Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of an Improved Voltage-balancing Control Method of Modular Multilevel Converter Based on Amplitude-adjustable Carrier

Realize the control of the balance of DC capacitor in power-unit is paramount for the normal operation of the modular multi-level converters. The traditional control method achieves the DC capacitor voltage balance by adjusting the trigger pulse of each power-unit. At the same time, the DC capacitor is constantly absorbing and releasing energy, so that the voltage of the DC capacitor cannot be maintained at a fixed value which means that the capacitor voltage fluctuation problem still exists while the traditional control method has been used. Moreover, the quality of the output voltage will also decrease. In this paper, an improved voltage-balancing control method based on amplitude-adjustable carrier is proposed to suppress the adverse effects of the DC capacitor voltage fluctuation. The improved DC capacitor voltage control method is based on volt-second equivalent, and the carrier of each power-unit is rebuilt according to the real-time voltage of the DC capacitor. Finally, we will get a reconstructed trigger pulse to achieve the control of the output voltage of each power-unit which effectively improve the quality of the output voltage of the modular multi-level converters. The simulation experiments and results built on MTALAB/SIMULINK have proved the effectiveness of the improved control method.
Multimodal Connections between Dockless Bikesharing and Ride-Hailing: An Empirical Study in New York City

Motivated by the meteoric rise in the adoption of both ride-hailing services (DiDi, Uber, Lyft, etc.) and dockless bikesharing services (Ofo, Mobike, LimeBike, etc.), we propose a multimodal system where passengers ride a dockless bikeshare to/from hubs where they switch modes to/from a carpool. The proposed mutlimodal system is a generalization of the existing Uber ExpressPool service. The goal of this paper is to test empirically the feasibility of the proposed multimodal system. We accomplish this goal with the aid of time-stamped taxi origin/destination data from New York City. The analysis has two steps: network design and trip assignment. First, we identify 17 carpool hub locations with a coverage of 1 km to capture all taxi trip demand within Manhattan during peak hours. After designing the network, we then assign trips to carpools, within each hub, that have similar trip start times and destinations. We formulate the assignment problem as an offline matching algorithm on a bipartite graph. We found that over 80 percent of all trips can be assigned to carpools at almost all hubs. Compared to a single-modal system, the multimodal system served the same number of passengers with 40 percent fewer taxis. We found the matching rate to be consistent for every month in 2015. These results provide initial evidence that multimodal connections between ride-hailing and dockless bikesharing are feasible, reduces passenger trip times, and decreases road congestion.
Finite-Frequency Fault Detection Filter Design for Discrete-Time Switched Systems

In this paper, the finite-frequency fault detection filter design for discrete-time switched systems is investigated. The frequencies of the faults and the unknown disturbance input are assumed to be finite and in three known intervals, qualified as low-, middle-, and high-frequency intervals. Based on the switched Lyapunov function and the generalized Kalman–Yakubovic–Popov lemma, efficient conditions are obtained to guarantee the existence of a finite-frequency fault detection filter, such that the error system is asymptotically stable with an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }/H_{-}$ </tex-math></inline-formula> performance index. Finally, a chemical reactor control system is employed to illustrate the obtained techniques.
Bidirectional Associations Between Self-Reported Gaming Disorder and Adult Attention Deficit Hyperactivity Disorder: Evidence From a Sample of Young Swiss Men

Background: Gaming disorder (GD) has been shown to co-occur with attention deficit hyperactivity disorder (ADHD), yet few studies to date have investigated their longitudinal associations. Method: The sample included 5,067 young Swiss men (mean age was 20 years at wave 1 and 25 years at wave 3). Measures were the Game Addiction Scale and the Adult ADHD Self-Report Scale (6-item screener). Longitudinal associations were tested using autoregressive cross-lagged models for binary measures of GD and ADHD, as well as continuous measures for GD score and ADHD subscales of inattention and hyperactivity. Results: ADHD at age 20 increased the risk for GD at age 25 (probit = 0.066 [0.023, 0.109]; p = 0.003). GD at age 20 also increased the risk for ADHD at wave 3 (probit = 0.058 [0.013, 0.102]; p = 0.011). Only the ADHD inattention subscale showed a bidirectional longitudinal relationship with the GD score (standardized Beta from inattention at age 20 to GD score at age 25: 0.090 [0.056, 0.124]; p < 0.001; from GD score at age 20 to inattention at age 25: 0.044 [0.016, 0.071]; p = 0.002), whereas associations between the hyperactivity subscale and GD were not significant. Discussion: GD had bidirectional longitudinal associations with ADHD, in that ADHD increased the risk for GD and GD increased the risk for ADHD, and they may reinforce each other. These associations may be linked more to the inattention ADHD component than to the hyperactivity ADHD component. Individuals with ADHD or GD should be screened for the other disorder, and preventive measures for GD should be evaluated in individuals with ADHD.
Application-Aware IoT Camera Virtualization for Video Analytics Edge Computing

