Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Key Performance Indicator Anomaly Detection

With the rapid development of the Internet, web services have penetrated into all areas of society, and ensuring the stability of web services has become more and more important. Generally, operations judge whether a web service is stable by monitoring various key performance indicators (KPI). If a KPI is abnormal, it often means that there is a problem with the related application, and KPI anomaly detection plays an important role in it. KPI is time serial which is different from static data. In this article, we show the investigation and summary of KPI anomaly detection. The KPI anomaly detection methods can be classified into three types: distance-based methods, density-based methods, and deep learning-based methods. We conclude the advantages and disadvantages of them. Finally, we give the possible future research directions.
DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm.
Association between the Rankings of Top Bioinformatics and Medical Informatics Journals and the Scholarly Reputations of Chief Editors

The scientometric indices, such as the journal Impact Factor (IF) or SCImago Journal Rank (SJR), often play a determining role while choosing a journal for possible publication. The Editor-in-Chief (EiC), also known as a lead editor or chief editor, usually decides the outcomes (e.g., accept, reject) of the submitted manuscripts taking the reviewer’s feedback into account. This study investigates the associations between the EiC’s scholarly reputation (i.e., citation-level metrics) and the rankings of top Bioinformatics and Computational Biology (BCB) and Medical Informatics (MI) journals. I consider three scholarly indices (i.e., citation, h-index, and i-10 index) of the EiC and four scientometric indices (i.e., h5-index, h5-median, impact factor, and SJR) of various journals. To study the correlation between scientometric indices of the EiC and journal, I apply Spearman (ρ) and Kendall (τ) correlation coefficients. Moreover, I employ machine learning (ML) models for the journal’s SJR and IF predictions leveraging the EiC’s scholarly reputation indices. The analysis reveals no correlation between the EiC’s scholarly achievement and the journal’s quantitative metrics. ML models yield high prediction errors for SJR and IF estimations, which suggests that the EiC’s scholarly indices are not good representations of the journal rankings.
GIS Models for Vulnerability of Coastal Erosion Assessment in a Tropical Protected Area

Coastal erosion is considered a major worldwide challenge. The vulnerability assessment of coastal areas, in relation to climate change, is a key topic of worldwide increasing interest. The integration of methodologies supported by Remote Sensing, Geographical Information Systems (GIS) and in situ monitoring has allowed a viable identification of vulnerable areas to erosion. In the present study, a model was proposed to the assessment of the estuarine system of Cananéia-Iguape (Brazil), by applying the evaluation and prediction of vulnerability models for the conservation and preservation of mangroves. Approximately 1221 Km2 were classified, with 16% of the total presenting high and very high vulnerability to erosion. Other relevant aspects, were the identification and georeferencing sites that showed strong evidence of erosion and, thus, having a huge influence on the final vulnerability scores. The obtained results led to the development of a multidisciplinary approach through the application of a prediction and description model that resulted from the adaptation of the study system from a set of implemented models for coastal regions, in order to contribute to the erosion vulnerability assessment in the mangroves ecosystems (and associated localities, municipalities and communities).
FMCW Multi-Person Action Recognition System Based on Point Cloud Nearest Neighbor Sampling Algorithm

This paper proposes a multi-person action recognition system based on frequency modulated continuous wave radar (FMCW). First, a Gaussian mixture model clustering method (GMM) is used to extract the active point cloud data of a single person from the data collected by the radar. Then point cloud nearest neighbor sampling algorithm is proposed for processing the radar point cloud data. Eventually, the long and short-term memory network (LSTM) is used to extract the features between time series data frames to realize action recognition. In this paper, a nearest neighbor sampling algorithm is proposed to solve the problem of sparse point cloud in single-person activity in the radar point cloud data processing. This method selects the reflection point closest to the centroid of the current frame based on the Euclidean distance, and merges multiple frames data with behavior information contained through a sliding window to fill in the information of the current frame and enrich the temporal and spatial sequence data. The radar point cloud data collected is then converted to fixed-dimensional feature information to meet the input requirements of the LSTM network. The experimental results show that the action recognition proposed in this paper has an average recognition accuracy of 98.68% for falling, sitting, and walking in a multi-person activity scene.
Performing manufacturing tasks with a mobile manipulator: from motion planning to sensor based motion control

