Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone Age Assessment by Deep Convolutional Neural Networks Combined with Clinical TW3-RUS

Bone age assessment is critical to diagnosis of various growth disorders in children, such as endocrine, nutritional disorders and dysplasia. X-rays of hand and wrist are the most common modality used to calculate bone age. In this paper, we propose a novel approach called DeepTW3 for automatic bone age assessment from X-ray images. DeepTW3 integrates Convolutional Neural Networks (CNNs) with expertise knowledge of TW3(Tanner-Whitehouse 3nd edition)-RUS(radius, ulna and short bones) bone age assessment system. The proposed method is tested on a dataset containing 1,100 hand bone X-ray images, all of which were manually annotated with selected region of interests(ROIs). Our method achieved mean absolute errors (MAE) of 0.2685, outperforming all state-of-the-art methods. For the task of grading skeletal maturity, our method using continuous stage distribution is complementary to using the clinical TW3-RUS categorical stages when interpreting critical cases of intermediate bone stage.
Comparing FlowVisor and Open Virtex as SDN-Based Site-to-Site VPN Services Solution

Site-to-Site Virtual Private Network (VPN) is a computer network technology made to establish secure and private data communication between geographically separated site for multiple tenants on the service provider network infrastructure, in a traditional network its implemented by using Multi Protocol Label Switching (MPLS). Software Defined Networking (SDN) is a computer network architecture, design and management paradigm, that is made for next generation computer networks and overcoming the limitation of the existing traditional computer networks. Like the traditional one, SDN-based computer network infrastructure are also able to run network various function including network virtualization. There are several SDN based network virtualization technology already available with different architecture and its own characteristics. This research will review and compare two network hypervisor software: FlowVisor and OpenVirtex as a base technology for site-to-site VPN implementation. An SDN network testbed deployed to test network virtualization functionality, firewall network function virtualization, network failover, network quality of services and traffic processing overhead in virtualized network infrastructure based on both software. SDN-based site-to-site VPN is more easily implemented rather than traditional site-to-site VPN. OpenVirtex promises as better future solution for SDN based virtualization solution rather than FlowVisor, but its still not reliable to implemented as a network virtualization solution in a real network infrastructure.
Unsupervised Anomaly Event Detection in Video Based on Autoencoders and Motion Features

In this article, we propose an unsupervised learning method for anomaly detection in videos based on autoencoders to learn the distribution of normal events. In our model, if the behavior deviates from normal distribution, it is considered as abnormal behavior. We use 2D convolution to extract the spatial feature of the video data. In order to compensate for the temporal information of video data, we incorporate optical flow features which include two consecutive frames of motion features. To improve the expressive ability of our model and fit more complex distribution functions, a ‘micro network’ is constructed and added into the model. Moreover, we add the convolutional long Short-Term Memory (ConvLSTM) in the middle section between encoder and decoder of our model to learn temporal evolution of spatial feature. The effectiveness of the proposed unsupervised anomaly event detection method is validated through experiments that are conducted on four publicly available data sets.
Constructing Medical Image Domain Ontology with Anatomical Knowledge

Extracting information from the medical imaging reports with domain ontology has attracted much attention in medical natural language processing field. Based on the unstructured characteristics of medical image report text, the existing image report domain ontology is constructed by ontology learning method, including ontology learning method based on language and ontology learning method based on machine learning. However, these existing methods ignore the anatomical knowledge embedded in medical imaging reports, which is very useful for extracting entity relationships from reports. In order to solve the above problems, this paper proposes a domain ontology construction method for medical image reporting based on anatomy knowledge, which combines the prior knowledge of pathology and anatomy to obtain the basic framework of the domain ontology as the knowledge driver. In particular, our proposed approach consists of two tasks. The first task is to convert each text report into a semantic tree through an anatomic knowledge based semantic subtree generation algorithm. Semantic subtree generation algorithm mainly consists of three parts: framework positioning, relation extraction and adding relation. Firstly, the text is located based on anatomical knowledge, and the relational extraction mainly adopts the method of dependency syntactic analysis to extract the three semantic relations contained in the text. Finally, add the relationship to the corresponding branch. The second task is to obtain the domain ontology by merging semantic subtree. This step is mainly to obtain the domain ontology by merging the nodes of the XML structure semantic tree. The experimental results show that this method can be used for relational extraction and domain ontology construction, laying a good foundation for the follow-up research.
Log-Euclidean Kernel-Based Joint Sparse Representation for Hyperspectral Image Classification

