Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Training of Deep Convolutional Neural Network for Multi-organ Segmentation from Multi-sequence MRI of the Abdomen

Automated delineation of organs play a vital role during the treatment planning for clinical practitioner. With magnetic resonance (MR) images being widely used in radio-therapy, the organ delineation from multi-sequence MR images has become more desirable. Segmenting abdominal organs from MR images is critical in surgical planning. However, the organs segmentation from MR volume is a challenging task, as there are different visual appearances for the same organ in the different sequences of MR images. Recently, convolutional neural network (CNN) methods have achieved promising results in automating organ delineation. However, the performance of deep learning (DL) to emerging approaches such as adversarial learning in the field of segmentation are rarely explored. In this paper, we have trained an encoder-decoder like architecture as a segmentation network, using segmentation loss minimization along with training it to adversarial attack another CNN implementing the visual Turing test discriminator. This way of adversarial training has shown significant improvement in performance. We have used the CHAOS-19 dataset of abdominal MRI for segmentation of the liver, left kidney, right kidney, and spleen. We have used Dice coefficient (DC) to measure the performance, and achieved an average DC of 0.97 with the faster convergence.
An ultra-wideband (UWB) MIMO Antenna for 5G Applications

In this paper, a compact (20×20×1.6 mm3) dual-port steering wheel shape multiple-input-multiple-output (MIMO) patch antenna is suitable for K & Ka band applications and in particular for 5G applications is presented. Three simulated antenna designs (Ant-1, Ant-2 and Ant-3) are systematically investigated and the specifications of Ant-3 have been optimized for desired antenna parameters and operation. Ultra-wideband (22.77-29.11 GHz) at port-1 and (22.66-29.11 GHz) at port-2 with impedance bandwidth of 24.44 % and 24.91 % respectively is observed. Maximum peak gain of 3.83 dBi at both port-1 and port-2 is observed. Simulated isolation less than − 20 dB, maximum envelope correlation coefficient (ECC) is 0.0011, diversity gain (DG) between 9.997-9.999 (dB), total active reflection coefficient (TARC) is less than −10 dB and radiation efficiency more than 70 % is obtained.
Kiwifruit vine extraction based on low altitude UAV remote sensing and deep semantic segmentation

Extraction and segmentation of kiwifruit vine range is an important part of precision agriculture in kiwifruit orchard. In this paper, depth semantic segmentation and traditional machine learning are used to segment and extract kiwifruit vines from orthophoto images, and the accuracy and image quality of vine segmentation based on pspnet, SVM and random forest classification in another test set are compared. Experimental results show that although the mean pixel accuracy of deep semantic segmentation is slightly lower than that of traditional machine learning segmentation, the segmentation image quality of deep semantic segmentation is better and the pixels are more continuous. The deep learning method effectively developed UAV remote sensing image data, improved the usability of remote sensing image, and provided necessary plant information for orchard precision agriculture.
WSGP: A Window-based Streaming Graph Partitioning Approach

Graph partitioning, a preliminary step of distributed graph processing, has been attracting increasing attention in the last decade. A high quality graph partitioning algorithm should facilitate graph processing by minimizing the communication overhead and maintaining the load balancing among distributed computing units. Offline partitioning algorithms usually require the knowledge of a complete graph, and therefore, are not adaptive to handle massive graph-structured data. On the contrary, streaming partitioning algorithms take edges or vertices as a stream and make partitioning decisions on the fly. However, the streaming manner faces dilemmas from time to time because of a lack of knowledge. Furthermore, an unmindful partitioning decision in such a dilemma could significantly decrease the partition quality. In this paper, we propose a novel window-based streaming graph partitioning algorithm (WSGP). WSGP leverages a greedy-based heuristic to perform edge partitioning. When facing a decision dilemma, WSGP utilizes a size-bounded window to buffer the edges. When the window is fully filled, an edge is poped and assigned to a partition. The assignment is decided by knowledge obtained from both the edges already settled and the ones still cached in the buffer window. Our experiments take into account various real-world benchmark graphs. The experimental results demonstrate that WSGP consistently has a smaller replication factor than the state-of-the-art algorithms by up to 23%, at a limited cost in terms of memory and comprehensive running time.
Helmet Detection using Machine Learning Techniques

