Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating and Improving Static Analysis Tools Via Differential Mutation Analysis

Static analysis tools attempt to detect faults in code without executing it. Understanding the strengths and weaknesses of such tools, and performing direct comparisons of their ef-fectiveness, is difficult, involving either manual examination of differing warnings on real code, or the bias-prone construction of artificial test cases. This paper proposes a novel automated approach to comparing static analysis tools, based on producing mutants of real code, and comparing detection rates over these mutants. In addition to making tool differences quantitatively observable without extensive manual effort, this approach offers a new way to detect and fix omissions in a static analysis tool's set of detectors. We present an extensive comparison of three smart contract static analysis tools, and show how our approach allowed us to add three effective new detectors to the best of these. We also evaluate popular Java and Python static analysis tools and discuss their strengths and weaknesses.
Comparison of Clustering Techniques for Thai Mutual Funds Fee Dataset

There are researches that study about clustering techniques e.g., K-Means, K-Medoids, and X-Means. Their works mainly focus on applying one technique on multiple data sets to find the pros and cons of each algorithm. In this work, we focus on study and comparing these three clustering techniques instead. The experiment is done by applying each technique on Thai mutual funds fee data set which consists of 2,595 funds. From our experiment, we found that the optimal K value is 22. K-Means use the least processing time while K-Medoids use the most time. K-Means also has the least average distant between each centroid while K-Medoids has the most average distant. From Davies-Bouldin index, X-Means has the lowest value while K-Medoids has the highest value. The most density cluster of K-Means and X-Means is cluster 0 but it is cluster 1 for K-Medoids.
An Exploration Into Secure IoT Networks Using Deep Learning Methodologies

With Internet of Things (IoT) emerging as a new technology connecting few devices at home to billions of devices at corporate, the prices of such connected devices have become significantly low over the years and therefore more prone to attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS). To enable resilience-continuous monitoring is needed alongside adaptive decision making. To address these challenges, Software Defined Networks (SDN) can help handling security threats in IoT networks dynamically and in an adaptive manner. Various research work has been done in the field of Cyber Security and the Deep learning methodologies to apply the techniques to secure the network by watermarking [4]. Honeypots show great results in overcoming the threat of DDoS attacks and provide dynamic protection for SDNs, however anti-honeypot software can identify the honeypots and nullify their actions. Pseudo-Honeypots are proposed to protect from such anti-honeypot attacks by proving several groups of Bayesian-Nash Equilibrium (BNE) as a part of the strategy. SDNs have been used in many of the proposed techniques that relies on the Long Short-Term Memory (LSTM) models for predicting attacks or anomalies in the traffic data or even classify malicious IoT nodes.
Loan Default Risk Prediction Using Knowledge Graph

Credit risk, also known as loan default risk, is one of the significant financial challenges in banking and financial institutions since it involves the uncertainty of the borrowers' ability to perform their contractual obligation. Banks and financial institutions rely on statistical and machine learning methods in predicting loan default to reduce the potential losses of issued loans. These machine learning applications may never achieve their full potential without the semantic context in the data. A knowledge graph is a collection of linked entities and objects that include semantic information to contextualize them. Knowledge graphs allow machines to incorporate human expertise into their decision-making and provide context to machine learning applications. Therefore, we proposed a loan default prediction model based on knowledge graph technology to improve the prediction model's accuracy and interpretability. The experimental results demonstrated that incorporating knowledge graph embedding as features can boost the performance of the conventional machine learning classifiers in predicting loan default risk.
Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
Automated Cause Analysis of Latency Outliers Using System-Level Dependency Graphs

Detecting performance issues and identifying their root causes in the runtime is a challenging task. Typically, developers use methods such as logging and tracing to identify bottlenecks. These solutions are, however, not ideal as they are time-consuming and require manual effort. In this paper, we propose a method to automate the task of detecting latency outliers using system-level traces and then comparing them to identify the root cause(s). Our method makes use of dependency graphs to show internal interactions between threads and system resources. With these graphs, one can pinpoint where performance issues occur. However, a single trace can be composed of a large number of requests, each generating one graph. To automate the task of identifying outliers within the dataset, we use machine learning density-based models and statistical calculations such as $Z$ -score. Our evaluation shows an accuracy greater than 97 % on outlier detection, making them appropriate for in-production servers and industry-level use cases.
A Gaussian Process Approach to Multiple Internal Models in Repetitive Control

