Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Low-cost Position Differential Positioning System Based on STM32

There exist some obstacles to realize high-precision positioning with using the position differential technique due to the limiting factors. However, with the advancement of chip performance and the establishment of multiple satellite navigation systems, position differential positioning can be realized to meet the civilian high-precision positioning demand while significantly reducing costs. This paper is to explore the engineering application and propose a new method. Through the experiments and data analysis, we have found the error correlation between two receivers in a small area and designed a set of positioning system, and programmed in the Keil µ Vision5 in C language. The system consists of the current mainstream low-cost STM32, UBLOX NEO 6M chip, NRF24L01 chip, GPS antenna and power supply module. Each two receivers can form a differential positioning system. At present, the system in the low-speed status is, in the urban environment, basically reaching within 0.5 – 3 m of the positioning error.
TE-nester: a recursive software tool for structure-based discovery of nested transposable elements

Eukaryotic genomes are generally rich in repetitive sequences. LTR retrotransposons are the most abundant class of repetitive sequences in plant genomes. They form segments of genomic sequences that accumulate via individual events and bursts of retrotransposition. A limited number of tools exist that can identify fragments of repetitive sequences that likely originate from a longer, originally unfragmented element, using mostly sequence similarity to guide reconstruction of fragmented sequences. Here, we use a slightly different approach based on structural (as opposed to sequence similarity) detection of unfragmented full-length elements, which are then recursively eliminated from the analyzed sequence to repeatedly uncover unfragmented copies hidden underneath more recent insertions. This approach has the potential to detect relatively old and highly fragmented copies. We created a software tool for this kind of analysis called TE-nester and applied it to a number of assembled plant genomes to discover pairs of nested LTR retrotransposons of various age and fragmentation state. TEnester will allow us to test hypotheses about genome evolution, TE life cycle and insertion history. The software, still under improvement, is available for download from a repository at https://gitlab.fi. muni.cz/lexa/nested.
Toward Explainable Recommendations: Generating Review Text from Multicriteria Evaluation Data

Explaining recommendations helps users to make more accurate and effective decisions and improves system credibility and transparency. Current explainable recommender systems tend to provide fixed statements such as "customers who purchased this item also purchased…. ". This explanation is generated only on the basis of the purchase history of similar customers, so it does not include the preferences of customers who have purchased the item or a description of the item. Since user-generated reviews generally contain information about the reviewer’s preferences and a description of the item, such reviews typically have more effect on purchase decisions. Therefore, using reviews to explain recommendations should be more useful than providing only a fixed statement explanation. Aiming to create a system that provides personalized explanations for recommendations, we have developed a recurrent neural network model that uses multicriteria evaluation data to generate reviews.
A unified scheme of text localization and structured data extraction for joint OCR and data mining

Both text detection and structured data extraction are imperative in an optical character recognition (OCR) processing pipeline. Text detection, especially for indistinct, diverse, multi-language text regions, is one of the most challenging tasks in computer vision and has attracted increasing attention recently. Moreover, although there are some studies in data mining related to structured data extraction, it has not received its deserved attention as one of important steps in OCR. The previous methods for structural data extraction, including layout template-based, rule-based, and natural language processing (NLP)-based methods, usually leads to either inaccurate results or complex modules. In this paper, we integrate text detection and structured data extraction into a unified deep learning-based Image Text Extraction (ITE) scheme. Our ITE is an end-to-end trainable model and able to handle multi-scale and multi-lingual text in a single process. Experiments on large-scale real-world passport and medical receipt datasets have demonstrated the superiority of the proposed method in terms of both effectiveness and efficiency.
Stackelberg Game for Resource Sharing in Mobile Cloud Workflow Management Via a Direct Approach

Mobile cloud computing(MCC) is a promising technology that fully utilizing the idle resources of mobile devices to improve the system performance. But the devices are self-interested and rational, they may be unwilling to share their resources with the devices of which workflows can not be processed immediately due to constrained resources. To address this problem, a Stackelberg game is formulated between the demanding users and supplier for MCC, which is a direct method rather than one that needs a broker or auctioneer in the previous works. First, we give the system model in which the satisfaction function is first considered in MCC. Next, the Stackelberg game is analysed and it is proved that there exists a unique equilibrium. From the perspectives of fairness and profit, separately, incentive mechanisms of uniform pricing and nonuniform pricing strategies for supplier are adopted, respectively. Finally, simulation results validate the efficiency of the proposed algorithm and demonstrate the non-uniform pricing outperforms the uniform pricing scheme. The effects of changing system parameters on the system performance are also shown.
Adaptive steering control system of the USV with friction compensation

