Miracle-dz/UniFAR-FTUs huggingface dataset:
Tightly Coupled Array Antennas for Ultra-Wideband Wireless Systems

Tightly coupled array (TCA) antenna has become a hot topic of research recently, due to its potential of enabling one single antenna array to operate over an extremely wide frequency range. Such an array antenna is promising for applications in numerous wideband/multi-band and multi-function wireless systems such as wideband high-resolution radars, 5G mobile communications, satellite communications, global navigation satellite systems, sensors, wireless power transmission, Internet of Things, and so on. Many papers on this topic have been published by researchers internationally. This paper provides a detailed review of the recent development on TCA that utilizes the capacitive coupling. The basic principles and the historical evolution of the TCAs are introduced first. Then, recent development in the analysis and design of TCAs, such as equivalent circuit analysis, bandwidth limitation analysis, array elements, feed structures, substrates/superstrates loading, and so on, are explained and discussed. The performances of the state-of-the-art TCAs are presented and a comparison amongst some TCAs reported recently is summarized and discussed. To illustrate the practical designs of TCA, one case study is provided, and the detailed design procedures of the TCA are explained so as to demonstrate the TCA design methodology. Simulated results including the VSWR at different angles of scanning, patterns, and antenna gain are shown and discussed. A conclusion and future work are given in the end.
Use of Systematic Code Based on Data Bits Weighing for Concurrent Error Detection Considering Error Structure Analysis

This paper describes the algorithm of formationof new code with weight-based summation. Authors prove that this code detect all single and twofold distortions in data bits. Also other properties of this code considering hardware and information redundancy are stated. The main attention is paid to the ability of this code to detect 100% of single errors in the tested circuit with relatively low redundancy and analysis of structure of its errors that allow to reach this goal. Results are verified by the experiments using the set of LGSynth '89 benchmarks.
Real-Time Flow Identification Based on Neural Network and OpenFlow Over SDN

This paper introduces a novel approach to flow identification by applying a backpropagation Neural Network over SDN. Thanks to the advantages of the global abstract of SDN controller, we get a convenient way to collect data from data-path and implement a real-time predictor of traffic types. Some flow features are selected from OpenFlow counters for each flow. In addition, we get some other flow features by intermittently duplicating flows and leading them to the controller. The data from OpenFlow counters and duplicated flows are integrated as input vector for Neural Network. Especially, we take into consideration packet length distribution and connection count with the same source and destination IP, which improves the accuracy and efficiency of video flow and P2P flow identification.
RNA interference of muscle actin and ATP synthase beta increases mortality of the phytoplasma vector Euscelidius variegatus.

BACKGROUND RNA interference (RNAi) techniques have emerged as powerful tools to develop novel management strategies for the control of insect pests. The leafhopper Euscelidius variegatus is a natural vector of chrysanthemum yellows phytoplasma and a laboratory vector of Flavescence dorée phytoplasma. Phytoplasmas are insect-borne bacterial plant pathogens that cause economically relevant crop losses worldwide. RESULTS In this study, we demonstrated that microinjection of muscle actin and ATP synthase β double-stranded (ds)RNAs into adult insects caused an exponential reduction in the expression of both genes, which began within 72 h of dsRNA administration and lasted for 14 days, leading to almost complete silencing of the target genes. Such silencing effects on muscle actin expression appeared to be both time- and dose-dependent. Our results also showed that the knockdown of both genes caused a significant decrease in survival rates in comparison with green fluorescent protein (GFP) dsRNA-injected control insects. CONCLUSION The effectiveness of RNAi-based gene silencing in E. variegatus guarantees the availability of a powerful reverse genetic tool for the functional annotation of its genes and the identification of those potentially involved in the interaction with phytoplasmas. In addition, this study demonstrated that muscle actin and ATP synthase β may represent candidate genes for RNAi-based control of E. variegatus. © 2018 Society of Chemical Industry.
Adaptive Anonymization of Data using b-Edge Cover

