Miracle-dz/UniFAR-FTUs huggingface dataset:
Quadratic Stabilization of Discrete-Time Bilinear Control Systems

A stabilization problem for discrete-time bilinear controlsystemsisconsidered.Usingthelinearmatrixinequality technique and the concept of quadratic Lyapunov functions, an approach is proposed to the construction of the so-called stabilizability ellipsoid such that the trajectories of the closedloop system starting from any point inside this ellipsoid asymptotically tend to the origin. The approach allows for an efficient construction of nonconvex approximations to stabilizability domains of bilinear control systems. The obtained results can be extended to various robust statements of the problem, to bilinear systems with many dimensional control, and to bilinear control systems subjected to exogenous disturbances.
Fine-Grained Age Group Classification in the wild

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value. Concerning the problem that accuracy of age estimation of face images under unconstrained conditions are relatively low for existing methods, we propose a method based on Attention LSTM network for Fine-Grained age group classification in the wild based on the idea of Fine-Grained categories and visual attention. This method combines ResNets models with LSTM unit to construct AL-ResNets networks to extract age-sensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets model pre-trained on ImageNet data set is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 data set for age estimation. Then, we fine-tune ResNets on the Adience data set to extract the global features of face images. To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, by combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets for age group classification in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience data set.
Estimation of Spliced Images in Photographs

Photographs are considered to be a trustful document which is used to store life events. But the recent advance in image editing softwares causes image manipulation more easier and common since it will not leave any visual clue of having it tampered. So it become difficult to identify whether an image is authentic or not. There is mainly three types of image forgery, they are image retouching, copy move and image splicing. There are different methods available to check the authenticity of the image. In this paper we propose a method to detect splicing in photo consisting of human. First we detect all the faces present in the image then it is transformed into different color space. Then illuminant map of each color space is obtained. Extract different features such as texture color andsome of the image quality measures(IQM). Naive Bayes classifier is used to detect whether the image is real or fake.
IOT Based Smart Community Monitoring Platform for Custom Designed Smart Homes

The world especially homes are getting smarter on a daily basis, by the arrival of the Internet of Things (IoT). As man is a social being the smart homes must not be standing alone, they must collaborate each other to build a smart community. It is a network of smart homes in a local geography. The smart community acts as an intermediate between smart homes and smart cities. Various papers propose smart community architecture where all homes in it are having the same functionality. But that is not the case in the real-world scenario. In a society, various homes will need various functionalities. Each home will have to monitor different parameters using different sensors and have to provide different responses and services accordingly. This paper presents a monitoring platform for a smart community, of which there are three custom-designed smart homes having different functionalities. The data is sent to community server via MQTT protocol. The data received at the server is saved in a database and is displayed in a webpage in the server. The monitoring platform was successfully implemented and was found functioning well.
Beamforming-Based Acoustic Source Localization and Enhancement for Multirotor UAVs

The problem of acoustic source localization and signal enhancement through beamforming techniques is especially challenging when the acoustic recording is performed using microphone arrays installed on multirotor unmanned aerial vehicles (UAVs), The principal source of disturbances in this class of devices is given by the high frequency, narrowband noise originated by the electrical engines, and by the broadband aerodynamic noise induced by the propellers. A solution to this problem is investigated, which employs an efficient beamforming-based spectral distance response algorithm for both localization and enhancement of the source. The beamforming relies on a diagonal unloading (DU) transformation. The proposed algorithm was tested on a multirotor micro aerial vehicle (MAV) equipped with a compact uniform linear array (ULA) of four microphones, perpendicular to the rear-front axis of the drone. The array is positioned slightly above the plane of propellers, and centered with respect to the drone body. The experimental results conducted in stable hovering conditions are illustrated, and the localization and signal enhancing performances are reported under various noise conditions and source characteristics.
Compound Password System for Mobile

Authentication on mobile devices deviates from the traditional text-based password system. The choice to use alternative password systems comes at a cost and in this paper we explore the current issues with said systems and propose a new method for authentication on mobile devices. In this paper we explore the current landscape of mobile authentication. The mobile authentication systems, Personal Identification Number and Pattern passwords both have issues which makes them weak to attacks. Our goal is to create a scheme that can strengthen the security of mobile passwords by combining both methods of authentication. When a new security system is implemented there is two factors that determine the system’s success, the complexity of the system and the usability or feasibility of the system. This paper looks to define the complexity of our scheme in terms of security.
Making Digital Nudging Applicable: The Digital Nudge Design Method

