Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization Algorithm for Wireless Sensor Networks Based on Node Degree Estimation Distance

In order to improve the positioning ability of wireless sensor networks, a positioning algorithm based on distance estimation of node degree is proposed. An optimized topological structure model of wireless sensor network information collection nodes is constructed, and a wireless sensor network transmission link model is designed by combining the Porter balanced modulation method of wireless sensor channels. Adaptive spread spectrum sequence recombination of wireless sensor network transmission links is carried out by adopting adaptive information conduction control technology, and a balanced scheduling model of wireless sensor network transmission links is constructed by adopting node balanced scheduling and information sensing fusion tracking methods. Combining with the node degree estimation method, this paper estimates the position information of the information collection nodes in wireless sensor networks, establishes the positioning model of wireless sensor networks, and realizes the positioning of wireless sensor networks by combining the distance parameter analysis of node degree estimation and fuzzy clustering methods. Simulation results show that this method has better intelligence and positioning accuracy, reduces the bit error rate of information transmission in wireless sensor networks, and improves the balanced deployment ability of information collection nodes in wireless sensor networks.
JNose: Java Test Smell Detector

Several strategies have been proposed for test quality measurement and analysis. Code coverage is likely the most widely used one. It enables to verify the ability of a test case to cover as many source code branches as possible. Although code coverage has been widely used, novel strategies have been recently employed. It is the case of test smells analysis, which has been introduced as an affordable strategy to evaluate the quality of test code. Test smells are poor design choices in implementation, and their occurrence in test code might reduce the quality of test suites. Test smells identification is clearly dependent on tool support, otherwise it could become a cost-ineffective strategy. However, as far as we know, there is no tool that combines code coverage and test smells to address test quality measurement. In this work, we present the JNose Test, a tool aimed to analyze test suite quality in the perspective of test smells. JNose Test detects code coverage and software evolution metrics and a set of test smells throughout software versions.
An empirical study of automatically-generated tests from the perspective of test smells

Developing test code can be as or more expensive than developing production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. A strategy to verify the tests quality is to analyze the presence of test smells in test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. Their presence could be used as a quality indicator. In this paper, we present an empirical study to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and Evo- Suite) with the existing unit test suite of twenty-one open-source Java projects. We analyze the unit test code to detect the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from the automated unit test generators and existing unit test suites.
Research on Improvement of Lateral Static Stability Based on Hydrodynamic Parameters of Underwater Glider

In order to improve the lateral static stability of underwater glider under ocean current, premised on certain gliding efficiency, the appropriate hydrofoil swept angle and vertical tail aspect-ratio parameters are designed and numerical simulation method is used to analyze their influence on lateral static navigation performance under conditions of diving and floating. The results show that increasing the hydrofoil swept angle in a certain range has a positive effect on the lateral static stability of underwater glider in different gliding conditions, and increasing the vertical tail aspect-ratio has a dual effect on the lateral static stability in different gliding conditions. The method provides a theoretical basis for the design of hydrodynamic parameters of underwater glider sailing under different working conditions.
Analysis of Private Industrial Control Protocol Format Based on LSTM-FCN Model

Protocol security is an important element in the field of industrial control system security. For industrial control systems filled with a large number of private protocols, accurate information on the format of private industrial control protocols is the basis of its security analysis. How to effectively analyze the format of private industrial control protocols is the key point to be solved in the security of the current industrial control system. In this paper, a private industrial control protocol formats analysis method based on Long short-term memory Fully convolutional neural network (LSTM-FCN) is proposed. The Long short-term memory Fully convolutional neural network (LSTM-FCN) model is used to learn the field types of public industrial control protocols. The trained model is then applied to the private industrial control protocols field type identification. Finally, the type recognition fields are fused based on the format characteristics of the public protocol fields. The results of the format analysis of the private industrial control protocol were obtained. The experimental results show that the method can accurately analyze the format of private industrial control protocols.
Rotating machinery vibration fault diagnosis model based on autoencoder

Rotating machinery is a large-scale equipment widely used in industrial production, and its safety issues should be paid attention to. Fault diagnosis of equipment is of great significance for ensuring safe production. With the rapid development of artificial intelligence, it is a trend to apply deep learning technology to vibration fault diagnosis of rotating machinery. This paper proposes a vibration fault diagnosis model for rotating machinery based on auto-encoder. Deeply under-complete auto-encoders, deep-noise-reduction auto-encoders, and deep-sparse autoencoders are constructed respectively. Four types of fault data of the test bench are used for verification. The results prove the effectiveness of this model.
An Anchor-free SAR Ship Detector with Only 1.17M Parameters

