Miracle-dz/UniFAR-FTUs huggingface dataset:
Extremal Graph Theory

The basic statement of extremal graph theory is Mantel’s theorem, proved in 1907, which states that any graph on n vertices with no triangle contains at most n2/4 edges. This is clearly best possible, as one may partition the set of n vertices into two sets of size bn/2c and dn/2e and form the complete bipartite graph between them. This graph has no triangles and bn2/4c edges. As a warm-up, we will give a number of different proofs of this simple and fundamental theorem.
Security Threats & Attacks in IoV Environment: Open Research Issues and Challenges

With advancement in technology, our lives have become safer, smarter and easier. There has been a large increase in number of vehicles connected to Internet. The rapid progress in technology has brought the concept of Internet of Vehicles (IoV) to real life. Networks involving Vehicle-to-Vehicle communication can be extended to IoV. With the help of AI knowledge of other cars and their activities, IoV helps to improve driving aids. IoV is the upgraded version of Vehicular Ad Hoc Networks (VANET), which was designed to provide safe driving. Due to lack of security, IoV environments might face threats from various attacks. There are a huge number of malicious attackers because of the self-organization and openness of IoV. Thus, the users of vehicles may face grave concern as whether the information received is genuine or not. In our paper, several attacks, threats and security aspects related to IoV paradigm has been discussed along with open issues and challenges faced by IoV environment.
A Quasi-Isotropic Antenna with Asymmetric Dual-Branch for Mobile Terminal Integrated Design

A compact quasi-isotropic antenna with asymmetric dual-branch which can be integrated with the mainboard of mobile terminal has been proposed in this paper. The proposed antenna locates in a corner of the mainboard, consisting two parallel L-shaped metal branches in each side of the mainboard and a vertical branch connecting and supporting them. The parallel and vertical branches together realize complementation of the radiation pattern. To prove the design, radiation performances of the proposed antenna have been studied by simulations. The antenna has a −10 dB bandwidth of about 40 MHz, from 2.43 GHz to 2.47 GHz. The efficiency of the proposed antenna is above 85% within the operation band. The gain variation versus angle of the proposed antenna is lower than 6.13 dB in all three cross sections.
DogGet: A Legged Manipulation System in Human Environments

The problem of population ageing in today's world is becoming increasingly serious, and the capacity gap in the service industry for elderly people is increasing. Especially in the current epidemic environment, the deartificialization of the service industry is imminent. For service tasks of seniors, there is no doubt that legged robots are the best choice for service tasks due to there flexibility and agility. However, complex service tasks require a mobile robotic system that integrates functions such as localization, navigation, recognition, and grasping. To this end, we propose a mobile robotic system to accomplish some daily service work such as finding and grasping some articles for daily use and then delivering them to a proposed place. We show that the system is universally applicable and highly efficient in daily environments. Videos of the experiments can be viewed at https://youtu.be/xPeBzDf6D_k or https://www.bilibili. com/video/BV1QZ4yli7qx.
Inspection methodologies and machine-learning approaches for defectivity data in semiconductor industry for automotive applications: case study for field-failure prevention

Big-data infrastructure and environment enable the ability to connect and store data to develop and deploy machine-learning activities and analysis in various industries. Equipment manufacturers are implementing new artificial-intelligence capabilities on their tool platforms, thus pushing manufacturers themselves to use data provided by these new functionalities and to link them with other data from their processes. Then, the manufacturer who uses this new artificial-intelligence data is seeking to connect them to some other of internal data, such as the electrical test results per die, in a new type of analysis, with the purpose to improve the screening offered by the electrical test, and thus to increase overall quality. This paper reports new capabilities in automated optical inspection, implemented in automotive-semiconductor manufacturing, which predict a probability of failure per die, computed by the inspection equipment from the features of the observed defects, and presents some significant results in the case of a product implemented in automotive RADAR products.
A Generalization Enhancement Approach for Deep Learning Segmentation Models: Application in COVID-19 Lesion Segmentation from Chest CT Slices

Deep learning has shown remarkable promise in medical imaging tasks, reaching an expert level of performance for some diseases. However, these models often fail to generalize properly to data not used during training, which is a major roadblock to successful clinical deployment. This paper proposes a generalization enhancement approach that can mitigate the gap between source and unseen data in deep learning-based segmentation models without using ground-truth masks of the target domain. Leveraging a subset of unseen domain's CT slices for which the model trained on the source data yields the most confident predictions and their predicted masks, the model learns helpful features of the unseen data over a retraining process. We investigated the effectiveness of the introduced method over three rounds of experiments on three open-access COVID-19 lesion segmentation datasets, and the results illustrate constant improvements of the segmentation model performance on datasets not seen during training.
Augmented Generative Adversarial Network-Based Method for Harmonic State Estimation

