Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-View Co-Training Clustering Algorithm Based on Global and Local Structure Preserving

Multi-view clustering which integrates the complementary information from different views for better clustering, is a fundamental and important topic in machine learning. In this paper, we present a multi-view co-training clustering algorithm based on global and local structure preserving. Here the global structure is referred to the integration of the within-cluster compactness and between-cluster separation; the local structure is referred to the neighborhood information. Our algorithm at first preserves both the global and local structure to the subspace in each view. And then, this algorithm obtains the clustering result in the subspace of each view, and utilizes the clustering labels of one view to guide the subspace clustering in another view. In this way, the differences and compatibilities among the multiple views are fused together to form the final cluster partition. Therefore, the clustering result takes full account of the global and local structure information of the multi-view data, which is helpful to improve the of clustering accuracy. Experimental results on the multi-view text datasets and image datasets demonstrate the effectiveness and correctness of the proposed algorithm.
Information-Driven Gas Source Localization Exploiting Gas and Wind Local Measurements for Autonomous Mobile Robots

Gas source localization (GSL) by an olfactory robot is a research field with a great potential for applications but also with numerous unsolved challenges, particularly when the search must take place in realistic, indoor environments that feature obstacles and turbulent airflows. In this work, we present a new probabilistic GSL method for a terrestrial mobile robot that revolves around the propagation of local estimations throughout the environment. By exploiting the geometry of the environment as the basis for this propagation, we avoid relying on analytical dispersion models, eliminating the need to assume controlled environmental conditions. Simulated and real experiments are presented in different indoor environments featuring multiple rooms and turbulent flows, demonstrating the suitability of our approach for locating the emitting gas source.
LVQ: A Lightweight Verifiable Query Approach for Transaction History in Bitcoin

In the Bitcoin system, transaction history of an address can be useful in many scenarios, such as the balance calculation and behavior analysis. However, it is non-trivial for a common user who runs a light node to fetch historical transactions, since it only stores headers without any transaction details. It usually has to request a full node who stores the complete data. Validation of these query results is critical and mainly involves two aspects: correctness and completeness. The former can be implemented via Merkle branch easily, while the latter is quite difficult in the Bitcoin protocol. To enable the completeness validation, a strawman design is proposed, which simply includes the BF (Bloom filter) in the headers. However, since the size of BF is about KB, light nodes in the strawman will suffer from the incremental storage burden. What’s worse, an integrated block must be transmitted when BF cannot work, resulting in large network overhead. In this paper, we propose LVQ, the first lightweight verifiable query approach that reduces the storage requirement and network overhead at the same time. To be specific, by only storing the hash of BF in headers, LVQ keeps data stored by light nodes being little. Besides, LVQ introduces a novel BMT (BF integrated Merkle Tree) structure for lightweight query, which can eliminate the communication costs of query results by merging the multiple successive BFs. Furthermore, when BF cannot work, a lightweight proof by SMT (Sorted Merkle Tree) is exploited to further reduce the network overhead. The security analysis confirms LVQ’s ability to enable both correctness and completeness validation. In addition, the experimental results demonstrate its lightweight.
Theoretical Modeling and Analysis of Circularly Polarized Annular Leaky-Wave Antenna Based on Travelling-Wave Structure

In this paper, a theoretical model based on the electric field integration method is proposed to analyse radiation properties of the slotted annular Leaky-Wave Antenna (LWA), which can generate a Circularly Polarized (CP) conical beam or broadside beam. The annular LWA contains radiating elements arranged along a circular travelling-wave structure with equal intervals. By combining the proposed model and LWA theory, the design procedure for the annular SIW LWA is presented. To verify the model, three examples of LWAs with different beam angles have been designed and fabricated. Calculation results from the proposed model are consistent with the simulation and measurement results. The model can significantly reduce the design period and is an instructive tool for antenna design.
Design Optimization for Low-Complexity FPGA Implementation of Symbol-Level Multiuser Precoding

