Miracle-dz/UniFAR-FTUs huggingface dataset:
Region-Based Convolutional Neural Networks for Profiled Fiber Recognition

Profiled fiber recognition using electron-microscopic (EM) images is one of the main tasks in textile quality inspections. However, many existing methods require fiber samples being located and segmented from their EM image before fed to classifiers manually. Delicate spatial patterns such as sample overlap and shape variability among the same kind of fibers cannot be dealt with by classic pattern recognition methods. As an advanced object detection method, region-based convolutional neural networks (R-CNN) shows great potentials in profiled fiber recognition. Adapting the framework of R-CNN, we carry out the task in three major steps: hierarchical segmentation by selective search, automatic generation of fiber samples by self-designed selection rules and cascade classification by CNN along with some other classifiers. Within the system, the selection rules of region proposals recommended by the selective search method are tailored to spatial features of fiber samples, which are fed to a fine-tuned deep-structured classification network to recognize multiple shapes of fibers. The experimental results indicate the proposed method outperforms the backpropagation network (BP) and the support vector machine (SVM), especially when cascade classification is employed.
Plant data visualisation using network graphs

Background The amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets. Method This study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation. Results A visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation. Discussion The relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.
Construction of non‐commutative surfaces with exceptional collections of length 4

Recently de Thanhoffer de Völcsey and Van den Bergh classified the Euler forms on a free abelian group of rank 4 having the properties of the Euler form of a smooth projective surface. There are two types of solutions: one corresponding to P1×P1 (and non‐commutative quadrics), and an infinite family indexed by the natural numbers. For m=0,1 there are commutative and non‐commutative surfaces having this Euler form, whilst for m⩾2 there are no commutative surfaces. In this paper, we construct sheaves of maximal orders on surfaces having these Euler forms, giving a geometric construction for their numerical blowups.
Electromagnetic transduction therapy and shockwave therapy in 86 patients with rotator cuff tendinopathy: A prospective randomized controlled trial

ABSTRACT Rotator cuff (RC) tendinopathy is the most common cause of shoulder pain. The effectiveness of electromagnetic transduction therapy (EMTT), a high energetic pulsed electromagnetic field therapy in this field has not been tested yet in combination with extracorporeal shock wave therapy (ESWT). A total of 86 patients with RC tendinopathy were randomized to undergo three sessions of ESWT in combination with 8 sessions of EMTT or sham-EMTT. Both intervention groups experienced significant and clinical relevant decrease of pain at all follow-up visits, and the functionality of the shoulder evaluated by the Constant Murley score increased significantly as well. The combination of EMTT + ESWT produced significantly greater pain reduction in the visual analogue scale compared to ESWT with sham-EMTT after 24 weeks, during which the Constant Murley score improved significantly when the combination of ESWT and EMTT was employed. In patients with RC tendinopathy, electromagnetic transduction therapy combined with extracorporeal shock wave therapy significantly improves pain and function compared to ESWT with sham-EMTT.
An Overview of Sparse Representation Based Action Recognition in Video

Most of the action recognition researchers are undergoing intense study on sparse representation, due to its discriminative nature. The basis of the sparse representation is sparse coding, which give way for creating sparse dictionaries. The aim of this paper is to show the details about action recognition in video; the basics of sparse representation and its usage in action recognition; advancements in sparse representation; practical issues of recent sparse representation methods and future possibilities to solve such issues. This paper also provides various models and techniques used for the sparse representation based action recognition.
An Optimized Density-based Algorithm for Anomaly Detection in High Dimensional Datasets

In this study, the authors aim to propose an optimized density-based algorithm for anomaly detection with focus on high-dimensional datasets. The optimization is achieved by optimizing the input parameters of the algorithm using firefly meta-heuristic. The performance of different similarity measures for the algorithm is compared including both L1 and L2 norms to identify the most efficient similarity measure for high-dimensional datasets. The algorithm is optimized further in terms of speed and scalability by using Apache Spark big data platform. The experiments were conducted on publicly available datasets, and the results were evaluated on various performance metrics like execution time, accuracy, sensitivity, and specificity.
High Gain Circularly Polarized Substrate Integrated Coaxial Line Fed Antenna Array for RFID Band

