Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Distributed Battery Switch Based Electro-Mobility Using Publish/Subscribe System

With the growing popularization of Electric Vehicle (EVs), Electro-mobility (in terms of where to charge EV) has become an increasingly important research problem in smart cities. One of the major concerns is the anxiety of EVs, as drivers may suffer from discomfort due to long charging time. In this article, we leverage the battery switch technology to provide an even faster charging than plug-in charging service, by cycling switchable (fully-recharged) batteries at Charging Stations (CSs). Upon that, a cost-efficient Publish/Subscribe (P/S) system is provisioned, to facilitate the design of distributed charging manner for privacy guarantee. The proposed communication framework utilizes Mobile Edge Computing (MEC)-functioned Road Side Units (RSUs) to bridge, process and aggregate the information flow between CSs and EVs. We further design an advanced reservation based charging system, in which the knowledge of EVs’ reservations is utilized to predict how likely a CS will be congested. This benefits to a smart transportation planning on where to charge, in order to improve charging comfort. Results show the advantage of our enabling technology comparing to other benchmark solutions, in terms of minimized waiting time for the battery switch (as the benefit for EV drivers), and a higher number of batteries switched (as the benefit for CSs).
The Influence of Current Ripples on the Lifetime of Lithium-Ion Batteries

In electric vehicles (EVs) and other applications, lithium-ion batteries experience variable load profiles with frequencies up to several kilohertz, as caused by power electronics. It is crucial to know if certain frequencies accelerate battery degradation and should be avoided. This applies in particular for EV batteries with an expected lifetime of more than ten years. This study investigates the influence of alternating current (ac) profiles on the lifetime of lithium-ion batteries. High-energy battery cells were tested for more than 1500 equivalent full cycles to practically check the influence of current ripples. The applied load profiles consisted of a constant current with superimposed ac frequencies. The frequencies were chosen with regards to the impedance spectra and ranged from 30 kHz to 1 Hz. The measurement results are compared to published results and to a second series of experiments conducted with high-power battery cells. The results of the experiments indicate that lithium-ion battery cells cycled with low frequencies experience a 1% to 2% higher impedance increase and capacity fade than battery cells cycled with high ac frequencies. These differences in aging characteristics established within the first 50 cycles of the brand-new battery cells and remained almost constant thereafter. For these experiments, the antithesis is disproved since no accelerated degradation occurred at higher frequencies. Therefore, with regards to battery lifetime, high frequencies can be tolerated as long as temperatures are considered as well. This new finding may help us to reduce the costs of products with complex battery systems, such as EVs.
A Survey of Routing Mechanisms in ICNs

The success of the current Internet architecture over the past decades has been attributed to its simple layered host-centric communication model. Unfortunately, this same design is now posing a significant challenge to newly developed applications such as media streaming, social networking and location-based services that are content- rather than host-centric. Information Centric Networks (ICNs) were introduced to address this challenge by allowing applications to retrieve contents using their names while being agnostic to their physical location. The key enabler to achieving this task is the development of efficient, robust and scalable name-based routing mechanisms. This paper discusses the main requirements of these mechanisms, analyzes existing key name-based routing approaches and highlights future research directions in that area.
Improved YOLOv2 Object Detection Model

Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2. The number of parameters on the convolution layer is reduced by 78.83%. Secondly, the Feature Pyramid Network is introduced into the detection model to replace the YOLOv2’s image feature fusion method and perform object detection tasks on multi-scale image features. As a result, the ability of the improved YOLOv2 detection model to detect the small-size object is enhanced. Experimental results on PASCAL VOC 2007 datasets show that the improved YOLOv2 has a competitive accuracy to YOLOv2 and better performance on the small-size object.
A W-Band Low Loss, High Power SPDT Switch Using Reverse Saturated $0.13\ \mu \mathrm{m}$ SiGe HBTs

This paper presents a sigle-pole double-throw (SPDT) switch using a $0.13\ {\mu} \mathbf{m}$ reverse saturated SiGe HBTs. The switch is based on ${\lambda}/4$ transmission line and the performance of insertion loss and isolation is improved by adding a small area of substrate contacts around the NPN transistors and an isolation-boosting inductance between the collector and emitter, respectively. A measured insertion loss of 1.4-1.6 dB is achieved at 81-98 GHz and < 2.5 dB in the whole W-band frequencies. The measured output port-to-port isolation is > 25 dB at 78-97 GHz and > 20dB in W-band frequencies. The return loss is better than 10 dB at 75-102 GHz. To the best of the authors' knowledge, the proposed switch demonstrates the lowest insertion loss in the silicon-based SPDT switches in W-band frequencies.
Design and Analysis of SCMA Codebook Based on Star-QAM Signaling Constellations

