Miracle-dz/UniFAR-FTUs huggingface dataset:
Static prediction of parallel computation graphs

Many algorithms for analyzing parallel programs, for example to detect deadlocks or data races or to calculate the execution cost, are based on a model variously known as a cost graph, computation graph or dependency graph, which captures the parallel structure of threads in a program. In modern parallel programs, computation graphs are highly dynamic and depend greatly on the program inputs and execution details. As such, most analyses that use these graphs are either dynamic analyses or are specialized static analyses that gather a subset of dependency information for a specific purpose. This paper introduces graph types, which compactly represent all of the graphs that could arise from program execution. Graph types are inferred from a parallel program using a graph type system and inference algorithm, which we present drawing on ideas from Hindley-Milner type inference, affine logic and region type systems. We have implemented the inference algorithm over a subset of OCaml, extended with parallelism primitives, and we demonstrate how graph types can be used to accelerate the development of new graph-based static analyses by presenting proof-of-concept analyses for deadlock detection and cost analysis.
A machine learning approach for medical device classification

The growth of medical device innovation over the last decades has necessitated the need for strong regulatory control in order to ensure the safety and performance of such devices. Medical devices are categorised according to the risk posed to the public. However, the legislation describing the classification rules are often dense and difficult to read. In order to facilitate device classification, the medical device regulator in Australia, the Therapeutic Goods Authority (TGA), provides online digital support tool for device classification. In this work, we (i) evaluate the online tool and (ii) make a further a proposal for using machine learning as means to provide more effective results. For the first part of this work, we asses whether the tool increases the readability of the legislative rules by evaluating the Flesch reading ease score of the legislation and the tool. While the online tool provides some degree of simplicity and readability over the legislation, we argue that the TGA can make more use of its data in order to provide more effective services. In the second part, we develop a proof-of-concept machine learning model to classify a device based on its stated purpose. The results of the experiment show a 82% weighted accuracy across four class labels, indicating that a more data-driven approach could be adopted by the authority.
Vector-tracking-based GNSS/INS Deep Coupling and Experiment Platform for Urban Scenarios

Vector-tracking-based GNSS/INS deep coupling is a promising method for high-precision and robust navigation in urban challenging environments, especially for weak and dense multipath scenarios. However, the research on this topic is far from enough due to the complexity of multipath interference and system structure. Herein, a typical vector delay/frequency-locked loop is introduced. Then, based on this vector tracking loop, two different deep coupled schemes are proposed according to different approaches to estimating the corrections for the inertial navigation system. At last, the experiment based on a vehicular field collection and a software-defined platform was designed in which all the commonly used stand-alone-receiver-based and GNSS/INS-based methods are tested and compared. The results preliminarily illustrate the effectiveness and superiority of the deep coupling methods designed, on positioning and measurement improvement, especially in a weak signal and multipath scenario.
Trinetra App: A Companion for the Blind

The life of a blind person is always dependent on another person who needs some kind of assistance, whether it is mobility or any other chore. Just like any other normal person, the blind should be able to lead their life. Trinetra App- a companion for the blind is aimed to provide a better experience for the blind. Trinetra App helps blind people by interacting with them and can tell what is happening around them. The design is incorporated with cutting-edge technologies like computer vision, speech recognition, image processing, and OCR. Trinetra App is designed using Flutter Framework which can run on both Android OS and IOS. Trinetra App can find objects in front of the user, recognize text, and convey information like time, weather, phone battery level. The detected and recognized results are conveyed through audio to the user. This research work details the methods used for design and implementation and experimental results thus obtained.
Cloud Detection Using Fully Convolutional Network with Zynq SoC for Spaceborne Application

Cloud detection is an important step to avoid the interference of contaminated areas in the remote sensing image. At present, the onboard cloud detection using deep learning is an attractive idea to provide the solution for detecting cloud contaminated region with high accuracy in real-time. However, the method based on deep learning has a large amount of model parameters and requires high computation resources, which is difficult for deployment in the onboard scenario. To address this issue, the cloud detection using the fully convolutional network with Zynq SoC is proposed in this article. Multiple convolution layers in a fully convolutional network are used to extract deep semantic features to improve the accuracy of cloud detection in different scenarios. And a custom computing architecture with full-precision parameters is conducted, which utilizes the loop tiling for feature maps and general matrix multiplication with parallel computing for convolution. The proposed network is deployed under the limited hardware resource. Experimental results indicate that the mean intersection over union of the proposed method is 90.39%, and the pixel accuracy reaches 95.79%. Compared with the implementation on ARM, the proposed method can achieve about 18.84 times speedup.
Viral Pneumonia Detection Using Modified GoogleNet Through Lung X-rays

