Miracle-dz/UniFAR-FTUs huggingface dataset:
Advance Switching Technique for Single Phase Voltage Source Inverters

Nowadays single phase voltage source inverters are widely used in different applications including power supplies for superconductor insertion devices where power conversion efficiency highly depends upon the switching algorithms. Traditional modulation techniques cause considerable losses and show poor conversion efficiency. In this paper, an advanced switching technique is proposed which provides lower total harmonic distortion and power conversion losses. The proposed technique not only improves the inverter efficiency, increases the life time. The proposed technique is verified with a laboratory test platform.
SHAstor: A Scalable HDFS-Based Storage Framework for Small-Write Efficiency in Pervasive Computing

It is well known that small files are often created and accessed in pervasive computing in which information is processed with limited resources via linking with objects as encountered. And the Hadoop framework, as a de facto big data processing platform though very popular in practice, cannot effectively process the small files. In this paper, we propose a scalable HDFS-based storage framework, named SHAstor, to improve the throughput in processing of small-writes for pervasive computing paradigm. Compared to the classic HDFS, the essence of this approach is to merge the incoming small writes into a large chunk of data, either at client side or at server side, and then store it as a big file in the framework. As a consequence, this could substantially reduce the number of small files to process the pervasively gathered information. To reach this goal, the framework takes the HDFS as the basis and adds three extra modules for merging and indexing the small files during the read/write operations in pervasive applications are performed. To further facilitate this process, a new ancillary namenode is also optionally installed to store the index table. With this optimization, SHAstor can not only optimize the small-writes, but also scale out with the number of datanodes to improve the performance of pervasive applications.
Multi-user Multi-provider Resource Allocation in Cloud Computing

In a cloud computing environment, cloud service providers offer cloud services to users. How to achieve reasonable and efficient service resource allocation to meet a user's demands is an important problem. Considering a multi-user and multi-provider environment, we propose a service resource allocation framework to optimize an objective function of load and completion time. We design an improved differential evolution algorithm for optimal resource allocation given a batch of multiple tasks. Experimental results show that proposed method can better balance loads among computing resources while achieving better optimized results for the entire system than some existing methods.
Vulnerability Analysis Toolkit for IEEE 802.11 Wireless Networks: A Practical Approach

This paper aims to propose a novel toolkit to assess the 10 main vulnerabilities of WiFi networks, which have been identified by OWISAM methodology. In this context, controlled scenarios were deployed and the corresponding practical analysis were carried out. Identification of the attacks that can be implemented to verify each of the vulnerabilities in the Top 10, and the utilization of novel tools with details of the performed procedure are the main contributions of this paper, which represents a toolkit to perform a vulnerability analysis of wireless networks.
Planetary terminal descent hazard avoidance using optical flow

An algorithm that uses optical flow to detect landing hazards for a descending spacecraft is described. Image edge points are tracked between frames of a motion sequence, and the range to the points is calculated from the displacement of the edge points and the known motion of the camera. A novel variable-sized edge detector is used to compensate for the change in distance from one image to the next. Kalman filtering is used to incrementally improve the range estimates to those points and provide an estimate of the uncertainty in each range. Errors in camera motion and image point measurement are also modeled. A surface is then interpolated to these points, providing a complete map from which hazards such as steeply sloping areas can be detected. The algorithm has been applied to synthetic and real image sequences, with resulting range accuracy on the order of 1-3% of the range. <<ETX>>
Integrating Social Links into Wireless Networks: Modeling, Routing, Analysis, and Evaluation

Social connections among network nodes have been well investigated as an additional opportunity in network design (e.g., in routing strategies and trusted networking). This paper presents a paradigm shift that explores the design and performance analysis of combining social links jointly with communication links for message delivery in wireless networks. In a combined multi-layer social and communication network, communication links are based on conventional wireless technologies (e.g., WiFi, Bluetooth) and social links are overlaid over a communication infrastructure (e.g., cellular network) that provides an alternative way for data transmission. The goal is to characterize the performance analytically when routing is designed by combining social and communication links. A distance discretization technique is applied to model the reliability and delay of message delivery. The analytical foundation is developed to analyze the end-to-end delay and success probability under various effects of persistent transmission, potential error in distance estimation, and mobility. Systematic routing strategies that employ network inference are then designed to improve the performance in different aspects, such as delivery delay, delivery success probability, and energy-saving. A network emulation testbed is implemented with actual radios and real-world social network datasets to measure the performance of a heterogeneous network with social and communication links. The results in this paper show that the integration of social links in wireless network routing as a multi-layer design leads to substantial performance improvement for delay and reliability of message delivery.
A Partial Energy Input Based Three-Port Cascade Multi-level Hexverter and Its Loop Current Control

