Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Encoder-Decoder Architecture for Table Border Segmentation of Scanned Documents

Robotic Process Automation (RPA) has been widely used in business and enterprises to automate the processing of digital documents and collect information and acquire knowledge. Table structure reconstruction in scanned documents has been extensively studied as an essential application of RPA. However, the detection of table borders often ignores broken borders, which makes it unsuitable for natural scenes. To address this, our paper heavily employs a data augmentation approach to synthesize fake scanned documents to train our table-border semantic segmentation model. We propose a novel segmentation model for table borders based on semantic segmentation. We compare traditional morphology-based line detection algorithms with existing semantic segmentation-based approaches. The results indicate that our proposed algorithm can solve the frame line detection problem effectively, even for low-quality scanned images. Actual cases show that we can reconstruct the table’s structure and obtain the knowledge in the table.
Agenda of Solutions to Mitigate the Challenge of Polarization of Extreme Positions in Social Media Environments

This paper proposes an agenda of possible solutions relevant to the challenge of polarization of extreme positions. The challenge of mitigating polarization of extreme positions has been a hot topic in politics, journalism, new media, and, more recently, in CSCW and HCI research. In this study, the main focus is to investigate the harmful effects of polarization from a neuroscience point of view. The suggested solutions are based on theories of social cognitive neuroscience and encourage awareness of inappropriate behavior, an increase in the diversity of arguments, and the exercise of individual and collective responsibility, to mitigate the problem of polarization that threatens public debate and, consequently, democracy.
Modeling of a Soft Robotic Tongue Driven by Compressed Air

A novel soft robotic tongue is proposed for the purpose of presenting typical tongue movements in speaking, which is pneumatically actuated and supposed to be applied in pronunciation teaching device and humanoid robot to gain verisimilitude. The actuation and control system is mainly composed of solenoid proportional valves (ITV0010, SMC, Japan), DAQ card (PCIe6321, NI, USA) and motion tracking system (OptiTrack, NaturalPoint, USA). A theoretical model based on continuum mechanics is established building the relationship between input air pressure and output deformation. On the basis of the proposed theoretical model and Artificial Neural Network (ANN), an integrated control algorithm is preliminarily raised for different purpose of use in static and dynamic control. A series of verification experiments are carried out based on OptiTrack system in order to test motion controllability and working performance of the robotic tongue. The experimental results indicate that the proposed theoretical model is more suitable for static shape control due to obvious deformation delay compared with input air pressure. High deformation repeatability guarantees the precision of ANN based control method which is more suitable for dynamic motion control. The results of performance test show that actual movements of the robotic tongue generally meet the demand in demonstrating tongue shape when speaking.
Mitigation of Subsynchronous Control Interaction in DFIG-Based Wind Farm Using Repetitive-PI

The subsynchronous interaction between the wind farm’s grid-side converter and the series-compensated transmission line can trigger subsynchronous oscillations (SSO). The wind power system oscillation frequency depends on the actual operational conditions. This paper proposes a repetitive-PI control strategy to mitigate oscillations under various conditions. The repetitive-PI control offers fast dynamic performance and high steady-state accuracy, which can eliminate steady-state errors and improve the system’s robustness. Firstly, the repetitive-PI composite controller is derived based on the frequency domain expansion of the repetition controller. Then, the stability of the repetitive-PI composite controller is analyzed, and its main parameters of it are designed. Finally, to demonstrate the efficacy of the proposed suppression technique for SSO, simulations are conducted under different wind speeds, series compensation levels, and numbers of operating wind turbines. The time-domain simulation results and impedance model-based analysis demonstrate that the proposed repetitive-PI control strategy enhances the system’s damping performance and improves its disturbance resistance.
Detection of osteochondritis dissecans using convolutional neural networks for computer-aided diagnosis of baseball elbow

Baseball elbow is a kinetic disorder of the elbow caused by repetitive pitching in baseball. Osteochondritis dissecans (OCD), one of the most common forms of baseball elbow, is a disorder of the humeral capitellum of the elbow, and early detection of OCD is important. This study aims to create a model to detect OCD from ultrasound images of the elbow. The model is based on VGG16. The proposed method was validated by using 67 OCD subjects and 91 normal subjects. The results showed that the model achieved an accuracy of 88.5%, a precision of 87.9%, a recall of 97.0%, an F1 score of 0.910, and an AUC of 0.971.
Easily Reconstructable Logic Functions