The continuous mobilization of vehicles has led to a surge in the number of road accidents across the world. To get better of this, government is trying to focus on the safest and preventive measures in traffic. So, our main idea is to introduce a helmet detection mechanism as most of the deaths caused are due to the absence of helmet. The practice of direct observation is found to be time taking and a lot of human effort is needed. This project attempts to implement a detection process through a few machine learning algorithms by using pre-defined libraries. This system notices a person with/without a helmet thereby imposing fines on the detected candidates. Further, this research work concludes that the automatic identification of helmets can overcome the challenges faced by manual data collection process. Moreover, this research work assumed that, through data collection, the algorithm can help to track the helmet use and promote its active use by people in order to ensure the road safety
The Outage Probability Analysis of Network-Centric Clustering in C-RAN

The baseband unit (BBU) pool in the cloud radio access network (C-RAN) realizes the centralized deployment of baseband processing units, and the centralized signal processing promotes the implementation of coordination multi- point (CoMP) between remote radio heads (RRHs). Network- centric clustering mode is considered to be an effective solution to improve network coverage probability. As an adjustable parameter in the network, cluster size and frequency reuse distance play an important role in improving the quality of service. However, due to the uncertainty of wireless networks and the resulting aggregated interference distribution, it is a severe challenge to evaluate the performance of wireless networks. The goal of this paper is to study the impact of cluster size and frequency reuse distance on the outage probability of users. The network-centric hexagonal clustering method is adopted, in which each user is associated with the nearest RRH in the cluster. In order to mitigate the intra- cluster interference, we assume that the channel state information (CSI) can be perfectly obtained by the RRHs in the same cluster, and the intra-cluster interference can be eliminated by beamforming. By modeling RRHs and users as binomial point process (BPP), and the outage probability is obtained in integral form. The accuracy of the derived outage probability expression is verified by Monte Carlo simulation.
Driver’s warning notifications by using FM RDS technology

In this paper, the use of Software Defined Radio (SDR) transceivers for broadcasting drivers’ warning notifications is tackled. More precisely, by employing the Radio Data System (RDS) protocol of a local Frequency Modulation (FM) transmission, road traffic’s dangerous areas (rail crossing, unmarked crossroads, etc.) are announced. The code of a warning message is broadcasted in the close vicinity of the dangerous area and, at the receiver side, a corresponding image is displayed on the car’s tablet PC. In order to study the coexistence of such kind of transmissions with the regional or national ones, the performance in terms of bit error rate (BER) with respect to the difference between the two received power levels is studied. Then, based on a simplified channel model, this paper gives a method to estimate the local FM transmission range, able to cover the dangerous area without transmission errors on the RDS part.
Sentiment Time Series Calibration for Event Detection

Event detection based on sentiment time series, which describe the trend of users’ emotions or attitudes towards specific topics over time, has been widely applied in the analysis of social network or text mining. Most of the contributions directly generate time series sequences by classifiers. However, due to the missing corpus labels or the limited performance of the classifier, such generated sentiment time series may not correspond to the actual values, especially when the sentiment value changes drastically, called extreme value. We propose a new method to calibrate sentiment times series for event detection based on evaluation on a sampling dataset. Theoretical analysis of the calibration method is explicated, and it is proved that the sampling error of the performance indicators can be limited to a minimal range for extreme values, thus sentiment value error can be reduced. Experiments on simulated datasets and real-world datasets illustrate the effectiveness and robustness of our method.
edgeRouting: Using Compute Nodes in Proximity to Route IoT Data

Due to the proliferation of edge computing, cloud providers have started offering compute nodes at the edge of the network in addition to traditional compute nodes in data centers. So far, various systems have been proposed for processing Internet of Things (IoT) data on both edge and cloud compute nodes in order to reduce the communication latency. However, such systems do not typically consider that the network bandwidth between an edge node and a cloud node can be orders of magnitude higher than the bandwidth between an IoT device and a cloud node. As a result, the IoT data are commonly sent selectively to either edge or cloud nodes disregarding alternative network paths through edge nodes, which may have higher network bandwidth, and lower communication latency. To avoid this, in this paper we analyze the latency of sending data to edge and cloud compute nodes of cloud providers. Based on this analysis, we propose edgeRouting which routes the data through the closest edge compute node. By doing that, edgeRouting exploits both the low propagation delay of nodes at the edge, and the high bandwidth among edge and cloud compute nodes of cloud providers. To evaluate our approach, we perform experiments on a real-world setup with nearby and remote compute nodes of a cloud provider, and we show that edgeRouting reduces the communication latency by up to 55% compared to alternative methods.
Sentimental Poetry Generation