Converter driven motors may cause energy accumulation in the DC-link capacitor of the converter when it is in the braking mode, and without a way to transmit this energy, this part of the energy will threaten the voltage balance. To reduce the impact as well as make use of the braking energy, a three-port cascaded multi-level hexverter (TCMH) based on partial energy input is proposed in this paper. Made up by six bridge arms of which only three have power inputs, it is able to drive two high power motors at the same time. By a loop current control also proposed in this paper, energy interaction exists among the six bridge arms. When the two motors are in asynchronous modes, the energy of the braking motor converter can be fed directly to the motoring mode motor, which solves the power balance problem as well as increases the power utilization rate. Finally, simulations are conducted to verify the effectiveness of the proposed hexverter and its loop current control.
Event detection from Twitter - a survey

Purpose “What’s happening? ” around you can be spread through the very pronounced social media to everybody. It provides a powerful platform that brings to light the latest news, trends and happenings around the world in “near instant” time. Microblog is a popular Web service that enables users to post small pieces of digital content, such as text, picture, video and link to external resource. The raw data from microblog prove indispensable in extracting information from it, offering a way to single out the physical events and popular topics prevalent in social media. This study aims to present and review the varied methods carried out for event detection from microblogs. An event is an activity or action with a clear finite duration in which the target entity plays a key role. Event detection helps in the timely understanding of people’s opinion and actual condition of the detected events. Design/methodology/approach This paper presents a study of various approaches adopted for event detection from microblogs. The approaches are reviewed according to the techniques used, applications and the element detected (event or topic). Findings Various ideas explored, important observations inferred, corresponding outcomes and assessment of results from those approaches are discussed. Originality/value The approaches and techniques for event detection are studied in two categories: first, based on the kind of event being detected (physical occurrence or emerging/popular topic) and second, within each category, the approaches further categorized into supervised- and unsupervised-based techniques.
Lightweight Distributed Attack Detection and Prevention for the Safe Internet of Things

The paper investigates the causes of widespread use by cybercriminals of the Internet of Things for organizing distributed network attacks including DDoS and other illegal use. An analysis of existing approaches and technologies for protecting network computer devices is presented, as well as the main factors that prevent their use in the world of Internet of Things. An original approach is suggested that ensures the integration of lightweight protective mechanisms directly into the construction of Smart Things with the defense on the side of a telecom operator. Variants of technology implementation are considered. Key aspects and potential ways of implementation of the proposed approach are noted. Advantages and disadvantages are discussed.
RetiNet — Feature Extractor for Learning Patterns of Diabetic Retinopathy and Age-Related Macular Degeneration from Publicly Available Datasets

Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are two common vision threatening eye conditions. In a large-scale screening environment DR and AMD can be assessed by detecting specific retinal findings in fundus images. In this paper, we introduce a new deep learning based feature extractor for automatic classification of DR and AMD from fundus images. We used a small dataset containing 60000 images with four severity levels of DR and two classes of AMD to design and fine-tune a deep learning model called RetiNet. This dataset, which consisted of two publicly available datasets (MESSIDOR and Kaggle), was augmented and employed to evaluate RetiNet. RetiNet can achieve diagnosis performance comparable to retina experts on the MESSIDOR dataset with cross-dataset testing (i.e., the feature extractor was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an average accuracy of 88% on the validation set.
Investigation of a parasitic-inductance reduction technique for through-hole packaged power devices

Parasitic inductance in power electronic circuits can be problematic, giving rise to unwanted overshoots and ringing after switching transients. Whilst the aim is usually to minimize parasitic inductance, the use of through-hole packaged devices with relatively large insertion inductance may be unavoidable, for example due to heat dissipation requirements or device availability. This paper investigates the effectiveness of a technique for reducing the insertion inductance of through-hole packaged devices. The technique is demonstrated with a custom heatsink design for a T0220-packaged Schottky diode. The technique is to exploit the image-plane concept, bringing conductive layers as close as possible to the current loops within the device and its leads. Measurements in a switch-mode GaN test circuit show that the heatsink reduces the parasitic inductance of the diode by 17% when compared to a conventional heatsink.
Autonomic Methods for Mitigating Threats to the Internet of Things (IoT)