We present a framework combining mobile manipulation planning and sensor based motion control. The result of manipulation planning is a reference robot trajectory composed of segments. For each segment, a hierarchical task based controller is automatically built. Some of the segments correspond to motions where some parts of the robot and of the environment are known to be close to each other. The corresponding controller implements a task of relative pose between these elements. The task error is measured by vision sensors using AprilTags. The higher priority of this task with respect to the reference joint trajectory greatly improves the accuracy of the task affected by the poor accuracy of the robot base localization and other sources of error.
Profile of Fatty Acids and Spectroscopic Characteristics of Selected Vegetable Oils Extracted by Cold Maceration

Abstract Biodiesel has become more attractive material for its properties such as biodegradability, renewability and very low toxicity of its combustion products. A higher quality of this fuel is essential in its potential commercialization. Analytical methods used in biodiesel analysis are constantly refined. The most popular analytical techniques include chromatography and molecular spectroscopy. The ATR-FTIR spectroscopy is one of the most important methods of spectroscopy. This paper presents the results of studies on selected oils of natural origin using ATR-FTIR infrared absorption spectroscopy. Three types of oils from pumpkin seeds and winter rapeseed were analysed. The main fatty acids were also determined in all the samples.
Distributed Aggregative Game for High-order Systems under Disturbances

In this paper, we consider a distributed aggregative game problem for high-order systems with uncertain disturbances under a directed communication topology. The embedded technique is used to decouple the original problem into two subproblems: path planning and tracking control. We first construct a virtual single-integrator system and design a distributed dynamic average consensus algorithm to estimate the unknown aggregative parameter, and then propose a gradient-based distributed game algorithm for this virtual system to generate the reference trajectory of the aggregative game. In the tracking part, we design a dynamic state feedback controller for the high-order system to track the reference trajectory and reject external disturbances. Finally, simulations are given to validate the effectiveness of the theoretical analysis.
Design and Control of Two-Wheeled and Self-Balancing Mobile Robot

The paper focuses on the design and development of a two-wheeled and self-balancing robot as well as its control. The problem is equivalent to the inverted pendulum principle of balancing robots. Dynamic model based PD controller and empirical controller were designed. These controllers are used in closed loop system to provide the robot balance, even when robot is slightly pushed, which normally causes it to fall. The equations of robot motion were derived using Lagrangian and mapped to a transfer function in the complex s-domain. The controller parameters are initially tuned using PID Tuner in Simulink. Then, the zero-order-hold discretization method was applied to implement this control on the Arduino microcontroller. Furthermore, the controller parameters are additionally adjusted through experiments in order to exhibit better control performance. Moreover, the effect of an unexpected disturbance on the robot was taken into consideration. The effectiveness of the designed controllers was verified experimentally.
Microwave in-situ polymerization of poly lactic acid and carbon nanotubes and study of its electrical conductivity

Poly lactic acid (PLA) is linear aliphatic thermoplastic polyester produced from renewable resources. Its degradable nature makes it very useful. High molecular weight PLA is produced by condensation method ring opening polymerization of Lactide. Carbon nanotubes are allotropes of carbon with a cylindrical nanostructure. It has extraordinary thermal conductivity, mechanical and electrical properties. Carbon nanotubes find applications as additives to various structural materials. Nanocomposite is a multiphase solid material where one of the phases has one or more than dimensions of less than 100 nanometers (nm). It is a solid combination of a bulk matrix and nano-dimensional phases differing in properties due to dissimilarities in structure. These materials show improvements in mechanical, thermal, electrical and barrier properties. In this study properties of nanocomposite of PLA and carbon nanotubes are studied, in which lactide is the monomer and carbon nanotube as the nanofillers.The in-situ polymerization was done in a Monomode microwave reactor. Microwave heating has been used for the synthesis of composite. Microwave heating is a faster method of heating as compared to the other methods. Fourier transform infrared microscopy confirmed the formation of nanocomposites. SEM and TEM images showed that the CNT is dispersed over the polymer matrix. CNT has very high electrical conductivity and thus synthesized nanocomposite expectedly shows high electrical conductivity. The electrical conductivity of nanocomposite spectacularly increases with the increase in concentration of nanofiller up to a certain value.
Detection of Hijacking DDoS Attack Based on Air Interface Traffic

Massive machine type communication (mMTC) network contains massive access devices, which increases the risk of distributed denial of service (DDoS) attacks. Especially, the DDoS attack launched by hijacked devices cannot be detected through traditional cryptography-based authentication because that the hijacked devices have legitimate identities. In this letter, we propose to exploit the characteristics of access traffic at the air interface to detect this kind of DDoS attack, and then identify the suspicious devices. We analyze both the characteristics of access traffic based on MTC traffic arrival model and the random access mechanism proposed by 3GPP to construct test statistics, and propose a sequential change point detection (SCPD) scheme to test whether there are anomalies as quickly as possible. After that, we identify the hijacked devices based on the abnormal traffic. Simulation experiments confirm the effectiveness of our detection scheme.
Configuration Estimation of Continuum Robots Using Piecewise Constant Curvature Generalized Epi-Polar Constraint Model

