Miracle-dz/UniFAR-FTUs huggingface dataset:
A Students’ Action Recognition Database In Smart Classroom

With the development of human action recognition, it is possible to automatically recognize students’ actions in classroom, providing a new direction for classroom observation in teaching research. Training effective students’ action recognition algorithms depends significantly on the quality of the action database. However, only a few existing action databases focus on learning environment. In this paper, we contribute to this topic from two aspects. First, a novel students’ action recognition database is introduced. The spontaneous action database consists 15 action categories, 817 video clips of 73 students, which are collected in real smart classroom environment. Second, a benchmark experiment was conducted on the database using two kinds of recognition algorithms. The best result is achieved by Inception V3 with 0.9310 accuracy. Such a spontaneous database will help in the development and validation of algorithms for action recognition in learning environment.
A comparison of face-to-face and online instructional delivery methods in large-group settings in a university undergraduate wellness course

Utilization of online delivery of the lecture component in an undergraduate wellness course is unique and undocumented in research. Wellness courses are traditionally taught with face-to-face lectures plus an activity component. This discipline is distinctive in terms of its activity components that are not conducted online. The purpose of this study was to investigate two large group face-to-face and online courses to determine if the students enrolled were demographically equivalent and if there were differences in academic achievement in terms of final course grades and between subgroups of students. For the fall 2006 and spring 2007 semesters, 1,814 students were enrolled in the Personal Wellness course. Chi-square test of independence and independent-samples Mests results revealed there were demographic differences between the face-to-face and online delivery methods (p < .05). Significant differences were found for gender, ethnicity, major, ACT scores, and semester course load. Students' final course grades were examined using an independent-samples f-test to determine if students were academically more successful in the face-to-face or online delivery format. No significant differences were discovered in students' final course grades between the two delivery methods (t{2) = . 244, p > . 05). ANOVA and independent-samples Mest results revealed significant differences in final course grades among various student subgroups (p < .05). Differences were found based on gender, ethnicity, major, and year in school. Based on these results, it was recommended that both face-to-face and online sections of Personal Wellness be offered and that different content be offered to best meet the needs of the diverse students enrolled. A significant difference was found in student subgroups' and their final course grades based on their choice of face-to-face or online delivery. These conclusions should influence how the Personal Wellness course is taught in the future at this particular university. Future research should further examine these characteristics which may have educational impact for teachers, administrators, and students themselves.
Tucumã: A toolbox for spatiotemporal remote sensing image analysis [Software and Data Sets]

Remote sensing images have been broadly employed over the past decades to detect and investigate temporal changes on Earth's surface. Appropriate tools are needed to support complex analyses of temporal data in various domains, ranging from purely scientific to educational. There are many existing tools, but it is often necessary to switch among them several times. Typical interoperability issues include system mismatch and the variety of user profiles and data types.
Microburst Aware Congestion Control for Storage Traffic

Data center network speeds are advancing on a yearly basis. As of now, servers have network interfaces of more than 25 Gbps. However, high-speed interfaces cannot be sufficiently utilized because the current Transmission Control Protocol (TCP) has a lot of software overhead. Therefore, Remote Direct Memory Access (RDMA) technologies, which decrease Central Processing Unit (CPU) overhead and eliminate data copies, are being used in Ethernet. In Ethernet, RDMA is enabled by RDMA over Converged Ethernet v2 (RoCEv2), which uses Data Center Quantized Congestion Notification (DCQCN) as a congestion control method. On the other hand, RoCE enables Non-volatile Memory Express over Fabrics (NVMe-oF), which can access remote Non-volatile Memory (NVM) at high speeds comparable with those of local NVM. However, to achieve suitable remote access performance, network congestion needs to be alleviated. In this paper, we show an example of how remote access performance is decreased by network congestion. We also find that the existing microburst-aware congestion control method for TCP does not work for RDMA. We propose Microburst Aware Congestion Control for Storage Traffic (MARCS) as a congestion control method for RDMA. In MARCS, a flow uses a dedicated transmission queue when the flow is at the beginning stage. Furthermore, by controlling the bandwidth of the queue, the performance of other continuous flows is kept high. MARCS also utilizes a mechanism to prevent packet reordering that heavily affects the performance of RDMA. Using simulation and real devices, we show that MARCS dramatically improves the throughput when congestion occurs.
A Balanced Feed Triple Frequency Patch Loaded Printed Dipole Antenna for WiMAX/WLAN Applications