The goal of digital nudging, a concept based on insights from behavioural economics, is to influence decision-making in digital choice environments. Information systems scholars increasingly see digital nudging as a promising research field, as do practitioners in the field of user interface, user experience, and digital service design. However, the use of digital nudging is not widespread because practitioners are often unaware of the concept or they do not have a systematic approach with which to apply it. Using a design science research approach, we develop the Digital Nudge Design method and evaluate its applicability and usefulness in practice. The method is based on requirements deduced from literature on digital nudging and persuasive systems and frominterviews with practitioners from five case organizations. The study contributes to research that seeks to develop methods for influential user interface design, and the method supports researchers and practitioners in designing digital nudges.
Dynamic Team Heterogeneity in Cooperative Coevolutionary Algorithms

We propose Hyb-CCEA, a cooperative coevolutionary algorithm for the evolution of genetically heterogeneous multiagent teams. The proposed approach extends the cooperative coevolution architecture with operators that put the number of coevolving populations under evolutionary control. Populations are dynamically merged based on behavioral similarity, thus decreasing team heterogeneity, and stochastic population splits are used to explore increased team heterogeneity. Hyb-CCEA is capable of converging to suitable team compositions for the given task, be it a completely homogeneous team where all agents share the same control logic, a heterogeneous team where each agent has distinct control logic, or a partially heterogeneous team. By placing both team composition and agent controllers under evolutionary control, Hyb-CCEA can be applied to domains for which the experimenter has limited or no knowledge about possible solutions. We study Hyb-CCEA extensively in an abstract domain, and conduct a series of validation experiments with four simulated multirobot tasks: two multirover foraging tasks and two robotic soccer tasks. The results show that Hyb-CCEA takes advantage of partial heterogeneity and frequently outperforms the standard cooperative coevolution approach, both in terms of fitness scores achieved and number of evaluations needed to evolve solutions.
K-truss decomposition for Scale-Free Graphs at Scale in Distributed Memory

We update our prior 2017 Graph Challenge submission [11] on large scale triangle counting in distributed memory by extending it to compute the full $k$-truss decomposition [6] of large scale-free graphs. We build on heuristics to minimize ‘wedge checks', by operating on an ordered directed graph, and describe an algorithm to ‘unroll’ triangle counts when they are scheduled for pruning by the $k$-truss decomposition. Our $k$-truss algorithm is implemented using HavoqGT, an asynchronous vertex-centric graph analytics framework for distributed memory. We present a brief experimental evaluation on two large, real-world, scale-free graphs: a 128B edge web-graph and a 1.4B edge twitter follower graph. To our knowledge, the 128B edge web-graph is the largest real-world graph to have its $k$-truss decomposition computed.
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds

Network slicing allows network operators to build multiple isolated virtual networks on a shared physical network to accommodate a wide variety of services and applications. With network slicing, service providers can provide a cost-efficient solution towards meeting diverse performance requirements of deployed applications and services. Despite slicing benefits, End-to-End orchestration and management of network slices is a challenging and complicated task. In this chapter, we intend to survey all the relevant aspects of network slicing, with the focus on networking technologies such as Software-defined networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and Cloud Computing platforms. To build the required background, this chapter begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their interplay. Then we cover the 5G vision for network slicing and extend it to the Fog and Cloud computing through surveying the state-of-the-art slicing approaches in these platforms. We conclude the chapter by discussing future directions, analyzing gaps and trends towards the network slicing realization.
OCR (Optical Character Recognition) Based Reading Aid

There is need to convert paper books and documents into text. OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. A multilingual voice creation toolkit that supports the user in building voices for the Open source MARY TTS platform, for two state-of-the-art speech synthesis technologies: unit selection and HMM-based synthesis. For languages not yet supported by MARY TTS, the toolkit provides the necessary tools and generic reusable run-time system modules for adding support for a new language. Here, discussed the design and implementation of assistive platforms, and combine today's growing technology along with smart phone capabilities with the advantages offered by the high scalable and reactive cloud resources, making it a more economical and cost-effective way
Micro-Expressions Detection Based on Micro-Motions Dense Optical Flows