In this paper, a high gain microstrip patch antenna array for radio frequency identification (RFID) applications is presented. A four-probes-fed microstrip square patch antenna fed by a series power divider is considered as the radiation element of the array. After that, a 2 × 2 antenna array is realized with a second level series power divider feed network implemented with substrate integrated coaxial line (SICL) technology. The series power dividers are used to implement a two levels sequential rotation feed network for generating circular polarization (CP). The SICL based feed network is found to effectively reduce coupling effects between feed network and radiating elements. Measurement results show a $S_{11}\leq-15$ dB bandwidth of 840 – 980 MHz, and gain of 11.8 dBi at the frequency 915 MHz. This antenna array is suitable for outdoor long-distance RFID communication systems.
The Classification of Teachers' Knowledge of 1-to-1 Elementary Education

In this study, we interviewed elementary school teachers about the use of Information Communications Technology (ICT) in the classroom. In this elementary school, a 1-to-1 tablet PC program was in place. Interview contents were analyzed using the TPACK framework. Among the classified forms of knowledge, Technical Knowledge (TK), Technical Pedagogical Knowledge (TPK), Technical Content Knowledge (TCK), and Technical Pedagogical Content Knowledge (TPACK) were further classified as "conventional teaching tools" or "1-to-1" depending on the object of knowledge. As per the result, among the seven knowledge types, the proportion is biased toward TPK, TCK, and TK; in particular, the proportion of TPK is high. In addition, a knowledge of the "1-to-1" was considered TPK, TCK, and TK.
Insertion Loss Characterization of Impedance Matching Networks for Low-Power Rectennas

An impedance matching network inserted between an antenna and a rectifier is essential for rectennas, but few studies have characterized the insertion loss of the matching network if nonideal matching elements are considered. In general, the matching elements, including lumped capacitors, lumped inductors, and transmission lines, are approximated as lossless; if the overall conversion efficiency decreases, the underlying reason seldom attributes to the loss of the matching network. In this paper, we derive the insertion loss of the matching network comprising actual lumped elements and transmission lines. The loss is quantified using the operating power gain of a two-port network, which varies with different levels of input power. When the input power ranges between −30 and 0 dBm, more than 95%, 90%, and 75% of power dissipates at 900 MHz, 1.8 GHz, and 2.4 GHz, respectively, due to the use of actual matching elements. The numerical results are verified by experimental data, and all the analyses demonstrate that the loss of the matching network becomes very serious as the input power is low. These observations are explained using the contour plot of operating power gain. Accordingly, this study provides a theoretical development for rectenna designers.
Log-Periodic Monopole Array With Uniform Spacing and Uniform Height

This paper presents a log-periodic monopole array (LPMA) with uniform spacing and uniform height operating in a wideband of 2–6 GHz. Different from the conventional LPMA with logarithmic spacing and height, the proposed antenna with uniform spacing and uniform height simplifies the design procedure and retains a low profile. The top-hat monopoles and folded top-hat monopoles are used to reduce the height of the antenna. A maximum bandwidth of 3:1 is theoretically proposed by considering the maximum and minimum spacing factor of the array. A meandering microstrip line is employed as the feeding line. The compensation of the length of the feeding line is proposed for the uniformly spaced LPMA compared to the conventional array. A prototype is fabricated and tested. Measured results indicate that an impedance bandwidth from 2 to 6 GHz (3:1) with VSWR < 2 is achieved. The measured peak realized gain is in the range of 6.6–8.7 dBi, which has great potential in many communication systems such as missile and airborne applications.
Lysine Stimulates Protein Synthesis by Promoting the Expression of ATB0,+ and Activating the mTOR Pathway in Bovine Mammary Epithelial Cells.