The paper investigates how well poetry can be generated to contain a speciﬁc sentiment, and whether readers of the poetry experience the intended sentiment. The poetry generator consists of a bi-directional Long Short-Term Memory (LSTM) model, combined with rhyme pair generation, rule-based word prediction methods, and tree search for extending generation possibilities. The LSTM network was trained on a set of English poetry written and published by users on a public web-site. Human judges evaluated poems generated by the system, both with a positive and negative sentiment. The results indicate that while there are some weaknesses in the system compared to other state-of-the-art solutions, it is fully capable of generating poetry with an inherent sentiment that is perceived by readers.
FPRA: A Fine-grained Parallel RRAM Architecture

Emerging resistive memory (RRAM) based crossbar array is a promising technology to accelerate neural network applications. RRAM-based CNN accelerators support a high-degree of intra-layer and inter-layer parallelism. The intra-layer parallelism duplicates kernels for each network layer while the inter-layer parallelism allows execution of each layer when a portion of input data is available. However, previously proposed RRAM-based accelerators do not leverage data sharing between duplicate kernels leading to significant idleness of crossbar arrays during inference. This shared data creates data dependencies that stall the processing of the next layer in the pipeline. To address these issues, we propose Fine-grained Parallel RRAM Architecture (FPRA), a novel architectural design, to improve parallelism for pipeline-enabled RRAM-based accelerators. FPRA addresses the data sharing issue with kernel batching and data sharing aware memory. Kernel batching rearranges the layout of the kernels and minimizes the data dependencies created by the input shared data. The data sharing aware memory uniformly buffers the input and output data for each layer, efficiently dispatching data to duplicate kernels while reducing the amount of data transferred between layers. We evaluate FPRA on eight popular image recognition CNN models with various configurations in a cycle-accurate simulator. We find that FPRA manages to achieve 2.0 $\times$ average latency speedup, and 2.1 $\times$ average throughput increase, as compared to the state-of-the-art RRAM-based accelerators.
ECO: Edge-Cloud Optimization of 5G applications

Centralized cloud computing with 100+ milliseconds network latencies cannot meet the tens of milliseconds to sub-millisecond response times required for emerging 5G applications like autonomous driving, smart manufacturing, tactile internet, and augmented or virtual reality. We describe a new, dynamic runtime that enables such applications to make effective use of a 5G network, computing at the edge of this network, and resources in the centralized cloud, at all times. Our runtime continuously monitors the interaction among the microservices, estimates the data produced and exchanged among the microservices, and uses a novel graph min-cut algorithm to dynamically map the microservices to the edge or the cloud to satisfy application-specific response times. Our runtime also handles temporary network partitions, and maintains data consistency across the distributed fabric by using microservice proxies to reduce WAN bandwidth by an order of magnitude, all in an application-specific manner by leveraging knowledge about the application’s functions, latency-critical pipelines and intermediate data. We illustrate the use of our runtime by successfully mapping two complex, representative real-world video analytics applications to the AWS/Verizon Wavelength edge-cloud architecture, and improving application response times by 2x when compared with a static edge-cloud implementation.
Seeing Molecules Interact with Total Internal Reflection Fluorescence Microscopy

Copyright: © 2016 Ho LS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author: Hung-Wing Li and Man Shing Wong, Department of Chemistry, Hong Kong Baptist University, Hong Kong, E-mail: hwli@hkbu.edu. hk, mswong@hkbu.edu. hk
Method for Nearby Product Marketing using Wi-Fi Aware Technology

There are various methods of business product advertisement like newspapers, television, posters, or ads on blogs. However, in these methods, we are targeting the audience using different metadata. In this paper, we will discuss a method for targeting a nearby audience using new - aware technology and providing a communication channel between buyer and seller. Wi-fi aware, also known as neighbor awareness network (nan) is a low power discovery mechanism that runs over wi-fi. System architecture, its benefits, and comparison with other existing methods are also explained.
Inertial Data Based Learning Methods for Person Authentication

A user authentication method based on biometric movement patterns is proposed. As input, the data acquired from the accelerometer of a wearable device is used. Using machine learning methods, i.e. k-Nearest Neighbors, Random Forests classifiers and a ID Convolutional Neural Network, the person which performs a known activity is identified from a set of 15 persons. Most approaches in the domain propose either the human activity type recognition or person identification using the gait pattern. In this research, the considered activities are: Walking and Working at computer. As features, sequences of 52 consecutive accelerations on the three axes are selected using the overlapping time window method. Even if computer work is a mainly static activity, the results obtained for identifying the person performing it are encouraging. The main advantage of the proposed method is that it does not involve computing of other features than those from the acceleration data.
An Overview of Architecture and Security Issues of a Smart TV

