Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on forklift obstacle detection and distance warning method based on deep learning

Aiming at the problems of personnel intrusion and random placement of goods affecting driving safety in the process of forklift operation, an obstacle target detection and distance detection method for forklift operation based on improved YOLOv5 network is proposed. First, in view of the problem that the target is small and difficult to detect when the distance between people is long, the YOLOv5 network is improved to improve the accuracy of target detection; for the problem of tracking loss caused by the detection target being occluded, the target tracking algorithm and ranging algorithm are introduced to realize the target detection tracking recognition and distance detection. The experimental results show that the detection accuracy of the improved YOLOv5 network is improved by 4%; within the dangerous distance range, the distance detection error is within 5%, which meets the requirements of real-time detection and distance detection and can be used to detect potentially dangerous situations during operation and give an early warning.
Lower level duality and the global solution of generalized semi-infinite programs

The reformulation of generalized semi-infinite programs (GSIP) to simpler problems is considered. These reformulations are achieved under the assumption that a duality property holds for the lower level program (LLP). Lagrangian duality is used in the general case to establish the relationship between the GSIP and a related semi-infinite program (SIP). Practical aspects of this reformulation, including how to bound the duality multipliers, are also considered. This SIP reformulation result is then combined with recent advances for the global, feasible solution of SIP to develop a global, feasible point method for the solution of GSIP. Reformulations to finite nonlinear programs, and the practical aspects of solving these reformulations globally, are also discussed. When the LLP is a linear program or second-order cone program, specific duality results can be used that lead to stronger results. Numerical examples demonstrate that the global solution of GSIP is computationally practical via the solution of these duality-based reformulations.
An approach to detect IoT botnet attacks based on hierarchical clustering

With the rise and development of Internet of Things (IoT) technology, more and more devices access the network. However, most of them usually ignore the security issues, the crisis such as large-scale DDoS attacks caused by IoT botnet becomes more and more severe. It is significant to study the behavior of botnet and the detection technology. In order to improve the detection performance of IoT botnet, we analyze the behavior of botnet based on the traffic in IoT environment and propose a detection approach based on hierarchical clustering. Firstly, we capture the network traffic as . pcap files and aggregate packets into data flows based on five-tuple, then extract the basic statistics features by using a time window. Secondly, we analyze the typical features of IoT botnet during waiting period and malicious active period and optimize them by hierarchical clustering. Finally, XGBoost algorithm is used to classify the botnet. To demonstrate the effectiveness of the proposed approach, we trained KNN, Decision Tree, Random Forest models over the same datasets to detect IoT botnet and compared their performance with our approach. The experimental results prove our method can efficiently detect botnet as compared to other trained models.
An Improved OFDM Channel Estimation Algorithm Based on DFT

Traditional DFT-based channel estimator for OFDM systems has lower computational complexity than MMSE channel estimators, and better performance than LS channel estimators. However, the traditional DFT-based channel estimator removes only the noise in the channel impulse response (CIR) beyond the length of the cyclic prefix (CP), while the noise within the length of CP is not suppressed at all, affecting the performance of the algorithm, and indicating that there is certain space for improving its performance. The traditional channel estimator is fairly improved and proposed. Only by introducing a decision threshold into the length of CP, the influence of the noise could be further removed by threshold processing. Simulation results show that the improved algorithm is superior to the original algorithm, and thus is of practical application value.
Modification of the segmentation based on Graph cut method

This article presents the implementation of methods to improve Graph cut segmentation during initialization by initialization contour. It deals with a modified iterative method for improving the segmentation of heterogeneous objects in the input images. The modifications bring less dependence on user-set constants of Graph cut segmentation and thereby simplify the user interaction. It also deals with a modification for establishing the value of t-links edges in the graph for Graph cut segmentation. These modifications are experimentally tested on the input images intended for segmentation.
Target product profile for readers of rapid diagnostic tests