Increasing improvements to wireless technology, coupled with enhancements to device miniaturization, have fueled the advancement of a networking infrastructure referred to as the Internet of Things (IoT). In the IoT devices and software within these devices are designed to interact directly with each other. As these IoT systems are ostensibly designed to require minimal human intervention it seems reasonable to suggest automated methods for securing these systems would be highly appropriate [2]. This paper seeks to evaluate significant developments in network security protection mechanisms as they apply to our current understanding of the Internet of Things and seeks to uncover as well as possibly suggest new areas of network security development in the IoT arena. Additionally, this paper will analyze information from scholarly sources and evaluate different methods, strategies and frameworks related to IoT security
VTET: A Virtual Industrial Control System Testbed for Cyber Security Research

Nowadays Industrial Control System (ICS) are suffering the increasing cyber attacks. The ICS testbed is a ideal platform since that it could make the security research more controllable and safe. In contrast to the physically replication testbed, the virtual testbed is more economic, safe and maintainable which will makes it advantageous in the laboratorial environment. In this paper, we present VTET, a virtual ICS testbed for cyber security research. VTET contains a virtual chemical industrial process, and both virtual and physical controllers. We provide a detailed description of VTET implementation such as the migration of controllers and the configuration of network. And we also demonstrate the 5 attack schemes simulated in VTET to prove its availability. Since the convenience of configuration and deployment, VTET is very suitable for academic ICS security research.
LED Driver Achieves Electrolytic Capacitor-Less and Flicker-Free Operation with an Energy Buffer Unit

In this paper, a cycle-by-cycle energy buffering LED driver has been proposed to achieve electrolytic capacitor-less flicker-free operation. An energy buffering unit (EBU), with high voltage film capacitors being the energy storage device, is introduced in the design to buffer imbalanced energy in every switching cycle. The switching current can be controlled to meet high power factor correction and deliver DC LED output current at the same time. Compared to previous electrolytic capacitor-less designs, it can reduce circulating power and, therefore, also reduce power conversion loss. A 15W experimental prototype had been built and tested to verify the proposed LED driving method.
Watching Closely: Shot Scale Influences Theory of Mind Response in Visual Narratives

Theory of mind, defined as attributing intentions, feelings, and thoughts to others, is a key capacity in building mental models of narratives. Shot scale, referring to the relative size of the figure on the screen, effectively regulates theory of mind relevant visual cues carried by faces. However, research into the effect of shot scale on theory of mind responses in a narrative is almost nonexistent. The aim of the present study was to investigate the extent to which shot scale influences theory of mind responding in film viewers. Four short animated movies were annotated for average shot scale and presented in a within-subject design. Employing mixed-method data collection, participants were asked to retell the story of the films and fill in questionnaires on narrative experience. Skin conductance was also measured during exposure. Story descriptions were content analyzed for theory of mind responses. In a Poisson regression model, average shot scale predicted theory of mind response indicating that increasing spatial proximity to the character triggered higher occurrence of mental state references in participants’ story descriptions. The findings elucidate how formal properties of character presentation affect an audience’s mental models of a story.
Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach

The challenging issue of “human–machine copilot” opens up a new frontier to enhancing driving safety. However, driver–machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver–vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver–IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.
Four-leg Inverter with Reduced Order Generalized Controller for Unbalanced Load Detection and Compensation

Unbalanced load condition occurs in a three phase four wire microgrid system due to uneven distribution of single phase loads. This unbalanced loading causes negative and zero sequence currents in addition to the positive sequence current in the grid. The four leg inverter can be controlled to provide negative and zero sequence currents to address this issue. The positive and negative sequence currents are generally detected using phase-locked loop (PLL) and d-q transformations for controlling the inverter. However, these detection techniques cause delay and phase error. In this paper, an unbalanced current detection technique using reduced order generalized integrator (ROGI) is proposed. The proposed method accurately and rapidly extracts the positive, negative and zero sequences of the load currents and compensates the unbalance. In addition, an improved vector decomposition technique using two-dimensional voltage space vectors has been proposed to modulate the four-leg inverter. Thus, the modulation of four leg inverter is simplified and computational time of controller is reduced. The proposed method has been validated by simulation and experiments.
Behavior Recognition of Moving Objects Using Deep Neural Networks