This paper presents a simple inexpensive balanced feed printed dipole antenna on FR-4 substrate with parasitic patches to enhance matching at the frequencies of interest. The antenna resonates in the 2.4GHz band, 3.5GHz band as well as the 5.2GHz band. The integrated balun ensures equal distribution of current to both dipole arms. The antenna is very simple in configuration having dimensions 70 × 58 × 1.52 mm3.
Hardware Design of DC-3GHz Compressed Sensing Receiver Based on Modulated Wideband Converter

Modulated Wideband Converter (MWC) is a sub-Nyquist sampling structure suitable for wideband sparse signals. Based on MWC, this paper designs a DC-3 GHz Compressed Sensing (CS) receiver system with commercial devices. Combined with some standard compressed sensing algorithms, this receiver can sense the spectrum from DC to 3 GHz and receive at most six 40 MHz real signals simultaneously without prior knowledge of the carrier frequencies. This receiver with 60 dB dynamic range is designed to have six physical branches, and each branch contains an Analog-to-Digital Conversion (ADC) which operates at 245 MHz. The total sample rate of 1470 MHz is much smaller than the Nyquist sampling rate of 6 GHz. Based on a classical algorithm named SBR4, multiple sets of multiband signals with different input power are successfully blindly reconstructed.
Keyword Analysis and Topic Extraction of Hospital Violence News

Currently disputes between doctors and patients have increased in China, as has hospital violence. How to improve the relationship between them is an important problem. This paper focuses on the online news about hospital violence from 2013 to 2018. If we can find out the characteristics of hospital violence news, we will have some possibilities to help to improve the doctor-patient relationship from the point of media coverage. In this study, we collect 90,499 news articles in total from online mainstream media, such as People’s Daily, Tencent News, NetEase News, etc. First, we analyze the news by extracting keywords using three algorithms as word frequency, TF-IDF and TextRank respectively. The results are compared and TextRank is shown the most effective. Second, we analyze the news by building the Latent Dirichlet Allocation topic model and extract news topics automatically. And then these topics are grouped into five themes. Finally, we discuss the characteristics of hospital violence news according to the keywords and topics.
WiGrus: A Wifi-Based Gesture Recognition System Using Software-Defined Radio

With the proliferation of WiFi devices and infrastructures, the ubiquitous WiFi signals are used to transmit user data. Besides it is also capable of sensing and identifying human gestures. In this paper, we propose a WiFi-based gesture recognition system, namely WiGrus, which solves the problems of user privacy and energy consumption compared with the approaches using wearable sensors and depth cameras. WiGrus leverages the fine-grained Channel State Information (CSI) extracted from WiFi signals to recognize a set of hand gestures. First of all, we utilize timestamps attached to the extracted CSI values to split continuously received WiFi packets into gesture instances. Second, a Principal Component Analysis (PCA)-based method and the first order difference are employed to reduce the noise and mitigate multipath effects caused by the environment changes. Then, massive features are extracted from the processed CSI values to present the intrinsic characteristics of each gesture. Finally, a 2-stage-RF algorithm is proposed to classify the gestures. Our experiments are implemented with a wireless router and a Software Defined Radio (SDR) device, more specifically Universal Software Radio Peripheral (USRP), which are used as WiFi signal transmitter and receiver respectively. The experimental results demonstrate that WiGrus can achieve an average accuracy of 96% in Line-of-sight (LOS) scenario and 92% in Non-Line-of-Sight (NLOS) scenario in the office environment and is robust to the environment changes.
Towards Software-defined Fog Computing via Named Data Networking

According to the fog computing paradigm, a plethora of Internet of Things (IoT) applications will benefit from the distributed execution of services and applications, processing and storage in proximity to data sources. However, new networking primitives and service orchestration mechanisms need to be designed to cope with the dynamic, distributed, and heterogeneous nature of the fog resources. A solution to such a challenging deployment would be the coupling of two revolutionary future Internet paradigms, Software-Defined Networking (SDN) and Named Data Networking (NDN). Indeed, on the one side, SDN can support wise orchestration decisions by leveraging a centralized intelligence and a global view of resources. On the other side, NDN well matches the service-centric nature of fog computing by letting (named) services be discovered regardless of the identity of the specific executor. In the paper, we dissect the strengths of this integrated solution, with a special focus devoted to evaluate the benefits of the NDN stateful data plane coupled with the centralized SDN control plane, when compared to a traditional IP-based host-centric software-defined approach.
Deep Reinforcement Learning for Dynamic Network Slicing in IEEE 802.11 Networks

