Miracle-dz/UniFAR-FTUs huggingface dataset:
Palladium nitrate–magnesium nitrate modifier for electrothermal atomic absorption spectrometry. Part 5. Performance for the determination of 21 elements

The determination of 21 elements was investigated in the presence of the palladium nitrate–magnesium nitrate (Pd–Mg) mixed modifier. Significantly higher pyrolysis temperatures could be used for the majority of elements compared with previously recommended modifiers, making possible an efficient removal of concomitants prior to the atomization of analyte elements. A 10 µg amount (1 g l–1) of NaCl had no influence on the determination of all of the elements investigated and most of the analyte elements were not affected even by a ten times higher concentration. The concentration of potassium sulfate, which had no influence on the determination, was lower for most of the elements. The tolerance towards NaCl and K2SO4 increased significantly for a number of elements when the pyrolysis temperature was lowered by 200–300 °C and the pyrolysis time was increased. For most of the elements investigated the characteristic mass values were comparable to, or better than published data. For some elements, however, such as bismuth, lead and tellurium, the characteristic mass was significantly higher. This resulted from the high stabilizing power of the Pd–Mg modifier, which necessitated higher atomization temperatures for these elements for which diffusional losses were more pronounced. The advantages and limitations of the Pd–Mg modifier are discussed.
An Infimal Convolution based Penalty for Sparse Signal Recovery

In this paper, we propose a nonconvex penalty function for sparse signal recovery by using infimal convolution approximation. First, we show that this penalty function is between the ℓ1-norm and the difference of ℓ1 and ℓ2-norm (ℓ1 2-norm), thus it can retain the advantages of these two norms at the same time, which means that it can induce the sparsity effectively for the low-amplitude components as the ℓ1-norm and relieve underestimating the high-amplitude components as the ℓ1 2-norm. Second, we present an iterative method to solve the nonconvex penalty minimization based on the difference of convex algorithm (DCA), which solves the subproblem by using alternating direction method of multipliers (ADMM) method. The experimental results demonstrate the effectiveness of the proposed method.
Local Bilinear Convolutional Neural Network for Spotting Macro- and Micro-expression Intervals in Long Video Sequences

To reduce the impact of low intensity on the spot micro-expressions in long video sequences when facial microexpressions occur, this paper presents a method based on Local Bilinear Convolutional Neural Network (LBCNN) for spotting macro- and micro-expressions in long videos sequences. Considering the low intensity of facial micro-expressions, and the occurrence of micro-expressions is only related to the local area of the facial, we turn the micro-expression spot in long videos sequences into a fine-grained image recognition. Bilinear Convolutional Neural Network (BCNN) has proven to be effective in fine-grained image recognition. Therefore, we use the BCNN structure to extract the global and local features of the face area of each frame of the image in the long video sequence and obtains the final classification result by fusing the global and local features. The metric F1-scores of our proposed methods were evaluated on the CAS(ME)2 and SAMM Long Videos dataset of the Third Facial Micro-Expression Grand Challenge (MEGC 2020). For the CAS(ME)2, the overall F1-scores are 0.0595 for macro- and micro-expressions; for SAMM Long Videos, the overall F1-scores are 0.0813 for macro- and microexpressions. The experiments show that our method achieved superiorly higher results than the baseline method (MDMD) provided. https://github.com/panhang1023/MEGC2020
Overfitting Mitigation Analysis in Deep Learning Models for Plant Leaf Disease Recognition

Over the last two decades, deep learning models have revolutionized the field of image processing in almost every application area including the field of agriculture. Recently, these models have successfully been applied to recognize leaf diseases of plants. However, deep learning models are complex in nature and are prone to overfitting. Therefore, the selection of proper training settings is very important to avoid overfitting. In this work, we explore the effect of data augmentation and various optimization techniques being used to partially train a pretrained DenseNet-121 model from the perspective of overfitting. In first phase, experiments are conducted for different combinations of data augmentation techniques, it was found that for some combinations, the network is more prone to overfitting. In the second phase, seven different optimizers were analyzed and it was observed that the Adadelta optimizer exhibits the best generalization ability on the available training data.
Towards Gait Analysis And Rehabilitation of Parkinson's Disease Patients

This paper aims at designing an efficient real-time gait analysis algorithm to detect a possible fall. Four-meter Go and Back (4G) scale has been designed for taking measurements of experiments. Two wireless accelerometers are put on waist positions of 17 subjects suffering from Parkinson's disease. Based on information captured, a real-time gait analysis algorithm is implemented to detect an ambulatory deviation. Furthermore, the proposed method is flexible to adjust to respond to the gait-related movements. Other researchers can use this algorithm in their gait-related studies. In this paper, different gait-related parameters are also discussed and represented through a 4GB scale.
Retinal Fundus Image Refinement with Contrast Limited Adaptive Histogram Equalization, Noise Filtration and Intensity Adjustment