Micro-expressions are one of the most reliable sources of deceit detection. They are fast, involuntary facial movements and they usually occur when a person tries to hide their true feelings. This paper presents a novel method for micro-expression detection in high speed video-sequences. We first define several free-form regions of interest on the face based on the location of the muscles of facial expression. Dense optical flow features are extracted from each region, and their magnitude and direction are used to spot micro-expressions in the video sequence. The direction of movement is used to filter out irrelevant, macro movements. The proposed algorithm for micro-expression detection is training free and it involves a simple voting procedure on the movement magnitude from each region. The proposed method was tested on a publicly available database.
Vehicle Detection using Dimensionality Reduction based on Deep Belief Network for Intelligent Transportation System

Vehicle detection is one of essential technology on Intelligent Transportation System. By detect the vehicle, we may know the presence and the number of vehicles on the road for some intervals of time. The challenge is the high dimensionality of features to represent a vehicle. To detect the vehicle, there are two processes, feature extraction and classification. The high dimensionality feature, which is produced on feature extraction step, make a heavy computation on the classification step. This research proposed a dimensionality reduction using Deep Belief Network (DBN) for vehicle detection. We try to detect cars and motorcycles. The feature extraction method is based on descriptive methods such as scaled invariant feature transform. DBN is used to reduce the high dimensionality features that is produced by descriptive methods. For classification task, we choose Support Vector Machine method. Moreover, UIUC dataset and our original data are chose to evaluate the proposed method performance. We are also compared DBN with Principal Component Analysis (PCA) and other method. The result indicates that using DBN as dimensionality reduction method performed better than PCA and others method in vehicle detection.
Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks

Existing domain adaptation approaches generalize models trained on the labeled source domain data to the unlabeled target domain data by forcing feature distributions of two domains closer. However, these approaches are likely to ignore the semantic information during the feature alignment between source and target domain. In this paper, we propose a new unsupervised domain adaptation framework to learn the cross-domain features and disentangle the semantic information concurrently. Specifically, we firstly combine the task-specific classification and domain adversarial learning to obtain the cross-domain features by mapping the data of both domains with the shared feature extractor. Secondly, we integrate the domain adversarial learning and the within-domain reconstruction to disentangle the semantic information from the domain information. Thirdly, we include a cross-domain transformation to further refine the feature extractor, which in turn improves the performances of the task classifier. We compare our proposed model to previous state-of-the-art methods on domain adaptation digit classification tasks. Experimental results show that our model achieves better performances than the other counterparts, which demonstrates the superiority and effectiveness of our model.
Design of Solar Powered Battery Charger: An Experimental Verification

This paper presents the design and implementation details of the embedded system to design a photovoltaic based battery charger for lead-acid battery. The battery is charged in float charging mode as well as in bulk charging mode. In bulk charging mode perturb and observe maximum power point tracking algorithm is used to charge the battery. Hardware realization of the PV based battery charger has been carried out and is tested in real time scenario.
Development of an Arrhythmia Monitoring System and Human Study

Electrocardiography (ECG) is a fundamental method not only commonly used in the hospital for clinical requirement but also widely adopted in home and personal healthcare systems to obtain the electrical activity of the heart. An arrhythmia monitoring system is proposed and used in a clinical trial. The proposed system has three parts. The first is a high-resolution, low-power analog front-end circuit for implementing bio-signal sensing circuits. This part is developed with a chopper-based pre-amplifier and a high-pass sigma–delta modulator. The features of the circuits are low complexity, high resolution, and low power consumption. The second part is a digital signal processor with a decimation filter and a universal asynchronous receiver/transmitter package generator. The last part is used to realize a software interface on smartphone for ECG signal recording, display, and classification. A wavelet-based classification method is also proposed to classify the rhythm. The chip used in the system is fabricated through the 0.18 $\boldsymbol {\mu }\text{m}$ standard complementary metal–oxide–semiconductor process, and the operation voltage is 1.2 V. The classification algorithm is verified with data from the MIT/BIH arrhythmia database. The accuracy of beat detection and arrhythmia classification is 99.4% and 95.83%, respectively. Eight patients are enrolled in a human study to verify the performance of the proposed arrhythmia monitoring system. Results show that the system can acquire and classify ECG signals.
DVC: An End-To-End Deep Video Compression Framework