Traditional harmonic state estimation methods are limited by few measurement devices, difficulty in obtaining accurate harmonic impedance, complex network topology, and changes in grid operation. These factors cause problems such as underdetermined measurement equations, non-global observable systems, and difficulty in extracting accurate coupling relationships between buses. This paper proposes a novel method based on an augmented generative adversarial network for harmonic state estimation. The efficient extraction of electrical image features by the neural network method is achieved by transforming the temporal sequence data into electrical images. The generator based on deep residual networks and the improved residual block structure are used to improve the feature learning capability of the generator. In addition, the loss function of the generator takes into account the difference between the real samples and the generated samples in the high-frequency component. The validity and accuracy of the proposed method have been verified by simulation analysis.
Autonomy for Ships: System Thinking and Engineering

Marine autonomy research has focused on al-gorithmic and technical developments, targeting autonomous craft in restricted areas where international rules and regu-lations are not prioritised. This paper addresses the system engineering aspect of a highly complex system in which the seamless, predictable, and secure interoperability of vendor-specific hardware and software subsystems is a fundamental requirement for designing and implementing cyber-physical systems with artificial intelligence to assist or replace the navigating officer, such as autonomous marine surface vehicles. It addresses international rules in the sector and exhibits a system architecture that can fulfil the criteria for safe behaviour in foreseen occurrences and the capacity to request human aid if the autonomous system cannot manage a problem. The system thinking and engineering provided in this article have been applied to The GreenHopper, a harbour bus currently under construction and intended to undergo certification and enter commercial service.
A Strategy for Smooth Microgrid Transitions without Phase Misalignment and Voltage Mismatch

In this work, it is proposed a modified control structure to provide smooth transitions of control modes of Grid-forming Converter without phase misalignment and voltage mismatch. A background of the traditional structure is provided and a carefully description of the method is introduced. At the results, a comparison with methods in literature is carried out and it was possible to prove that with the correct phase and voltage compensations, one can almost eliminate the transients in the grid voltage due to the switching of control modes of the Grid-forming Converter and transition of the MG operating mode.
Evidence-driven Data Requirements Engineering and Data Uncertainty Assessment of Machine Learning-based Safety-critical Systems

Reliance on data-centric machine learning (ML) models in complex systems has posed numerous challenges in the software engineering process, especially when the system is deployed in a high-risk environment Due to the inherent uncertainty of such ML models, the safety assurance of these systems is now a primary concern. Recently, many researchers are focusing on assuring safe outcomes of ML models. However, not enough attention is paid to evaluating the training data uncertainty before indulging in ML training. Currently, there are no specific guidelines on how to perceive, elicit and specify data requirements and assure the data quality depending on the ML objective and problem domain. To address these gaps, this research provides guidelines for a systematic data requirements engineering and data uncertainty assessment process involving diverse stakeholders. A three-layered framework is proposed that helps to explore the data space and elicit verifiable data requirements. Such requirements can facilitate the evaluation of the collective confidence of the experts in data quality. To accommodate epistemic uncertainty of such assessment (inconclusive due to lack of knowledge) Dempster Shafer’s theory of evidence is used. The application of this theory within the proposed framework aims to address the identified research gaps.
Customs import and export commodity specification declaration review model that integrating VAE and BERT

Standardized declaration review refers to judging whether the declaration information of import and export commodities filled in by enterprises meets the customs reporting standards in accordance with the standardized declaration catalogue formulated by the general administration of customs. Commodity declaration information is a short text composed of independent elements, and there are problems such as scattered keywords, semantic discontinuity, and elements that are not easy to identify and split. In view of these problems, this paper proposes a model that integrates variational encoders and BERT to review the product declaration text. The model reconstructs the text through a variational encoder, combines the word frequency to capture keywords under different elements of the text, so that the model learns the unique structure of the commodity text. Make full use of the customs corpus, combine and expand the BERT thesaurus, and use the tokenization method of word granularity to correctly divide the customs vocabulary, so that the extracted element information is more complete. The experimental results on the real customs commodity declaration data show that the proposed model is conducive to improving the intelligent level of standardized declaration and review, and the accuracy of the prediction results is relatively high.
Spherical Sector Harmonics Based Directional Drone Noise Reduction