Abstract While equipment-free visual interpretation is a benefit of lateral-flow assays, automating the reading of rapid diagnostic tests promotes proper test performance, interpretation and reporting of the results. We have created a target product profile that describes the minimal and optimal characteristics of various rapid diagnostic test readers. The product profile is intended to promote the development of effective, useful, sustainable rapid diagnostic test readers in support of health programmes worldwide. These readers may be custom hardware or software-only running on a general-purpose mobile device, for professional or lay use, and for medical or non-medical purposes. During the development of the product profile, the World Health Organization and FIND convened a development group of 40 leading scientists, experts, public health officials and regulators. We held a public consultation to which 27 individuals or organizations responded. The product profile calls for rapid diagnostic test readers that at minimum should: (i) interpret colorimetric tests with at least 95% agreement with expert visual interpretation; and (ii) automatically report results and associated data for the health programme. Optimally, readers should: (i) provide at least 98% agreement; (ii) operate with multiple models of rapid diagnostic tests; (iii) assist the user in performing each rapid diagnostic test according to the test instructions; and (iv) offer multiple configurations, operating modes and languages to suit the needs of diverse users, settings and health programmes.
Some remarks on the geometry of some surfaces of matrices associated with Toeplitz eigenproblems

Introduction Orbits and Quotients Matrices With Fixed Singular Values Matrices With Fixed Rank Matrices With Fixed Displacement Rank Matrices With Fixed Singular Values and Fixed Displacement Rank Hermitian Matrices With Fixed Eigenvalues Hermitian Matrices With Fixed Inertia Hermitian Matrices With Fixed Displacement Inertia Hermitian Matrices With Fixed Eigenvalues and Fixed Displacement Inertia Appendix: Tridiagonal Hermitian Matrices With Fixed Eigenvalues References Acknowledgements Introduction. I am interested in the following problem: Given a Toeplitz matrix, nd its singular values. This problem is quite important. For example, consider a timeinvariant, causal linear system. Such a system can be represented by means of a Volterra integral operator T having the following form:
Graph Global Attention Network with Memory for Fake News Detection

With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of natural language processing (NLP). This study addresses the problem of detecting fake news on social media, which poses a significant challenge to society. This study proposes a new approach named GANM for fake news detection that employs NLP techniques to encode nodes for news context and user content and uses three graph convolutional networks to extract features and aggregate users' endogenous and exogenous information. The GANM employs a unique global attention mechanism with memory to learn the structural homogeneity of news dissemination networks. The approach achieves good results on a real dataset.
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity

Modern machine learning increasingly supports paradigms that are multi-institutional (using data from multiple institutions during training) or cross-institutional (using models from multiple institutions for inference), but the empirical effects of these paradigms are not well understood. This study investigates cross-institutional learning via an empirical case study in higher education. We propose a framework and metrics for assessing the utility and fairness of student dropout prediction models that are transferred across institutions. We examine the feasibility of cross-institutional transfer under real-world data- and model-sharing constraints, quantifying model biases for intersectional student identities, characterizing potential disparate impact due to these biases, and investigating the impact of various cross-institutional ensembling approaches on fairness and overall model performance. We perform this analysis on data representing over 200,000 enrolled students annually from four universities without sharing training data between institutions. We find that a simple zero-shot cross-institutional transfer procedure can achieve similar performance to locally-trained models for all institutions in our study, without sacrificing model fairness. We also find that stacked ensembling provides no additional benefits to overall performance or fairness compared to either a local model or the zero-shot transfer procedure we tested. We find no evidence of a fairness-accuracy tradeoff across dozens of models and transfer schemes evaluated. Our auditing procedure also highlights the importance of intersectional fairness analysis, revealing performance disparities at the intersection of sensitive identity groups that are concealed under one-dimensional analysis.1
A Resonant Tank Based Approach for Realizing ZPA in Inductive Power Transfer Systems

A load-independent constant current (CC) - constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.
Linearizing the soft finger contact constraint with application to dynamic force distribution in multifingered grasping

