Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric Power System Transient Stability Assessment Based on Bi-LSTM Attention Mechanism

This paper puts forward a Bi-LSTM attention mechanism model based on voltage phasor for electric power system transient stability assessment. The Bi-LSTM attention mechanism is used to map the relationship between voltage phasor and power system transient stability, and by establishing a sample matrix of transient stability information in the initial stage of the perturbation level, the extracted features are more robust, effectively reducing the false and missing samples, thus improving the generalization ability and evaluation performance of the model. Furthermore, by adjusting the network structure parameters of the best evaluation indicator. The mapping model between input features and transient stability is established to further reduce false positives and sample loss, and to improve the accuracy of network model evaluation. The improved model combines Bi-LSTM feature extraction layer and attention mechanism to form a hybrid model for a transient stability classification model, and the IEEE-39 bus New England test system is used to verify the accuracy of the model, and the wide-area noise is introduced into the generated data to evaluate the robustness of the system. Finally, the method is used to realize the transient stability evaluation of the electric power system based on voltage phasor data, and the validity of the proposed model is authenticated by comparative analysis of the proposed hybrid model with another deep learning models.
Depression Detection Using Machine Learning Techniques on Twitter Data

Depression has become a serious problem in this current generation and the number of people affected by depression is increasing day by day. However, some of them manage to acknowledge that they are facing depression while some of them do not know it. On the other hand, the vast progress of social media is becoming their “diary” to share their state of mind. Several kinds of research had been conducted to detect depression through the user post on social media using machine learning algorithms. Through the data available on social media, the researcher can able to know whether the users are facing depression or not. Machine learning algorithm enables to classify the data into correct groups and identify the depressive and non-depressive data. The proposed research work aims to detect the depression of the user by their data, which is shared on social media. The Twitter data is then fed into two different types of classifiers, which are Naïve Bayes and a hybrid model, NBTree. The results will be compared based on the highest accuracy value to determine the best algorithm to detect depression. The results shows both algorithm perform equally by proving same accuracy level.
Global Multi-Level Attention Network for the Segmentation of Clinical Target Volume In The Planning CT For Cervical Cancer

Accurate segmentation of clinical target volume (CTV) in computed tomography (CT) images is of great significance for planning cervical cancer radiotherapy. However, manual delineation is time-consuming and subjective. In this study, we propose a novel method for the auto-segmentation of cervical cancer CTV based on the convolutional neural network (CNN). We adopt 3D UNet ++ as the backbone and develop the global multi-level attention (GML) module, which can help effectively establish global contextual attention and enhance the ability to learn differential representations. Besides, the atrous spatial pyramid pooling (ASPP) is employed at the central bottleneck to handle the multi-scale objects in CTV regions. We train and evaluate the proposed approach on a dataset of 87 CT scans. The experimental results show that our method has superior performance compared with typical methods. Meanwhile, the ablation experiments further validate the effectiveness of the GML module.
A Review of Machine Learning on Energy Management Strategy for Hybrid Electric Vehicles

In the context of oil shortages and serious emission of air pollutants, hybrid electric vehicles (HEVs) have become a very good choice for the automobile industry during transition period for merits of high energy efficiency and low emissions. Energy management strategy (EMS) is highly crucial to benefits of HEVs. In this work, the optimal problem of energy management is summarized firstly. Then, the current machine learning based EMSs have been discussed. And machine learning methods adopted in driving predictions, which contain other research fields highly related with EMSs, are also analyzed. This paper wishes to provide some suggestions for future EMSs designing, and helps to the research and development of new powertrain design of HEVs.
Understanding the Influencing Factors and the Preference of Infant Milk Formula: A PLS-SEM Approach

Infant Formula Milk (IMF) is the best alternative to human milk and breastfeeding, however selecting the best IMF for infants requires careful consideration primarily because infants are still in the most delicate stage of their life, hence choosing the right IMF for them present a challenge particularly for first time mothers. An online survey was conducted to be able to understand the process of thinking that mothers go through before deciding to buy IMF. This study has identified that compatibility (impact) and practicality (availability and price) are the main factors that most mothers consider when choosing the IMF for their child through Structural Equation Modelling (SEM) with formative construct model approach using a component - based / PLS method. The results derived from this study can aid mothers and parents alike decide when choosing the suitable IMF for their child.
Uber Related Data Analysis using Machine Learning