Disturbances that come from multiple originating domains, e.g., time, position, or commutation-angle domain, are often encountered in practice due to the increasing complexity of mechatronic systems. The aim of this paper is to present a generalized approach that enables asymptotic rejection of multi-dimensional disturbances which are periodic in the different originating domains, e.g., if speed changes, then spatially-periodic disturbances manifest themselves differently in the time domain. A multi-dimensional Gaussian process (GP) based internal model is employed in conjunction with a traditional repetitive control (RC) setting using non-equidistant observations, allowing to learn a multidimensional buffer for RC. A case study with a spatio-temporal disturbance confirms the benefit of this method.
RetinaMHSA: Improving in single-stage detector with self-attention

In recent years, object detection with two-stage methods is one of the highest accuracies, like faster R-CNN. One-stage methods which use a typical dense sampling of likely item situations may be speedier and more straightforward. However, it has not exceeded the two-stage detectors' accuracy. This study utilizes a Retina network with a backbone ResNet50 block with multi-head self-attention (MHSA) to enhance one-stage method issues, especially small objects. RetinaNet is an efficient and accurate network and uses a new loss function. We swapped c5 in the ResNet50 block with MHSA, while we also used the features of the Retina network. Furthermore, compared to the ResNet50 block, it contains fewer parameters. The results of our study on the Pascal VOC 2007 dataset revealed that the number 81.86 % mAP was obtained, indicating that our technique may achieve promising performance compared to several current two-stage approaches.
In-Vehicle Positioning for Public Transit Using BLE Beacons

Public transit has been affected disproportionately by the social distancing requirements consequent to the COVID-19 pandemic. Technologies such as effortless ticketing and crowdedness assessment have the potential to increase safety and instill confidence for transit users. One key component of these technologies is the ability to detect the presence of a passenger inside a bus vehicle, as well as their approximate location within the vehicle. We present a preliminary study demonstrating the potential of a system that uses Bluetooth Low Energy (beacons), placed inside a vehicle, to localize a passenger within the length of the vehicle with an accuracy better than 1 meter. Based on these preliminary results, we are working on a long-term experiment that will collect RSSI data from BLE beacons (as well as GPS and inertial data) from passengers using the transit system of our campus.
Evaluation of Geomagnetic Matching Algorithms for Indoor Positioning

Geomagnetic fingerprinting is a promising technology for supplying smartphone indoor navigation algorithms with infrastructureless and to some extent stable local position information. Geomagnetic disturbances in buildings impose a characteristic magnetic signature which can be detected by the phones magnetic sensor. A common approach is to create a database by recording reference fingerprints for example along a predefined known path. In the positioning phase, magnetic data is recorded along a certain time-or path length of the unknown path. The live data can be analyzed and compared against the prerecorded reference fingerprints. This magnetic matching procedure differs considerably from WiFi fingerprinting, where WiFi data from discrete points is compared. The main difference is that the described approach uses data recorded as time series. The matching has to consider not only the signal amplitude but also temporal-or spatial matching. In this paper several magnetic matching algorithms are evaluated for usage in an indoor positioning system. A public database is used as data source allowing comparison of the results with other works.
Reducing FTM Ranging and Location Attack Exposure With Crowd-Wisdom

802.11 Fine Timing Measurement is an indoor ranging technique. Because it is unauthenticated and unprotected, an adversary can implement ranging and location attacks, by inserting one or more rogue responders and causing an unsuspecting client to incorporate forged values into its location computation. We show in this paper that protection intended for attacks on comparable ranging techniques, like GPS, are ineffective in the case of FTM. However, we also show that a crowd-sourcing technique that confirms that one AP is known by the others can mitigate the attack exposure.
WiVisit: POI Visit Identification Based on Auto-Generated Wi-Fi Fingerprint

Point of Interest (POI) visit is critical information for many location-based services, such as POI recommendation and advertising push. However, most POI visit information is obtained from user’s check-in data on social networks, which inevitably contains false visits and misses parts of real visits. Some work has been done to attempt mining POI visits from users’ GPS trajectories, but they could not cover indoor POIs. In this paper, we proposed a Wi-Fi fingerprint-based POI visit identification system, WiVisit, in order to get accurate POI visit info, including indoor POIs. Different from traditional Wi-Fi fingerprint-based localization system, WiVisit can generate Wi-Fi fingerprints automatically with Wi-Fi and POI binding info for different POIs without any human effort. Therefore, WiVisit system could be easily and widely deployed in the real world. Moreover, a multi-model fusion based POI visit identification method was used in WiVisit to handle multiple POI types. Finally, extensive real POI visits were collected and used to assess the performance of WiVisit system. WiVisit achieved a 90% recall rate and 83% accuracy from the visited POIs, which already outperformed the state-of-the-art.
GAN+: Data Augmentation Method Using Generative Adversarial Networks and Dirichlet for Indoor Localisation