With the rapid development of modern road traffic network, the demand of automatic traffic understanding has become a vital issue for building the intelligent traffic monitoring system and self-driving techniques. In this paper, we focus on behavior recognition of moving objects at busy road intersections in a modern city. To achieve this, we first capture a 4K (3840×2160) traffic video at a busy road intersection of a modern megacity by flying an UAV during the rush hours, and then manually annotate locations and types of road vehicles to form a dataset for this research. Next we propose an innovative behavior recognition framework consists of advanced deep neural network based vehicle detection and localization, type (car, bus and truck) recognition, tracking and behavior recognition over time. We will present experimental results to demonstrate the effectiveness of our solution. This paper not only demonstrates the advantages of using the latest technological advancements (4K video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic analysis.
Efficient Directory Mutations in a Full-Path-Indexed File System

Full-path indexing can improve I/O efficiency for workloads that operate on data organized using traditional, hierarchical directories, because data is placed on persistent storage in scan order. Prior results indicate, however, that renames in a local file system with full-path indexing are prohibitively expensive. This article shows how to use full-path indexing in a file system to realize fast directory scans, writes, and renames. The article introduces a range-rename mechanism for efficient key-space changes in a write-optimized dictionary. This mechanism is encapsulated in the key-value Application Programming Interface (API) and simplifies the overall file system design. We implemented this mechanism in B&egr;-trees File System (BetrFS), an in-kernel, local file system for Linux. This new version, BetrFS 0.4, performs recursive greps 1.5x faster and random writes 1.2x faster than BetrFS 0.3, but renames are competitive with indirection-based file systems for a range of sizes. BetrFS 0.4 outperforms BetrFS 0.3, as well as traditional file systems, such as ext4, Extents File System (XFS), and Z File System (ZFS), across a variety of workloads.
A Succinct Provable Data Possession Mechanism for Lightweight Clients in Network Computing

The wide use of network computing technologies has promoted the popularity of outsourced storage services. Meanwhile, the provable data possession (PDP) techniques are widely studied as the outsourcing storage servers are not always trustworthy and dependable in maintaining the outsourced data. However, most existing PDP solutions are either costly for the current Internet of things (IoT) devices, or vulnerable to spoofing attacks. In this paper, we propose a succinct anti-spoofing provable data possession scheme for lightweight clients in network computing. We employ the basic integrity comparison-based verification to fit for the resource-constrained verifiers, and propose a random sentinel metadata setup mechanism and the true-fake blended challenge scheme to improve the robustness against spoofing attacks of untrusted servers. The analyses reveal that our approach is effective in data possession verification with the robustness against spoofing attacks, while the overhead on the verifier side is low.
Draft Genome Sequence of Bacillus cereus Strain UAEU-H3K6M1, a Bacterium with Potential Bioremediation Abilities, Isolated from Petroleum Sludge

Here, we report the draft genome sequence of Bacillus cereus strain UAEU-H3K6M1, which was isolated from petroleum sludge in the desert. It is composed of around 5.4 Mbp and has a GC content of 35%. ABSTRACT Here, we report the draft genome sequence of Bacillus cereus strain UAEU-H3K6M1, which was isolated from petroleum sludge in the desert. It is composed of around 5.4 Mbp and has a GC content of 35%. Functional annotation and pathway modeling showed multiple genes with potential bioremediation abilities.
A Dual-Band Dual-Polarized Antenna Array with Improved Isolation

In this work, a dual-band dual-polarized microstrip antenna array is presented with well cancelled self-interference resulting from the couplings within the array for co-time co-frequency full-duplex communications. The proposed single dual-band dual-polarized antenna uses multi-layer architecture, where feedings in shunt and in series are adopted for the two polarizations, respectively. A symmetrical array scheme collaborated with specified excitations is proposed for array applications to further improve the isolation level. A $\pmb{1 \times 4}$ array operated at 2.45 GHz and 3.5 GHz is designed for verification. Simulated results show that the isolations are higher than 60 dB for the two impedance bandwidths with $\pmb{\vert \text{S}_{1,1}\vert \leq-10\text{dB}}$.