The paper explains the working of an Uber dataset, which contains data produced by Uber for New York City. Uber is defined as a P2P platform. The platform links you to drivers who can take you to your destination. The dataset includes primary data on Uber pickups with details including the date, time of the ride as well as longitude-latitude information, Using the information, the paper explains the use of the k-means clustering algorithm on the set of data and classify the various parts of New York City. Since the industry is booming and expected to grow shortly. Effective taxi dispatching will facilitate each driver and passenger to reduce the wait time to seek out one another. The model is employed to predict the demand on points of the city.
COSMO: Code Coverage Made Easier for Android

The degree of code coverage reached by a test suite is an important indicator of the thoroughness of testing. Most coverage tools for Android apps work at the bytecode level and provide no information to developers about which source code lines have not yet been exercised by any test case. In this paper, we present COSMO, the first fully automated Android app instrumenter publicly available that operates at the source code level in a completely transparent way, making it fully compatible with existing system level testing technologies and Android test generators. The experiments that we have conducted on a large benchmark of Android apps show that COSMO can successfully instrument most apps without altering their execution traces, introducing a small, acceptable runtime overhead.
Optimization Analysis of Hydrogen Fuel Cell Hybrid Power Based on Neural Network and Sensor Networks

Hydrogen fuel cell vehicles have concerns about clean, pollution-free, high energy utilization, and the fast fuel refueling, and they have become a hot spot in the development of new energy vehicles. Inspired by this, the paper conducts the optimization analysis of hydrogen fuel cell hybrid power based on neural network and sensor networks. In our model, in order to improve the energy efficiency of the fuel cell hybrid power system, the control of the fuel cell should be optimized according to its efficiency change characteristics, and the consumption of additional power should be minimized; at the same time, the control strategy of the fuel cell and power battery hybrid power system should be determined according to the power demand. The neurons in full connection layer are fully connected and have most of the core training parameters, hence, the neural netwotks are optimized with senser model. The proposed model is validated based on the datasets collected, where the user experience is also studied.
A Review of Trajectory Data Mining Applications

With the rapid development of digital technologies, location-aware technologies have helped increase trajectory data availability that results from tracking moving objects. Through the collection and analysis of trajectory data, the extracted knowledge can be beneficial in many applications. Trajectories are widely known as a series of points that represent geographic locations. The researchers devoted their efforts to many trajectory data mining fields, where the benefit of these data can be gained and employed in many applications. This paper focuses on providing a comprehensive review of trajectory data mining applications such as path detection, urban services, environment services, objects behavior analysis, and so on.
Towards A Sustainable Development Cities Through Smart Shopping Trolly: A Response to the Covid-19 Pandemic

The use of trolleys in the shopping industry has always been very popular, especially among customers who frequently stop by the mall or supermarket to shop for their necessities. People need trolleys to make it easier for them to carry their groceries and other luggage. There are various types of trolleys according to their respective uses. Still, the trolleys in the shopping industry commonly used today can only help users carry goods from one place to another. However, this traditional type of trolley is still lacking in being a perfect fit in the shopping industry. It does not help reduce the crowds and the enormous queue in terms of inconvenience. Therefore, this project aims to design a smart trolley equipped with an Arduino NANO, RFID, and a barcode scanner. It can also be synchronized with the mobile application via a WIFI module. This new technology will replace the traditional one, which will help consumers maintain social distancing during the age of the COVID-19 pandemic.
Resonant converter topology impact on a Dynamic IPT application

This paper evaluates the impact of Electric Vehicles (EV) movement in dynamic inductive power transfer (DIPT) systems. Different resonant power converter topologies are considered, SS, SP, PS and PP topologies. In DIPT systems electric vehicles have inherent displacements due to the constant EV movement in the travel direction. Nonetheless, lateral and vertical misalignments may occur due to different driving and road conditions. Thus, DIPT systems must be capable of coping with different operating conditions which can lead to high currents and/or voltages in the converter primary-side, which can lead to components damage and converter failure. This makes the choice of the resonant power converter topology an important aspect in designing the system. The main goal of this paper is to analyze the behaviour of a DIPT system primary-side under different operating conditions, considering different coupling and under charging and no-charging conditions.
Synthesizing Multi-threaded Tests from Sequential Traces to Detect Communication Deadlocks