An lightweight anchor-free synthetic aperture radar (SAR) ship detector is proposed in this paper. Firstly, According to the SAR image characteristics, ResSARNet with only 0.69M parameters was proposed. Then, with ResSARNet as the backbone network, four improvements have been made to fully convolutional one-stage object detection (FCOS): Center-ness on bounding box regression branch, center sampling, Generalized Intersection over Union (GIoU) loss and adaptive training sample selection (ATSS). To verify the effectiveness of the proposed method, sufficient experiments have been conducted on the widely used SAR Ship Detection Dataset (SSDD). The results show that, the proposed network can achieve 61.5% average precision (AP) and 70.9% average recall (AR) with only 1.17M parameters, which are only 6.5% and 3.2% for RetinaNet@ResNet-18 and RetinaNet@ResNet-50, respectively.
Prevalence of Refractive Error and Visual Impairment among Rural Dwellers in Mashonaland Central Province, Zimbabwe

Purpose: To investigate the availability and content of educational statements or recommendations disseminated by U. S. ophthalmologic organizations regarding perioperative management of antithrombotic agents for ophthalmic and orbital surgery, given the highly variable management of these agents by U. S. ophthalmologists and limited consensus recommendations in the literature. Methods: National U. S. ophthalmic surgical organization websites were systematically examined for educational statements, which were reviewed for discussion of perioperative management of antithrombotic agents including antiplatelet and anticoagulant medications. A “statement” was defined as either: (a) a guideline directed toward ophthalmologists or (b) a surgical/clinical educational posting directed toward ophthalmologists or patients. Results: Fourteen surgical organizations were identified, with eight of these publishing clinical/surgical educational statements. A total of 3408 organizational statements were identified, with 252 (7.4%) and 3156 (92.6%) statements directed toward physicians and patients, respectively. In total, 0.3% (9/3408) of statements discussed perioperative management of antithrombotics. These accounted for 0.8% (2/252) of ophthalmologist-directed statements and 0.2% (7/3156) of patient-directed statements. The majority of patient-directed statements (57.1%, 4/7) recommended that patients discuss antithrombotic cessation with their ophthalmologists, though ophthalmologist-directed information regarding these medications was scant or absent. Conclusions: Educational material from U. S. ophthalmologic organizations regarding perioperative management of antithrombotics is notably lacking despite the fact that ophthalmic and orbital surgeries carry unique vision-threatening hemorrhagic risks. Given these risks, as well as the medicolegal consequences of hemorrhagic complications in ophthalmic surgery, increased dissemination of educational material, and consensus statements by ophthalmic surgical organizations on the perioperative management of antithrombotics may be justified.
Fault diagnosis of rotating machinery based on deep learning

In the current machinery manufacturing industry, rotating machinery occupies a very important position. Rotating machinery mainly refers to the machinery that can complete specific functions with the help of rotary action. The main vibration faults of rotating machinery include rotor unbalance, rotor misalignment, friction between moving and static parts and looseness of support parts. Generally, large rotating machinery is generally equipped with vibration monitoring protection and fault diagnosis system to ensure the safe operation of rotating machinery, but there are some limitations. In order to find the hidden danger in the machinery in time and avoid serious mechanical accidents, this paper combines the deep learning technology in machine learning to increase the accuracy and reliability of fault identification, reduce the risk of failure, and provide more guarantee for the production safety of machinery industry. Aiming at the main faults of rotating machinery, this paper proposes a fault recognition method of rotating machinery based on LSTM. Firstly, the sample data is screened and judged effectively, and then the abnormal data are classified by LSTM and softmax. Finally, the evaluation index and result of the fault category are obtained, which is compared with other classification algorithms to show the good applicability of the algorithm. At present, there are still some problems in the model, which need to be further discussed.
Oracles of Bad Smells: a Systematic Literature Review