Network slicing, a key enabler for future wireless networks, divides a physical network into multiple logical networks that can be dynamically created and configured. In current IEEE 802.11 (Wi-Fi) networks, the only form of network configuration is a rule-based optimization of few parameters. Future access points (APs) are expected to have self-organizational capabilities, able to deal with large configuration spaces in order to dynamically configure each slice. Deep Reinforcement Learning (DRL) can achieve promising results in highly dynamic and complex environments without the need for an operating model, by learning the optimal strategy after interacting with the environment. However, since the number of possible slice configurations is huge, achieving the optimal strategy requires an exhaustive learning period that might yield an outdated slice configuration. In this paper, we propose a fast-learning DRL model that can dynamically optimize the slice configuration of unplanned Wi-Fi networks without expert knowledge. Enhanced with an off-line learning step, the proposed approach is able to achieve the optimal slice configuration with a fast convergence, which is attractive for dynamic scenarios.
A Deep Siamese-Based Plantar Fasciitis Classification Method Using Shear Wave Elastography

Two-dimensional shear wave elastography (2D-SWE) is an effective and feasible method for plantar fasciitis (PF) evaluation. Until now, only experienced doctors have been able to give relatively accurate evaluation via ultrasound images, resulting in low efficiency and high cost. Therefore, designing automatic algorithms to recognize the pattern of these ultrasound images is urgently required. In recent years, deep learning (DL) has made considerable progress in computer- aided diagnosis (CAD). However, there have been no studies that apply DL to the diagnosis of PF. To achieve robust PF classification, this paper builds a deep Siamese framework with multitask learning and transfer learning (DS-MLTL), which learns discriminative visual features and effective recognition functions using 2D-SWE. The DS-MLTL model comprises two VGG-style branches and a multitask loss including a classification loss and a Siamese loss. The Siamese loss leverages the intrinsic structure (similarities) of different images and contains a contrastive constraint and a similar constraint. In our framework, visual features and the multitask loss are learned jointly, and they can benefit from each other. To train the DS-MLTL model effectively, the model transfers knowledge from the large-scale ImageNet dataset to the PF classification task. For model evaluation, an SWE dataset of plantar fascia, which contains 282 images of a PF pattern and 60 images of a healthy pattern, is collected. Experimental results show that the DS-MLTL method achieves favorable accuracy of 85.09 ± 6.67% and performs better than human-crafted features extracted from B-mode ultrasound and SWE. In addition, DS-MLTL also obtains the best performance compared with different DL models.
Latency-Constrained Dynamic Computation Offloading with Energy Harvesting IoT Devices

In this paper, we address the problem of dynamic computation offloading with Multi-Access Edge Computing (MEC), considering an Internet of Things (IoT) environment where computation requests are continuously generated locally at each device, and are handled through dynamic queue systems. In such context, we consider simple devices (e.g., sensors) with limited battery and energy harvesting capabilities. Hinging on stochastic optimization tools, we devise a dynamic algorithm that jointly optimize radio (e.g., power, energy) and computation (e.g., CPU cycles) resources, while guaranteeing a certain out of service probability (defined as the probability that the sum of local and remote queues exceeds a predefined value) and stability of the device batteries around prescribed operating levels. The method requires the solution of a convex optimization problem per time slot, and does not require apriori knowledge of channel, task and energy arrival distributions. Numerical results illustrate the advantages of the proposed method.
FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention

Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel interdependencies into account. The 1-D factorized kernel block provides extra convolutions layers with a minimum number of parameters to reduce the computations of the higher-order convolutions. Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i.e., constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github.com/vivek231/Skin-Project.
Demo: A Mobile Edge Computing-based Collision Avoidance System for Future Vehicular Networks

Mobile Edge Computing (MEC) is a key enabler for the deployment of vehicular use cases, as it guarantees low latency and high bandwidth requirements. In this paper, we propose a MEC-based cooperative Collision Avoidance (MECAV) system designed to anticipate the detection and localization of road hazards. It includes a Collision Avoidance service, allocated in the MEC infrastructure, which receives information of status and detected hazards from vehicles, processes this information, and selectively informs to each vehicle that either approaches to road hazards or to other vehicles. We have implemented a proof-of-concept of the MECAV system and we have validated its architecture and functionalities.
Poster Abstract: C-Continuum: Edge-to-Cloud computing for distributed AI