This paper proposes and validates a low-complexity FPGA design for symbol-level precoding (SLP) in multiuser multiple-input single-output (MISO) downlink communication systems. In the optimal case, the symbol-level precoded transmit signal is obtained as the solution to an optimization problem tailored for a given set of users’ data symbols. This symbol-by-symbol design, however, imposes excessive computational complexity on the system. To alleviate this issue, we aim to reduce the per-symbol complexity of the SLP scheme by developing an approximate yet computationally-efficient closed-form solution. The proposed solution allows us to achieve a high symbol throughput in real-time implementations. To develop the FPGA design, we express the proposed solution in an algorithmic way and translate it to hardware description language (HDL). We then optimize the processing to accelerate the performance and generate the corresponding intellectual property (IP) core. We provide the synthesis report for the generated IP core, including performance and resource utilization estimates and interface descriptions. To validate our design, we simulate an uncoded transmission over a downlink multiuser channel using the LabVIEW software, where the SLP IP core is implemented as a clock-driven logic (CDL) unit. Our simulation results show that a throughput of 100 Mega symbols per second per user can be achieved via the proposed SLP design. We further use the MATLAB software to produce numerical results for the conventional zero-forcing (ZF) and the optimal SLP techniques as benchmarks for comparison. Thereby, it is shown that the proposed FPGA implementation of SLP offers an improvement of up to 50 percent in power efficiency compared to the ZF precoding. Remarkably, it enjoys the same per-symbol complexity order as that of the ZF technique. We also evaluate the loss of the real-time SLP design, introduced by the algebraic approximations and arithmetic inaccuracies, with respect to the optimal scheme.
K-Means Tree for Fast Furthest Neighbor Approximation

Searching for the furthest neighbor in a given dataset is a linear time complexity problem. This complexity rises to be quadratic when we need to find the furthest neighbor for each point (example) in a dataset. That is, the brute force algorithm takes $O(n^{2})$ to find the furthest neighbor for all points. Such an algorithm is computationally expensive, particularly when the number of samples $n$ in a dataset is large. In this paper, we introduce an approximate tree-based searching method mainly to reduce the time complexity of the search. The proposed method recursively utilizes the k-means approach in order to split the data into sub-groups and then arranges them as a tree structure. Using such a structure, the searching process consumes $O(log(n))$ to find the approximated furthest neighbor from a given example; and $O(nlog(n))$ to find it for all examples in the dataset. Our experiments show that the proposed method is reliable and efficient in approximating the furthest neighbor, therefore, can be used in practice, particularly for big data.
A MATLAB GUI Program for LLC Resonant Converter

In this paper, a graphical user interference (GUI) program based on MATLAB was built for LLC resonant converter. The time domain analysis method is adopted to derive the mathematical model of LLC resonant converter in PO operation mode. The designed MATLAB GUI program has the following features and advantages: 1) key circuit voltages and currents operation waveforms are presented, which can help visualize the steady state operation of the converter; 2) the execution time is only around one second, which is much more faster than commercial simulation software; 3) important currents and voltages values are summarized and listed, which is convenient for users; 4) power loss model is used to obtain the efficiency and power loss distribution information, which is beneficial for LLC converter design; 5) high accuracy and small computation capacity make it suitable for industry applications. Brief introductions for the time domain analysis are presented in this paper. Comparisons between the proposed GUI program and commercial simulation tools are made.
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most of the existing approaches mainly leverage recurrence or convolution-based neural networks. However, while recurrent neural networks (RNNs) usually suffer from slow training speed due to sequential computation and encounter problems as vanishing gradient or bottleneck, CNN endures a trade-off between complexity and performance. In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches. The use of focal loss instead of negative log-likelihood helps the model focus more on low-frequency samples training. Moreover, to deal with the distortions and irregular texts, we exploit Spatial TransformerNetwork (STN) to rectify text before passing to the recognition network. We perform experiments to compare the performance of the proposed model with seven benchmarks. The numerical results show that our model achieves the best performance.
Multi-Armed-Bandit-based Shilling Attack on Collaborative Filtering Recommender Systems