Background l-lysine (Lys) is a critical dietary nutrient for mammary gland development and milk production. However, the specific pathways of Lys utilization and how milk protein synthesis is affected in bovine mammary epithelial cells (BMECs) are poorly understood. Objective We aimed to investigate the effects of Lys on milk protein synthesis and the mechanism of Lys uptake and catabolism in BMECs. Methods BMECs were cultured in 0, 0.5, 1.0, 1.5, 2.0, 5.0, and 10.0 mmol Lys/L to detect cell viability, or cultured in 0-2.0 mmol Lys/L with l-[ring-3H5] phenylalanine to study the effect of Lys on protein turnover, or cultured in Krebs buffer with [U-14C] l-Lys to quantify Lys metabolism. In some experiments, BMECs were cultured in a conditioned medium alone or including 1.0 mmol Lys/L and 2-amino-endo-bicyclo [2.2. 1] heptane-2-carboxylic acid (BCH) for 24 h to analyze the expression of amino acid transporter B (0+) (ATB0,+), mammalian target of rapamycin (mTOR), and Janus kinase 2 (JAK2)-signal transducer and activator of transcription 5 (STAT5) pathways. Results Including 1.0 mmol Lys/L in cultures increased cell viability by 17-47% and protein synthesis by 7-23%, whereas protein degradation was inhibited by 4-64% compared with BMECs cultured with 0, 0.5, or 2.0 mmol Lys/L (all P ≤ 0.05). Studies that used [U-14C] l-Lys showed that most Lys was incorporated into proteins (90%), whereas the remainder was either oxidized into CO2 (4%) or used as a substrate for aspartate (3%) and histidine synthesis (3%). Furthermore, Lys significantly increased expression of ATB0,+ (71% mRNA and 44% protein), STAT5 (27% mRNA and 21% phosphorylated proteins), and mTOR (51% mRNA and 22% phosphorylated proteins) compared with cells without Lys. Conclusions Lys promoted protein synthesis, mostly through enhancing uptake by ATB0,+ and the mTOR and JAK2-STAT5 pathways. Understanding the utilization of Lys in BMECs provides insights into the role of amino acid nutrition in bovine milk production.
Fast Data Delivery for Many-Core Processors

Server workloads operate on large volumes of data. As a result, processors executing these workloads encounter frequent L1-D misses. In a many-core processor, an L1-D miss causes a request packet to be sent to an LLC slice and a response packet to be sent back to the L1-D, which results in high overhead. While prior work targeted response packets, this work focuses on accelerating the request packets. Unlike aggressive OoO cores, simpler cores used in many-core processors cannot hide the latency of L1-D request packets. We observe that LLC slices that serve L1-D misses are strongly temporally correlated. Taking advantage of this observation, we design a simple and accurate predictor. Upon the occurrence of an L1-D miss, the predictor identifies the LLC slice that will serve the next L1-D miss and a circuit will be set up for the upcoming miss request to accelerate its transmission. When the upcoming miss occurs, the resulting request can use the already established circuit for transmission to the LLC slice. We show that our proposal outperforms data prefetching mechanisms in a many-core processor due to (1) higher prediction accuracy and (2) not wasting valuable off-chip bandwidth, while requiring significantly less overhead. Using full-system simulation, we show that our proposal accelerates serving data misses by 22 percent and leads to 10 percent performance improvement over the state-of-the-art network-on-chip.
Design of a Multi-Layer UAV Path Planner for Cluttered Environments

There is an ever-increasing urge to integrate unmanned air systems (UAS) into the same airspace as manned aircraft. A step towards achieving this goal is to develop Sense and Avoid (SAA) capabilities to provide UAS with the required technology to detect and maneuver around traffic. This paper details an algorithm which enables an unmanned vehicle to reach a target waypoint by following the shortest path and avoiding fixed obstacles and moving traffic. In this context, a multi-layer planner is developed to enable planning of trajectories in cluttered environment containing fixed and moving obstacles. The planner is segmented into three layers; a high-level planner using Voronoi techniques that enable the planner to segregate individual obstacles into broad no-fly zones; a mid-level planner for representing the no-fly zones as convex hulls and a low-level planner that solves the resulting planning problem using mixed-integer linear programming (MILP), taking into account the convex no-fly zones and all neighboring traffic. Preliminary performance results suggest that the multi-layer planner is able to consistently identify optimal conflict-free paths in cluttered environments with execution speeds that are suitable for fast off-line planning operations.
VeraGreg: A Framework for Verifiable Privacy-Preserving Data Aggregation

A lot of effort has been made to devise a scheme for verifiable and privacy-preserving outsourcing of arbitrary computations. However, such schemes rely on Fully Homo-morphic Encryption which is still far from practical. In our work, we instead focus solely on encryption schemes with single homomorphic operation, in particular addition. We define a rigorous framework that gives the data originator a possibility to check what values have been incorporated within provided homomorphic aggregate. We also propose a practical scheme that instantiates this framework and prove that it achieves Indistinguishability under Non-Adaptive Chosen Ciphertext Attack (IND-CCA1). The definition of our framework led us further to a straightforward modification of the security notions of Non-Malleability (NM) and Adaptive Chosen Ciphertext Attack (CCA2). Our modification aims at preventing trivial breach which is by principle unavoidable for plain homomorphic encryption. With our enhancement, the notions of security can serve as a novel security goal for any future verifiable homomorphic schemes.
Turning Trust Around: Smart Contract-Assisted Public Key Infrastructure