In this digital era, consumers are influenced by various intelligent computing technologies in the age of the Internet of Things (IoT) to enhance their comfort and quality of life. A Smart TV with broadcasting services is considered as an excellent illustration of the digital technologies. Smart TV applications and software are constantly developed, and it has been certified to satisfy EAL2 security requirements. These technologies understand hand movements and responds appropriately. They are, however, vulnerable to security breaches via email, cameras, internet connectivity, and media players. However, the risks can be reduced by utilizing passwords, antivirus software, secure Wi-Fi, and other methods. This research study is a review on the applications of Smart TV in an IoT driven environment. The purpose of this research is to develop and describe: (a) study architecture, (b) working (c) security issues, (d) testing, (e) advantages and (f) disadvantages.
Wireless Power Transfer Strategies for Medical Implants: Focus on Robustness and EM Compatibility

In the recent past, we have experienced astonishing progress in mapping human body functionalities and interconnections, encouraging a holistic view of physiological processes and medical analysis. Similar advances are occurring in modeling and theoretically investigating the interaction between electromagnetic (EM) fields and living systems, opening more and more opportunities to turn bio-EM interactions into a concrete, viable approach to the treatment of a variety of diseases. We are now able to understand the physiological implications of a certain EM stimulation at very deep levels-to cell membranes and beyond-and to quantify with high accuracy EM power density, even for very microscopic scales of analysis. And we can infer from this information the possible consequences of EM stimulation in terms of macroscopic effects on the whole biological system.
Chinese Character Relationship Extraction Method Based on BERT

With the continuous development of information technology and the advent of the era of big data, today’s society has entered the era of artificial intelligence. A variety of artificial intelligence application products continue to appear, playing various important roles in many fields, such as AI + agriculture, AI + medical, AI + autonomous driving, AI + education, etc. The combination of artificial intelligence and traditional industries The emergence of artificial intelligence has made these traditional industries shine. In the field of artificial intelligence, there is a very important sub field--Natural Language Processing (NLP). In the development of various industries, the scale of data generated by various industries is increasing, and the problem of information overload is becoming more and more serious, so how to quickly and accurately Obtaining key information in the data is of great significance. However, the text data generated by all walks of life is often unstructured and diverse in types. Therefore, in recent years, information extraction in the field of natural language has become a research hotspot, and person relationship extraction is an important branch of information extraction. Person relationship extraction is the core task of text mining and information extraction, and its task is to identify sentences. The semantic relationship between two person name entities, in recent years, person relationship extraction has been widely promoted and applied in various aspects, such as intelligent semantic search, character knowledge graph construction, question and answer systems, etc. It performs well. Therefore, it is of great significance to design and develop a set of Chinese character relationship extraction system. In response to this problem, this paper proposes a BERT-based Chinese character relationship extraction architecture. Its fusion model architecture is BERT+BI-LSTM+Multi-head-Self-Attention+FC. The current popular deep learning for Chinese character relationship extraction the model of has a better effect on the relationship extraction of a single character relationship pair. However, when it is extended to single-sentence multi-person relationship pairs and document-level semantic complexity, the evaluation index data of this model is not high. Through comparison, it is found that BERT has a significant improvement in the pre-training effect of the word2vec model, and the BI-LSTM+Multi-head-Self-Attention model has a significant improvement in the effect of a single LSTM model. Therefore, the proposed BERT+BI-LSTM +Multi-head-Self-Attention+ FC fusion model has certain practical value in Chinese character relationship extraction.
An Affordable Monitoring System for Room’s Temperature and Humidity (MS_RTH)

Automation becomes a mandatory option to be used in our daily life. This includes home automation systems which is referred to controlling home utilizations either by wired or wireless communication. This facility is even more required for an elder, disable, or any other vulnerable person in the house. On the other hand, Arduino is an easy to use, powerful, cost effective, and efficient microcontroller tool which is used in many automation systems. In this paper, an affordable monitoring system is proposed by integrating an Arduino-built system with a suitable smartphone application to measure and control a room’s temperature and humidity. Knowing that, mobile technology has changed everybody’s life in today’s world and people are carrying smartphones nearly all the time whether they are outside or inside their homes, it is suitable to have a smartphone application to measure and control any room’s temperature using them. For this paper requirements, Android smartphone platform was chosen to build the required application and connected successfully with the Arduino-built measurement system using MIT APP Inventor2 builder. The application was built and implemented effectively in measuring any room’s temperature and humidity frequently successfully. The Android application also provides a warning message if the temperature exceeds the permitted edge. The later facility is very important to avoid possible fire accidents or similar undesired room’s atmosphere.