming language, and our extensions: Tabletop Grace (designed for tabletops) and Mobile Grace (designed for tablets). Our results show that the majority of participants felt they were able to collaborate quickly and easily, and the cross device interaction would be particularly beneficial in an education setting. Fig. 1.Multi-Device Grace: multiple novice programmers simultaneously working together on different devices (laptop, mobile tablet, and digital tabletop) within different independent workspaces to develop a new blocks-based program.
Bounds on RF cooperative localization for video capsule endoscopy

Wireless video capsule endoscopy has been in use for over a decade and it uses radio frequency (RF) signals to transmit approximately fifty five thousands clear pictures of inside the GI tract to the body-mounted sensor array. However, physician has no clue on the exact location of the capsule inside the GI tract to associate it with the pictures showing abnormalities such as bleeding or tumors. It is desirable to use the same RF signal for localization of the VCE as it passes through the human GI tract. In this thesis, we address the accuracy limits of RF localization techniques for VCE localization applications. We present an assessment of the accuracy of cooperative localization of VCE using radio frequency (RF) signals with particular emphasis on localization inside the small intestine. We derive the Cramer-Rao Lower Bound (CRLB) for cooperative location estimators using the received signal strength(RSS) or the time of arrival (TOA) of the RF signal. Our derivations are based on a three-dimension human body model, an existing model for RSS propagation from implant organs to body surface and a TOA ranging error model for the effects of non-homogenity of the human body on TOA of the RF signals. Using models for RSS and TOA errors, we first calculate the 3D CRLB bounds for cooperative localization of the VCE in three major digestive organs in the path of GI tract: the stomach, the small intestine and the large intestine. Then we analyze the performance of localization techniques on a typical path inside the small intestine. Our analysis includes the effects of number of external sensors, the external sensor array topology, number of VCE in cooperation and the random variations in transmit power from the capsule.
Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets

The rise of social media paves way for unprecedented benefits or risks to several organisations depending on how they adapt to its changes. This rise comes with a great challenge of gaining insights from these big data for effective and efficient decision making that can improve quality, profitability, productivity, competitiveness and customer satisfaction. Sentiment analysis is the field that is concerned with the classification and analysis of user generated text under defined polarities. Despite the upsurge of research in sentiment analysis in recent years, there is a dearth in literature on sentiment analysis applied to banks social media data and mostly on African datasets. Against this background, this study applied machine learning technique (support vector machine) for sentiment analysis of Nigerian banks twitter data within a 2-year period, from 1st January 2017 to 31st December 2018. After crawling and preprocessing of the data, LibSVM algorithm in WEKA was used to build the sentiment classification model based on the training data. The performance of this model was evaluated on a pre-labelled test dataset generated from the five banks. The results show that the accuracy of the classifier was 71.8367%. The precision for both the positive and negative classes was above 0.7, the recall for the negative class was 0.696 and that of the positive class was 0.741 which shows the prediction did better than chance in addition to other measures. Applying the model in predicting the sentiments of the five Nigerian banks twitter data reveals that the number of positive tweets within this period was slightly greater than the number of negative tweets. The scatter plots for the sentiments series indicated that, majority of the data falls between 0 and 100 sentiments per day, with few outliers above this range.
Machine Learning based Prediction System for Detecting Air Pollution

— In today’s world, one of the most common problems we are facing worldwide is Air Pollution. Air of most cities is polluted these days and newer pollutants have also been added in the air rendering it more poisonous. Both human and natural activities can produce Air Pollution. Pollutants like Sulphur Oxides, Carbon Dioxide (CO2), Nitrogen Oxides, Carbon Monoxide (CO), Chlorofluorocarbon (CFC), Lead, Mercury etc. are being added in the air due to human activities. In this paper, data has been collected from the two sources in the Bengaluru region: government website and static sensors built using Arduino. The level of CO is measured using three machine algorithms namely Random Forest Regression (RFR), Decision Tree Regression (DTR) and Linear Regression (LR). The results show that RFR gives least error of the three and hence more accuracy. Command line interface has also been created to see the CO level prediction.
Automatic analysis of crowd dynamics using computer vision and machine learning approaches

As the population of the world increases, urbanization generates crowding situations which pose challenges to public safety, security and to the management of crowd. Manual analysis of crowded situations is a tedious job and usually prone to errors. Computer scientists have been trying to bring about the cognitive video understanding abilities of human brains onto video systems using computer vision techniques. However, crowd analysis is a complex phenomenon in computer vision because of the intrinsic difficulties such as perspective distortion, occlusion, shadowing effect and variations in scale, pose and illumination. These difficulties degrade the performance of a computer vision system. This research proposes solutions based on classical machine learning approaches as well as state-of-the-art deep learning approaches to automate the process of visual surveillance. This thesis will investigate three research areas of visual analysis of crowds: crowd counting and density estimation in low-to-medium density crowds, crowd motion analysis, and describing crowd motion with useful motion information. To this end, we investigated the texture features for crowd counting and density estimation. We outline an SVM-based framework for the evaluation of different texture features and also studied the performance of the various combination of features for crowd counting and density estimation. Currently, object detection using Deep Convolutional Neural Networks have shown tremendous improvement over hand-crafted feature extraction techniques. Current state-of-theart object detectors were used for head detection task. Furthermore, Faster R-CNN was used for counting by detection, and the performance is improved with the proposed Motion Guided Filter by using spatio-temporal information. In this study, we also propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. It is very important for a computer vision system to capture movement as accurately as possible. For such a system, a motion field is generated by computing the dense optical flow. The performance of our approach is evaluated on different real-time videos. Furthermore, a framework is developed that extract motion information from the crowd scene by generating trajectories using particle advection approach. The trajectories obtained are clustered using unsupervised hierarchical clustering algorithm into flows. The motion information extracted includes, the speed, density and mean direction of dominant flows/global flows. This study will provide real-time and practical solutions for the autonomous vision system used in the surveillance. The analysis of information before the occurrence of any disaster or disruptive event will result in the indication of dangerous and disruptive behaviour which seeks urgent attention.
Collaborative Traffic Measurement in Virtualized Data Center Networks