Retinal Fundus disease is an explosively increasing research topic today for several reasons. The problem with images is the quality that makes it difficult for health care providers to interpret the condition accurately. In this study, a method has been proposed to improve the quality of retinal fundus to solve this problem. Test data for the research comes from a set of STARE datasets with different procedures, which include combined contrast-limiting histogram equalization, noise filtration, and intensity adjustment. In this study, we used the Mean Square Value (MSE) and the Peak Signal To Noise Ratio (PSNR) as parameters to compare the image quality of retinal fundus of each process. The experimental results of this study justify that these procedures can be used to improve image quality and convert low-quality images to a high-quality image.
Extraction of Seasonal Signal of GPS Coordinate Time Series Using Atomic Norm Minimization

Global positioning system (GPS) coordinate time series shows obvious seasonal oscillation, especially in the vertical direction. In order to separate tectonic movement from the other signals accurately and obtain reliable long-term movement trend, it is necessary to obtain accurate seasonal signal estimation. In this paper, seasonal signals in the GPS time series are modelled by time-varying sinusoidals, and the Atomic Norm Minimization (ANM) is used to extract the seasonal signals. To verify the efficiency of the proposed algorithm, a set of experiments is carried out on both the synthetic dataset and real GPS time series. The results of ANM are compared with the state-of-the-art methods under different noise levels. All the elicited results indicated that the ANM has certain advantages and can be used as an effective alternative way to extract the seasonal signals.
Progressive Kernel Pruning Based on the Information Mapping Sparse Index for CNN Compression

Network pruning can effectively reduce a model’s capacity and computational load, thereby making model deployment in mobile devices less difficult than that without pruning. To improve the pruning rate of the model while maintaining the kernel’s feature extraction ability, this paper designs a progressive kernel pruning method for CNN model compression based on the proposed information mapping sparsity index. This method first prunes the kernels in the filter and then prunes the kernels in the convolution layer when the model reaches a certain compression ratio. The whole process is called progressive kernel pruning (PKP). For the kernel pruning process, this paper defines the information mapping sparse index (IMSI), which is used to measure the mapping ability of the convolution kernel related to the amount of information transferred by the convolution operation. When pruning the kernels of filters and layers, according to the IMSI, the kernels with the strongest mapping abilities are retained to transfer as much information as possible with the least number of kernels. Progressive kernel pruning can make use of the characteristic that the model is easy to optimize when kernel pruning in the filter, and it avoids having the model easily fall into local optima when kernel pruning in the layer directly. The experimental results on the CIFAR-10/100 and ImageNet datasets show that compared to the existing CNN model compression methods, the IMSI-based progressive kernel pruning method exhibits better compression performance in processing the model compression tasks that are currently popular. In particular, pruning VGG-16 on CIFAR-10 with our model achieves a compression ratio of 80.8x and an acceleration ratio of 14.8x, which are 5.8x and 4.2x higher than the best results at present, respectively, and the classification accuracy decreases by only 0.59% relative to that of the baseline.
A New Truck-Drone Routing Problem for Parcel Delivery Services Aided by Parking Lots

Collaborative routing aided by drones in last-mile delivery (LMD) has been an extensively studied topic in recent years. In this article, a new optimization problem for collaborative routing with a truck and a fleet of drones in LMD is introduced. The problem proposes a new strategy to coordinate a truck and a fleet of drones to serve a given set of customers. The plan is based on identifying the locations where the truck can park and where the drones fly to serve the customers. A procedure to locate these parking lots conveniently is given. For the transportation network, which includes these points, a mixed linear integer programming formulation is provided, which aims to minimize the makespan of serving all the customers. Computational experiments on a set of problem instances were conducted to analyze the model solutions’ characteristics and find their computational limits. The experiments showed that the proposed model could significantly improve the results obtained when only the truck delivers. Additionally, we propose a GRASP metaheuristic to solve instances of greater size. Its computational performance was studied when applied to a set of instances with different characteristics. The paper discusses some insights obtained from the computational experimentation and presents future research directions.
Physics Reasoning for Intrusion Detection in Industrial Networks