Video analytics edge computing exploiting IoT cameras has gained high attention. Running such tasks on the network edge is very challenging since video and image processing are both bandwidth-hungry and computationally intensive. Unlike traditional computing systems, IoT cameras are heavily dependent on the environmental factors such as brightness of the view. In this paper, we propose an edge IoT camera virtualization architecture. For this, we leverage an ontology-based application description model and virtualize the IoT camera with container technology that decouples the physical camera and support multiple applications on board. We also develop an IoT camera reconfiguration scheme that allows IoT cameras to dynamically adjust their configuration to environmental context changes without degrading application QoS. Experimental results based on our prototype implementation show that the responsiveness of our system is 2.8x faster than existing approaches in reconfiguring to the environmental context changes.
Synergistic Approach for Combining SVM Algorithms for Wind Speed Prediction

This work presents synergistic approach for combining Directed Acyclic Graph (DAG), Binary Tree (BT) and Binary Decision Tree (BDT) based Support Vector Machine (SVM) algorithms for predicting wind speed. The proposed approach and individual algorithms are evaluated on wind speed data that has many samples divided into training and test data sets. The proposed approach of using the synergistic approach produces better results than Directed Acyclic Graph, Binary Tree, and Binary Decision Tree (BDT) based multiclass SVM algorithms individually.
Extend Cloud to Edge with KubeEdge

In this paper, we introduce an infrastructure in edge computing environment, KubeEdge, to extend cloud capabilities to the edge. In the new form of cloud architecture, Cloud consists of computing resources both at centralized data centers and at distributed edges. KubeEdge infrastructure connects and coordinates two computing environments for applications leveraging both computing resources to achieve better performance and user experience. Technically, KubeEdge provides the network protocol infrastructure and the same runtime environment on the edge as in the cloud, which allows the seamless communication of applications with components running on edge nodes as well as cloud servers. It also allows the existing cloud services and cloud development model to be adopted at edge. Based on Kubernetes [1], KubeEdge architecture includes a network protocol stack called KubeBus, a distributed metadata store and synchronization service, and a lightweight agent (EdgeCore) for the edge. KubeBus is designed to have its own implementation of OSI network protocol layers, which connects servers at edge and VMs in the cloud as one virtual network. KubeBus provides a unified multitenant communication infrastructure with fault tolerance and high availability. The distributed metadata store and sync service is designed to support the offline scenario when edge nodes are not connected to the cloud. EdgeController component in KubeEdge architecture is a controller plugin for Kubernetes [1] to manage remote edge nodes and cloud VMs as one logical cluster, which enables KubeEdge to schedule, deploy and manage container applications across edge and cloud with the same API.
SPaaS-NFV: Enabling Stream-Processing-as-a-Service for NFV

Network Function Virtualization (NFV) as new network paradigm provides an opportunity to accelerate network innovation in the next generation mobile network (MN). The monitoring, in this case, helps the analysis and gives better insights into the network function in real time. Performance indicators and runtime conditions related data can be streamed to enhance this process. In order to enable the deployment of the stream processing service automatic, we present the SPaaS-NFV framework, which is implemented to demonstrate the concept of streaming-processing-as-a-service for NFV. SPaaS-NFV can automate the deployment of the stream processing services by receiving user's request in Json. This framework intends to make user focus on data and business insights, without the worry of building stream processing infrastructure and tooling in the NFV environment.
A class of half‐bridge quasi‐Z‐source inverters: Detailed steady‐state analysis in various operating states, design considerations, and derivation of general topology

In this paper, 2 types of half‐bridge quasi‐Z‐source inverters with detailed analysis are proposed. Unlike the classical half‐bridge inverter, both types of proposed half‐bridge inverters can generate zero voltage level in output. Moreover, lack of continuous input current in conventional half‐bridge inverters is resolved by the second type of proposed inverters. One of the important advantages of proposed inverters is using the 1 impedance network only with 2 capacitors and 2 inductors; whereas, most of the half‐bridge Z‐source inverters have more than 1 impedance network or use additional semiconductor elements. In this paper, operation principles of proposed inverters in different operating modes are discussed. Then, design considerations and calculation of critical Inductance in boundary conditions between 2 different states are provided. Next, power circuit of general topology with multiple DC sources and derivation of subcircuits are proposed. Finally, to verify validity of derived equations, simulation results using PSCAD/EMTDC software and experimental results are provided.
A Dynamic Resource Overbooking Mechanism in Fog Computing