Friction exists widely in the relative movement of mechanical devices. Considering the rotation friction in the autopilot of Unmanned Surface Vehicle (USV), an adaptive steering control system with friction compensator is designed in this paper. Taking the nonlinearity and parameters uncertainties of USV into account, the sliding mode control technology combined with a friction compensator is utilized to realize the steering control. The Radial Basis Function (RBF) neural network is integrated to approximate the friction disturbance online. The weights of the neural network are updated according to the adaptive law, which is derived by using the Lyapunov method. To solve the system chattering problem existing in the control, the sign function which is in the switch control is substituted by a saturation function. The controller proposed in this paper works well in the presence of friction disturbance which is verified by the simulation results. Compared with the traditional sliding mode controller, it has higher tracking accuracy.
A Consideration on LUT Linearization of Stochastic ADC in Sub-Ranging Architecture

A stochastic ADC is naturally a non-uniform non-linear quantizer, in which post-correction is typically needed. In addition, even though it can achieve high precision, the range of a stochastic ADC is limited. Therefore, it is beneficial to use the stochastic array as a fine-stage in a sub-ranging architecture. In this work,look-up-table linearization with full mapping and sigma-based mapping for a stochastic ADC and sub-ranging stochastic ADC are examined and compared experimentally by Monte Carlo simulation and measurement. A test chip is fabricated for 4-bit coarse flash stage and 127 comparator arrays on 0.18 μm CMOS. Measurements confirmed simulation results, and it is shown that full mapping can improve equivalent number of bit by 2 bits compared to sigma-based mapping. Furthermore, the results suggest that the coarse stage comparator with 5 times better input offset variation than fine stage comparator is suitable for sub-ranging implementation.
GPU-accelerated CellProfiler

CellProfiler excels at bridging the gap between advanced image analysis algorithms and scientists who lack computational expertise. It lacks however high performance capabilities needed for High Throughput Imaging experiments where workloads reach hundreds of TB of data and are computationally very demanding. In this work, we introduce a GPU-accelerated CellProfiler where the most time-consuming algorithmic steps are executed on Graphics Processing Units. Experiments on a benchmark dataset showed significant speedup over both single and multi-core CPU versions. The overall execution time was reduced from 9.83 Days to 31.64 Hours.
Softmax Cross Entropy Loss with Unbiased Decision Boundary for Image Classification

Considering that in neural network based on softmax cross entropy loss, the output probability is mainly based on linear computation of parameter vectors of each class in the last layer and hidden features in the layer of sample points. Therefore, the final output of neural network is effected by of the L2-norm of parameter vector of each class. Taking binary-class as an example, if the parameter vector of a class has a large L2-norm. , decision boundary is close to another class with smaller L2-norm. , so that sample points will be easily assigned to the class with large L2-norm. Based on it, this paper proposes a new softmax cross entropy loss, which adjusts the position of decision boundary so that it is not biased to any class. Experimental results on the LabelMe dataset and the UIUC-Sports dataset show that the proposed loss is superior to softmax cross entropy loss.
Online Rolling Motion Generation for Humanoid Falls Based on Active Energy Control Concepts

This paper introduces a novel online rolling over control technique based on energy concepts to alleviate impact forces during humanoid fall overs. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack angles. In addition, energy-injection velocity is proposed as an auxiliary control command to ensure sequential multiple contacts in rolling. The online rolling controller is synthesized to compute the optimal values of three rolling parameters: the first two parameters are to construct the humanoid's posture as a polyhedron by selecting suitable contacts points. This polyhedron distributes the energy gradually across multiple contacts; and the last one is to inject additional energy into the system during the fall, to overcome energy drought and tip over successive contacts, i.e., rolling. Accordingly, the proposed controller exploits energy injection, minimization, and distribution techniques and renders a rolling like motion. Numerical experiments with a segmented planar model and a full humanoid model verify that it significantly reduces impact forces.
Clustering and Comparison of Hierarchies in the Spatial Organization of Chromatin

Recent development of high-throughput 3C-based technologies made it possible to study spatial organization of chromatin in the nucleus and revealed new units of chromatin organization– Topologically Associating Domains (TADs). Each such domain is comprized of DNA regions actively contacting with each other while having fewer contact outside the domain. TADs are conserved between cell lines and even between related species. While there exist numerous bioinformatics tools for the TADs identification, the choice of a method and its adjustable set of parameters influences the number of the identified TADs and their characteristics. Besides, one more common disadvantage is that the most methods may leave gaps between the identified TADs. This study aimed to develop a robust and universal tool which would overcome these limitations. We have developed and implemented an algorithm, that builds a hierarchical tree on Hi-C contact matrices and methods to calculate the differences between such trees.
A Time-Efficient Approach to Solve Conflicts and Deadlocks for Scheduling AGVs in Warehousing Applications