Drones are becoming a part of our lives, but its noise pollution is an annoying problem. In this paper, we propose a spherical sector harmonics based noise reduction method that can reduce the directional drone tonal noise. The method works for both continuous secondary sources and discrete secondary sources. To address power limitations in real applications, simulations are conducted under a secondary source power constraint to investigate the noise reduction performance for different secondary source setups. The proposed directional noise reduction method is also feasible for applications that aim to reduce directional noise and have restrictions to conduct measurements in a directional region.
Les Matemàtiques de GOOGLE: l'algorisme PageRank

The mathematics of Google: The PageRank algorithm. In this paper we present and analyze the PageRank algorithm, used by Google to rank its search results. We focus on the mathematical background of this algorithm, which involves nonnegative matrices, graphs and Markov chains. There are some other web ranking algorithms, based on the computation of eigenvectors, like the HITS algorithm, which we briefly explain at the end of the paper.
Pushing the Boundaries of Small Tasks: Scalable Low-Overhead Data-Flow Programming in TTG

Shared memory parallel programming models strive to provide low-overhead execution environments. Task-based programming models, in particular, are well-suited to cope with the ubiquitous multi- and many-core systems since they allow applications to express all available concurrency to a scheduler, which is tasked with exploiting the available hardware resources. It is general consensus that atomic operations should be preferred over locks and mutexes to avoid inter-thread serialization and the resulting loss in efficiency. However, even atomic operations may serialize threads if not used judiciously. In this work, we will discuss several optimizations applied to TTG and the underlying PaRSEC runtime system aiming at removing contentious atomic operations to reduce the overhead of task management to a few hundred clock cycles. The result is an optimized data-flow programming system that seamlessly scales from a single node to distributed execution and which is able to compete with OpenMP in shared memory.
Ambisonics Capture using Microphones on Head-worn Device of Arbitrary Geometry

3D auditory scenes for Extended Reality (XR) environments are usually recorded using spherical microphone arrays and then binaurally rendered through head-wearable devices. Considerable research is ongoing to integrate spatial audio recording capabilities into these devices. Plausible microphone array designs mountable on head-devices will have arbitrary geometry and unknown scattering effects. In this case, we can not apply conventional eigenbeam processing methods of regular arrays. In this paper, we propose an alternative method using Wearable-Device-Related Transfer Functions (WDRTFs) to extract higher-order ambisonics from the recordings of the head-device. WDRTFs are directional responses incorporating the sound wave transformations caused by the device's and wearer's body. The inherent diversity of these functions is exploited to estimate the ambisonics of the recorded soundfield. This paper presents a preliminary evaluation of this method using two irregular microphone array designs worn on a spherical head and the results are promising in comparison to an equivalent SMA. Therefore, we can further adapt this method to achieve perceptually accurate binaural rendering using the microphone recordings of the head-device.
Detection of Human Action Intention by Electromyography (EMG)

Due to the growing proportion of elderly people in modern society, there is significant need for an increased understanding of the mechanisms of skeletal muscle structures and functions. This will allow robots to be developed to provide support for the elderly. Wearable robots have been developed for applications related to assistance and rehabilitation. The control strategy of a wearable robot is a subtle balance between the generation of muscle force from a human and the generation of force from a robot assistant. The intention of standing should be detected instantaneously, as the time window for the effect of assistance is much shorter. According to the literature, electronic signals generated from muscles are present before the force itself. Therefore, surface electromyography (sEMG) is a suitable approach for detecting the action of human movement. An approach for detecting human action intention based on sEMG is presented in this paper. By using the Kalman Filter, the response time can be shrunk within 20ms, which will provide an instant signal to the robot control system and improve the robot assistant's performance.
BALA-CPD: BALanced and Asynchronous Distributed Tensor Decomposition