This paper shows that sum-of-product expression (SOP) minimization produces the generalization ability. We show this in three steps. First, various classes SOPs are generated. Second, minterms of SOP are randomly selected to generate partially defined functions. And, third, from the partially defined functions, original functions are reconstructed by SOP minimization. We consider Achilles heel functions, majority functions, monotone increasing cascade functions, functions generated from random SOPs, and monotone increasing random SOPs. As for generalization ability, the presented method is compared with Naive Bayes, multi-level perceptron, support vector machine, JRIP, J48, and random forest.
Multi-Target Cross-Dataset Palmprint Recognition via Distilling From Multi-Teacher

Cross-dataset palmprint recognition is an important and popular topic, which has attracted more and more attention. In previous study, researchers are mainly focused on the scenarios of single-target or multisource cross-dataset recognition. However, in practical application, the query images may be collected from multiple devices and environment, called multi-target cross-dataset palmprint recognition, which is much more challenging. In this article, an approach is presented for multi-target cross-dataset palmprint recognition using knowledge distillation and domain adaptation, named distilling from multi-teacher (DFMT). The source dataset is first paired with each of the multiple target datasets. Then, a teacher feature extractor is constructed to extract the adaptive knowledge of each pair using domain adaptation. Then, a student feature extractor is established to learn the adaptive knowledge from teacher feature extractors. Particularly, multilevel distillation losses are constructed to help transfer the adaptive knowledge more effectively. Experiments are carried out on multiple palmprint databases such as contact database, contact-less database, constrained database, and unconstrained database. Experimental results demonstrate the superiority of DFMT compared with other competitive algorithms.
Community-based Matrix Reordering for Sparse Linear Algebra Optimization

Sparse linear algebra kernels achieve sub-optimal performance due to their poor cache locality. Matrix reordering is an effective pre-processing optimization that improves cache locality and performance of these kernels. While many reordering techniques have been proposed, most prior work on matrix reordering suffer from two key limitations: (1) they evaluate their reordering proposal on a small set of arbitrarily-selected inputs and (2) they do not quantify the additional headroom for improvement after reordering is applied. To address these two limitations, we perform a detailed characterization of reordering techniques across a broad set of 50 input matrices where we quantify the ability of matrix reordering techniques to bring sparse linear algebra kernels close to hardware limits. Our analysis reveals that community-based matrix reordering is most effective at optimizing the execution of sparse linear algebra kernels, bringing the cuSPARSE SpMV kernel to within 54% of ideal run time on an NVIDIA A6000 GPU on average. However, community-based reordering is not uniformly effective across all 50 input matrices. We investigate the reasons when community-based reordering falls short and propose an enhanced version of community-based reordering that provides up to 1.57× additional performance improvements for the SpMV kernel.
Intrusion Detection Based on Sampling and Improved OVA Technique on Imbalanced Data

Network-based Intrusion Detection(NID) is an effective means to deal with network attacks. NID is able to detect different types of network attacks by analyzing network traffic. However, in the real world, network traffic contains majority and minority class attacks as well as a large number of normal traffic samples. The imbalance in the number of training samples of various types of network traffic makes network intrusion detection very poor. Due to the lack of training samples, traditional NID can’t learn the characteristics of minority class attacks, which leads to the failure of NID to detect minority class attacks. Therefore, in order to solve the problem brought by imbalanced data, we propose a network intrusion detection algorithm based on the sampling and improved One-vs-All(OVA) technique. The dataset is balanced by downsampling the majority class data based on K-means clustering and oversampling the minority class data based on Auxiliary Classifier Generative Adversarial Network(ACGAN), improve classification accuracy through OVA-based model training and testing. We conduct validation experiments on the NSL-KDD dataset, and the experimental results show that the proposed method achieves excellent results in terms of Accuracy, Precision, Recall and F1-score. Compared with existing state-of-the-art methods, the proposed method not only achieves excellent detection performance with low false positive rate, but also addresses the learning problem of imbalanced data more effectively.
Is the Future Cold or Tall? Design Space Exploration of Cryogenic and 3D Embedded Cache Memory