Motivated by the superior performance of region covariance descriptor, we use covariance matrices as new features to replace the original spectral pixel features, and employ a Log-Euclidean metric to characterize the geodesic distance between symmetric positive definite (SPD) covariance matrices. Based on the covariance features and Log-Euclidean metric, we propose a Log-Euclidean kernel-based joint sparse representation (LogEKJSR) model for the classification of hyperspectral images (HSIs). In the implementation of LogEKJSR, we first reduce the dimensionality of HSI by employing extended multiattribute profile (EMAP) transformations, and then extract the region covariance matrix features associated with each pixel on the EMAPs. The EMAP can model homogeneity and texture structure of HSI by aggregating multiple morphological attribute profiles, the covariance matrix feature contains both local spectral correlation and spatial structure information, and the Log-Euclidean kernel is a matrix-to-matrix similarity metric rather than vector-to-vector similarity metric. Finally, a LogEKJSR model is obtained by replacing the traditional kernel in the KJSR with the Log-Euclidean kernel. Experimental results on three benchmark hyperspectral data sets demonstrate that our proposed LogEKJSR is more effective than existing JSR, KJSR, and support vector machine methods.
Modeling and Estimating User Influence in Social Networks

It is of importance to model and estimate the user influence in social networks, especially for advertisers who conduct viral marketing. In this paper, we are interested in the number of received messages incurred by a node generating a message, and introduce the concepts of individual influence and type influence, while type influence is got by averaging out individual influence over nodes of the same type. We propose a user behavior model and use generating function to analyze type influence (including the mean and variance) and diffusion threshold, and find these results are not accurate in finite-size networks. We then classify nodes into subtypes and redefine the network model, which achieves much more accurate results. We also propose a scalable approach to estimate individual influence, and find it can get good approximates for individual influence, subtype influence and type influence by only considering local neighbors and out-of-date information, which is useful in large-scale networks. All analysis results are verified by simulations in real-world networks. Models in this paper can be extended to consider more realistic situations, and we believe these results are of use in understanding the diffusion dynamics in social networks.
Music Emotion Classification Based on Indonesian Song Lyrics Using Recurrent Neural Network

Music is one of the entertainments for the community both in Indonesia and throughout the world. Music is enjoyed in the form of instruments and has lyrics that can express emotions. The emotions produced by a song can be distinguished based on the lyrics. Songs that are in accordance with the mood are sometimes needed to enjoy music, so we need tools to distinguish emotions in the song called classification. This study uses the Recurrent Neural Network method to classify emotions based on song lyrics. The parameters of the Recurrent Neural Network that were tested in this study were hidden size, learning rate, and dropout. Data in this study were divided into development dataset and dataset testing. K-fold cross-validation is used in the model training process. The highest accuracy obtained was 82.4 percent during the testing process. Accuracy is obtained by using a hidden size parameter of 128, a learning rate of 0, 01, and a dropout of 0, 4. The highest accuracy model is used as a basis for classifying emotions based on Indonesian song lyrics into happy and sad classes. When the live process uses 10 complete songs, the average accuracy is 83.13 percent.
The comparison of technical capabilities of six pulse generators for biological applications

In this paper we present the comparison of technical parameters for three electric pulse generators and three magnetic pulse generators, which were developed for the research application in electroporation and magnetoporation fields respectively. The concept of each system is introduced and the comparison of the parameters like nature of pulse, waveform, maximum pulse amplitude, rise time, fall time, number of pulses and repetition frequency etc. is provided. The comparison result highlights the novelty of each system.
An Implementation of IoT-Based Weather Monitoring System

The living environment monitoring is a critical issue today. Many related researches focused on developing the living environment monitoring systems by using the web-based technology, but such information systems would increase the difficulty of promotion and migration to others, because of needing well-trained engineers to achieve and maintain such information system. This research focuses on developing the weather monitoring system. The main goal of the research is to use the technology of message queuing telemetry transport (MQTT) as roles of the communication layer instead of direct-connecting database, which can isolate the system migration complexity from heterogeneous relational database management system (RDBMS) and construct a distributed information system easily. Finally the contributions are demonstrated as implementation of the IoT-based weather monitoring system. The prototyping of the weather monitoring system is implemented and deployed at Wu-Tso elementary school for the students' learning of nature science and activity environment. In addition, the system is implemented using open sources and is easy to be deployed and scalable.
Course Finder Based On An Ensemble of Similarities And Social Network Analysis