Multi-threaded libraries, including the ones advertised as thread-safe, may contain concurrency bugs, and worse, may not include relevant test cases that can drive the program execution towards bug-prone interleavings. Also, the effectiveness of dynamic verification, a prominent concurrency bug detection technique, depends critically on the availability of relevant test cases. Generating such test cases automatically to assist dynamic verification is, therefore, a significant problem.Among hard-to-detect concurrency bugs in multi-threaded Java libraries are communication deadlocks, which occur due to the incorrect usage of wait() and notify() communication primitives. In this work, we present a novel technique to systematically synthesize multi-threaded test cases to expose communication deadlocks. We model these deadlocks as global constraints over the events of two sequential program traces of the library APIs. The task of predicting the relevance of combining two sequential program traces is delegated to an SMT solver. We implement our technique in a prototype tool named REVELIO, and evaluate it on fifteen classes of popular multi-threaded Java libraries. We find that REVELIO is able to synthesize precise tests exposing communication deadlocks, which the state-of-the-art tools cannot.
An ontology based text approach for culture aware emotion mining: A Moodle plugin

Learning analytics is the list of methods allowing the measurement, collection, analysis and use of relevant data for the purpose of understanding the behavior of learners and improving learning materials. Generally, learning analytics methods focus on data analysis about the performance of learners and the interaction with the learning environment. A key characteristic that may impact negatively or positively the learner performance is their emotional state. Considering the learner's emotions during the learning process may enhance the learning experience by improving the motivation and the engagement of the learners. Furthermore, depending on the sociocultural context to which they belong and through which they have evolved, learners transmit, control, and regulate their emotions in different ways. These cultural variations need also to be considered when dealing with emotions. In this paper, we will first explore the idea of emotion and its relations with learning, collaborative learning, and culture. Second, we will present a set of selected existing works which have used learning analytics and especially text mining when mining emotion. Then, we will introduce our approach called EmoMining which is ontology-based learning analytic approach for emotion detection taking into account cultural differences. Finally, we present the developed Moodle plugin evaluate and validate the proposed approach.
Development and Evaluation of a Smartphone-Based Electroencephalography (EEG) System

The aim of the study was to design, develop and evaluate a general-purpose EEG platform which integrates with a smartphone. The target specification was a system with 19 EEG channels and data stored onto the smartphone via a Wi-Fi connection. The hardware was developed using three ADS1299 integrated circuits, and the game engine, Unity, was used to develop the smartphone app. An evaluation of the system was conducted using recordings of alpha waves during periods of eye closure in participants (Bland-Altman statistical comparison with a clinical grade EEG system). The smartphone was also used to deliver time-locked auditory stimuli using an oddball paradigm to evaluate the ability of the developed system to acquire event related potentials (ERP) during sitting and walking. No significant differences were found for the alpha wave peak amplitude, frequency and area under the curve for the intra-system (two consecutive periods of alpha waves) or inter-system (developed smartphone-based EEG system versus FDA-approved system) comparisons. ERP results showed the peak amplitude of the auditory P300 component to deviant tones was significantly higher when compared to standard tones for sitting and walking activities. It is envisaged that our general-purpose EEG system will encourage other researchers to design and build their own specific versions rather than being limited by the fixed features of commercial products.
Dense Pixel-Labeling For Reverse-Transfer And Diagnostic Learning On Lung Ultrasound For Covid-19 And Pneumonia Detection

We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We compare the performance of U-Net trained with dense and sparse labels to segment A-lines, B-lines, and Pleural lines on a custom dataset of lung ultrasound scans from 4 patients. Our experiments show that dense labels help reduce false positive detection. We study the classification capability of the dense and sparse trained U-Net and contrast it with a non-pretrained U-Net, to detect and differentiate COVID-19 and Pneumonia on a large ultrasound dataset of about 40k curvilinear and linear probe images. Our segmentation-based models perform better classification when using pretrained segmentation weights, with the dense-label pretrained U-Net performing the best.
Analysis of Individual Purchase Decision Based on Bayesian Personalized Ranking

In recent years, text-based transaction data has been highly valued by merchants. It not only reflects the degree of consumer preference on goods, but also reflects the consumer’s satisfied and potential needs. In this paper, we used sentiment analysis, Naive Bayes Algorithm and other methods to build a personalized recommendation model based on texts and time. The paper will conduct model assumptions and tests from the two main bodies: Users and items.
Synthesizing General-Purpose Code Into Dynamically Scheduled Circuits