Indoor localisation is indispensable to many service applications ranging from hospitals, malls, to parking lots. Several machine learning techniques have been explored with limited success. This is partly due to insufficient training data. This paper presents a data augmentation workflow, GAN+, which uses Dirichlet distribution coupled with a Generative Adversarial Network. It aims to improve the performance of the model by synthetically producing additional training samples without the need for any extra human effort. The proposed technique was tested using the multi-building dataset, UJIndoorLoc [1]. Experimental results show that the dataset generated by GAN+ can achieve an average location error that is more than ten times lower than that of the original dataset. Therefore, the proposed data augmentation scheme validates the feasibility of using GAN’s in the domain of indoor localisation.
A Simulation Tool for the Analysis of TDOA Methods

Localization applications are widely used as an essential service in many systems, such as 5G device positioning, location of things (LoT), and speech source localization. Unlike classical GPS, the time difference of arrival (TDOA) approach estimates the angle of arrival using the signals received from a sensor array, allowing to determine the position of an emitter with more accuracy. This paper presents the Simulation PlAtform for PosiTIon LoCatiOn (SimPatico), a simulation tool for evaluation and analysis of TDOA methods, providing a precise way to compare their performance considering specific scenarios. TDOA methods implemented in SimPatico are shown in terms of mathematical formulation and operational properties. We also explore the main features of the simulator, including source signal representations, addition of distortion on them and simulation parameters. Simulation outcomes are presented, exemplifying SimPatico as a benchmark platform and a versatile tool for performance analysis of TDOA methods.
A Stepwise Simple Self-Calibration Method for Low-Cost MEMSIMU

The low-cost MEMS-IMU is affected by the biases, the scale factors and the misalignment errors, which will cause the measurement errors of the IMU, and then cause the large system errors. Traditional calibration method based on laboratory turntable is complicated and expensive, so it has become a trend to study the self-calibration method without relying on special equipment such as turntable. In traditional self-calibration methods, 12 full error parameters of accelerometer are usually estimated at one time with optimization estimation algorithm, which will cause the problem of high complexity, long estimation time and low accuracy. In this paper, we proposed a stepwise self-calibration method by estimating the biases and other 9 error parameters step by step, under which the estimation accuracy can be improved and the algorithm complexity can also be reduced. In addition, considering the characteristics of gyroscope biases successive start fluctuation, online calibration strategy of the gyroscope biases is also adopted. Turntable experiment shows that the 24 error parameters of accelerometer and gyroscope can be calibrated quickly in 8 minutes by the method proposed in this paper. The estimation efficiency is 10~20 times faster than that of one-time estimation method. The calibration errors of the accelerometer are within 0.2 m/s, the calibration errors of the gyroscope are within 0.5 °/s, and the calibration accuracy is more than 95%.
O-CNN

We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the 3D shape surface. We design a novel octree data structure to efficiently store the octant information and CNN features into the graphics memory and execute the entire O-CNN training and evaluation on the GPU. O-CNN supports various CNN structures and works for 3D shapes in different representations. By restraining the computations on the octants occupied by 3D surfaces, the memory and computational costs of the O-CNN grow quadratically as the depth of the octree increases, which makes the 3D CNN feasible for high-resolution 3D models. We compare the performance of the O-CNN with other existing 3D CNN solutions and demonstrate the efficiency and efficacy of O-CNN in three shape analysis tasks, including object classification, shape retrieval, and shape segmentation.
The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts

In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional content in the production of automotive components and parts traded between the three partner countries, United States, * CAPP Research Centre for the Analysis of Public Policies, University of Modena and Reggio Emilia, Italy 1 Department of Economics, University of Modena and Reggio Emilia, Modena, Italy margherita.russo@unimore.it, corresponding author 2 Department of Economics, University of Modena and Reggio Emilia, Modena, Italy, fabrizio.alboni@unimore.it 3 Facultad de Economía, Universidad Nacional Autónoma de México, Mexico, jcarreto@economia.unam. mx 4 Complex Multilayer Networks Lab, Center for Information Technology, Fondazione Bruno Kessler (FBK), dedomenico@fbk.eu 5 Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), University of Catania, giuseppe.mangioni@unict.it 6 Department of Economics, Ca' Foscari University of Venice Italy; simone.righi@unive.it 7 Former Professor Dipartimento di Economia e Diritto, Sapienza Università di Roma, Italy, annamaria.simonazzi@uniroma1.it The paper is part of the research project "Geopolitical and technological challenges for the automotive global value chains", PI Annamaria Simonazzi, funded by INET. The application on Mexico has been developed under the complementary project "Digital transformation in the automotive supply chain in Mexico", PI Jorge Carreto Sanginés, funded by UNAM PAPIIT IN309620. The authors wish to thank Emmanuel Lazega, Pietro Panzarasca and Riccardo Righi for their comments on the presentation of preliminary results of this paper at the ARS'19 Seventh International Workshop, and comments and suggestion emerging in the discussion with participants at the c.MET05 Policy Research Meeting on 31st March 2021, Italy, at the Gerpisa 2021 Colloquium, at the Conference on "L'industria dell'auto e gli investimenti della Cina nell'era dell'auto elettrica e della mobilità sostenibile", Modena University, 2021; and at the EUSN 2021 Conference. A special thanks to Martin Rosvall, who highlighted which output files could be used in the analysis of Infomap flow between clusters, and to Emanuele Murgolo for copy editing.
Zero-shot Cross-Linguistic Learning of Event Semantics

Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face. In this paper, we look specifically at captions of images across Arabic, Chinese, Farsi, German, Russian, and Turkish and describe a computational model for predicting lexical aspects. Despite the heterogeneity of these languages, and the salient invocation of distinctive linguistic resources across their caption corpora, speakers of these languages show surprising similarities in the ways they frame image content. We leverage this observation for zero-shot cross-lingual learning and show that lexical aspects can be predicted for a given language despite not having observed any annotated data for this language at all.
Improved Characteristics of CdSe/CdS/ZnS Core-Shell Quantum Dots Using an Oleylamine-Modified Process

CdSe/CdS with ZnS/ZnO shell quantum dots (QDs) are synthesized by a one-pot method with various oleylamine (OLA) contents. The crystal structures of the QDs were analyzed by X-ray diffractometry, which showed ZnS diffraction peaks. It was represented that the ZnS shell was formed on the surface of the CdSe/CdS core. Interestingly, QDs with a high OLA concentration exhibit diffraction peaks of ZnS/ZnO. As a result, the thermal stability of QDs with ZnS/ZnO shells exhibits better performance than those with ZnS shells. In addition, the photoluminescence intensity of QDs with ZnS/ZnO shells shows a relatively slow decay of 7.1% compared with ZnS shells at 85 °C/85% relative humidity aging test for 500 h. These indicate that QDs with different OLA modifications can form ZnS/ZnO shells and have good stability in a harsh environment. The emission wavelength of QDs can be tuned from 505 to 610 nm, suitable for micro-LED display applications.
ROTOR DESIGN AND STRENGTH ANALYSIS OF HIGH SPEED PERMANENT MAGNET MACHINE

In this paper, the design features of high speed permanent magnet machines are introduced. More discussions are paid to the choice of permanent magnet (PM) material and rotor structure, determination of main rotor dimensions, and analysis method of rotor strength. Nowadays, the sintered neodymium-iron-boron (Nd-Fe-B) material is used in most of PM machines. This kind of PM material has large compression strength but small tensile strength, and can not withstand the large centrifugal force due to the high rotation speed. A nonmagnetic steel enclosure is needed to cover the PM material.The tensile press of PM can be reduced by pre-pressure applied to the outer surface of the PM through shrink-fitting into the enclosure. The rotor strength analysis methods of high speed PM machine by means of analytical method as well as finite element analysis are introduced. As an example, the strength analysis results for a high speed PM machine with rated speed of 60000r/min are presented.
Analysis and Research on Nonlinear Complex Function Approximation Problem Based on Deep Learning

Shallow models have limited ability to express high-dimensional nonlinear complex functions. Based on deep learning, a Gaussian radial basis function neural network (RBFNN) is proposed, which is an analysis method of nonlinear complex function approximation based on the Gaussian-RBFNN model. The proposed method can approximate single-variable and binary complex nonlinear functions, and the approximation error is less than 0.1, which can achieve the ideal approximation effect. Finally, the proposed method is applied to analyze the nonlinear complex stock prediction approximation problem, and the effectiveness and practicality of the method are further verified. It can be seen that compared with the BP neural network model, this model performs better in average training time, training standard error, Ratt, RATR, and trmse For RBFNN; the training only needs 50 iterations, while the traditional BP needs 7100 training to gradually converge. In the actual function test, after 275 times of training, the convergence speed gradually tends to be stable, and the predicted value is closer to the actual value. Therefore, the model can be used to analyze practical nonlinear complex function approximation problems.