In recent years, continuum robots have attracted more attention for they can work in more severe environments. However, at present, most of the research focuses on the mechanical structure innovation, and there are few pieces of research on the control of this kind of robot. Since continuum robots are deformable, their shape is a general curve in space. Therefore, they are not fully defined by actuator positions, which are different from the traditional rigid robots. To achieve more accurate control, a method of sensing robot configuration in real-time is necessary. However, the existing visual-based approaches all adopt external global cameras, which is difficult to adapt to the demand of unknown unstructured environments. This paper presents a system capable of estimating the configuration of continuum robots under piecewise constant curvature (PCC) assumption from cameras mounted on each constant curvature segment. Specifically, we first proposed the PCC 2R model, which is equivalent to each cc-segment of PCC continuum robots by two joints rigid bodies, thereby reducing the problem complexity and improving the numerical stability of the estimation. Then, based on the PCC 2R model, we proposed the PCC generalized epi-polar constraint to completely constrain the four degrees of freedom of each cc-segment in planar, it can be solved through one corresponds, to estimate the configuration of continuum robots under PCC. Finally, the above approach is verified by experiment.
Magnetometer Error Compensation Algorithm Based on Improved Particle Swarm Optimization Algorithm

In order to effectively aid inertial navigation, the geomagnetic navigation requires precise prior geomagnetic information. As the core sensor of geomagnetic navigation, three-axis magnetometer has the problem of low error accuracy. In order to improve the adaptability of the inertia weight, this paper proposes improved particle swarm optimizations (APSO) to realize the estimation of the parameters of the magnetometer. Firstly, based on instrument error of magnetometer and magnetic field error of carrier interference, the error model of three-axis magnetometer is constructed; then APSO is proposed to fit the error model parameters. The experiment results show that APSO has a more accurate value and effectively reduces the measurement error of the magnetometer.
Stress Engineering for Drive Current Enhancement in Silicon Carbide (SiC) Power MOSFETs

The poor interface quality of the Silicon Carbide/oxide (SiC/SiO2) interface severely degrades the electron surface channel mobility in SiC-based power devices. Based on transfer characteristic simulations (with a deck calibrated to experimental data), this work predicts improved mobility with stress engineering, a well-established technique for performance enhancement in low power silicon (Si) transistor technology. Process simulation of Si and SiC-based devices with Silicon Nitride (Si3N4) stressor layer has been carried out to estimate the stress generated in the channel. SiC D-MOSFET we have also computed the effect of varying stress magnitude, direction, and position in the device.
Research on Innovation of Integrated Management Mode of Supply Chain in Cross-border E-commerce Service

China's cross-border e-commerce exports still have risks in policy, logistics and payment. This paper studies the innovative model of integrated management mode of supply chain in optimized cross-border e-commerce services. This paper analyzes the development status of cross-border e-commerce service cross-border e-commerce, focuses on the operation of supply chain in cross-border e-commerce service, constructs PEST-SWOT matrix, analyzes the development of supply chain in cross-border e-commerce service from four aspects of politics, economy, society and technology, and puts forward the optimization path of cross-border e-commerce service cross-border e-commerce supply chain development. The supply chain in cross-border e-commerce service is the largest cross-border B2C retail platform in China. Combined with correlation index analysis, the correlation feature quantity of reliability panel data of supply chain integrated management mode in cross-border e-commerce service is extracted, and the reliability data fusion of supply chain integrated management mode in cross-border e-commerce service is carried out by the method of prior information distributed detection. The empirical analysis shows that the optimized integrated management mode improves the integration and management ability of supply chain in cross-border e-commerce services, and has a good confidence, thus improving the integration of supply chain in cross-border e-commerce services.
Design and Analysis of a Low-Power Two-Stage Dynamic Comparator with 40ps Delay in 65nm CMOS Technology