High-level synthesis (HLS) tools generate hardware designs from high-level programming languages and should liberate designers from the details of hardware description languages like VHDL and Verilog. HLS tools typically build datapaths that are controlled using a centralized controller, which relies on a compile-time schedule to determine the clock cycle when each operation executes. Such an approach results in high-throughput pipelined designs only in cases where memory accesses are provably independent and critical control decisions are determinable during code compilation. Unfortunately, when this is not the case, current tools must make pessimistic assumptions, yielding inferior schedules and lower performance. Recent advances in HLS have explored methods to overcome the conservatism in static scheduling and to remove the inability of HLS tools to handle dynamic events. Dataflow circuits play a significant role in this context: they are built out of units that communicate using point-to-point pairs of handshake control signals and this distributed control mechanism effectively implements a dynamic schedule, adapted at runtime to particular memory and control outcomes. Dataflow circuits can exploit the same optimization opportunities as standard HLS circuits (i.e., pipelining and resource sharing), but also introduce to HLS features similar to those of modern superscalar processors (i.e., out-of-order memory accesses and speculative execution), which are key for HLS to be successful in new contexts and broader application domains.
Improving Domain Generalization in Segmentation Models with Neural Style Transfer

Generalizing automated medical image segmentation methods to new image domains is inherently difficult. We have previously developed a number of automated segmentation methods that perform at the level of human readers on images acquired under similar conditions to the original training data. We are interested in exploring techniques that will improve model generalization to new imaging domains. In this study we explore a method to limit the inherent bias of these models to intensity and textural information. Using a dataset of 100 T2-weighted MR images with fat-saturation, and 100 T2-weighted MR images without fat-saturation, we explore the use of neural style transfer to induce shape preference and improve model performance on the task of segmenting the kidneys in patients affected by polycystic kidney disease. We find that using neural style transfer images improves the average dice value by $\sim0.2$. In addition, visualizing individual network kernel responses highlights a drastic difference in the optimized networks. Biasing models to invoke shape preference is a promising approach to create methods that are more closely aligned with human perception.
Research on the Trajectory Tracking Control of a Swinging Spherical Robot

The spherical robot was a kind of mobile robot which used the spherical shell to roll to realize its own motion. It had the characteristics of a small turning radius and omnidirectional motion. Besides the structure of the spherical robot was multitudinous, a kind of two degrees of freedom of the oscillating XK-II spherical robot was designed in this paper. Likewise, the motion principle and the structure of the XK-II spherical robot were introduced in this paper. The relation of speed vector projection and the description method of Euler Angle were used in the paper to establish the two inputs and five outputs kinematics equation of the XK-II type spherical robot. Then, the kinematics equation was solved by using the four order-five order Runge-Kutta algorithm, at the same time, the trajectory tracking controller of line, roundness, sinusoid and Lissajous figures were designed by combining PID. Finally, the kinematics model was validated by simulation and real experiment, and the trajectory tracking control of the spherical robot was realized effectively.
Contrast-Enhanced Brain MRI Synthesis With Deep Learning: Key Input Modalities and Asymptotic Performance

Contrast-enhanced medical images offer vital insights for the accurate diagnosis, characterization and treatment of tumors, and are routinely used worldwide. Acquiring such images requires to inject the patient intravenously with a gadolinium-based contrast agent (GBCA). Although GBCAs are considered safe, recent concerns about their accumulation in the body tilted the medical consensus towards a more parsimonious usage. Focusing on the case of brain magnetic resonance imaging, this paper proposes a deep learning method that synthesizes virtual contrast-enhanced T1 images as if they had been acquired after the injection of a standard 0.100mmol/kg dose of GBCA, taking as inputs complementary imaging modalities obtained either after a reduced injection at 0.025mmol/kg or without any GBCA involved. The method achieves a competitive structural similarity index of 94.2%. Its asymptotic performance is estimated, and the most important input modalities are identified.
Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

Tracking assembly lines in manufacturing to provide assistance is one of the essential requirement in Smart Industry. Nevertheless, these given assistance and guidelines should be offered to operators when needed. Otherwise, it can be deemed patronising in some cases, e.g., experienced operators may require less assistance than junior operators. Therefore, to provide tailored guidance and assistance in assembly lines, the operators' experience-level should be classified at different levels. In this paper, we introduce three scenarios to achieve the classification of operators expert levels in a real case study (micro-step time-series data from a factory assembly line). We implement a Convolutional Neural Network model for time-series classification, using 5 convolutional layers, max-pooling layers and 5 dense layers with dropout to avoid overfitting. We compare the results of our approach with the ground truth and also with other classifiers as K-nearest neighbours, Random Forest and Naive Bayes classifier. Results show an accuracy of 77 to 98% and 71 to 88% for two of considered scenarios.