Tensor decomposition is widely used in machine learning, recommendation systems, and social networks. Large real-world tensors require parallel algorithms running on distributed memory systems. Parallel algorithms suffer two major performance bottlenecks: load imbalance and communication cost, which are difficult to overcome due to the inherent tradeoff among the multiple types of computations and communications, especially for irregular sparse tensors. Previous work predominately focuses on balancing the load within the tensor-related computation, resulting in imbalance for multiple matrix-only computations and increased communication costs. It also extensively uses collective communication operations and bulk-synchronous computations by interleaving stages of global communication and stages of local computation, failing to hide the communication cost. In this paper, we present a novel algorithm BALA-CPD, which achieves the best overall workload balance, and effectively overlaps communication and computation for the popular distributed Canonical Polyadic Decomposition (CPD) algorithms. BALA-CPD uses a workload and data partition scheme that prioritizes the load balance for all the matrix-only computations and all the communications. When necessary, BALA-CPD adjusts to mitigate the load imbalance for the tensor-related computation. Departing from the bulk-synchronous approaches, BALA-CPD breaks down computation and communication in consecutive stages, and masks the communication costs by a combination of one-sided asynchronous communication and a fine-grained interleaving of communication and computation. We implement BALA-CPD and evaluate it on a 64-node cluster with 1280 processors. Experimental results show BALA-CPD is scalable and outperforms the state-of-the-art distributed implementations by up to 1.8× on 1280 processors.
Utilizing the Electroluminescence of SiC MOSFETs as Degradation Sensitive Optical Parameter

Bias Temperature Instability (BTI) is a major reliability challenge of SiC MOSFETs due to the high defect density of the gate oxide compared to Si MOSFETs. Charge trapping at these defects causes gradual threshold voltage drift and degradation of the on-state resistance, which leads to higher device losses and temperatures. To avoid critical failures, the health state of the gate oxide can be monitored by measuring the BTI-related degradation. This work demonstrates that the electroluminescence (EL) emitted by the body diode of SiC MOSFETs can be utilized as such an aging precursor. As trapped charges at the gate oxide influence the current proportions between the channel and the light-emitting body diode during third quadrant operation, the EL exhibits a sensitivity to BTI that can be sensed as part of online condition monitoring. This paper investigates the EL sensitivity to BTI by means of accelerated aging tests and EL measurements on an industry-standard SiC power MOSFET. The measurement results show a strong increase in the EL intensity for a positive threshold voltage drift, thereby validating the proposed sensing approach.
Maximal Renewable Power Penetration Computation with Augmented Lagrangian Alternative Direction Inexact Newton Method

Global warming and environmental pollution concerns have promoted dramatic penetration of renewable energy sources all over the world. The problem of estimating the maximal power penetration of renewable energy can be formulated as an optimal power flow problem. In this paper, we apply augmented Lagrangian alternating direction inexact Newton (ALADIN) method to solve this optimization problem. The ALADIN method uses inexact Hessians which can reduce the communication compared with alternating direction of multipliers method (ADMM). The numerical studies on 30 bus system indicating that the ALADIN method is much faster than the ADMM and can reduce the number of iterations by about one order of magnitude.
A review of DC DC converters for renewable energy and EV charging applications

DC-DC converters are integral part of electric vehicles and PV systems. The modifications applied on the configuration in these converters will improve overall performance of the system. This paper present overview of various converter topologies used for renewable and EV charging applications and also put forward the best among that in terms of lesser ripple current and higher efficiency. From the comparison study, a high gain quasi resonant SEPIC converter is considered as the superior one for renewable energy applications. To improve the voltage conversion ratio, it uses a voltage multiplier combined with passive clamp circuit and coupled inductor. The converter is free from power dissipations due to soft switching feature. Thus, the proposed converter can provide higher voltage gain and efficiency.
Computer Technology in Intelligent Detection of Agricultural Machinery Equipment

In recent years, with the deep development of applied computer technology, test and inspection technology, technical economics and other disciplines related to its engineering, the research on new algorithms for automation equipment has made great progress in recent ten years. Accompanied by various Internet application technology gradually rise up research and development and large-scale application of the product, based on real-time position and the real-time exchange of data collection service relationship in consistently use way, more and more related areas of science and technology have begun to pay attention to its development focus gradually in agriculture data itself, the precision agriculture it is undoubtedly that such a biological information Technology development is a hot field in the direction of science and technology. With the detection function of real-time sensor and the application technology of biological data information mining, people have been able to control digital agriculture precision farming more accurately, timely and effectively, and achieve large-scale production and benefit maximization. In this paper the main research content is by using computer technology such as data mining methods to start integrating of the analysis on farmland environment geographic information system in a large number of agricultural machinery tillage operation dynamic information, for a variety of information related to farmland farming status indicators for scientific and accurate analysis calculation, but also to the agricultural farming in quality level to conduct a comprehensive monitoring and evaluation, to make all kinds of agricultural machinery The operator can adjust the state of the farming environment reasonably as soon as possible so as to greatly reduce the loss and waste of the value of the national agricultural resources caused by the abnormal farming environment in the field.