A university offers multitude of courses each having different constraints, pre-requisites, focus as well as sequence in which these need to be taken. A student accordingly attempts to design his credits. This is equally applicable to a faculty who tries to focus on a basket of courses according to his/her interests. An attempt to automate this process by clustering the textual descriptions of the course offerings alone however has not been very productive and commonly it is done manually as the course offerings evolve. In this work, to achieve this goal, the method elaborated in this paper uses the implicit social network of offered courses coupled with a bottom up ensemble approach that tends to balance both topology and topic, semantics and context. Contrary to clustering that depends on the attributes of each node(individual course), community focused techniques utilize network details if such an implicit social graph based on similarity of courses can be envisaged. The course descriptions themselves are represented by vectors that are derived from dense (derived from word2vec) as well as sparse vector (derived from TF-IDF) representation of various aspects of a course description. As a next step, an innovative ensemble approach is used to calculate similarity based on various similarity algorithms combined with the similarity detected by a traditional clustering algorithm. Finally the derived similarities from this ensemble are used to form edge weights in the adjacency matrix of the course social graph so that standard community detection algorithms can be used. The analysis carried out here shows that this approach is much more successful in detecting course clusters compared to classical text clustering approach. The main contribution of this work is a bottom up ensemble scheme coupled with community detection techniques that can be way more successful than classical text clustering methods.
A Navigation Architecture for Ackermann Vehicles in Precision Farming

In this letter, inspired by the needs of the European H2020 Project PANTHEON,11[Online]. Available: https://www.project-pantheon.eu. we propose a full navigation stack purposely designed for the autonomous navigation of Ackermann steering vehicles in precision farming settings. The proposed stack is composed of a local planner and a pose regulation controller, both implemented in ROS. The local planner generates, in real-time, optimal trajectories described by a sequence of successive poses. The planning problem is formulated as a real-time cost-function minimization problem over a finite time horizon where the Ackermann kinematics and the presence of obstacles are encoded as constraints. The control law ensures the convergence toward each of these poses. To do so, in this letter we propose a novel non-smooth control law designed to ensure the solvability of the pose regulation problem for the Ackermann vehicle. Theoretical characterization of the convergence property of the proposed pose regulation controller is provided. Numerical simulations along with real-world experiments are provided to corroborate the effectiveness of the proposed navigation strategy.
A 0.4-V 1.9 μ W Amplifier for Low Power Biomedical Applications

This manuscript presents a fully differential amplifier with a supply of 0.4V for low power biomedical applications. It is designed in a $0.13 \mu \mathrm{m}$ CMOS technology with regular VT transistors. By employing a gate-body input class-AB output topology and novel local common-mode feedback, it achieves large output swing, less noise and fast speed, with low power consumption. It also obtains less current variation against supply variation, which is critical for improving battery duration especially for ultra-low-voltage (ULV) operation. Its robust performance against process and supply variations is also verified by extensive corner simulations. Simulation results indicate that this amplifier has an open loop DC gain of 51dB, a unity gain bandwidth of 1.95MHz and a phase margin of 74° with a load of 24pF//1.16M$\Omega$ at 0.4V supply. The power consumption of the amplifier is only $1.9 \mu \mathrm{W}$.
Multi-Stage Attention-Unet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation

Bleeding in gastrointestinal tract (GI) is caused by many different diseases and may lead to serious consequence if not being treated correctly. The most effective method to locate bleeding area is using wireless capsule endoscopy (WCE). However, the amount of WCE images of one patient is so large that even a professional physician may take a long time on analyzing them, and human errors may also occur with work time increasing. Thus the computer-aided diagnosis methods become more attractive. In this paper, we propose a novel deep learning based method for WCE images bleeding area segmentation. We design multi-stage architecture and attention blocks to deal with small areas segmentation. The latter stages' input is the combination of feature maps transferred from former stages so that even very deep layers can obtain small areas features, and attention blocks help shallow layers to better extract small areas features thus they can transfer more useful information to latter stages. Extensive experiments are conducted on public available WCE image dataset to show the effect of multi-stage architecture and attention blocks. Compared with other bleeding areas segmentation methods, our approach achieves state-of-the-art performance with 98.5% overall accuracy, 90.1% mean accuracy and 86.3% mean intersection over union (IoU). There is a 10.7% improvement on mean IoU compared with the previous state-of-the-art WCE bleeding segmentation method.
Critical infrastructure adaptive computer control of standard and smart subsystems