Automated guided vehicles (AGVs) have been widely used in automated warehouses. Coordination between AGVs and real-time performance in a large-scale system are key issues. To address these challenges, this paper proposes a time-efficient method to solve conflicts and deadlocks in multi-AGV systems for warehouses. The method includes four main parts: map initialization, candidate route planning, scheduling strategy planning for routes, and selection of the strategy having minimum makespan. It solves conflicts and deadlocks using time windows and improves the real-time performance with a high-efficient time window arranging operation. The proposed method is validated by means of simulation in a real warehouse environment.
A More Streamlined U-net for Nerve Segmentation in Ultrasound Images

U-net [1] is used in medical image segmentation [2]. Its core ideal is to build a concatenation [3] between the feature map of upsampling from the expansive path and the correspondingly cropped feature map from the contraction path. The cropping is very important because of the loss of border pixels when convolution is performed. However, we think that too many concatenations will bring too many cropping and convolution operations, which will reduce the information of the image, and will ultimately weaken the segmentation effect. So we remove a concatenation between the expansive path and the contracting path, and a new network called mini-u-net is proposed. In order to prove the superiority of our network, we have also created a network with one-more concatenation, we call it onemore-u-net. We put onemore-u-net, original u-net, and mini-u-net for comparison. The dataset uses the neural image data of the kaggle2016 game. The experimental comparison found that the dice-coef’, precision, recall of mini-u-net are all better than the other two networks, and then we put The mini-u-net compares the results of two papers which use the same dataset and finds that our results in precision and dice-coef are better than those mentioned in the paper.
Kernel Collaborative Representation With Local Correlation Features for Hyperspectral Image Classification

Spatial information has widely been used in hyperspectal image (HSI) classification to improve classification accuracy. However, the structural information may not be fully explored when using spatial information, this paper proposes the joint collaborative representation classification with correlation matrix (CRC-CM) for HSI by using spatial correlation features in patches, which could keep the local intrinsic structure in band images. Considering spatial heterogeneity in a patch, local correlation matrices of a target neighborhood patch and training neighborhood patch are improved by a binary weight matrix and shape-adaptive neighborhood. To explore nonlinear nature of spatial features, corresponding kernel CRC-CM is also proposed. To evaluate the effectiveness of the proposed methods, three real HSIs with different degree of heterogeneity are used. The experimental results show that the proposed spatial correlation features outperform the original spectral feature and other spatial features which widely used in HSI classifiers.
A Cascaded Thermometer-Coded Current-Steering Digital-to-Analog Converter

A fully thermometer-coded cascaded current-steering digital-to-analog converter (DAC) is reported in this paper. The described solution is implemented and simulated as a 7-bit DAC. The circuit achieves a $|Dl\mathrm{V}L|\lt 0.9^{*}\mathrm{L}\mathrm{S}\mathrm{B}$ for extracted view Monte Carlo simulations, temperatures in the range between $-40^{0}\mathrm{C}$: $125^{0}\mathrm{C}$ and voltage supply in the range 2. 5V: 3.3V. In the 55nm high-voltage CMOS technology, 6V devices are used. The total current consumption is $9\mu \mathrm{A}$ in the supply range from 2.5V to 3. 3.3V.
Non-asymptotic Bound on the Performance of k-Anonymity against Inference Attacks

Internet of Things (IoT) applications bring in a great convenience for human's life, but users' data privacy concern is the major barrier towards the development of IoT. k-Anonymity is a method to protect users' data privacy, but it is presently known to suffer from inference attacks. Thus far, existing work only relies on a number of experimental examples to validate k-anonymity's performance against inference attacks, and thereby lacks of a theoretical guarantee. To tackle this issue, in this paper we propose the first theoretical foundation that gives a non-asymptotic bound on the performance of kanonymity against inference attacks, taking into consideration of adversaries' background information. The main idea is to first quantify adversaries' background information, and from the point of the view of adversaries, classify users' data into four kinds: independent with unknown data values, local dependent with unknown data values, independent with certain known data values, and local dependent with certain known data values. We then move one step further, theoretically proving the bound on the performance of k-anonymity corresponding to each of the four kinds of users' data through cooperating with the noiseless privacy. We argue that such a theoretical foundation links k-anonymity with noiseless privacy, theoretically proving k-anonymity provides noiseless privacy. Additionally, our work theoretically explains why k-anonymity is vulnerable to inference attacks using the modified Stein method. We believe that our work can bridge the gap between design and evaluation, enabling a designer to construct a more practical k-anonymity technique in real-life scenarios to resist inference attacks.
Modeling and Testing of Aging Faults in FinFET Memories for Automotive Applications