Industrial control systems (ICS) exchange network traffic carrying payloads that are closely related to the physics of industrial equipment and processes. We leverage this factor to develop a machine reasoning approach that inspects network packet payloads in terms of their relationship to physics. We found that exploits and malware are unambiguously detected, since they inject machine instructions, addresses, and other data that clearly depart from physics. We developed an ontology integrated with the knowledge of physics, which we tested against exploits of a large number of public vulnerabilities that affect industrial control systems. We also ran our approach in several case studies that involved ICS control of an electrical motor, which we describe in the paper.
Social Network Analysis Visualization Tools: A Comparative Review

Social network analysis helps analysts to study the relationships among different nodes of a network and get valuable insights. The common output of social networks analysis is measuring the node's centrality and graphical visualization, which helps us to understand the degree and nature of relationships between different nodes of the network. Online Social networks produce data that provides tons of knowledge. But it is challenging to extract the data and get useful information through its analysis and visualization. Getting insights from small-scale networks is comparatively easy since they can be manually constructed and graphically represented. But it gets very time-consuming and hectic process as the network expands. Several social network analysis tools are available for this purpose. These tools also come with embedded techniques for analyzing social networks, reducing the overhead for the manual study of complex networks. The focus of this paper is on the analysis and comparison of popular tools that are often used by experts for analysis of large-scale data-sets. In this paper, we have addressed the various characteristics, functionalities, support comparison, strengths, and limitations of SNA tools based on a literature survey and expert opinions. This will help researchers and practitioners to choose a suitable tool for social network visualization and analysis.
Visualization Analysis for Articles Published in the Journal of Medical Education Online from 2010 to 2019 based on Citespace

Object: To understand the development process, hotspots and trends of international medical education research and provide references for medical education research in future; Methods: Retrieve academic papers published by Medical Education Online from 2010 to 2019, and use bibliometrics and graph theory of scientific knowledge to perform statistics and analysis on the papers; Results: A total of 412 papers were included, and the number of papers published was between 18 and 75 each year. The institution with the largest number of papers published by the University of California, Formed a team of multiple core authors. The citation rate of the paper is 86.7%, the average number of citations is 7.63 times, the H-index index is 22, and there are 27 keywords with a frequency of 5 times; Conculsion: High-level universities pay more attention to medical education research, publish a large number of papers and form multiple research teams; medical education research covers a wide range, high simulation simulation, teaching evaluation and feedback, pediatric residency training, transition care skills. are hot topics of research; the content of medical education research is constantly evolving. Undergraduate medical education is the most central frontier field. Professional literacy, standardized patients, online education, academic performance, and educational continuity are also important frontier fields
A Survey Comparing Specialized Hardware And Evolution In TPUs For Neural Networks

This survey paper is based on the evolution of TPUs from first generation TPUs to edge TPUs and their architectures. This paper compares CPUs, GPUs, FPGAs and TPUs, their hardware architectures, their similarities and differences will be discussed. Modern neural networks are immensely used these days but they require more time, computation and energy. Due to the greater demand and attractive options for architects to explore, companies are continuously working to reduce training and inference response time. Due to the demands and cost factors different kinds of ASICs (application specific integrated circuits) are developed and research is increased in this area. Many models of CPUs, GPUs and TPUs have been developed to support these networks and to improve training and inference phase. Intel developed CPUs for this purpose, NVIDIA developed GPUs and Google developed cloud TPUs. The hardware of CPUs and GPUs can be sold to businesses while Google offers TPU processing for everyone from the cloud. When the data is away from the computational source, it increases the overall cost and to reduce this cost companies implements memory management and caching techniques close to ALUs.
Virtualizing Analog Mesh Computers: The Case of a Photonic PDE Solving Accelerator

Innovative processor architectures play a critical role in sustaining performance improvements under severe limitations imposed by feature size and energy consumption. The Reconfigurable Optical Computer (ROC) is one such innovative, Post-Moore’s Law processor. ROC is designed to solve partial differential equations in one shot as opposed to existing solutions, which are based on costly iterative computations. This is achieved by leveraging physical properties of a mesh of optical components that behave similarly to electrical resistances. However, building large photonic arrays to accommodate arbitrarily large problems is not yet feasible. It is also possible to have problems that are smaller than the size of the accelerator array. In both cases, virtualization is necessary. In this work, we introduce an architecture and methodology for light-weight virtualization of ROC. We show that overhead from virtualization is minimal, and our experimental results show two orders of magnitude increased speed as compared to microprocessor execution while keeping errors due to virtualization under 10%.
Experimental study on the impact of reciprocating compressor systems on the power quality in electrical power supply systems.