In past, several Certificate Authority (CA) compromise and subsequent mis-issue of certificate raise the importance of certificate transparency and dynamic trust management for certificates. Certificate Transparency (CT) provides transparency for issued certificates, thus enabling corrective measure for a mis-issued certificate by a CA. However, CT and existing mechanisms cannot convey the dynamic trust state for a certificate. To address this weakness, we propose Smart Contract-assisted PKI (SCP) - a smart contract based PKI extension - to manage dynamic trust network for PKI. SCP enables distributed trust in PKI, provides a protocol for managing dynamic trust, assures trust state of a certificate, and provides a better trust experience for end-users.
Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points

Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorize-minimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating process, we show that with high probability, the algorithm converges to a value that is within statistical error of the true change-point. A fast implementation of the algorithm using Markov Chain Monte Carlo is also introduced. The performances of the proposed algorithms are evaluated on synthetic data sets and the algorithm is also used to analyze structural changes in the S&P 500 over the period 2000-2016.
Case marking in German-speaking children with specific language impairment and with phonological impairment

ABSTRACT Identification of children with specific language impairment (SLI, now known as Developmental Language Disorder) remains challenging. Morphosyntax difficulties have been proposed as potential linguistic ‘markers’ for SLI across a number of languages. This study investigates the existence of such a clinical marker in German-speaking children with SLI, looking in particular at German case marking, and makes comparisons with matched typically developing groups and a group with isolated phonological impairment (PI). A case-control study was completed with 66 pre-school children in four groups (1) SLI, (2) PI, (3) age-matched typically developing children (TD-A) (4) language-matched typically developing children (TD-L). Errors in nominative, accusative and dative marking were analysed from the production of articles in elicitation tasks and spontaneous language samples. The performance of the SLI group was poorer than the TD-A group across all three grammatical cases investigated, but there was little supportive evidence for this being a diagnostic marker. It is, however, suggested that poor case marking may alert clinicians to the need for further linguistic assessment. No significant differences were found between the SLI and PI groups; rather scores for the PI group suggested they fell on a gradient between the SLI TD-A groups. Findings are discussed in relation to the proposed new diagnostic category of developmental language disorder.
An Algorithm Based on Influence to Predict Invisible Relationship

The researches of social networks are in full swing under the era of big data, especially in the field of computer research, and link prediction in social networks has attracted an increasing number of researchers. However, most of the current studies had focused on the prediction of visible relationship between users, ignoring the existence of invisible relationships. The same as visible relationships, invisible relationships are also indispensable part of social networks, and invisible relationships can discover more potential relationships between users. In order to do a better research of invisible relationship, the definition, types and characteristics of invisible relationship have been introduced in the paper, and the influence algorithm is proposed to speculate the existence of invisible edges between users. The algorithm is based on three indicators, which are the occasional contact degree, interest coincidence degree and the popularity of users, and it takes the influence as reference, by comparing with the threshold, Θ, defined in advance, users with relationships beyond it are viewed as possessing invisible relationship. The feasibility and accuracy of the algorithm are proved by extensive numerical experiments through comparing with one well-known and widely used method: the common neighbors (CN).
RGBD Image Matting Using Depth Assisted Active Contours

This paper proposes a depth assisted active contours for RGBD image matting. The method adopts the confidence map to decide the weights of color and depth images, then uses the weights in order to guide the depth assisted active contours to segment the given image, which results in a good trimap in the process of trimap generation. Take the color image, depth image, and trimap as input, our method extends the alpha matting to estimate the color and depth mattes. Besides, the method provides an appropriate way to combine the two mattes. The experiments with complex natural images demonstrate that our RGBD matting approach is able to generate good matting results.
Vision-Based Autonomous Landing of a Multi-Copter Unmanned Aerial Vehicle using Reinforcement Learning

This paper presents vision-based landing guidance of multi-copter Unmanned Aerial Vehicle (UAV) using reinforcement learning. In this approach, the guidance method is not designed or proposed by a human, but deployed by a neural network trained in simulated environments; which contains a quad-copter UAV model with Proportional-Integral-Derivative (PID) Controller, ground looking camera model that gives pixel deviation of targeting landing location from the center of an image frame, and laser rangefinder that gives altitude above ground level. Since we aimed for various types of multi-copter UAVs to track targeting ground location, reinforcement learning method has been used to generate proper roll and pitch attitude commands in multiple situations. Series of flight experiments show that a multi-copter UAV equipped with a proper attitude controller and trained artificial intelligence pilot can guide a multi-copter UAV to a ground target position.