Fog Computing (FC - similarly edge computing) as new computing paradigm can support distributed domain-specific or area-specific applications with cloud-like quality of service (QoS). This promising paradigm thus can find its wide applications in various industrial scenarios and smart cities in which the resource requirements will be divided into peak-hour or non-peak-hour. To deal with such features of applications, a flexible resource allocation approach based on pricing model can be critical for the success of such paradigm. To the best of our knowledge, we have not seen such pricing based resource allocation approach ever been reported for FC scenarios. In this paper, we propose a novel pricing based dynamic resource allocation model through overbooking mechanism, and it is realized through three steps: 1) According to different QoS requirements of user tasks, methods of on-demand billing, daily billing, and auction billing are designed, in which we allow the resource to be overbooked; 2) For auction billing, we design an auction approach including pricing rule and winner determination rule. We prove that our auction approach guarantees individual rationality, computational efficiency, and truthfulness. 3) To overbook as much resource as possible with a high degree of QoS satisfaction of on-demand and daily billing, we overbook the resource based on a resource utilization prediction using neural network and service level agreement violation feedback. In the end, we validate the mechanism with real-world data trace. Experimental results show that our auction approach achieves desirable properties, and our dynamic resource overbooking mechanism maximizes the profit of nodes with a high degree of QoS satisfaction of on-demand and daily billing and a high resource utilization prediction accuracy rate.
Optimal Design and Stability Analysis of a Fuzzy PID Controller

This paper presents a solution to the problem of stabilizing a given Takagi-Sugeno (TS) Fuzzy model by PID controllers using the particle swarm optimization (PSO). It is based on searching the best parameters kp, ki and kd existing in the stability region. So, a new algorithm is proposed. this algorithm relies on the use of a new objective function taking into account both the performance indices and the error signal. The advantages of this approach were discussed through simulations on a numerical example.
A Novel Combination of Bees and Firefly Algorithm to Optimize Continuous Problems

Although Bees algorithm (BA) has presented great performance so far in optimizing different problems, it still has weak convergence rate in some optimizations. Therefore, in this investigation in order to resolve its weakness and boost the exploitation of algorithm, it was hybridized by Firefly algorithm (FA). The hybrid bees and firefly algorithm (BA-FA) was implemented on twelve numerical benchmark functions to evaluate it. The BA-FA was also compared with four famous algorithms, including particle swarm optimization (PSO), invasive weed optimization (IWO) and so on. The results showed that BA-FA had the best performance and convergence in optimizing the mentioned functions.
A Haar Classifier Based Call Number Detection and Counting Method for Library Books Kütüphane Kitaplarında Yer Numaralarını Bulmakve Saymakiç in HaarSınıflandırıcı Tabanlıbir Yöntem

Counting and organization of books in libraries is a routine and time-consuming task. The task gets more complicated by misplaced books in shelves. In order to solve these problems, we propose an automated visual call number (book-id) detection and counting system in this paper. The method employs a Haar feature-based classifier from OpenCV library and cloud-based OCR system to decode characters from images. To develop and test the method, we have acquired and organized a dataset of 1000 book call numbers. The proposed method has been tested on 20 bookshelves images that contain 233 call numbers, which resulted in a true detection rate of 96% and false detection rate of 1.75 per image. For OCR step, the number of false recognized characters per call number was 0.76.
The Big Win Strategy on Multi-Value Network: An Improvement over AlphaZero Approach for 6x6 Othello

The AlphaZero approach got a great success and achieved superhuman performance across many challenging games, but we think there are at least three problems that can be improved. Firstly, AlphaZero only estimates win, draw, or lose but ignores how many points it will get or lose. Secondly, AlphaZero uses Monte-Carlo Tree Search to derive an average value among all the children nodes' values in a subtree. Thirdly, AlphaZero does not consider the depth rewards during the Monte-Carlo Tree Search. To solve these three problems, we introduce a general-purpose framework, the Big-Best-Quick win strategy in Monte-Carlo Tree Search, to try to surpass the AlphaZero approach. In this paper, we mainly focus on the Big-win strategy to improve the performance of AlphaZero without human knowledge. We are pleased to derive some promising results in which our Big-win approach has improved the strength of the 6x6 Othello program with win rate=63%, lose rate=28%, and draw rate=9% comparing to the original AlphaZero approach based on a fair training and playing time conditions.
Recent Trends in Deep Generative Models: a Review

With the recent improvements in computation power and high scale datasets, many interesting studies have been presented based on discriminative models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for various classification problems. These models have achieved current state-of-the-art results in almost all applications of computer vision but not sufficient sampling out-of-data, understanding of data distribution. By pioneers of the deep learning community, generative adversarial training is defined as the most exciting topic of computer vision field nowadays. With the influence of these views and potential usages of generative models, many kinds of researches were conducted using generative models especially Generative Adversarial Network (GAN) and Autoencoder (AE) based models with an increasing trend. In this study, a comprehensive review of generative models with defining relations among them is presented for a better understanding of GANs and AEs by pointing the importance of generative models.
A Complete System for Vision-Based Micro-Aerial Vehicle Mapping, Planning, and Flight in Cluttered Environments