This paper makes an analysis based on experimental studies on the impact of reciprocating compressor systems on the power supply system itself. Due to the periodic change of the drag torque of the reciprocating compressors, fluctuations in the current occurs. This in turn, depending on the ratio of the power supply and the load as a reciprocating compressor system, we analyze the power quality in the power supply system. Based on the experiments performed, this impact was assessed and recommendations are made to minimize the negative impact of these consumers on the power quality.
ALIGNMENT EFFECT VERSUS ENTRENCHMENT EFFECT: THE IMPACTS OF CONTROLLING SHAREHOLDER CHARACTERISTICS ON ACCOUNTING QUALITY

This research examines the association between the controlling shareholders and accounting quality of listed firms in the Stock Exchange of Thailand (SET) during 2008-2012. Accounting quality measurement is based on four accruals models from Dechow and Dichev (2002), Dechow, Sloan and Sweeney (1995), Yoon, Miller and Jiraporn (2006), and the adjusted accruals model proposed by this research. The results support both entrenchment and alignment effects. The entrenchment effect is presented when the controlling shareholders do not have comprehensive power in the firm’s activities whereas the alignment effect is confirmed when the controlling shareholders have the sufficient absolute power to manage or block the firm’s importance activities.
Application of Machine Learning in Classification and Prediction of Breast Cancer

—Cancer misdiagnosis is extremely common. We attempt to build different machine learning models that can predict occurrences of cancer traits in a patients. Being said that cancer is often misdiagnosed, when it comes to cancer, spotting the disease earlier can quite literally mean the difference between life and death. Predictive models obtained by using machine algorithms may be a key in such cases. This can be used by any medical institutes for faster, economical and accurate cancer diagnosis. Machine learning incorporates varieties of statistical, probabilistic and optimization techniques that allow computers to “learn” from past examples and to detect hard-to-diagnosed patterns from massive, noisy or complex datasets This project allows us make fast, real-time and accurate diagnosis and prediction of breast cancer. The software uses support vector machine algorithm to do the prediction and diagnosis of breast cancer. The simplicity and almost accurate results for support vector machine algorithm is very suitable for implementation.
Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Technologies used in ICS and Smart Grid are overlapping. The most discussed attacks on ICSs are Stuxnet and Black energy malware. The anatomy of these attacks not only pointed out that the security of ICS is of prime concern but also demanded to execute a proactive approach in practicing ICS security. Honeypot is used to implement defensive measures for security. The Honeynet group released Honeypot for ICS labelled as Conpot in 2013. Though the Conpot is low interactive Honeypot, it emulates processes of different cyber-physical systems, typically Smart Grid. In the literature, the effectiveness of Honeypot operations was studied by challenging limitations of the existing setup or proposing new variants. Similar approaches are followed for Conpot evaluation. However, none of the work addressed a formal verification method to verify the engagement of Honeypot, and this makes the presented work unique. For proposed work, Coloured Petri Net (CPN) tool is used for formal verification of Conpot. The variants of Conpot are modelled, including initial state model, deadlock state model and livelock model. Further evaluation of these models based on state space analysis results confirmed that Conpot could lure an attacker by engaging him in an infinite loop and thereby limiting the scope of the attacker from exploring and damaging the real-time systems or services. However, in the deadlock state, the attacker’s activity in the conpot will be restricted and will be unable to proceed further as the conpot model incorporates deadlock loop.
Preparation of chitosan bicomponent nanofibers filled with hydroxyapatite nanoparticles via electrospinning

Chitosan (CS) bicomponent nanofibers with an average diameter controlled from 100 to 50 nm were successfully prepared by electrospinning of CS and poly(vinyl alcohol) (PVA) blend solution. Finer fibers and more efficient fiber formations were observed with increased PVA contents. On this contribution, a uniform and ultrafine nanofibrous CS bicomponent mats filled with hydroxyapatite (HA) nanoparticles were successfully electrospun in a well devised condition. An increase in the contents of HA nanoparticles caused the conductivity of the blend solution to increase from 1.06 mS/cm (0 wt % HA) to 2.27 mS/cm (0.5 wt % HA), 2.35 mS/cm (1.0 wt % HA), respectively, and the average diameter of the composite fibers to decrease from 59 ± 10 nm(0 wt % HA) to 49 ± 10 nm (0.5 wt % HA), 46 ± 10 nm (1.0 wt % HA), respectively. SEM images showed that some particles had filled in the nanofibers whereas the others had dispersed on the surface of fibers, and EDXA results indicated that both the nanoparticles filled in the nanofibers and those adhered to the fibers were HA particles. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010