Automotive has become one of the most prevailing sectors of the modern semiconductor industry. Due to strict requirements for safety, reliability, and security the proposed test & repair solutions for automotive applications undergo a circumstantial verification before exploitation. Traditionally production defects and soft errors occurring in the operation mode were considered to be the main source of failures for System-on-Chips (SoC). Nevertheless, aging-induced faults especially in modern technology nodes also pose certain challenges for SoC lifetime and impact the overall Failure in Time (FIT) rate of the system. Meanwhile keeping hold of low FIT rate is one of the main criteria for reliability. In this paper, a comprehensive study on aging faults is conducted for FinFET memories and an efficient test & repair methodology is proposed that meets the automotive requirements and allows decreasing the FIT rate of the system.
Inpainting of Remote Sensing SST Images With Deep Convolutional Generative Adversarial Network

Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation regions. This letter introduces a deep convolutional generative adversarial network to recover the RS sea surface temperature images with cloud occlusion from the big historical image records. We propose a new loss function for the inpainting network, which adds a supervision term to solve our specific problem. Given a trained generative model, we search for the closest encoding of the corrupted image in the low-dimensional space using our inpainting loss function. This encoding is then passed through the generative model to infer the missing content. We conduct experiments on the RS image data set from the national oceanic and atmospheric administration. Compared with traditional and machine learning methods, both qualitative and quantitative results show that our method has advantages over existing methods.
Virtual Touch-Point: Trans-Domain Behavioral Targeting via Transfer Learning

Behavioral targeting (BT) is an important function for a company to reach a wide range of potential users. Trans-domain BT which targets potential users of one (source) domain (e.g. E-Commerce) who lie in another (target) domain (e.g. Ad Network) is a promising method to expand the range. However, it is difficult for trans-domain BT to keep its targeting quality high in case when ID linkage across domains is limited. To realize high quality trans-domain BT with limited ID linkage, we propose a method to cross-connect private touchpoints to users in each domain, which we call Virtual Touch-Point (VTP). Here, we utilize transfer learning to acquire knowledge to tie two domains. We made a VTP prototype by implementing typical transfer learning algorithms and evaluated its effectiveness using real-world data of two domains: (source) E-Commerce → (target) Ad Network.
Heparin as an anticoagulant for the dielectric measurement of blood

Testing blood ex-vivo allows physicians to gain an understanding of what is happening within the human body. Once blood is extracted from the body, it begins to clot. This process can affect the outcome of blood tests, particularly for dielectric measurements. As a result, the dielectric measurements recorded are dependent on time after extraction. To solve the clotting problem, and to allow measurements at any time, anticoagulants are employed. However, a number of anticoagulants have been shown to greatly alter the dielectric properties of fresh blood. In this paper, we analyze the anticoagulant heparin, which works by a different mechanism to stop coagulation than other previously studied anticoagulants. Specifically, heparin acts as a cofactor, accelerating the natural anticoagulation process by several orders of magnitude rather than chelating with clotting factors. We compare intra-individually the dielectric measurements of pure and heparin blood samples at 37°C, taken from four healthy adult volunteers. We also compare the heparin results to previous data on other anticoagulants, specifically, ethylene diamene tetra acetic acid (EDTA) and sodium citrate. In this exploratory study, we found that the effects of heparin on blood are 2.66% or less for the relative permittivity and conductivity, and that heparin has the least effect on dielectric measurements when compared to the other commonly used anticoagulants. These results suggest heparin as the anticoagulant of choice for dielectric measurement.
pyMIC-DL: A Library for Deep Learning Frameworks Run on the Intel® Xeon Phi™ Coprocessor

Deep learning plays a vital role in broad spectrum of scientific fields, such as computer vision, speech recognition, natural language processing, and so on. In order to support deep learning, many frameworks are created with the aim of setting up artificial neural networks as quickly as possible. Such frameworks can be run on systems including either Graphics Processing Unit or Intel Xeon Phi the second generation - Knights Landing coprocessor. However, very few deep learning frameworks can be run on legacy systems containing Intel Xeon Phi Knights Corner. For that reason, we propose and develop pyMIC-DL which is a NumPy-like library supporting deep learning frameworks run on such legacy systems. pyMIC-DL that is an extension of offloading module pyMIC implements basic functions so that it can be easily integrated into deep learning frameworks. The experimental findings show that pyMIC-DL outperforms NumPy in terms of two hardware platforms with the similar theoretical peak performance. Moreover, pyMIC-DL has shown its high effectiveness when being integrated into a well-known deep learning framework Chainer with a very impressive performance. Hence, pyMIC-DL is a fairly promising NumPy-like library to facilitate deep learning frameworks run on the legacy systems.