Viral pneumonia occurs in lungs by the viral infection and is a life threating disease if not treated at the right time. Age is a critical factor in this regard and the effect of viral pneumonia varies from person to person diversly. People of an older age and infants are at considerable risk due to viral pneumonia that affects the lungs. It is difficult and time consuming for radiologists to detect the viral pneumonia by manually analyzing the lungs x-rays. So, Deep learning-based approaches are employed on lung x-rays for the accurate detection of viral pneumonia disease to avoid wrong medication. Therefore, it is necessary to propose an automated method that can accurately detect viral pneumonia patients to assist the medical doctors in their decision-making process. In this paper, we employed three different pretrained models such as AlexNet, GoogleNet, and ResNet18 to investigate the performance of transfer learning-based classification task to detect the viral pneumonia patients. We fined tuned all the three models. Along with this, we applied data augmentation to increase the amount of data to avoid the overfitting problem, which is common if the data is small for training the model. Among the three pretrained customized models, we achieved remarkable performance results on GoogleNet and obtained remarkable accuracy of 96.64%, precision of 96.99%, recall of 96.26%, specificity of 97.01%, and F1-score of 96.63%. More specifically, our method effectively detected the viral pneumonia patients accurately and precisely. Experimental results and comparative analysis with existing state-of-the-art methods illustrate that our method is useful in reliable detection of viral pneumonia patients and can be used by radiologists in their decision-making process.
SMPL-Based 3D Pedestrian Pose Prediction

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently prone to suffer from error accumulation along the kinematic chain, which results in unnatural human poses. In joint-rotation-based pose prediction, Skinned Multi-Person Linear (SMPL) parameters are widely used to represent pedestrian pose. In this work, a novel SMPL-based pose prediction network is proposed to address the centrality of each SMPL component by distributing the network weights among them. Furthermore, to constrain the network to generate only plausible human poses, an adversarial training approach is employed. The effectiveness of the proposed network is evaluated using the PedX and BEHAVE datasets. The proposed approach significantly outperforms state-of-the-art methods with improved prediction accuracy and generates plausible human pose predictions.
A Representation Separation Perspective to Correspondence-Free Unsupervised 3-D Point Cloud Registration

3-D point cloud registration in remote sensing field has been greatly advanced by deep learning-based methods, where the rigid transformation is either directly regressed from the two point clouds (correspondences-free approaches) or computed from the learned correspondences (correspondences-based approaches). Existing correspondence-free methods generally learn the holistic representation of the entire point cloud, which is fragile for partial and noisy point clouds. In this letter, we propose a correspondence-free unsupervised point cloud registration (UPCR) method from the representation separation perspective. First, we model the input point cloud as a combination of pose-invariant representation and pose-related representation. Second, the pose-related representation is used to learn the relative pose w.r. t. a “latent canonical shape” for the source and target point clouds, respectively. Third, the rigid transformation is obtained from the above two learned relative poses. Our method not only filters out the disturbance in pose-invariant representation but also is robust to partial-to-partial point clouds or noise. Experiments on benchmark datasets demonstrate that our unsupervised method achieves comparable if not better performance than state-of-the-art supervised registration methods. The source code will be made public.
Investigation on Object Detection Models for Plant Disease Detection Framework

Plant diseases are the most common and severe threat to precision agriculture. Therefore, identification and diagnosis of illnesses at a premature stage are vital. In addition, manual observation according to specific selection criteria is difficult and expensive. While various deep learning-based solutions have been proposed for this process, they usually suffer from lengthy training/testing times with massive datasets. In this paper, to address this problem, we explore the potential of computer vision-based object detection methods for early plant disease detection. A comparative study has been performed with three different benchmark object detection models YOLOv4, EfficientDet, Scaled-YOLOV4. The experimental results were evaluated with precision, recall, F1-score, and mean Average Precision (mAP) as performance metrics. All models are trained using the PlantVillage dataset. Empirical results show that the Scaled-YOLOv4 model is a well suitable object detection model providing a real-time solution in detecting even small infected regions of the plant leaves within less time duration. Therefore, detection and diagnosis of diseases at an early stage of infection are essential.
FEA Based Design of a Low Cost Ferrite Magnet Surface Permanent Magnet Synchronous Motor for An Industrial Exhaust Fan

