Miracle-dz/UniFAR-FTUs huggingface dataset:
TADS: Temporal Autoencoder Dynamic Series Framework for Unsupervised Anomaly Detection

Deep learning models for time series anomaly detection are mainly divided into supervised learning, semi-supervised learning, and unsupervised learning. Unsupervised learning has grown in importance for identifying anomaly features in time series without labels. Unsupervised learning of anomaly detection needs to be improved in two aspects. One is the calculation time, the other is the anomaly score. Some excellent unsupervised learning algorithms utilize clustering algorithms to filter out normal features, which takes too much time. Moreover, these algorithms use a fixed-length window to calculate anomaly scores, leading to inaccuracies and fluctuations. This paper proposes a Temporal Autoencoder Dynamic Series (TADS) framework. Here are the original contributions of the paper. Firstly, in the process of network compression coding, an enhanced Channel Attention Mechanism is used to identify critical features. Secondly, it uses Dynamic Series to replace the fixed window which improves the inaccuracy and fluctuation and also avoids the random error behavior caused by parameter adjustment. In addition, it is unnecessary to use clustering algorithms to obtain normal features. Finally, on the public MIT-BIH dataset, the experimental results show that TADS can improve the F1 score from 79% to 91% and accuracy from 89% to 93%. Meanwhile, the calculation time and fluctuation decreased by 42% and 19.8%, respectively. The experimental results demonstrate the performance of TADS for unsupervised anomaly detection on the MIT-BIH time series of ECG dataset through qualitative and quantitative methods.
A Textile-Based Wireless Power Transfer System Made of Slot Yagi-Uda Antennas for Wearable and Sensor Applications

This paper proposes a low-profile and sustainable novel misalignment-resilient wireless power transfer, harvesting, and sensing system (WPT-HSS) to operate at 433 MHz. The resonators used in the WPT-HSS were slot Yagi-Udas that exhibited an average wireless power transfer efficiency of 80% when the transmitting and receiving slot Yagi-Udas were misaligned considering three degrees of freedom with misalignment distances of up to 24 cm between their exciters. The low-profile characteristic, high efficiency, and resilience to misalignment make the proposed WPT-HSS appealing to IoT/IoMT applications. To the author's knowledge, this is the first time a misalignment-resilient wireless power transfer system using slot Yagi-Udas has been proposed. This design can be a stepping stone for future sustainable IoT/IoMT designs.
Emotion and Mentality Monitoring Assistant (EMMA)

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. There are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. The proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. The proposed solution has been developed by considering both regular users and mental health practitioners. The user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely.
Metamorphic Testing of an Automated Parking System: An Experience Report

Automated Driving Systems (ADSs) have gained popularity recently. However, the unstable and unsafe ADSs have caused many traffic accidents and received widespread attention. One way to alleviate such issues is to enhance the correctness and efficiency of testing ADSs. Due to the difficulty of checking ADSs’ behavior such as parking the car, confirming the correctness of the actual behavior may be non-trivial or impossible. This kind of problem is called the test oracle problem. Unlike traditional software testing, Metamorphic Testing (MT) does not focus on the correctness of the actual strategy but examines whether or not the inputs and outputs of multiple executions of a Software Under Test (SUT) satisfy certain relations of the SUT, called Metamorphic Relations (MRs). The paper also implements Mutation Analysis (MA) on Baidu Apollo ADS to evaluate our MT. MA involves small modifications to a program’s source code to see if test-cases can detect these changes. This work was part of a larger endeavour to create an Open Educational Resource (OER) to support learning about how to apply MT to ADSs. This paper reports on an experience of implementing MT to test the Automated Parking System (APS) of Apollo ADS and applying MA to evaluate the MT.
Augmenting Monitoring Infrastructure For Dynamic Software-Defined Networks

Software-Defined Networking (SDN) and virtual environment raise new challenges for network monitoring tools. The dynamic and flexible nature of these network technologies requires adaptation of monitoring infrastructure to overcome challenges of analysis and interpretability of the monitored network traffic. This paper describes a concept of automatic on-demand deployment of monitoring probes and correlation of network data with infrastructure state and configuration in time. Such an approach to monitoring SDN & virtual networks is usable in several use cases, such as IoT networks and anomaly detection. It increases visibility into complex and dynamic networks. Additionally, it can help with the creation of well-annotated datasets that are essential for any further research.
Design, Modeling, and Parametric Analysis of a Syringe Pump for Soft Pneumatic Actuators

Syringe pump has been applied to actuate soft pneumatic robots. Most previous works focus on designs of the syringe pump, its applications, and improvement of its problems such as leaking air, inefficient motions, etc. This paper introduces dynamical modeling and parametric analysis of a syringe pump. The syringe pump is made of a commercial syringe and a linear actuator. The dynamic equation is derived from the motions of the linear actuator, the air dynamics in the syringe, and the airflow inside the soft actuator. Because of the high-elastic materials, the volume of the soft actuator is a time-varying parameter. Therefore, the variation of volume is estimated by the Kalman filter instead of relying on the traditional design method. The dynamic model is also utilized to select optimal parameters which are verified by the experiments for the syringe pump. Two system controllers are designed with and without consideration of the pressure dynamics. The controller considering pressure dynamics outperforms. This work shows the benefits of pressure dynamic of the syringe pump for both system design and advanced controller design.
MAPPO-Based Cooperative UAV Trajectory Design with Long-Range Emergency Communications in Disaster Areas