In this paper, a novel codebook design method for sparse code multiple access (SCMA) is proposed and an analytical framework to evaluate the bit error rate (BER) performance is developed. In particular, to meet the codebook design criteria based on the pairwise error probability, a novel codebook with large minimum Euclidean distance employing the star quadrature amplitude modulation signal constellations is designed. In addition, with the aid of the phase distribution of the presented SCMA constellations, BER of SCMA system over downlink Rayleigh fading channel is obtained in closed-form expression. The simulation and analytical results show that the presented SCMA codebook outperforms the existing codebooks and low-density signature, and the proposed design is more efficient for the SCMA codebook with large size and/or high dimension. Moreover, the derived theoretical BER results match well the simulation results, especially in the high signal-to-noise ratio regimes.
Investigating the Relationship between Believability and Presence during a Collaborative Cognitive Task with a Socially Interactive Robot

The use of socially interactive robots in people's homes and workplaces will continue to grow. To test such systems before deployment, most human-robot interaction experiments are usually conducted in a research laboratory or require the physical presence of the robot. These experiments are usually time-consuming, costly, and, in most cases, require an experimenter to be present. To address this problem, we have developed a web-based research platform in which participants can remotely interact with a socially interactive robot shown via video based on a behavior profile programmed via a Wizard-of-Oz framework. To help us validate the believability when using this platform, we compared participant's responses to a physical presence robot with that to a remote presence robot during a problem-solving scenario. Participants were asked to complete a series of cognitive tasks with the help of the robot. We analyzed participant's tendency to take the robot's advice, their feelings before and after the experiment, and a quantitative assessment about believability. The experimental results indicate that there was a high degree of agreement between participants' behaviors in the two conditions and only minor changes in feelings before and after the experiment. The survey responses found no significant difference in their overall rating for believability, suggesting the equivalency of believability between the two conditions.
Research on Skin Cancer Cell Detection Using Image Processing

Out of the three basic types of skin cancer, namely, Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma, Melanoma is the most dangerous in which survival rate is very low. Early detection of Melanoma can potentially improve survival rate. The skin cancer detection technology is broadly divided into four basic components, viz., image preprocessing which includes hair removal, de-noise, sharpening, resize of the given skin image, segmentation which is used for segmenting out the region of interest from the given image. Different methods can be used for segmentation. Some commonly used segmentation algorithms are k-means, threshold in histogram etc., features extraction from the segmented image and classification of the image from the features set extracted from segmented image. Different classification algorithms can be used for this purpose. The recent skin cancer detection technology uses machine learning and deep learning based algorithms for classification. The most commonly used classification algorithms are support vector machine (SVM), feed forward artificial neural network, deep convolutional neural network. This paper provides a study and analysis on In this paper, an extensive literature survey of current technology is made for skin cancer detection and an accurate comparison among state of the art algorithm for the same.
Analysis and Design of Spoke-type Ferrite Permanent Magnet Motor with Rotor Overhang

This paper describes the analysis and design of a spoke-type motor using ferrite permanent magnet (PM). In order to strengthen the manufacturing cost competitiveness, the improvement design is proposed with the goal of reducing the size and weight compared to the conventional model. Through the PM overhang structure and the shape optimization of the rotor and stator, the output power density is increased by 20%. The operation stability and mass productivity of the improved model are confirmed by analyzing the magnetization and demagnetization characteristics of the PM. The prototype is manufactured and the simulated results are verified experimentally.
Trypanosomes can initiate nuclear export co-transcriptionally

Abstract The nuclear envelope serves as important messenger RNA (mRNA) surveillance system. In yeast and human, several control systems act in parallel to prevent nuclear export of unprocessed mRNAs. Trypanosomes lack homologues to most of the involved proteins and their nuclear mRNA metabolism is non-conventional exemplified by polycistronic transcription and mRNA processing by trans-splicing. We here visualized nuclear export in trypanosomes by intra- and intermolecular multi-colour single molecule FISH. We found that, in striking contrast to other eukaryotes, the initiation of nuclear export requires neither the completion of transcription nor splicing. Nevertheless, we show that unspliced mRNAs are mostly prevented from reaching the nucleus-distant cytoplasm and instead accumulate at the nuclear periphery in cytoplasmic nuclear periphery granules (NPGs). Further characterization of NPGs by electron microscopy and proteomics revealed that the granules are located at the cytoplasmic site of the nuclear pores and contain most cytoplasmic RNA-binding proteins but none of the major translation initiation factors, consistent with a function in preventing faulty mRNAs from reaching translation. Our data indicate that trypanosomes regulate the completion of nuclear export, rather than the initiation. Nuclear export control remains poorly understood, in any organism, and the described way of control may not be restricted to trypanosomes.
An Extensible Semantic Search Engine for Biomedical Publications