We explore the problem of sharing data that pertains to individuals with anonymity guarantees, where each user requires a desired level of privacy. We propose the first shared- memory as well as distributed memory parallel algorithms for the adaptive anonymity problem that achieves this goal, and produces high quality anonymized datasets. The new algorithm is based on an optimization procedure that iteratively computes weights on the edges of a dissimilarity matrix, and at each iteration computes a minimum weighted b-Edge Cover in the graph. We describe how a 2-approximation algorithm for computing the b-Edge Cover can be used to solve the adaptive anonymity problem in parallel. We are able to solve adaptive anonymity problems with hundreds of thousands of instances and hundreds of features on a supercomputer in under five minutes. Our algorithm scales up to 8K cores on a distributed memory supercomputer, while also providing good speedups on shared memory multiprocessors. On smaller problems where an a Belief Propagation algorithm is feasible, our algorithm is two orders of magnitude faster.
Visual Recognition of Container Number with Arbitrary Orientations Based on Deep Convolutional Neural Network

The visual recognition of container number is vital for unmanned ports. Most of the existing methods focus on the simple case where the orientation of the container number is in horizontal direction. However, the orientation of the container number in images captured by the camera in unmanned ports is often inclined. Hence, this paper presents an approach to complete a pipeline for visual recognition of container number with arbitrary orientations based on deep convolutional neural network (CNN). In order to deal with various complex cases, the processing of the pipeline is divided into three stages, namely the coarse localization, the fine localization and the text recognition. The proposed approach is implemented by TensorFlow. Experimental results show that the pipeline realizes excellent detection and recognition of container number with arbitrary orientations and various arrangements. According to the experimental results, the total accuracy of the proposed approach is over 85% at present.
Work-in-Progress: Dynamic Data Management for Automotive ECUs with Hybrid RAM-NVM Memory

Non-Volatile Memory (NVM) can be utilized to improve performance of automotive electronic systems, but frequent writings on NVM will decrease its lifetime. In this paper, we propose a Vehicle Dynamic Data Management (VDDM) scheme, which can distinguish the hot or cold data generated by vehicle Electronic Control Units (ECUs) and sensors, and manage the data to reduce the writing operations on NVM of the hybrid main memory. Experimental results show that VDDM can significantly reduce writing operations and prolong lifetime of NVM compared with other approaches.
An Ensemble Model for Breast Cancer Prediction Using Sequential Least Squares Programming Method (SLSQP)

Cancer is a collection of diseases which is obsessed by uncontrolled growth of cells in the body to produce abnormal cells. Breast cancer is the most common cancer among women. In cancer, abnormal cells spread in other body part that is advance stage of cancer; at this stage survival rate is very low. So, Cancer detection at early stage is highly required to reduce the mortality rate. Here, Mortality means death because of cancer. With the improvement of innovation and machine learning techniques, cancer detection has made advances. Machine learning (ML) allows system to learn on the basis of past experiences and take decision using various statistical and probabilistic methods with minimal human intervention. This research work exhibited an ensemble model to predict the breast cancer using four machine learning techniques which are Support Vector Machine, Logistic Regression, Decision Tree and K-Nearest Neighbour. Results shows that proposed ensemble framework is more accurate in contrast of tradition single classification system. In this research work, SLSQP method is used to assign weight to each classification model and prediction of each classifier is combined using soft voting technique.
Predictability Classification for Software Effort Estimation

In this paper, focusing on the problem that estimation accuracy of software development effort greatly varies among software projects, we propose a predictability classification method for software projects before conducting effort estimation. In the proposed method, given a project to be estimated, we first evaluate whether the effort can be accurately estimated or not by identifying the project as ”predictable” or ”unpredictable”. In case of predictable projects, we conduct the effort estimation. Otherwise, estimation is avoided. As a result of an experiment to assess the effectiveness of the proposed method using six industry datasets, (i) the mean square residual and residual variance are shown to be suitable measures for recognition of predictability; and (ii) the average absolute error is significantly reduced in five datasets, by avoiding the estimation when a project belongs to the unpredictable class, which proves the effectiveness of the proposed method. By using the proposed method, practitioners become aware of cases when they can rely on the estimation and when they cannot.
Identification of Relevant Contextual Dimensions Using Regression Analysis