Currently, most efficient algorithms for force-closure analysis and dynamic force distribution utilize linear programming, but friction models are nonlinear. Substituting polyhedral cones for circular cones of Coulomb friction realizes the linearization of the frictional point contact constraint. So far, however, there is no approach to soft finger contact. This paper present such an approach. Then the foregoing algorithms can be extended to grasping with soft finger contact. Herein an optimal force distribution algorithm for soft multifingered grasps is developed with an illustrative example.
Quantum Speed Limit for Time-Fractional Open Systems

The Time-Fractional Schr\"odinger Equation (TFSE) is well-adjusted to study a quantum system interacting with its dissipative environment. The Quantum Speed Limit (QSL) time captures the shortest time required for a quantum system to evolve between two states, which is significant for evaluating the maximum speed in quantum processes. In this work, we solve exactly for a generic time-fractional single qubit open system by applying the TFSE to a basic open quantum system model, namely the resonant dissipative Jaynes-Cummings (JC) model, and investigate the QSL time for the system. It is shown that the non-Markovian memory effects of the environment can accelerate the time-fractional quantum evolution, thus resulting in a smaller QSL time. Additionally, the condition for the acceleration evolution of the time-fractional open quantum system at a given driving time, i.e., a tradeoff among the fractional order, coupling strength, and photon number, is brought to light. In particular, a method to manipulate the non-Markovian dissipative dynamics of a time-fractional open quantum system by adjusting the fractional order for a long driving time is presented.
Feature Embedding Clustering using POCS-based Clustering Algorithm

An application of the POCS-based clustering algorithm (POCS stands for Projection Onto Convex Set), a novel clustering technique, for feature embedding clustering problems is proposed in this paper. The POCS-based clustering algorithm applies the POCS's convergence property to clustering problems and has shown competitive performance when compared with that of other classical clustering schemes in terms of clustering error and execution speed. Specifically, the POCS-based clustering algorithm treats each data point as a convex set and applies a parallel projection operation from every cluster prototype to corresponding data members in order to minimize the objective function and update the prototypes. The experimental results on the synthetic embedding datasets extracted from the 5 Celebrity Faces and MNIST datasets show that the POCS-based clustering algorithm can perform with favorable results when compared with those of other classical clustering schemes such as the K-Means and Fuzzy C-Means algorithms in feature embedding clustering problems.
LIMOT: A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at https://github.com/tiev-tongji/LIMOT.
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning

Cameras digitize real-world scenes as pixel intensity values with a limited value range given by the available bits per pixel (bpp). High Dynamic Range (HDR) cameras capture those luminance values in higher resolution through an increase in the number of bpp. Most displays, however, are limited to 8 bpp. Naïve HDR compression methods lead to a loss of the rich information contained in those HDR images. In this paper, tone mapping algorithms for thermal infrared images with 16 bpp are investigated that can preserve this information. An optimized multi-scale Retinex algorithm sets the baseline. This algorithm is then approximated with a deep learning approach based on the popular U-Net architecture. The remaining noise in the images after tone mapping is reduced implicitly by utilizing a self-supervised deep learning approach that can be jointly trained with the tone mapping approach in a multi-task learning scheme. Further discussions are provided on denoising and deflickering for thermal infrared video enhancement in the context of tone mapping. Extensive experiments on the public FLIR ADAS Dataset prove the effectiveness of our proposed method in comparison with the state-of-the-art.
Change point detection in low-rank VAR processes

Vector autoregressive (VAR) models are widely used in multivariate time series analysis for describing the short-time dynamics of the data. The reduced-rank VAR models are of particular interest when dealing with high-dimensional and highly correlated time series. Many results for these models are based on the stationarity assumption that does not hold in several applications when the data exhibits structural breaks. We consider a low-rank piecewise stationary VAR model with possible changes in the transition matrix of the observed process. We develop a new test of presence of a change-point in the transition matrix and show its minimax optimality with respect to the dimension and the sample size. Our two-step change-point detection strategy is based on the construction of estimators for the transition matrices and using them in a penalized version of the likelihood ratio test statistic. The effectiveness of the proposed procedure is illustrated on synthetic data.
A Blueprint of IR Evaluation Integrating Task and User Characteristics: Test Collection and Evaluation Metrics