A bad smell is an evidence of a design problem that may be harmful to the software maintenance. Several studies have been carried out to aid the identification of bad smells, by defining approaches or tools. Usually, the evaluation of these studies' results relies on data of oracles bad smells. An oracle is a set of data of bad smells found in a given software system. Such data serves as a referential template or a benchmark to evaluate the proposals on detecting bad smells. The availability and the quality of bad smell oracles are crucial to assert the quality of detection strategies of bad smells. This study aims to compile the bad smell oracles proposed in the literature. To achieve this, we conducted a Systematic Literature Review (SLR) to identify bad smell oracles and their characteristics. The main result of this study is a catalog of bad smell oracles that may be useful for research on bad smells, especially the studies that propose tools or detection strategies for bad smells.
Design of Life Cycle Characteristic Parameter Management System for Smart Hydropower Stations

The life cycle management of characteristic parameters of equipment assets is an important part of smart hydropower stations, which involves seven different periods, including planning and design period, material procurement period, equipment manufacturing period, material supply period, installation and commissioning period, operation and maintenance period, and asset management period. Due to the great number of equipment, a plenty of characteristic parameters are generated. But, the values of the characteristic parameters are stored in different system, leading to a large number of information islands, which can't meet the requirements of smart hydropower stations. In order to improve the management level of characteristic parameters of equipment asset in hydropower stations, a life cycle characteristic parameter management system (LCCPMS) has been designed. Firstly, the characteristic parameters of equipment assets have been studied. Subsequently, the coding roles of the characteristic parameters have been established. And then, the main development procedure has been summarized. Finally, the application modules of the LCCPMS have been designed.
Optimal "interval" Rules Acquisition in Set-Valued Ordered Information Systems

For set-valued ODIS, we utilize the interval which is viewed as the fundamental knowledge elements to construct the approximations relation. Using the interval knowledge granule, we induce useful "interval" decision information from the set-valued ordered decision information systems. To obtain optimization decision rules, we raise the concept of relative reducts, then, we use the corresponding discernibility function method to obtain the relative reduct.
Prediction of Microblog Users' Forwarding Behavior Based on Interactive and Active Information

As a mainstream online social media platform, microblog predicts the individual forwarding behavior from the perspective of a single user, which is helpful to grasp the information dissemination characteristics in microblog network from the micro level and realize further prediction and analysis of information dissemination paths and even dissemination trends. From the perspective of a single user, this paper predicts the forwarding behavior of microblog users. This paper summarizes the previous researches on the influencing factors of microblog users' forwarding willingness, considers the influence of users' interaction and users' activity on forwarding willingness, proposes a prediction method of microblog users' forwarding behavior based on interactive and active information, and verifies its feasibility. Finally, the shortcomings of this method are summarized and the prospect is put forward.
Application of Salting-Out Agent to Enhance the Hydrophobicity of Weakly Hydrophobic Bacterial Strains

Hydrophobicity is a vital parameter for initial cell adhesion that ultimately leads to biofouling of surfaces and loss of system performance and health issues. The efficiency of a number of biological systems could be improved by increasing the hydrophobicity of concerned bacteria. Here we used ammonium sulfate (salt) to enhance the bacterial hydrophobicity, as measured by a commonly used liquid–liquid partitioning based hydrophobicity assessment assay — the MATH test. We observed successive increases in bacterial hydrophobicity with incremental increase in salt concentration for Gram-negative bacteria. Upon addition of 2 M salt, three closely related E. coli strains were easily distinguishable from one another. Gram-positive bacteria exhibited different trends than Gram-negative strains, with no change in the hydrophobicity of S. salivarius HB cells and a sharp decline followed by an increase in hydrophobicity for D. radiodurans. Cell size measurements revealed that Gram-positive cells exhibited a change in cell size on hydrocarbon exposure, while the Gram-negative cultures remained mostly unaffected. Overall, salt addition was observed to enhance the hydrophobicity of different test strains, especially at the higher concentrations used here of 1.5 and 2 M. Salt addition in conjunction with the MATH test successfully differentiated and quantified otherwise weakly hydrophobic bacteria, thus enhancing the range of this laboratory assay. Our results demonstrate the effectiveness of salt addition in increasing the bacterial hydrophobicity, which could potentially be used in diverse areas, ranging from applied microbiology and engineering to oral care.
Quantification of sunscreen 2-phenylbenzimidazole-5-sulfonic acid in cosmetic products and water samples by HPTLC/densitometry with fluorescent detection