The tremendous growth of information on the web has necessitated the need for recommendation systems. Although users' preferences used to vary under different situations which urge the requisite of context-aware recommendation systems. But the major issue to be addressed in context-aware recommendation systems is an efficient utilization of contextual dimensions, under which an item is consumed, are not equally important. Therefore, in this study, the determinants are analyzed that influences the user decision and their satisfaction towards watching movies. Thus a logistic regression model is developed to induce out the foremost factors that prevail the user satisfaction. The key findings of the study indicate that dominantEmo, endEmo, interaction, and weather are the most relevant contextual dimensions which integrated into the model would boost the performance of the model.
A drama movie activates brains of holistic and analytical thinkers differentially

Abstract People socialized in different cultures differ in their thinking styles. Eastern-culture people view objects more holistically by taking context into account, whereas Western-culture people view objects more analytically by focusing on them at the expense of context. Here we studied whether participants, who have different thinking styles but live within the same culture, exhibit differential brain activity when viewing a drama movie. A total of 26 Finnish participants, who were divided into holistic and analytical thinkers based on self-report questionnaire scores, watched a shortened drama movie during functional magnetic resonance imaging. We compared intersubject correlation (ISC) of brain hemodynamic activity of holistic vs analytical participants across the movie viewings. Holistic thinkers showed significant ISC in more extensive cortical areas than analytical thinkers, suggesting that they perceived the movie in a more similar fashion. Significantly higher ISC was observed in holistic thinkers in occipital, prefrontal and temporal cortices. In analytical thinkers, significant ISC was observed in right-hemisphere fusiform gyrus, temporoparietal junction and frontal cortex. Since these results were obtained in participants with similar cultural background, they are less prone to confounds by other possible cultural differences. Overall, our results show how brain activity in holistic vs analytical participants differs when viewing the same drama movie.
TriCore: Parallel Triangle Counting on GPUs

Exact triangle counting algorithm enumerates the triangles in a graph by identifying the common neighbors of two vertices of each edge. In this work, we present TriCore, a scalable GPU-based triangle counting system that consists of three major techniques. First, we design a binary search based algorithm that can increase both the thread parallelism and memory performance on Graphics Processing Units (GPUs), both of which are absent from prior work. Second, in contrast to prior attempts which require multiple graph representations, i.e., compressed sparse row (CSR), edge list, and bitmap, to be present in the GPU memory, TriCore evenly partitions and distributes the partitioned CSR data across all the GPUs, and uses a streaming buffer to load the edge list from the CPU memory on the fly. This design enables TriCore to process the graphs that are orders of magnitude larger than the GPU memory. Third, we further develop a dynamic workload management technique to balance the workload across GPUs. our evaluation demonstrates that TriCore on a single GPU can count the triangles in the billion-edge Twitter graph within 24 seconds, that is, 22× faster than the state-of-the-art CPU project which uses CPUs that are 8× more expensive. When processing big graphs (up to 33.4 billion edges) that are ∼22× larger than the memory size of a single GPU, it achieves 24× speedup when scaling from 1 to 32 GPUs.
The Application of Spark in Medical Multidimensional Data Visualization and Statistical Analysis

With the increasing size, complexity and multidimensionality of medical research data, traditional statistical methods are becoming more and more difficult in analysis. Visualization can present data in an intuitive, visual, and easy-to-read format, which helps medical researchers understand data, and discover scientific views from the interpretation of data. At the same time, as a new parallel computing model, spark technology can also greatly improve the efficiency of the operation. In this article, we explored the application of data visualization methods and spark technology in the study of medical multidimensional data, and developed a visualization scientific discovery platform named datasparking for medical multidimensional data. The platform integrates the functions of interactive chart analysis and statistical analysis methods. It can complete the display of medical multi-dimensional data and analysis of statistical methods, and has the ability to conduct exploratory analysis of medical data. Also, the platform is implemented with Spark as the bottom layer, which improves the efficiency of statistical methods and visualization.
No evidence for sibling or parent–offspring coadaptation in a wild population of blue tits, despite high power