This paper investigates the cooperative real-time trajectory design issue for multiple unmanned aerial vehicles (UAVs) to support long-range emergency communications in disaster areas. To quickly restore the communication links between mobile users (MUs) and base stations, UAVs equipped with a radio frequency (RF) module and a free space optical (FSO) module serve as relay nodes. Given the difficulty of setting up a central controller for UAVs and the urgency of emergency communication, the UAV trajectory design issue is formulated as a distributed cooperative optimization problem. A collaborative multi-UAV trajectory design method based on multi-agent proximal policy optimization (MAPPO) is adopted to improve RF/FSO channel throughput. Compared with the state-of-the-art DRL methods, MAPPO can achieve higher RF allocation efficiency and increase the FSO communication backhaul capacity.
The Anatomy of Software Changes and Bugs in Autonomous Operating System

Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.
Preliminary Architecture and a Pilot Implementation for a Malicious Emails Detection Solution

Cyber-attacks are one of the most common and dangerous actions that can affect the activity of a small business, when personal data or any other type of sensitive information are stolen. The aim of this paper is to present a preliminary architecture and a pilot implementation, created around a plug-in, that detects malicious emails. The main idea is to collect a series of emails with suspected malicious content, to use text mining techniques to identify the essential words in the emails, and to create classification models that the plug-in could use to detect malicious emails. The pilot implementation was tested on several emails both malicious and clean, the include plug-in being able to distinguish the emails that are a source of infection with a high degree of accuracy. The novelty of our work consists in the resulting efficient and easy to use tool, based on Machine Learning algorithms, appropriate in the environment of small enterprises.
Has the Social Robot Been Hacked? An Emotion Detection System with Distance-Range Parameters for Feature Selection

As robots become more social and present in living spaces, they are also more likely to attract the attention of cyber attackers. Ranging in the level and method of communication with the users, social robots are able to collect, share and analyze a great variety and large volume of personal data. Adding to the danger of a hacked robot to be perceived as asocial or antisocial, it may also expose its users’ security and privacy. In this paper, we highlight important and unique intersections of social robots and cybersecurity. We also model one such interaction in order to explain the validity of our claim which is, disturbing impact on user emotion through the interaction with social robots. We design an emotion detection system for users that interact with social robots on a regular basis. The negative emotions that arise with the interaction with the robot could signal a misconfigured/inaccurately responding or compromised social robot. Our model is designed as a part of a future anomaly detection system, which should run on an external device that resides in the same space as the human and the robot to monitor the audio utterances. The emotion detection model has to be lightweight to install in simpler devices, fast and accurate in order to identify emotions in real time. Therefore we have designed a new parameter-based metric learning wrapper method for optimized feature selection in machine learning model, which generates fast and accurate results for binary classification of emotions, This also addresses the imbalanced class problem in the data. Our results on an acted, multi-speaker input set show comparable or better performance than state of the art methods.
Ag/ AgCl-Covered Flexible Microneedle Array Dry Electrode for Electrocardiography Recording

This paper reports a novel Ag/AgCl-covered flexible microneedle array dry electrode (Ag/AgCl-MNAE). The Ag/AgCl layer with rough and porous microstructure can uniformly cover the microneedle array substrate to enhance charge injection capability at the electrode–skin interface and the microneedle array can pierce the high-impedance stratum corneum to contact the epidermis layer directly. Therefore, the Ag/AgCl-MNAE has extremely low electrode–skin interface impedance (EII). Besides, the fabrication process is simple and low-cost without using expensive photolithography or physical vapor deposition equipment. The Ag/AgCl-MNAE and customized printed circuit board are integrated to realize the recording and wireless transmission of high-quality electrophysiological signals. Bio-signal recordings of electrocardiography (ECG) are successfully demonstrated with superior accuracy, demonstrating the great potential of this system in wearable health monitoring.
A Comparison Study of Deep Learning Algorithms for Metasurface Harvester Designs

The paper compares three deep learning artificial intelligence algorithms used for metasurface design. In-house design code for designing metasurface structures was developed with Python, the NumPy library. To facilitate the study, the three algorithms used are AdaBelief, Adam, and Yogi. According to the numerical comparison study, Adam has a better performance in terms of model generalization with a large dataset (in our case 7000 samples), while Adabelief and Yogi show a better performance in terms of a low dataset (in our case 4,000 samples), and Yogi has a better performance with a lower dataset correlation between the predicted performance of the energy harvester obtained from three algorithms. Yogi and Adablief performance could be improved by manipulating the hyper-parameters.
Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model

The term “Internet of Things” (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods.
Deep learning on mobile devices: a review

Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted much attention. This paper provides a timely review of this fast-paced field to give the researcher, engineer, practitioner, and graduate student a quick grasp on the recent advancements of deep learning on mobile devices. In this paper, we discuss hardware architectures for mobile deep learning, including Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), and recent mobile Graphic Processing Units (GPUs). We present Size, Weight, Area and Power (SWAP) considerations and their relation to algorithm optimizations, such as quantization, pruning, compression, and approximations that simplify computation while retaining performance accuracy. We cover existing systems and give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute Engine (MACE), and Paddle-mobile deep learning platform. We discuss resources for mobile deep learning practitioners, including tools, libraries, models, and performance benchmarks. We present applications of various mobile sensing modalities to industries, ranging from robotics, healthcare and multimedia, biometrics to autonomous drive and defense. We address the key deep learning challenges to overcome, including low quality data, and small training/adaptation data sets. In addition, the review provides numerous citations and links to existing code bases implementing various technologies. These resources lower the user’s barrier to entry into the field of mobile deep learning.
An accurate heston implementation with Usd-Cop Data

This study find by empirical evidence a fast and accurate way to calculate the price of a European Call using the Heston (1993) model. It calculate and uses a benchmark price calculated with the mentioned Heston 1993 pricing approaches and the trapezoidal rule with a = 1e-20000; b = 300; N = 10000000, to find which combination of Heston pricing process and numerical schems leads to a computationally faster and more accurate price process. Two equivalent pricing methods and seven numerical schemes are calculated in order to find wich combination take less time to be compute and is closes to the benchmark as posible. The study uses Q-measure in the sense of spot data, and the other P-measure in the sense of historical data. That mean the study calculate two parameter sets. one under mesure Q and other under P by Maximum Likelihood and non-linear least square function, respectively, to somehow proof the conclution dose not depents on how the parameter are found. Study stands that the accuraste way to calculate the Heston price in the Colombian FX market data used is consolidating the integrals for the probability P1 and P2 that the original framework propose and solve the integral using Gauss-Legendre or Gauss-Laguerre.
A Novel Approach for Hyperspectral Image Classification using Bat Algorithm to Optimize a CNN Classifier

We propose a novel hybrid classifier for hyperspectral images using Bat Algorithm (BA) to optimize the architecture of a Convolutional Neural Network (CNN). BA is applied by minimization of the CNN cross-entropy on the validation set. The objective function is represented as a matrix containing the parameters which define the CNN architecture. The proposed BAT-CNN classifier is evaluated on three hyperspectral datasets: Indian Pines, Pavia University and Salinas. The experiments lead to a better accuracy for the proposed hybrid classifier by comparison to the standalone CNN classifier, for each of the three considered datasets.
An improved U-Net network for medical image segmentation

In many computer-aided spinal imaging and disease diagnosis, automating the segmentation of the spine and cones from CT images is a challenging problem. Therefore, in this paper, we propose a triple channel expansion attention segmentation network based on U-Net for spinal CT images. We design a triple channel expansion attention to solve the problem of low accuracy caused by the loss of important feature information in the downsampling process of ordinary convolution, which uses different sizes of convolution set kernels to extract different features. Then through this attention, we output a feature image for each layer of the down-sampling, and finally skip connection with it during the up-sampling. Finally, many experimental results on VerSe 2019 and VerSe 2020 datasets show that our proposed network is superior to other prior art segmentation networks.
Efficient Visualization of Parameter Space for Rendering Translucent Objects

Translucent objects have a characteristic appearance due to a phenomena called subsurface scattering of light. In computer graphics, many methods have been proposed for rendering the translucent objects realistically. However, it is often difficult to generate an image with the desired appearance; the user must repeatedly generate images with different parameter settings, which is tedious and time-consuming. Our goal in this paper is reducing such user’s burden for the parameter tuning. We propose an efficient visualization method of the parameter space for the subsurface scattering so that the user can explore the space interactively. Our basic idea is creating a database consisting of images rendered with randomly sampled parameters and generating images with arbitrary parameters by interpolation. However the parameter space is usually high-dimensional, the costs for the database storage and the interpolation calculation are both expensive. We address this problem by combining the principal component analysis (PCA) and the radial basis functions (RBF). Using our method, the images with different parameters are calculated quickly, allowing her/him to efficiently find the optimal parameters that produce the desired appearance.
EDP-BGCNN: Effective Defect Prediction via BERT-based Graph Convolutional Neural Network

Software defect prediction (SDP) is a critical task that aims to identify potential defects and allocate resources for testing to enhance software reliability. In this study, we present a novel defect prediction framework called EDP-BGCNN, which leverages the power of BERT and graph convolutional neural networks to represent code. Our approach first extracts the code’s structural semantic features based on its abstract syntax tree (AST), followed by applying BERT for embedded learning to extract the code’s semantic features. We then use latent Dirichlet allocation (LDA) to extract descriptive semantic features and convert them into a numeric vector. The code and descriptive semantic features are then combined and processed by GraphSMOTE to address the class imbalance problem. Finally, we obtain a more comprehensive representation using graph convolutional neural networks. We evaluated our approach on five open-source projects and compared it with three state-of-the-art deep-learning methods. Our experimental results demonstrate that EDP-BGCNN can achieve significant improvements in AUC (4.9% - 23%) and F1-measure (6.6% - 10.7%) on average.