Mobile autonomous systems are supposed to deeply impact in manufacturing, space exploration, rescue, defense, transportation, and everyday life. Autonomous air-ground vehicles, for example, will become normal tools in the next few years, providing a natural platform for distributed artificial intelligence applications including, for example, disaster rescue and recovery, area surveying, autonomous driving, etc. The raise of autonomous cooperating robots will pose new challenges in networking, distributed systems and resource management. Heavy computational tasks will be dispatched to the closest edge node for processing and the core-cloud will be involved as last resort in an effort to reduce latency and increase the global system capacity leveraging application and resource locality. Massive amounts of data and computations will be required. For example, in the autonomous driving scenario Intel estimates that each driver-less vehicle will produce over 4 TeraBytes of data each day1. While most of this data is consumed in-car, cooperating autonomous vehicles will have to exchange some percentage of the 4TB and eventually offload some computation and data to the local edge or the core-cloud. This is particularly relevant when locally gathered and labeled data can be used to refine the model and ultimately increase the global “intelligence”. This approach is often taken by autonomous driving automakers.
Poster Abstract: iSDR: SDR-in-the-loop Simulation

Discrete event network simulators are used to evaluate the performance of a variety of designs in the wireless communication field. They enable researchers to build new network protocols and architectures in a simplified and organized development environment with adherence to the fundamental software engineering principles. However, even though they embody different mathematical models to simulate wireless medium, it is still far from taking an excessive number of channel conditions that directly affect wireless communication into consideration. In this study, we integrate the interface of the software-defined radio (SDR) USRP B210 into the physical layer model of the discrete event simulator OMNeT++ to create an integrated SDR (iSDR) framework. iSDR opens the way to easily develop new network protocols using various OMNeT++ modules and run simulations in wireless medium via the SDR interfaces for realistic performance results. We present working principles of iSDR and discuss significant design issues to support complex simulation scenarios.
A Research on the Education Mode of Innovative Software Talents Oriented to Emerging Engineering

The new generation of technologies, represented by cloud computing, artificial intelligence (AI), internet of things (IoT), and mobile internet, is driving the great renovation of human society as well as the rapid development of new economy and new industries. New requirements are then emerged for the software engineering education at this new era. Hunan University (HNU) relies on outstanding platforms, such as the National Demonstration Software College, the National Featured Software Engineering Major, and the National Pilot Zone for Software Talents Education, carrying out researches on teaching mode renovation, course scheme renovation, and practice scheme renovation within a series of teaching renovation projects. As a result, the Innovative Software Talents (IST) education scheme has been established with accountable achievements and it also has been widely introduced, demonstrated, and applied among Chinese universities.
Wideband Multibeam-Electronically Steerable Parasitic Array Radiator (ESPAR) for 5G Applications

This work presents a dielectric resonator on patch-electronically steerable parasitic array radiator (ESPAR) with wide impedance bandwidth for modern wireless applications. Enhancement of impedance bandwidth has been achieved by employing dielectric resonator on top of patch resonator as a compact constituent radiating element of the ESPAR array. A 3-element switched beam ESPAR array has been implemented at 15 GHz. Switching is based on PIN-diodes. The achieved impedance bandwidth is more than 24% which is the widest in comparison to DR based ESPARs in the literature. The antenna achieves beam steering in 0°, +25° and −25° directions with maximum gain of 8.26 dBi. Full wave simulations have been presented.
Clarification of Topic Differences Between Shops and Consumers for Personalization of Stories

In recent years, story marketing has been used in order to achieve differentiation of products that are becoming homogeneous. However, it cannot be always said that the information provided by a store matches what the individual consumer demands. In this research, we propose a method to create stories reflecting the interests and preferences of individual customers and consumers. In the proposed method, a topic group expressing the customer’s interests and preferences are obtained through the topic analysis of the texts of store interview and the customers ’past experiences. The interest of an individual customer for each topic is estimated based on the obtained topic group. The estimated interests help stores to determine what stories they should provide for each customer. In order to verify the validity of the proposed method, an experiment was conducted to reveal differences in interests among consumers as well as differences in the information provided by the store and the interests of each customer. From the experiment, it was found that when there are multiple topics in the information by the store, the interest in the topic is different by each subject. Additionally, it was demonstrated that the customer may be interested in topics that the store has not included.