Brushless permanent magnet (PM) motors are gaining popularity in the appliance industry due to their improved efficiency and high power density. This paper presents the finite element analysis (FEA) based design of a low cost ferrite magnet surface permanent magnet synchronous motor (SPMSM). The objective here is to improve the efficiency and torque ripple profile of the existing motor for an industrial exhaust fan. The single-phase induction motor (SPIM) utilized in the available industrial exhaust fan is modified to an SPMSM. The main dimensions and electrical performance of commercially available SPIM for an industrial exhaust fan are analyzed. The data obtained from an analytical design of SPMSM is validated against FEA. FFT analysis of radial flux density in the air gap and line back emf for different slot-pole combinations of the motor are analyzed, and the best suitable topology is selected. The improved efficiency results in the reduction in energy consumption of the appliance and hence its operating cost.
A Sensitive Data Leakage Detection and Privacy Policy Analyzing Application for Android Systems (PriVot)

Mobile applications can have access to various sensitive information to accomplish the business requirements as well as user requirements. Due to the sensitivity of this information, app developers are bound by the regulations to provide a privacy policy that describes their data collection practices. However, there were many incidents where the privacy policies were inconsistent with the actual data practices. Additionally, the privacy policies are often too long and difficult to grasp just by reading them due to their complex language. To address this hurdle, we propose a mobile application “PriVot”. PriVot has a privacy policy analyzer built with a hierarchical classifier using convolutional neural networks to provide a detailed and unambiguous summary indicating the data that is being collected by each app and their purpose for being collected Furthermore, it monitors the network traffic of the device with the aid of a Transport Layer Security(TLS) proxy, a Forwarder, and a Traffic Analyzer that operates on-device without requiring root privileges to identify potential data leakages and privacy policy violations. We present “PriVot” which achieved a 67.4% accuracy on privacy policy analysis and a 72.5% throughput at a low latency overhead with the network traffic monitoring.
Comparative Study of Deep Learning Techniques Used for Speech Enhancement

Speech degradation is caused by various reasons such as environmental disturbances captured during the speech signal recording, like reverberation, babble, etc. The objective of speech enhancement algorithms is to reduce noise while improving the overall quality and intelligibility of degraded speech signals and reducing auditory fatigue. Speech enhancement finds its application in various areas that require the need for noise-free clean speech signals, such as, speaker recognition, speech recognition, hearing aids, and almost every communication medium that is based on voice. This paper aims to study the temporal and spectral features of the speech signal which were exploited to create Neural Network models such as CNN, RNN, and LSTM models to perform the task of speech enhancement. Finally, an end-to-end deep learning model, WaveNet+LSTM, which takes raw audio data or spectral magnitude using STFT asinput and maps it to the targeted clean speech and learns by minimizing an energy-conserving loss function was created and analyzed. This model was compared to the other neural network models and the results analyzed. Speaker-specific training of the WaveNet+LSTM modelwas done to compare its performance when trained using frequency domain and time domain features.
Damage Analysis of Suspended Submarine Cable under Current Force Based on Finite Element Simulation

In this paper, 500kV AC submarine cable was taken as the object, the finite element software COMSOL was used to establish the simulation model of fluid-structure coupling, and the force of the suspended submarine cable was analyzed. The stress distribution of the lead alloy sheath of the cable in four specific directions and the location of the maximum stress value was obtained. On this basis, the relationship between the suspended length and the stress value was obtained by changing the suspended length of the submarine cable. According to the initial yield stress value of the lead alloy sheath, the reference suspended length of the preliminary damaged cable structure was given, which provides some theoretical analysis for the online monitoring of the submarine cable. The results show that when the suspended length reached 7.05 meters, the maximum stress value of the lead alloy sheath at the initial yield stress is 1.97×107(N/m2).
Study on Load Forecast of City Gas with Unstable Time Series Model

Based on theory of time series analysis, this the sis introduces establishing the unstable time series model process with the properties of unstable time series for the city gas load, and puts forth the improvement method of forming model on a foundation of Box and long autoregression of white noise, and establishes unstable time series model for the city gas load with Fourier series model to describe the cyclical variation regularity and AR model to describe the stable time series. 
Dynamic path planning for AGV based on Tent chaotic sparrow search algorithm