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information. In this paper, taking advantage of both classical architecture in the conventional video compression method and the powerful non-linear representation ability of neural networks, we propose the first end-to-end video compression deep model that jointly optimizes all the components for video compression. Specifically, learning based optical flow estimation is utilized to obtain the motion information and reconstruct the current frames. Then we employ two auto-encoder style neural networks to compress the corresponding motion and residual information. All the modules are jointly learned through a single loss function, in which they collaborate with each other by considering the trade-off between reducing the number of compression bits and improving quality of the decoded video. Experimental results show that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard H.265 in terms of MS-SSIM. Code is released at https://github.com/GuoLusjtu/DVC.
Detection of Food Intake Events From Throat Microphone Recordings Using Convolutional Neural Networks

Food intake analysis is a crucial step to develop an automated dietary monitoring system. Processing of eating sounds deliver important cues for the food intake monitoring. Recent studies on detection of eating activity generally utilize multimodal data from multiple sensors with conventional feature engineering techniques. In this study, we target to develop a methodology for detection of ingestion sounds, namely swallowing and chewing, from the recorded food intake sounds during a meal. Our methodology relies on feature learning in the frequency domain using a convolutional neural network (CNN). Spectrograms extracted from the recorded food intake sounds through a laryngeal throat microphone are fed in to the CNN architecture. Experimental evaluations are performed on our in-house food intake dataset, which includes 8 subject, 10 different food types covering 276 minutes of recordings. The proposed system attains high detection rates of the swallow and chew events with high sensitivity and specificity, and delivers a potential for food intake monitoring under daily life conditions in future studies.
GRANet: Global Refinement Atrous Convolutional Neural Network for Semantic Scene Segmentation

The main problems of complex-scene understanding and semantic scene segmentation are caused by mismatched relationships, confusion categories, and inconspicuous classes. Towards above issues, we propose a global refinement atrous convolutional neural network (GRANet) for semantic scene segmentation. To enlarge the receptive field of filters, we use atrous convolution instead of the downsampling operators. To handle the challenge caused by the existence of objects at multiple scales in a scene, we adopt multiple rates atrous convolution structure. And to overcome the problem that the current semantic segmentation architecture can not make good use of global information, we propose a multiple pooling module schemes to utilize the global context information to boost the performance of our GRANet. The proposed GRANet achieves state-of-the-art performance on the SiftFlow Dataset and attains comparable performance with other state-of-the-art works on Cityscapes dataset.
The effects of macrolides in children with reactive airway disease: a systematic review and meta-analysis of randomized controlled trials

Purpose Childhood reactive airway diseases (RADs) are concerning problems in children’s airways and may be preceded by bronchiolitis and may progress to childhood asthma. The severity of the disease is indicated by deterioration in pulmonary functions, increased usage of rescue medications, and recurrent wheezing episodes. Macrolides have both antimicrobial and anti-inflammatory functions and have been used as adjunctive therapy in childhood RADs. Patients and methods We conducted a meta-analysis to evaluate the effect of macrolides in children with RAD. Literature searches were systematically conducted using an electronic database from inception to August 2018. The Cochrane review risk of bias assessment tool was used to assess the quality of each randomized controlled trial. Results Sixteen randomized controlled trials comprising 1,415 participants were investigated in this meta-analysis. Children treated with macrolide therapy showed significantly better pulmonary functions in both forced expiratory volume in one second (% predicted) (difference in means=−9.77, 95% CI=−14.18 to −5.35, P<0.001; I2=0%) and forced expiratory flow 25–75 (% predicted) (difference in means=−14.14, 95% CI=−26.11 to −2.18, P=0.02; I2=29.56%). In addition, the short-acting β-agonist usage days and recurrent wheezing risk were significantly lowered in children with macrolide treatment (standardized difference in means=−0.34, 95% CI=−0.59 to −0.09, P=0.007, I2=27.05% and standardized difference in means=−0.53, 95% CI=−0.81 to −0.26, P<0.001, I2=0%, respectively). Furthermore, the growth of Moraxella catarrhalis from nasal swabs was less in children treated with macrolides (odds ratio=0.19, 95% CI=0.11–0.35, P<0.001). Children who took macrolides had a lower risk of adverse events (risk ratio=0.83, 95% CI=0.70–0.98, P=0.024, I2=0%). Conclusion This current meta-analysis suggested that adjunctive therapy with macrolides is safe and effective for achieving better outcomes in childhood RAD.