Memory latency, density, and power efficiency are key bottlenecks in a variety of computing systems, and the need for efficient and dense memory solutions is exacerbated by the continued importance of data-intensive applications such as machine learning, graph processing, and scientific computing. A myriad of emerging technologies and approaches aim to address the limitations of current systems. For example, 3D integration can enable highly dense memory structures, and multiple alternative device technologies such as STT and PCM have emerged as compelling solutions to improve memory system density and efficiency. Additionally, cryogenic operation of computing systems (i.e., ultra-low temperature cooling) is becoming a compelling solution as thermal hotspots have become a primary roadblock to conventional transistor scaling. This work probes, evaluates, and compares the potential capabilities of 3D integration, embedded non-volatile memories (eNVMs), and cryogenic operation towards improving future memory systems by presenting the first design space exploration of cryogenic operation and 3D integration applied towards the largest on-chip memory structure, the last level cache, as well as presenting and providing open-source tools for future, related design studies. This work specffically evaluates the applicationlevel benefits or limitations of such proposals by leveraging a cross-computing-stack simulation approach. Our studies reveal that the most compelling solution varies depending on the expected memory traffic patterns and workloads of interest, which in turn exposes several opportunities for future optimization and customization. For example, due to potentially high costs of cooling to cryogenic operation, we find that SRAM or 3T-eDRAM operating at 77K is sub-optimal compared to room-temperature SRAM and eNVM solutions, but exhibits advantages for relatively low-traffic workloads.
A Hybrid TOA/TDOA Localization Algorithm Based on Modified Taylor Weighting Technique under NLOS Conditions

In indoor positioning system, the mitigation of bias induced by non-line-of-sight (NLOS) errors is an urgent problem that needs effective solutions. The traditional Taylor weighting algorithm has high positioning accuracy in NLOS environments, with the limitation that the initial value of the interested target must be sufficiently close to its actual position, with significant computational complexity. This paper proposed a random point Taylor weighting (RPTW) technique to address these problems. The base station data is first grouped, and random points are scattered according to the Time of Arrival (TOA) data. The point with the smallest distance error from the measured value is selected as the initial estimated coordinates. The error value obtained from the Taylor iteration is used as the weight to weigh all the results to obtain the final estimated coordinates. The numeric simulation results show that the localization algorithm with the proposed RPTW technique can achieve optimal performances with randomly selected initial value under NLOS scenarios.
Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing

Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework.
FDI-X: An Occlusion-based Approach for Improving the Explainability of Deep Learning Models in Fault Detection and Isolation

Explanation for the deep learning model is necessary for high-stake decision-making, particularly in the context of fault detection and isolation (FDI). This is especially crucial in situations where incorrect FDI decisions can have serious consequences. This paper presents FDI-X (explainable FDI), an occlusion/masking-based approach to explain the decision made by the deep-learning model, increasing the trustworthiness of the model and making it suitable for use in industrial applications. It presents a hybrid fault diagnosis method that combines bond-graph-based residuals with a convolutional neural network (CNN) for classifying system faults in PV-cell. The proposed explanation method is demonstrated using a PV-cell simulation and various types of faults, showing that the CNN gives greater importance to the residuals that are most sensitive to the fault being diagnosed. The occlusion-based explanation method proves to be a valuable tool for improving the interpretability and reliability of deep learning models in FDI tasks.
Improved Distance vector based Kalman Filter localization algorithm for wireless sensor network