A dynamic path planning method of automatic guided vehicle(AGV) on the basis of the Tent chaotic sparrow search algorithm is proposed for the problems of population diversity reduction, easy to get caught up in local best and slow convergence during the iterative process of dynamic path planning solution of automatic guided vehicle (AGV). Based on the introduction of Tent mapping to initialize the individual sparrow positions to increase the initial population diversity; the global path optimality is improved and the convergence speed is accelerated by introducing the previous generation global optimal solution and adding adaptive weights m. The simulation results show that the proposed AGV dynamic path planning method has better convergence and solution accuracy, and the global search capability is greatly improved and a safe feasible path with optimal cost and satisfying constraints can be obtained quickly.
Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation

Modern day conversational agents are trained to emulate the manner in which humans communicate. To emotionally bond with the user, these virtual agents need to be aware of the affective state of the user. Transformers are the recent state of the art in sequence-to-sequence learning that involves training an encoder-decoder model with word embeddings from utterance-response pairs. We propose an emotion-aware transformer encoder for capturing the emotional quotient in the user utterance in order to generate human-like empathetic responses. The contributions of our paper are as follows: 1) An emotion detector module trained on the input utterances determines the affective state of the user in the initial phase 2) A novel transformer encoder is proposed that adds and normalizes the word embedding with emotion embedding thereby integrating the semantic and affective aspects of the input utterance 3) The encoder and decoder stacks belong to the Transformer-XL architecture which is the recent state of the art in language modeling. Experimentation on the benchmark Facebook AI empathetic dialogue dataset confirms the efficacy of our model from the higher BLEU-4 scores achieved for the generated responses as compared to existing methods. Emotionally intelligent virtual agents are now a reality and inclusion of affect as a modality in all human-machine interfaces is foreseen in the immediate future.
Multi-scale Mode Denoising (MSMD) Method for Weak Gear Fault Feature Extraction

Features caused by the gear fault are often submerged by the inference of the strong noise, which will lead to difficulties for the feature identification of the weak fault at the early stage. In addition, the desired weak feature would be modulated into several frequency bands, and then weaken the effect of the fault information identification. Motived by this issue, a MSMD method to extract and enhance the early weak fault signals is proposed in this paper. In this method, the collected raw vibration signals are decomposed into a series of band-limited intrinsic mode functions by variational mode decomposition, and then the sensitive modes including rich periodic amplitude modulation information are selected via proposed syncretic modulated impact index. The denoised signals can then be obtained through reconstructing the sub-signals generated from the sensitive modes using minimum entropy deconvolution. Finally, the fault feature can be identified clearly in the envelope spectrum of denoised signals. The results of simulation and experiment illustrate that the proposed method can effectively extract and enhance the weak fault features of gears.
Owcsimpy: A 3D Simulator for Indoor Optical Wireless Channels

In this paper, a 3D simulator for indoor optical wireless channels, named owcsimpy, is introduced. One of the advantages of owcsimpy is the ability to generate channel impulse responses (CIRs) with realistic environments. Envi-ronments with different furniture, blocking objects (such as human bodies), randomly-oriented stations, or mobile users can be modeled with owcsimpy. In addition, owcsimpy is a lightweight library that can be easily installed in many different machines, which makes it easy to produce a dataset of CIRs. In this paper, different use cases of owcsimpy are discussed. First, we demonstrate how to generate different scenarios ranging from a simple one, e.g., an indoor room with a light emitting diode (LED) and a photodiode (PD), to more complicated scenarios, e.g., an open office room with multiple LEDs and PDs. Second, CIRs that are generated by considering randomly-oriented mobile stations are used to calculate the coherence time of indoor wireless optical channels. Other potential applications of owcsimpy that show the advantage of the library to enable future research on LiFi are also presented at the end of the paper.
Concrete Bridge Damage Detection Based on Transfer Learning with Small Training Samples

In this paper, a transfer learning approach is applied to solving the problem of low detection accuracy in a smaller number of concrete bridge damage training samples. First, we used the YOLO v3 detection model to train a pre-trained model by pretraining on a dataset with a large number of samples. Then, we carried out transfer learning on a small sample of concrete bridge damage dataset. Finally, we used the SPP module to improve the YOLO v3 target detection model. Experiments show that the transfer strategy proposed in this paper greatly improves the accuracy of concrete bridge damage recognition with few training samples, and the network performance is further improved by adding the SPP module.