Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.
Evaluation of IGBT Module Remaining Lifetime in Wind Power Converters Considering Impacts of Failure Location

Operational and health management for the reliability of power electronic converters requires sensitive condition monitoring and accurate estimation of the remaining lifetime. This study adds to the second aspect by examining the effects of failure locations on junction temperature profiles under different conditions. It is found that die-attach solder fatigue and direct bonding copper (DBC) solder fatigue, which occur frequently during the serving time, have different effects on thermal loads. This article investigates the dependence of thermal stress effects on failure locations and output electrical frequencies using finite-element models and proposes an updated strategy for the Cauer-type thermal model according to the failure location. Experimental results validate the proposed model. The remaining lifetime of the doubly fed induction generator converter is calculated under different health conditions and failure locations using a modified lifetime model. This article is intended for the health management of converter systems and is supposed to be in service reliably.
Application of Empirical Wavelet Transform for Analyzing Inter-turn Fault in FEM Based Closed Loop Speed Controlled Induction Motor

The objective of the work is to analyze the finite element method based inter-turn fault in the stator winding of an induction motor (IM) controlled in a closed loop speed manner by utilizing empirical wavelet transform. Diagnosing inter-turn fault involving less than 5% of turns is a major challenge for electrical machine researchers. The analysis of such faults becomes more complex when the machine is operated from an inverter in a closed loop speed controlled manner due to its operation at variable speed and varying load. In this direction, a 7.5 kW IM is modeled in ANSYS Maxwell with various inter-turn fault severities like 30T (12.5%), 15T (6.25%) & 10T (4.2%) and correlated with ANSYS Simplorer for power supply and loading measures. MATLAB Simulink is utilized to model a closed loop speed controlled inverter and co-simulated with Simplorer to synthesize all the tools in a collective framework for realistic simulation. The airgap flux density analysis shows additional noise harmonics in its spatial FFT spectrum for shorted turns involving even less than 5%. Then empirical wavelet transform is used to analyze the line current data obtained from finite element IM model for various fault severities. The result shows that amplitude of a decomposed component of line current increases in a particular frequency band for 10T short compared to healthy data whereas additional components are induced in the spectrum when fault severity increases to 30T short.
Adaptive Lazy Compaction with High Stability and Low Latency for Data-Intensive Systems

Data-intensive systems are typical applications in the big data era. Most data-intensive systems employ LSM (Log-Structured Merge)-tree-based key-value stores, such as RocksDB and LevelDB. The LSM-tree has a multi-level data structure and can transform random writes into sequential ones by a special operation called compaction. However, compaction operations are costly and will increase the processing latency and lower the throughput of the LSM-tree. In this paper, we propose an adaptive lazy compaction method called ALC for the LSM-tree. The main idea of ALC is to delay compactions operations when the current workload is high, and to perform compactions when the system is low-loaded. With this mechanism, we can lower the impact of compactions on the system's performance and achieve high stability and low latency. Our preliminary experiments show that ALC outperforms RocksDB and the state-of-the-art method SILK in latency and throughput-stability.
A Comparative Study of Deep Learning Networks for COVID-19 Recognition in Chest X-ray Images

The COVID-19 pandemic is devastatingly affecting the health and well-being of the worldwide population. A basic advance in the battle against it resides in effective screening of infected patients, with one of the key screening approaches such as radiological imaging based on chest radiography. Faced with this challenge, various artificial intelligence (AI) frameworks, mostly based on deep learning, have been proposed and results have been getting better and very promising as the precision of positive cases recognition is constantly refined. In the light of previous work on automated X-ray image screening, we train several deep convolutional networks for the classification of chest pathologies into: normal, pneumonia, and COVID-19. We use three open-source and one private dataset for the validation of our findings. Unfortunately, data scarcity remains a big challenge hurdling COVID-19 automatic recognition research. In our case, we used a total of 518 COVID-19 positive X-ray images. We evaluate different architectures for COVID-19 recognition with different deep neural architecture.
Analysis on the influence of urban living environment on healthy human capital based on health production function