Localization is a crucial concern in many wireless sensor networks (WSN) applications. Moreover, getting accurate information about the geographic positions of nodes in the sensing field is necessary in order to make the collected data useful and meaningful. In this context, the heuristic distance vector called DV-Distance is widely used for range-free localization in multi-hop WSN thanks to its advantages such as its simplicity and good accuracy. However, its localization accuracy may be relatively low due to environmental conditions. In this paper, we propose an improvement of the DV-Hop algorithm based on distance computation via a received signal strength indicator (RSSI) and a simple polynomial approximation technique. The proposed technique consists of RSSI measurement for distance estimation between sensor nodes thanks to low cost and simplicity compared to other techniques based on time and angle of arrival, i.e. TOA, AOA and TDOA which require more specialized equipment. However, as RSSI values may be affected by conditions environment such as noise, we propose to use the Kalman Filter in order to deal with this issue and improve localization accuracy. Test results proved that the proposed technique has a higher localization accuracy than DV-Distance by up to 50% and by up to 18% when compared with the state-of-the-art algorithms cited above and other DV-Hop-based localization methods.
A Constraint-based Recommender System via RDF Knowledge Graphs

Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences.
Emotion Recognition from Audio-Visual Information based on Convolutional Neural Network

In recent years. , emotion recognition becomes a heavily researched field to consider in any project related to affective computing. Due to the almost limitless applications of this new discipline. , the development of emotion recognition systems has emerged as a lucrative opportunity in the corporate sector. Emotion recognition can be detected in many ways. , face. , speech. , text. , gestures. , etc. This paper proposes an emotion recognition approach using multiple information sources. Our approach involves both visual and speech information and it is based on Convolutional neural networks. The experiments prove the effectiveness of the proposed approach and the importance of combining visual with audio data.
Probing Planetary Formation and Evolution: Transiting Planets and Occulting Disks

The circumstellar environments of young stellar objects (YSOs) involve complex dynamical interactions between dust and gas that directly influence the formation of planets. However, our understanding of the evolution from the material in the circumstellar disk to the thousands of planetary systems discovered to date, is limited. One means to better constrain the size, mass, and composition of this planet-forming material is to observe a YSO being eclipsed by its circumstellar disk. Through this dissertation project, we are discovering and characterizing both disk eclipsing systems and exoplanets using the Kilodegree Extremely Little Telescope (KELT) project. KELT is a photometric survey for transiting planets orbiting bright stars (8 < V < 11); such bright planet host targets are well-suited for atmospheric characterization of the planets. KELT has discovered 15 planets transiting stars brighter than V ~11 to date. I will present some of the recently discovered planets from the survey and discuss their potential to advance our understanding of planetary atmospheres. In addition, KELT provides photometric monitoring of ~3 million stars, presenting the opportunity to perform multi-year studies of stellar variability generally and rare disk occultations specifically. Using time-series photometry from KELT we are conducting the Disk Eclipse Search with KELT (DESK) survey to look for disk eclipsing events, specifically in young stellar associations. To date, the survey has discovered and analyzed four previously unknown large dimming events around the stars RW Aurigae, V409 Tau, AA Tau, and TYC 2505-672-1, the latter now representing the longest-period eclipsing object known (period ~ 69 years). The results from this project on planet atmosphere characterization and protoplanetary disk structure and composition, will provide a framework to search for these kinds of systems in future surveys such as LSST.
Pedometers for Smartphones: Analysis and Comparison of Real-Time Algorithms

The recent years have witnessed the rise of an enormous number of software algorithms that implement pe-dometers (or step counters), which led to the development of several context-aware IoT-based smartphone apps for sports and healthcare, among others. While the number of scientific works in this context is high, there is no comparison study that analyzes the different proposal at implementation level. In this paper we first perform a literature review of software implementations of pedometers for smartphones and then classify them into a taxonomy. With this, we highlight the similarities of their scheme, which is based on a number of defined steps to be applied in a pipeline. We then develop a smartphone application that implements all the configurations of these steps found in literature and evaluates them in various scenarios. Finally, we present comparative results obtained by running extensive and real tests that show the importance of a carefully designed filtering step.
Secrecy in the UAV Ad Hoc Network

In this paper, secrecy is investigated for the UAV ad hoc network. The analysis is conducted under the framework of stochastic geometry in the 3D space. A new feature of the present work is the relaxation of integer path-loss exponents or their ratio. The theoretical challenge is well taken. With the aid of a few advanced special functions, several closed-form solutions are derived. Numerical results are presented and discussed.