The ever increasing amount of publications in the biomedical domain leads to the challenge of finding the right answer to a specific question within a vast sea of information. For instance, the biomedical search engine PubMed has an index of over 27 million publications. PubMed implements a keyword-based search. While easy to operate, the results are shown as a paginated textual list, which is time-consuming to navigate. To give the users a more convenient way of searching for information and displaying it, a variety of interfaces which operate on top of PubMed have emerged. One of those platforms is ViLiP, developed as visual exploratory interface to PubMed and primarily used in the neuroscience domain. ViLiP presents the result of a user’s query in form of an in-situ heat map. In this work, ViLiP is extended by an NLP-based semantic search engine, for the use-case of detecting drug information within a query. Based on linguistic annotations, potential candidates for drug names are selected from an RDF data source, and matching publications are searched for.
Improving Physical Layer Security via a UAV Friendly Jammer for Unknown Eavesdropper Location

In this correspondence paper, we consider an unmanned aerial vehicle (UAV) equipped with an air-to-ground friendly jammer assisting secure communications between a legitimate transmitter–receiver pair for unknown eavesdropper location. To enhance the reliability and security of this system, we examine the impact of the UAV jamming power and its three-dimensional (3-D) spatial deployment on the outage probability (OP) of the legitimate receiver and the intercept probability (IP) of the eavesdropper. Based on the IP, we develop a new security measure, which we refer to as the intercept probability security region (IPSR). The IPSR defines the region within a target area where the IP is less than a given threshold. We design a low-complexity iterative algorithm to maximize the IPSR subject to an OP constraint by jointly optimizing the 3-D deployment and jamming power of the UAV jammer. Numerical results show that our proposed iterative algorithm performs close to an exhaustive search with significantly reduced complexity.
A Lightweight Edge Computing Platform Integration Video Services

In Internet of Things (IoT) scenarios, edge computing, one emerging paradigm, becomes a feasible supplementary solution under the cloud architecture by increasing computing and storage resources available at the network edge. Considering the performance of cloud architecture in terms of response delay and communication burden under the IoT, simply relying on centralized cloud computing is often not the best strategy. To solve the issue, we propose a lightweight edge computing platform relying on the emerging lightweight virtualization and the Edge Intelligent Server(EIS) which integrates computing and networking capabilities. In addition, we design a scheme of video services based on the platform and provide performance evaluations. The results show that EIS has low network forwarding delay and cost-effective collaborative computing capabilities. The platform effectively reduces the response time of video services and improves Quality of Experience(QoE) of users.
An Unsupervised Feature Selection Method Based on Improved ReliefF and Bisecting K-means

Due to the fact that a large number of data are unlabeled with many high-dimensional in practical machine learning problems, this paper proposes an unsupervised feature selection model BK-IRfF based on Bisecting K-means and the improved ReliefF algorithm considering the redundancy information between features, combined with the idea of iteration. The model uses Bisecting K-means to label the unlabeled data, uses improved ReliefF algorithm for feature selection, and iterates the above process until the desired subset of features is output. Experiments show that this unsupervised feature selection method has good performance.
Cyadox regulates the transcription of different genes by activation of the PI3K signaling pathway in porcine primary hepatocytes

Cyadox, a new derivative of quinoxalines, has been ascertained as an antibiotic with significant growth promoting, low poison, quick absorption, swift elimination, brief residual period, and noncumulative effect. Seven differential expressed genes, including Insulin‐like Growth Factor‐1 ( IGF‐1), Epidermal Growth Factor ( EGF), Poly ADP‐ribose polymerase ( PARP), the Defender Against Apoptotic Death 1 ( DAD1), Complement Component 3 ( C3), Transketolase ( TK) and a New gene, were induced by cyadox in swine liver tissues by messenger RNA differential display reverse transcription polymerase chain reaction (DDRT‐PCR) in our laboratory. However, the signal mechanism that cyadox altered these genes expression is not completely elucidated. The signaling pathways involved in the expressions of seven genes induced by cyadox were determined in porcine primary hepatocytes by RT‐qPCR and the application of various signal pathway inhibitors. 3‐(4,5‐Dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) assay revealed that cyadox could stimulate proliferation of porcine primary hepatocytes in a time‐dependent manner. In porcine primary cultured hepatocytes, phosphoinositide 3‐kinase (PI3K) and transforming growth factor‐β (TGF‐β) signal pathways were the main signal pathways involved in the expressions of seven genes induced by cyadox. Taken together, these results demonstrate for the first time that seven cyadox‐related genes expressions in porcine primary hepatocytes treated with cyadox are mediated mainly through the PI3K signaling pathway, potentially leading to enhanced cell growth and cell immunity. EGF might be the early response gene of cyadox, and a primary regulator of the other gene expressions such as IGF‐1 and DAD1, playing an important role in cell proliferation promoted by cyadox.
Efficient Scheduling in Training Deep Convolutional Networks at Large Scale