Based on urban living environment on the health of human capital as the research core, the new healthy production function was constructed by integrating the urban environment pollution, urban infrastructure, city appearance cleaning and other 11 factors that have influence on the human capital of health into the healthy production function proposed by Grossman. In addition, the provincial panel data from 2008 to 2017 in China were used to conduct regression analysis on the specific impact by using the fixed-effect model. The estimated results show that :(1) urban environmental pollution will cause serious harm to the health of urban residents. For every 1% increase in sulfur dioxide emissions, the health human capital will decrease by 0.0228%;(2) medical resources and the education level of the population are bottlenecks affecting human capital of health, and insufficient resources will have a negative impact on human capital of health; (3)strengthening the construction of urban infrastructure, harmless treatment of garbage, investment in urban pollution control, urban public green space area and construction of cultural venues can help reduce the harm of urban gas, solid and water pollution, reduce the breeding of viruses and bacteria, and make cities more livable;(4)scientific and technological innovation depends not only on the number of authorized patents, but also on the quality and efficiency of the transformation of patents into scientific and technological achievements.
MagIK: A Hand-Tracking Magnetic Positioning System Based on a Kinematic Model of the Hand

In this article, we present a hand-tracking system based on magnetic positioning. A single magnetic node is mounted on each fingertip and two magnetic nodes on the back side of the hand. A fixed array of receiving coils is used to detect the magnetic field, from which it is possible to infer the position and orientation of each magnetic node. A kinematic model of the whole hand has been developed. Starting from the positioning data of each magnetic node, the kinematic model can be used to calculate the position and flexion angle of each finger joint, plus the position and orientation of the hand in space. Relying on magnetic fields, the hand-tracking system can work also in non-line-of-sight conditions. The gesture reconstruction is validated by comparing it with a commercial hand-tracking system based on a depth camera. The system requires a small number of electronics to be mounted on the hand. This would allow building a light and comfortable data glove that could be used for several purposes: human–machine interface, sign language recognition, diagnostics, and rehabilitation.
SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection

Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim’s age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications o f s uch m agnitude, a nd existing cyberbullying datasets suffer from quite severe class imbalances. To combat these challenges, we establish a framework for the automatic generation of balanced data by using a semi-supervised online Dynamic Query Expansion (DQE) process to extract more natural data points of a specific c lass f rom T witter. W e also propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings. With our DQE-augmented dataset, which we have made publicly available, we compare our GCN model using eight different tweet embedding methods and six other classification models over two sizes of datasets. Our results show that our proposed GCN model matches or exceeds the performance of the baseline models, as indicated by McNemar statistical tests.
Wind Farm Generators Thermal Condition Monitoring Based on Long Short-Term Memory

In recent decades, wind farms were the focal point of investing due to their considerable advantages over conventional fossil fuel-based power plants. Since a lot of the wind farms are installed more than 10 years ago, it is essential to develop a practical and cost-effective condition monitoring strategy that can address most of the common failures in wind farms using the data provided by existing SCADA systems. The need for a modern condition monitoring strategy coincides with the emergence of Deep Learning (DL) in the recent decade. In this paper, a novel condition monitoring is proposed to monitor the temperature of generator windings in a wind farm using Long Short-Term Memory (LSTM) model which is a Recurrent Neural Network (RNN) method. This DL model is a practical and unique choice for prediction due to its ability for considering long-term dependencies among input sequential features, which in this case are the provided time-series datasets by SCADA system. The novel proposed model in this paper is evaluated in healthy and abnormal operation modes of a wind farm in Quebec Province, Canada as case scenarios. Moreover, the error signal is calculated between real and predicted signals to evaluate the proper performance of the model. Finally, this model is compared with Multiple Linear Regression model to show the effectiveness and high precision of the proposed model.
Reinforcement Learning for Robotic Assembly Using Non-Diagonal Stiffness Matrix

Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, have been extensively studied for task automation. Precision assembly, a typical example of contact-rich tasks, requires high time constants to cope with the change in contact state. Therefore, this letter proposes a local trajectory planning method for precision assembly with high time constants. Because the non-diagonal component of a stiffness matrix can induce motion at high sampling frequencies, we use this concept to design a stiffness matrix to guide the motion of an object and propose a method to control it. We introduce reinforcement learning (RL) for the selection of the stiffness matrix because the relationship between the desired direction and the sensor response is difficult to model. An architecture with various sampling rates for RL and admittance control has the advantage of rapid response owing to a high time constant of the local trajectory modification. The effectiveness of the method is verified experimentally on two contact-rich tasks: inserting a peg into a hole and inserting a gear. Using the proposed method, the average total time needed to insert the peg in the hole is 1.64 s, which is less than half the time reported by the best of the existing state of the art studies.
A Unified Deep Learning Approach for Foveated Rendering & Novel View Synthesis from Sparse RGB-D Light Fields

Near-eye light field displays provide a solution to visual discomfort when using head mounted displays by presenting accurate depth and focal cues. However, light field HMDs require rendering the scene from a large number of viewpoints. This computational challenge of rendering sharp imagery of the foveal region and reproduce retinal defocus blur that correctly drives accommodation is tackled in this paper. We designed a novel end-to-end convolutional neural network that leverages human vision to perform both foveated reconstruction and view synthesis using only 1.2% of the total light field data. The proposed architecture comprises of log-polar sampling scheme followed by an interpolation stage and a convolutional neural network. To the best of our knowledge, this is the first attempt that synthesizes the entire light field from sparse RGB-D inputs and simultaneously addresses foveation rendering for computational displays. Our algorithm achieves fidelity in the fovea without any perceptible artifacts in the peripheral regions. The performance in fovea is comparable to the state-of-the-art view synthesis methods, despite using around 10x less light field data.
Experimental Implementation of TinyIPFIX Protocol for Arduino and Raspberry Pi Platform

The aim of this article is to implement the TinyIPFIX protocol for the Arduino and Raspberry Pi platform using the Python programming language. This protocol is derived from the IPFIX protocol and is used to enable efficient communication between devices within the Internet of Things environment. At the beginning, this article deals with the analysis of related communication protocols, the analysis of criteria for the TinyIPFIX protocol for its possible operation and the analysis of the TinyIPFIX message format, of which parts such a message consists. Subsequently, the architecture design procedure for the TinyIPFIX protocol is discussed. The main part is the experimental implementation of the TinyIPFIX protocol for Arduino and Raspberry Pi platforms. At the end of this work, experimental testing is evaluated, according to which the benefits of the implemented TinyIPFIX protocol are clarified.
Text Image Deconvolution Using Re-blurry Process And $L_{0}$ Norm

For single image blind deblurring issue, we need to estimate the kernel and the latent image from an observed blurry image. It is well known that the statistical results of clear text image with re-blurring process is very different with that of blurry text image. Furthermore, according to the prosperity of text image, the $L_{0}$ norm has been proofed effectively in text image deblurring. Therefore, inspired by observing distinct properties of text image with or without re-blurring process, we propose a new deblurring method to process text image by using the re-blurry process and $L_{0}$ norm. First, we employ the proposed regularization terms to the image deblurring framework. Then, we propose an effectively optimized method to estimate kernel and latent image. Finally, comparing with the state-of-the-art methods are evaluated quantitatively and qualitatively. In conclusion, the proposed method performs favorably against the state-of-the-art methods on processing blurry text images.
Unsupervised Discriminative Deep Hashing With Locality and Globality Preservation

Deep hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. However, deep unsupervised hashing can hardly achieve impressive performance due to the lack of the semantic supervision. This letter proposes Unsupervised Discriminative Deep Hashing (UD<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>H) to fulfill this gap. UD<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>H is formulated to jointly perform hash code learning and clustering, and trained in an asymmetric manner to improve the efficiency. The cluster labels supervise the training of deep model to enable hash code discriminative. Based on the outputs of the deep model, UD<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>H adaptively constructs a similarity graph that considers the local and global structures. Experiments on three benchmark datasets show that the proposed UD<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>H outperforms the state-of-the-art unsupervised deep hashing methods.
Repeating Link Prediction over Dynamic Graphs