AbstractThe water-soluble sunscreen 2-phenylbenzimidazole-5-sulfonic acid (PBS) was quantified in a sun-care product and water samples by thin layer chromatography followed by densitometric scanning in fluorescence mode (cut-off filter 370 nm, wavelength of excitation — 300 nm). Normal phase TLC was performed on silica gel 60 as stationary phase. Mobile phase used was ethyl acetate-ethanol-water 70:35:30 (v/v/v). The limit of detection (LOD) was 0.0004 μg spot-1, and the limit of quantification (LOQ) was — 0.001 µg spot−1 without any sample pre-concentration.
An LSTM-based Traffic Prediction Algorithm with Attention Mechanism for Satellite Network

Due to the response to the topological time-varying of satellite network, the satellite management system puts forward higher requirements for the network traffic prediction algorithm. The traffic prediction algorithm of ground network is not suitable for satellite network. In this manuscript, a neural network model of long and short-term memory with attention mechanism is proposed. Considering that the input and output of traffic prediction is a sequence, the long short-term Memory (LSTM) model in this manuscript balances the effects of different parts of input on output by adding attention mechanism. The simulation results show that compared with ARIMA, random forest and traditional Recurrent Neural Network (RNN), the prediction accuracy of this model is significantly improved. Meanwhile, compared with the model after removing the attention network, the model verifies the effectiveness of the attention network.
Fairy Lights in Femtoseconds

We present a method of rendering aerial and volumetric graphics using femtosecond lasers. A high-intensity laser excites physical matter to emit light at an arbitrary three-dimensional position. Popular applications can thus be explored, especially because plasma induced by a femtosecond laser is less harmful than that generated by a nanosecond laser. There are two methods of rendering graphics with a femtosecond laser in air: producing holograms using spatial light modulation technology and scanning of a laser beam by a galvano mirror. The holograms and workspace of the system proposed here occupy a volume of up to 1 cm3; however, this size is scalable depending on the optical devices and their setup. This article provides details of the principles, system setup, and experimental evaluation, and discusses the scalability, design space, and applications of this system. We tested two laser sources: an adjustable (30--100fs) laser that projects up to 1,000 pulses/s at an energy of up to 7mJ/pulse and a 269fs laser that projects up to 200,000 pulses/s at an energy of up to 50μJ/pulse. We confirmed that the spatiotemporal resolution of volumetric displays implemented using these laser sources is 4,000 and 200,000 dots/s, respectively. Although we focus on laser-induced plasma in air, the discussion presented here is also applicable to other rendering principles such as fluorescence and microbubbles in solid or liquid materials.
Doctoral Symposium: Placement of Distributed Stream Processing over Heterogeneous Infrastructures

Data Stream Processing (DSP) applications can extract, in a timely manner, valuable information from distributed data sources (e.g., sensing devices, social networks). These applications are subject to unpredictable and varying workloads and have to satisfy strict quality requirements, usually expressed in terms of latency, availability, and throughput. To successfully execute DSP applications, recent trends investigate the possibility of exploiting decentralized computing resources, which nonetheless pose new challenges due to the network and system heterogeneity, geographic distribution, and non-negligible network latencies. The doctorate work, presented in this paper, investigates the deployment of DSP applications with Quality of Service (QoS) requirements over a distributed infrastructure of heterogeneous computing and networking resources. Specifically, to support our study, we extend an open-source DSP system, Apache Storm, by providing mechanisms for executing distributed QoS-aware placement policies and self-adaptation. Then, we provide a general formulation of the optimal placement problem for DSP applications, modeling the heterogeneity of the execution environment. The ongoing research aims at investigating the following directions. First, we will design heuristics able to determine the best placement in a feasible amount of time. Second, we will investigate runtime adaptation strategies and online placement algorithms. Third, to prove the generality of our approach, we will customize the designed solutions for similar problems (e.g., service selection, container deployment).
Optical textures observed during the shearing of thermotropic liquid-crystal polymers

We report optical observations of a number of main-chain thermotropic liquid-crystal polymers. In situ measurements were carried out at elevated temperatures using an apparatus that is capable of providing a controlled translational oscillatory shearing motion to samples of typically 1–10 µm thickness. In static samples we observe either a general birefringence with local scattering from line defects which we believe to be disclinations, or in thicker samples a texture dominated by light scattering due to the presence of dence disclinations. Superimposed shearing appears to result in both the multiplication of disclinations and the progressive decrease in the distance between individual disclinations. At high shear rates pure birefringence in the direction of flow is observed. Finally we report on the optical relaxation behaviour of oriented samples together with an explanation for the skin core effect observed when thermotropic liquid-crystal polymers are extruded through dies.