Parent and offspring behaviors are expected to act as both the agents and targets of selection. This may generate parent–offspring coadaptation in which parent and offspring behaviors become genetically correlated in a way that increases inclusive fitness. Cross‐fostering has been used to study parent–offspring coadaptation, with the prediction that offspring raised by non‐relatives, or parents raising non‐relatives, should suffer fitness costs. Using long‐term data from more than 400 partially crossed broods of blue tits (Cyanistes caeruleus), we show that there is no difference in mass or survival between crossed and non‐crossed chicks. However, previous studies for which the evidence for parent–offspring coadaptation is strongest compare chicks from fully crossed broods with those from non‐crossed broods. When parent–offspring coadaptation acts at the level of the brood then partial cross‐fostering experiments are not expected to show evidence of coadaptation. To test this, we performed an additional experiment (163 broods) in which clutches were either fully crossed, non‐crossed, or partially crossed. In agreement with the long‐term data, there was no evidence for parent–offspring coadaptation on offspring fitness despite high power. In addition there was no evidence of effects on parental fitness, nor evidence of sibling coadaptation, although the power of these tests was more modest.
A Collision Resolution Algorithm for RFID Using Modified Dynamic Tree With Bayesian Tag Estimation

A new tree-based anti-collision protocol for radio-frequency identification systems is proposed to achieve a very high tag identification efficiency. The proposed algorithm works in two phases. In the first phase, the number of competing tags is estimated through the proposed Bayesian estimation technique, while in the second phase, tags are identified using our modified dynamic tree algorithm. The system efficiency is mathematically derived and verified through simulation. Numerical results show that the proposed algorithm achieves a tag identification system efficiency of 45% and a time system efficiency of 78.5%, thus outperforming any existing collision resolution algorithms.
Crack Detection Of Concrete Surface Based On \Newline Convolutional Neural Networks

Apparent quality of concrete is one of the important indexes of engineering quality assessment, occupying a very important position in the quality of engineering. As an important evaluation index of the apparent quality of concrete, the detection of cracks is particularly important. The traditional detection method mainly relies on the inspection personnel to carry out on-site inspection, which has low efficiency and large environmental limitations. In this paper, a method of crack image recognition based on convolution neural network is proposed, which can realize the automatic recognition of concrete surface cracks, and the recognition accuracy rate is as high as 93.7{\%.
QoE-Driven Optimized Load Balancing Design for Hybrid LiFi and WiFi Networks

The hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks are regarded as a promising solution for future indoor wireless communication. In this letter, we investigate the traffic load balancing (LB) issue for this hybrid networks. In our design, considering that user terminals (UTs) may have different data rate demands, we utilize the complementary advantages of the wide bandwidth of the LiFi network and the wide coverage area of the WiFi network to serve the UTs. With considerations of the access point heterogeneity and UTs’ quality of experience (QoE) demands for data rate, we then formulate the LB issue as a mixed-integer non-linear programming problem with the aim of maximizing the system throughput under proportional fairness constraints, and propose a distributed optimal algorithm and a simplified suboptimal sequential algorithm to solve it. Simulation results demonstrate that using the presented QoE-driving LB scheme, more UTs are served with higher QoE while maintaining high system throughput and proportional fairness.
Development of a Semi-Autonomous Robotic System to Assist Children with Autism in Developing Visual Perspective Taking Skills

Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.
A New Filter Feature Selection Based on Criteria Fusion for Gene Microarray Data

In machine learning and data mining, feature selection aims to seek a compact and discriminant feature subset from the original feature space. It is usually used as a preprocessing step to improve the prediction performance, understandability, scalability, and generalization capability of classifiers. A typical gene microarray data set has the characteristics of high dimensionality, limited samples, and most irrelevant features, and these characteristics make it difficult to discover a compact set of features that really contribute to the response of the model. In this paper, a score-based criteria fusion feature selection method (SCF) is proposed for cancer prediction, and this method aims at improving the prediction performance of the classification model. The SCF method is evaluated on five open gene microarray data sets and three low-dimensional data sets, and it shows superior performance over many well-known feature selection methods when employing two classifiers SVM and KNN to measure the quality of selected features. Experiments verify that SCF is able to find more discriminative features than the competing methods and can be used as a preprocessing algorithm to combine with other methods effectively.