Data center network monitoring can be carried out at hardware networking equipment (e.g., physical routers) and/or software networking equipment (e.g., virtual switches). While software switches offer high flexibility to deploy various monitoring tools, they have to utilize server resources, especially CPU and memory, that can no longer be reserved fully to service users' traffic. In this paper we closely examine the costs of ($i$) sampling packets on a virtual switch for monitoring purposes; (ii) sending them to a user-space program for measurement; and (iii) forwarding them to a remote server where they will be processed in case of lack of resources locally. Starting from empirical observations, we derive an analytical model to accurately predict (R2= 99.5%) the three aforementioned costs, as a function of the sampling rates, and pave the way for a collaborative monitoring approach where servers delegate monitoring tasks to each other via port mirroring in case they lack resources.
Intensity Guided Depth Upsampling Using Edge Sparsity and Super-Weighted $L_0$ Gradient Minimization

Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. Depth images have the physical nature of being represented by smooth regions and edges in between them, i.e. depth images have high edge sparsity in the gradient domain. In this paper, we propose intensity guided depth upsampling using edge sparsity and super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization. First, we get mutual structure between intensity and depth using joint mutual structure filtering. Second, we generate an initial depth image using recursive interpolation. Next, we generate weights for <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization based on gradient and entropy of the intensity image, and upsample the depth image using super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization. In super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization, we combine two main terms: Hybrid data fidelity and weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient regularization. The hybrid data fidelity term combines both zero-order and first-order data differences to suppress staircase artifacts, while the weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient regularization term preserves depth structures and removes noise. Finally, we further refine the depth image using adaptive fast weighted median filtering. Experiments on Middlebury and real-world scene datasets verify that the proposed method produces edge preserving depth upsampling results and outperforms state-of-the-arts in terms of both visual quality and quantitative measurements.
An Enhanced Network Admission and Communication Scheme for Internet of Things Based Body Area Network Healthcare System

 Abstract —As an important part of Internet of Things (IoTs), body area networks (BAN) have been developed for providing medical diagnostics. A large number of wearable or embedded body sensors are used to sense and collect patient’s personal physiological information and transmit it to the backend of the medical systems for healthcare monitoring. However, due to its openness, protecting sensitive data from unauthorized access is a vital issue in medical-related system. Recently, Deng et al. proposed a secure and lightweight network admission and communication scheme based on Internet of Things for body area network healthcare system. However, after a detailed inspection of their scheme, we found Deng et al . ’s scheme is not secure enough as they claimed and then demonstrated that their scheme is vulnerable to various attacks, such as data replaying attack and user traceability attack. To eliminate the vulnerabilities of Deng et al . ’s scheme, in this paper, we proposed an enhanced network admission and communication scheme for IoT-based body area network healthcare system while also providing mutual authentication, data confidentiality and data integrity.
Role of UAVs in Public Safety Communications: Energy Efficiency Perspective

Unmanned aerial vehicles (UAVs) have acquired remarkable popularity, thanks to their variety of applications in numerous domains spanning from surveillance, health to agriculture and smart cities. UAVs are also enabler in wireless communication that has potential features such as ubiquitous and reliable connectivity, fast and easy deployment, adaptive altitude, higher chance of line of sight (LOS) propagation path, higher mobility and flexibility. There are numerous surveys that summarized these advantages for different situations and scenarios. However, none of these surveys discussed the role of UAVs in public safety communications from the energy efficiency perspective. In this paper, we review the existing literature for UAV communication with taking into account the energy consumption criteria, and propose a multi-layered network architecture incorporating UAVs for public safety communication. Future research directions are also discussed.
Real Time Soccer Field Analysis from Monocular TV Video Data

Soccer games, respectively their video records, are an issue of extensive challenging research in image sequence processing. Topics of particular interest are player and field tracking. In this paper we will do an important step in this direction with a new approach to detect players and field lines in monocular TV video data. To achieve this goal, the field area and the grass colors are to be determined. This is done by involving contextual knowledge as well as a new method for color segmentation, which frame wise selects polyhedrons within the RGB cube. As an addition, the resulting algorithms are able to detect non field view images and work in real time.
Real-time binocular stereo vision system based on FPGA

Binocular stereo vision, as a typical technique of computer vision, is versatile in three-dimensional shape measurement. However, the efficiency and speed are limited by the inherent instruction cycle delay within traditional computers, leading to large quantities of image data and computational complexity. Consequently, this paper describes a real-time binocular stereo vision system based on FPGA implementation. Considering FPGA’s parallel architecture, both in storing and calculating, the whole system is a full-pipeline design and synchronized with the identical system clock so that different parts of the stereo processing can work simultaneously to improve the processing speed. As the complete image processing framework contains rectification, stereo correspondence and the left-right consistency check is realized by only one FPGA chip without other external devices, making system highly integrated and low cost. To avoid unnecessary cost of the FPGA resource, the dual-camera calibration is done offline by MFC-based software while the intrinsic and extrinsic parameters are transmitted into the FPGA through system interaction.