We present a complete system for micro-aerial vehicle autonomous navigation from vision-based sensing. We focus specifically on mapping using only on-board sensing and processing, and how this map information is best exploited for planning, especially when using narrow field of view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations are presented. We compare multiple global planning and path smoothing methods on real maps made in realistic search and rescue and industrial inspection scenarios.
Incremental Acquisition and Reuse of Multimodal Affective Behaviors in a Conversational Agent

To feel novel and engaging over time it is critical for an autonomous agent to have a large corpus of potential responses. As the size and multi-domain nature of the corpus grows, however, traditional hand-authoring of dialogue content is no longer practical. While crowdsourcing can help to overcome the problem of scale, a diverse set of authors contributing independently to an agent's language can also introduce inconsistencies in expressed behavior. In terms of affect or mood, for example, incremental authoring can result in an agent who reacts calmly at one moment but impatiently moments later with no clear reason for the transition. In contrast, affect in natural conversation develops over time based on both the agent's personality and contextual triggers. To better achieve this dynamic, an autonomous agent needs to (a) have content and behavior available for different desired affective states and (b) be able to predict what affective state will be perceived by a person for a given behavior. In this proof-of-concept paper, we explore a way to elicit and evaluate affective behavior using crowdsourcing. We show that untrained crowd workers are able to author content for a broad variety of target affect states when given semi-situated narratives as prompts. We also demonstrate that it is possible to strategically combine multimodal affective behavior and voice content from the authored pieces using a predictive model of how the expressed behavior will be perceived.
Phish-IRIS: A New Approach for Vision Based Brand Prediction of Phishing Web Pages via Compact Visual Descriptors

Phishing, a continuously growing cyber threat, aims to obtain innocent users' credentials by deceiving them via presenting fake web pages which mimic their legitimate targets. To date, various attempts have been carried out in order to detect phishing pages. In this study, we treat the problem of phishing web page identification as an image classification task and propose a machine learning augmented pure vision based approach which extracts and classifies compact visual features from web page screenshots. For this purpose, we employed several MPEG7 and MPEG7-like compact visual descriptors (SCD, CLD, CEDD, FCTH and JCD) to reveal color and edge based discriminative visual cues. Throughout the feature extraction process we have followed two different schemas working on either whole screenshots in a “holistic” manner or equal sized “patches” constructing a coarse-to-fine “pyramidal” representation. Moreover, for the task of image classification, we have built SVM and Random Forest based machine learning models. In order to assess the performance and generalization capability of the proposed approach, we have collected a mid-sized corpus covering 14 distinct brands and involving 2852 samples. According to the conducted experiments, our approach reaches up to 90.5% F1 score via SCD. As a result, compared to other studies, the suggested approach presents a lightweight schema serving competitive accuracy and superior feature extraction and inferring speed that enables it to be used as a browser plugin.
A Deep Analysis of the Existing Datasets for Traffic Light State Recognition

Traffic lights classification is a very important task that should be accomplish using computer vision techniques. RADAR or LIDAR sensors are suitable to detect traffic lights. However, they are not able to distinguish between traffic light states. This critical task embraces passengers safety during autonomous driving and it can only be solved using computer vision approaches. In this paper, a wide analysis of the state of the art regarding traffic lights classification is performed. The proposed approach is based on a ResNet architecture and it is compared against a more complex architecture (MobilNet) and also against a traditional feature-based classifier (Random Trees). Due to the importance of high quality training data, a comparative analyisis of the existing datasets related with traffic light classification is presented. The analysis take into account image patch size, number of labels and number of samples for each label.
Enabling the Internet of Arctic Things With Freely-Drifting Small-Satellite Swarms

The widespread deployment of Internet-capable devices, also known as the Internet of Things (IoT), reaches even the most remote areas of the planet, including the Arctic. However, and despite the vast scientific and economic interest in this area, communication infrastructures are scarce. Nowadays, existing options rely on solutions such as Iridium, which can be limited and too costly. This paper proposes and evaluates an alternative to existing solutions, using small satellites deployed as a freely-drifting swarm. By combining these simpler and more affordable satellites with standard protocols, we show how IoT can be supported in the Arctic. Networking protocols and link characteristics are emulated for 3 different satellite orbits and 4 ground nodes. The impact of different protocols and communication conditions is assessed over a period of 49 days and a cross-layer routing approach proposed. The obtained results reveal that a communication overhead bellow 27 % can be achieved and that the implemented satellite-aware route selection allows reducing the end-to-end time of a request up to 93 min on average. This confirms that freely-drifting small-satellite swarms may enable the Internet of Things even in the most remote areas.