The deep convolutional network is one of the most successful machine learning models in recent years. However, training large deep networks is a time consuming process. Due to a large number of parameters in these networks, the efficiency of data parallel methods is usually limited by the communication speed of networks. In this paper, we introduce two new algorithms to speedup training large deep networks with multiple machines: 1) propose a new scheduling algorithm to reduce communication delay in gradient transmission and 2) present a new collective algorithm based on reverse-reduce tree to reduce link contentions. We implement our algorithms on a well-known library Caffe and obtain near linearly scaling performance on commodity Ethernet networks.
Relationships Between Crime and Everyday Factors

Technological development in every aspect of human life has formed wider analytical approach to the crime. The genesis and structure of crime, its intensity, and dynamics are subjects of intense scientific research carried out by researchers in various fields of science. At the same time, the crowd-sourced open data-sets as social media and Internet data-sets can be a valuable source of knowledge about various behavior patterns, and social phenomena, including those of criminal nature. This paper aims to present some results of observations, simulations, and prediction of crimes and their correlations to the physical environmental factors like weather or distance to the point of interest. The results are based on qualitative and quantitative data obtained from existing open resources. The research may be useful to model forces and police means to prevent and combat crime effectively.
Graphene Based Monopole Terahertz Antennas on Polyimide Substrate

This paper presents the modelling and analysis of graphene based monopole antennas for Terahertz (THz) Band communications in micro-networks. Graphene is used as a conductor for radiating patch and ground in monopole antenna structures. Polyimide of thickness 143 µm is used as substrate with relative permittivity of 3.5. Two types of antennas were modelled; one is narrowband resonating at 0.1 THz and the other is wideband operating in 0.030-0.117 THz band. The second antenna has broader impedance bandwidth (>10%) in the band of operation.
Securing Collaborative Deep Learning in Industrial Applications Within Adversarial Scenarios

Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imposed a shift from the traditional centralized deployment to a more collaborative one. However, this has opened up several vulnerabilities caused by compromised nodes and inputs, with traditional crypto primitives and access control models exploited to offer protection means. Providing security can be costly in terms of higher energy consumption, calling for a wise use of these protection means. This paper exploits game theory to model interactions among collaborative deep learning nodes and to decide when using actions to support security enhancements.
Quantitative Comfort Evaluation of Eating Assistive Devices based on Interaction Forces Estimation using an Accelerometer

Robot usage in the fields of human support and healthcare is expanding. Robotic devices to assist humans in the self-feeding task have been developed to help patients with limited mobility in the upper limbs but the acceptance of these robots has been limited. In this work, we investigate how to quantitatively evaluate the comfort of an eating assistive device by estimating the interaction forces between the human and the robot when eating. Rather than using expensive or commercially unavailable devices to directly measure the forces involved in feeding, we use an accelerometer to estimate these forces, which are calculated using a previously observed estimation of the system mass and the measured acceleration during the feeding process. We experimentally verify our concept with a commercially-available eating assistive device and a human subject. The evaluation results demonstrate the feasibility of our approach.
Solar Image Matching Based on Improved Freak Algorithm

FREAK algorithm has the defects of not having scale invariance and single feature point matching strategy, being prone to unsatisfactory results. Based on AKAZE and RANSAC algorithm, an improved FREAK algorithm is proposed, named AKAZE-FREAK. The image pyramid was constructed by using the nonlinear diffusion filter and the numerical solution was obtained by fast display diffusion (FED) to obtain the image point coordinates with sub-pixel precision. Then the feature points were described by the FREAK descriptors to distribute directions for the feature points. Finally, based on the initial matching of the hamming distance to the eigen vector, RANSAC is used to eliminate the error matching point. In this paper, this algorithm is applied to the recognition of repeated shooting solar images. After the improved algorithm is compared with the SIFT-FREAK, SURF-FREAK and FREAK algorithms, it shows an improvement in the image feature point matching accuracy with good robustness on the scale difference, illumination difference and rotation difference of the image. It also improves the accuracy of the judgement of the late repeat.