In this paper, a new structure is presented to realize a high-speed high-precision two-stage comparator. Positive feedback is employed in the first stage of the offered comparator to reduce the delay time. Furthermore, by using an NMOS transistor between the differential output nodes of each stage in the reset mode, the comparison speed is enhanced. Moreover, the second stage of the offered comparator is activated with a preset delay to improve the speed and accuracy of the comparison. Furthermore, by using intermediate transistors between the two stages of the comparator, the delay time and the comparison accuracy is also improved. The delay and offset equations of this comparator are also extracted, and the main contributors in decreasing them are recognized. The proposed circuit is simulated in 65 nm CMOS technology. The post-layout results show that the total delay and standard deviation of input-referred offset voltage of this comparator are 40 ps and 5.69 mV, respectively. This comparator consumes 395.3 μW @ 6 GHz and 38 μW @ 1 GHz with a single supply voltage of 1.2 V. Moreover, the proposed comparator occupies the area of 115.92 μm2 (12.6 μm× 9.2 μm).
LoRa-based Smart Grid Neighborhood Area Network Resource Access and Address Allocation Mechanism

In smart grid wireless neighborhood area network (NAN) communication, LoRa star topology will cause the devices to fail to successfully access the network due to the long distance between the newly added intelligent LoRa terminal device and the gateway, which will affect the reliability of wireless NAN communication. Designing a suitable address allocation mechanism to improve the NAN communication service by considering the characteristics of the smart grid NAN topology is greatly required. Therefore, the LoRa-based smart grid neighborhood area network resource access and address allocation mechanism, namely RAN-LoRa, was proposed. First, the LoRa device address format and address allocation mechanism is put forward on the basis of wireless NAN cluster topology characteristics; Secondly, the corresponding routing scheme is designed; Finally, the mechanism is applied to the smart grid wireless NAN model. The simulation result shows that RAN-LoRa can effectively extend the maximum communication distance, increase the network access rate, and greatly reduce the packet loss rate of wireless NAN data transmission. The application of RAN-LoRa to smart grid wireless NAN communications helps ensure the reliability of smart grid.
The situation and the survival of MSMEs restaurant under COVID-19 pandemic in Thailand

The restaurant and service industry is a very important contributor to the global economy and the Thai economy. In particular, micro, small and medium enterprise (MSMEs) restaurant in Bangkok, the capital city of Thailand, accounts for 41.78% of the total restaurants in the country. However, these sectors are vulnerable and susceptible to natural hazards such as the COVID-19 outbreak. This research aimed to study the degree of the impact of COVID-19, measures already taken to address this issue and measures to implement in the future for the issues. This research collection data from 100 respondents via interview by telephone. We will find the decision and adjustment of the restaurant business. It depends on consumer behavior, society and technology that makes consumers more comfortable. It also reduces the risk of infection.
Planning in-hand dexterous micro-manipulation using 3-D rotations decomposition

This paper aims to contribute to the improvement of dexterity in contact micro-manipulation by performing in-hand dexterous micro-manipulation planning. Previous experimental works on planar micro-manipulation showed that such an approach allows for large rotations of arbitrary shaped objects. Moving from planar to 3-D manipulation significantly increases the complexity of the manipulation planning, especially when considering the rolling of the fingers on the object during the manipulation. We propose in this paper a dexterous manipulation planning algorithm that leverages the complexity of 3-D manipulation planning by decomposing the desired 3-D rotations into three successive rotations within two different planes. Optimal paths of the manipulating fingers are thus obtained in the planar spaces and then combined to form the trajectories in the 3-D space. Besides the relevance of the approach, the simulation results show that exploiting adhesion forces improves the robustness of the manipulation and extends the manipulation capabilities, but at the expense of the computation time.
APAS: Application-Specific Accelerators for RLWE-Based Homomorphic Linear Transformations

Recently, the application of multi-party secure computing schemes based on homomorphic encryption in the field of machine learning attracts attentions across the research fields. Previous studies have demonstrated that secure protocols adopting packed additive homomorphic encryption (PAHE) schemes based on the ring learning with errors (RLWE) problem exhibit significant practical merits, and are particularly promising in enabling efficient secure inference in machine-learning-as-a-service applications. In this work, we introduce a new technique for performing homomorphic linear transformation (HLT) over PAHE ciphertexts. Using the proposed HLT technique, homomorphic convolutions and inner products can be executed without the use of number theoretic transform and the rotate-and-add algorithms that were proposed in existing works. To maximize the efficiency of the HLT technique, we propose APAS, a hardware-software co-design framework consisting of approximate arithmetic units for the hardware acceleration of HLT. In the experiments, we use actual neural network architectures as benchmarks to show that APAS can improve the computational and communicational efficiency of homomorphic convolution by <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula>, respectively, with an energy reduction of up to <inline-formula> <tex-math notation="LaTeX">$26\times $ </tex-math></inline-formula> as compared to the ASIC implementations of existing methods.