Relevance is generally understood as a multi-level and multi-dimensional relationship between an information need and an information object. However, traditional IR evaluation metrics naively assume mono-dimensionality. We ask: How to deal with multidimensional and graded relevance assessments in IR evaluation? Moreover, search result evaluation metrics neglect document overlaps and naively assume gains piling up as the searcher examines the ranked list into greater length. Consequently, we examine: How to deal with document overlap in IR evaluation? The usability of a document for a person-in-need also depends on document usability attributes beyond relevance. Therefore, we ask: How to deal with usability attributes, and how to combine this with multidimensional relevance assessments in IR evaluation? Finally, we ask how to define a formal model, which deals with multidimensional graded relevance assessments, document overlaps, and document usability attributes in a coherent framework serving IR evaluation?
On the investigation of similarity measures for product resolution

Entity resolution is an important information integration problem that has been looked at by a number of research communities. Resolution of a specific type of entity, products, however, is a largely unexplored area. This work sets out a series of first steps in that direction. We devise similarity measures for comparing products based on specific attributes. To estimate the discriminative power of these similarity functions we introduce a metric termed pairwise discriminative power. We report on experimental results using two purpose-built test sets, corresponding to two very different e-commerce segments: electronics and toys.
Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition

In low-resource computing contexts, such as smartphones and other tiny devices, Both deep learning and machine learning are being used in a lot of identification systems. as authentication techniques. The transparent, contactless, and non-invasive nature of these face recognition technologies driven by AI has led to their meteoric rise in popularity in recent years. While they are mostly successful, there are still methods to get inside without permission by utilising things like pictures, masks, glasses, etc. In this research, we present an alternate authentication process that makes use of both facial recognition and the individual's distinctive temporal facial feature motions while they speak a password. Because the suggested methodology allows for a password to be specified in any language, it is not limited by language. The suggested model attained an accuracy of 96.1% when tested on the industry-standard MIRACL-VC1 dataset, demonstrating its efficacy as a reliable and powerful solution. In addition to being data-efficient, the suggested technique shows promising outcomes with as little as 10 positive video examples for training the model. The effectiveness of the network's training is further proved via comparisons with other combined facial recognition and lip reading models.
META-SMGO-$\Delta$: Similarity as a Prior in Black-Box Optimization

When solving global optimization problems in practice, one often ends up repeatedly solving problems that are similar to each others. By introducing a rigorous definition of similarity to exploit priors obtained from past experience to efficiently solve new (similar) problems, in this work we incorporate the META-learning rationale into SMGO $-\Delta$, a global optimization approach recently proposed in the literature. Through a benchmark numerical example we show the practical benefits of our META -extension of the baseline algorithm, while providing theoretical bounds on its performance.
Reliable Quantum Communications based on Asymmetry in Purification and Coding

The reliable provision of entangled qubits is an essential precondition in a variety of schemes for distributed quantum computing. This is challenged by multiple nuisances, such as errors during the transmission over quantum links, but also due to degradation of the entanglement over time due to decoherence. The latter can be seen as a constraint on the latency of the quantum protocol, which brings the problem of quantum protocol design into the context of latency-reliability constraints. We address the problem through hybrid schemes that combine: (1) indirect transmission based on teleportation and purification; (2) direct transmission, based on quantum error correction (QEC). The intuition is that, at present, the quantum hardware offers low fidelity, which demands purification; on the other hand, low latency can be obtained by QEC techniques. It is shown that, in the proposed framework, the purification protocol gives rise to asymmetries that can be exploited by asymmetric quantum error correcting code (QECC), which sets the basis for unique hybrid purification and coding design. Our results show that ad-hoc asymmetric codes give, compared to conventional QEC, a performance boost and codeword size reduction both in a single link and in a quantum network scenario.