The interruptions in the Critical Infrastructure services may rise as interruptions of production, service as well as damaging of production lines. The size, geographical distribution as well as complexity of critical infrastructures require additional systems for the control and monitoring of CIS on requested level. Municipal networks: water distribution, district heating, sewage networks etc. operate under harsh environmental conditions, however, being a part of the Critical Infrastructure they have to meet reliability and safety requirements. Their maintenance municipal networks depend on the data collected on their status, and the decisions on due corrective actions. Condition Based Maintenance (CBM) or Proactive Maintenance (PM), rather than scheduling maintenance actions, which are based on operating hours and servicing volume, promises to be more reliable and even more effective. Thus, PM requires the effective combination of data collection, analysis, presentation and decision making processes. The modular wireless sensor network based solutions was designed that incorporates selectable communication interfaces, which integrate supplementary monitoring subsystems in this line in order to have different service control and feedback control.
Evolved Binary Neural Networks Through Harnessing FPGA Capabilities

The exponential progress of semiconductor tech-nologies has enabled the proliferation of deep learning as a prominent area of research, where neural networks have demon-strated its effectiveness to solve very hard multi dimensional problems. This paper focuses on one in particular, Binary Neural Networks (BNN), which use fixed length bits in its connections and logic functions to perform excitation operations. Exploiting those characteristics, hardware accelerators that integrate field-programmable gate arrays (FPGAs) have been adopted to hasten inference of deep learning networks, given its proficiency to maximize parallelism and energy efficiency. This work will show how the algorithm Binary Spectrum-diverse Unified Neuroevolution Architecture (BiSUNA) can perform training and inference on FPGA without the need of gradient descent. Source code can be found in github.com/rval735/bisunaocl
Literature Recommendation Technology Based on User Characteristics and Semantic Computing

Obtaining the firsthand literature resources related to research is one of the main tasks of intelligence personnel. Different from the open users to whom content recommendation service is oriented, intelligence personnel have a clear professional background and specific research needs. This paper proposes a personalized recommendation algorithm for scientific literature that integrates user characteristics and semantic computing. On the one hand, using the rich background information and scientific research experience of intelligence personnel were used to generate recommendations for static information modeling ; on the other hand, user behavior was used to explore the potential semantic relationship between the literatures. Finally, the two methods are combined. Through the effective integration of user static characteristics and user behavior information, the dependence on the user interaction behavior was weakened, and the cold start problem was effectively alleviated. The experimental results showed that the proposed method was significantly improved compared with the traditional method, which proved the feasibility of the method.
A Cost-Effective Algorithm for Selecting Optimal Bandwidth to Clear Malicious Codes

Malicious code has posed a severe threat to modern society. Delivering antivirus program to networks is an important task of a cybersecurity company. As the bandwidth resource in a company is limited and precious, cybersecurity companies have to make a tradeoff between the impact(i.e. the economic loss) of malicious codes and the bandwidth assigned to transmit the antivirus programs. This paper addresses the malicious code and bandwidth tradeoff(MCBT) problem. By developing a novel malicious code and antivirus program interacting model, the total loss, which is the sum of the bandwidth usage fee and the economic loss, is quantified. On this basis, the MCBT problem is modelled as a constrained optimization problem that we refer to as the MCBT model, where the independent variable stands for bandwidth, and the objective function stands for the total loss. Some optimal bandwidth is determined by solving the MCBT model. Based on this, we propose a heuristic algorithm named DOWNHILL, which outperforms random strategies. Finally, the influence of some factors on the optimal bandwidth and the corresponding optimal total loss is uncovered through numerical simulations. To our knowledge, this is the first time the MCBT problem is treated in this way.
Parameter Optimization of DPFC Controller Based on Multi-objective

The interaction among control objectives of distributed power flow controller (DPFC) series-side converter is transformed into a multi-objective optimization problem. The variance between the three control objectives of active power, reactive power and DC capacitor voltage controller and the corresponding reference value is taken as the performance index to design the multi-objective function. The Pareto solution set is obtained by optimizing the objective function using multi-objective particle swarm optimization algorithm, and the best compromise solution based on membership function is adopted to select the best parameter solution. Finally, it is verified that the controller parameters designed by multi-objective particle swarm optimization algorithm can reduce the interaction between DPFC series-side multi-converters in PSCAD/EMTDC.
Automatic Matching Method for Calibration Points Based on an Angular Ordered Sequence

The calibration of a camera in wide scene generally requires a large number of non-coplanar control points. This process often needs a large amount of extra auxiliary work to match the object points and the image points, and in some cases, this matching is difficult for users with insufficient experience. A feature representation and optimal priority matching method based on the angular ordered sequences (AOS) of a control point are proposed in this paper with the goal of solving the object-image auto-matching problem for control points. We also provide a strategy for dealing with the difficult situation for automatic calibration when the camera’s optical center is nearly collinear with two object points. The simulation results demonstrated that the proposed method was robust with errors for the initial value of the optical center, object points, and image points, and it could quickly achieve object-image matching for automatic calibration.