Graphs are a vastly useful and widely used form of modeling and representation of systems, processes, entities, events, objects, components etc., in various domains of discourse, that reflects relations or connections of modeled entities. Graphs are vital to diverse data mining applications, as they capture relationships between data items, such as dependencies or interactions, and graph analysis can reveal valuable insights for many application domains including machine learning, anomaly detection, clustering, recommendations, social influence analysis, bioinformatics, and others. The analysis of the evolutionary behavior of dynamic graphs provides the means to continuously predict the appearance, and also, the disappearance of new graph links, i.e., to perform the Dynamic Link Prediction Task. Dynamic Link Prediction has been explored widely in the past years; however, the majority of these works focus on discovering new edges (by implicitly assuming ever growing dynamic networks). However, very few works focus on the repeating edges, i.e., links that continuously vanish and reappear in the dynamic network, but which size (in terms of number of nodes and edges) does not significantly change over long periods of time. In this work, we first study the literature for link prediction in the static settlement, then, we focus on dynamic link prediction, underlining the strengths and weaknesses of every approach studied. We discover that traditional methods do not work well with repeating links as they are unable to encode temporal patterns associated with the edges while also considering the topological graph features. We propose a novel method, Temporal Edge Embedding Neural Network (TEEN), which is based on a deep learning architecture that jointly optimizes the prediction of the correct edge labels as well as the proximity of two nodes’ pairs in their latent space at every time step. Our solution benefits of node embeddings created with deep encoders from where an edge embedding is created for every time step. Our evaluation experiments on transactional graphs show that TEEN is able to outperform state-of-the-art models by over 8% on AUC and over 7% on F1-Score. We show that our approach brings significant improvements in the scenario of transactional graphs.
A Fault Localization Approach Derived From Testing-based Formal Verification

The rapid development of safety-critical systems eagerly demands for the formal verification or enormous testing to ensure the correctness of programs. Testing-based formal verification with symbolic execution (TBFV-SE), a technique that integrates both formal verification and testing, taking the advantages of both methods without so much cost to meet such need. In order to further pinpoint the problematic positions in the code when TBFV-SE gives the incorrect program paths, we describe a fault localization algorithm to do this work through making use of the information provided by TBFV-SE. A case study demonstrates that our algorithm can more effectively and efficiently shrink the scale of suspicious lines of the code.
Health Management for PEM Fuel Cells Based on an Active Fault Tolerant Control Strategy

Health management can effectively extend the service life of proton exchange membrane fuel cells and reduce the maintenance cost. This paper proposes a health management method for the proton exchange membrane fuel cells based on an active fault tolerant control method. With the advantages of low cost and high speed, a stack voltage model is developed for the condition monitoring. The fast electrochemical impedance spectroscopy measurement is performed when the condition monitoring module detects faults. Then, the distribution of relaxation time is used to extract the characteristics of electrochemical impedance spectroscopy for fault diagnosis. Thereafter, based on the diagnosis results, the performance recovery strategies are incorporated to cope with the faults. This paper adopts three typical fuel cell faults, namely flooding, membrane drying, and air starvation, to demonstrate the effectiveness of the proposed health management. Experimental results show that the proposed health management can realize real-time health monitoring and rapid fault diagnosis, and achieve effective recovery of performance under faulty conditions, thereby significantly improving the reliability of the proton exchange membrane fuel cell system.
Large-scale Data-driven Segmentation of Banking Customers

This paper presents a novel big data analytics framework for creating explainable personas for retail and business banking customers. These personas are essential to better tailor financial products and improve customer retention. This framework is comprised of several components including anomaly detection, binning and aggregation of contextual data, clustering of transaction time series, and mining association rules that map contextual data to cluster identifiers. Leveraging rich transaction and contextual data available from nearly 60,000 retail and 90,000 business customers of a financial institution, we empirically evaluate this framework and describe how the identified association rules can be used to explain and refine existing customer classes, and identify new customer classes and various data quality issues. We also analyze the performance of the proposed framework and show that it can easily scale to millions of banking customers.