Collaborative Filtering (CF) is a popular recommendation system that makes recommendations based on similar users’ preferences. Though it is widely used, CF is prone to Shilling/Profile Injection attacks, where fake profiles are injected into the CF system to alter its outcome. Most of the existing shilling attacks do not work on online systems and cannot be efficiently implemented in real-world applications. In this paper, we introduce an efficient Multi-Armed-Bandit-based reinforcement learning method to practically execute online shilling attacks. Our method works by reducing the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach. Such practical online attacks open new avenues for research in building more robust recommender systems. We treat the recommender system as a black box, making our method effective irrespective of the type of CF used. Finally, we also experimentally test our approach against popular state-of-the-art shilling attacks.
A Rigorous Analysis of Biomedical Edge Computing: An Arrhythmia Classification Use-Case Leveraging Deep Learning

Biomedical Edge computing is an exciting area of interdisciplinary research involving the Internet of Medical Things (IoMT) sensors and devices with lightweight Artificial Intelligence (AI) logic. To address the rapidly growing need for smart and portable biomedical devices with localized decision-making capability, we present a proof-of-concept logic-in-sensor design with an arrhythmia analytics use-case. Existing signal processing techniques for arrhythmia analytics such as discrete wave transform (DWT) and non-linear delay differential equation (DDE) lead to high complexity and computational burden on biomedical edge devices due to expensive preprocessing steps. As a solution, we propose a deep learning-based lightweight arrhythmia classification method leveraging a customized one-dimensional (1-D) convolutional neural network (CNN). A rigorous analysis of the proposed method's performances and generalization potential are assessed using four publicly available datasets.
Synonymous Codon Usage Bias Factors Affecting Chloroplast Genome of Grape Wine Vitis vinifera

The genetic code uses 64 codons to represent the 20 standard amino acids and the translation termination signal. Each codon is recognized by a subset of a cell‘s transfer ribonucleotide acid molecules (tRNAs), and with the exception of a few codons that have been reassigned in some lineages (Osawa and Jukes, 1989: Osawa et al., 1990) the genetic code is remarkably conserved, although it is still in a state of evolution (Osawa et al., 1992).
Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several one-dimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.
Template Synthesis of Porous Polypyrrole in 3D Coordination Nanochannels

Oxidative polymerization of pyrrole in the 3D nanochannels of [Cu3(BTC)2]n (BTC = benzene-1,3,5-tricarboxylate) produced polypyrrole (PPy) inside the framework. Subsequent isolation of PPy by dissolution of the host framework in ammonia solution gave a functional porous PPy, which contrasts with the conventional bulk-synthesized PPy system.
Multiracial Identity and Political Preferences

Increases in racial minority populations are profoundly reshaping the composition of the American electorate, and multiracial identifiers are one of the fastest growing groups. Yet little is known about the political ideology and policy preferences of Americans who identify with more than one race. We address this gap here by examining the political views of the two biggest US multiracial groups—White-Asians and White-Blacks. We also advance prior work by assessing the role that linked fate, racial group closeness, and race salience play in explaining multiracials’ political attitudes. Leveraging the largest national political survey of multiracial adults to date, we find that White-Asian and White-Black multiracials are on the whole more progressive than Whites, expressing levels of linked fate and social and racial attitudes that are comparable to those of their minority race. We argue that the growth in these multiracial populations seems most likely to benefit the Democratic Party.
TEACHERS’ PERCEPTIONS ON THE DEVELOPMENT AND IMPLEMENTATION OF HISTORY 2166 SYLLABUS

In 1990, Zimbabwe underwent its first major post-colonial syllabus reform in the teaching of History at secondary school level. Scholars who have studied this reform usually overlook teachers’ perceptions when explaining its development and implementation. This qualitative study explores History teachers’ perceptions on their participation in the planning and implementation of the History 2166 syllabus reform. Using qualitative phenomenological design methodology, the researchers listened to the voice of the voiceless, as teachers expressed their perspectives, views, understanding, and interpretations on the syllabus reform during semi-structured interviews with five purposively sampled History teachers drawn from five different schools within the Glen View/Mufakose district in Harare province. The interviews were further transcribed, coded and categorised into meaningful themes. All the participants signed consent forms to demonstrate their willingness to participate in the study. This study appreciates that teachers are the chalk-face implementers of syllabus reforms in any given context of change and their views matter. The key finding was that the ‘top-down’ approach used during this syllabus reform proved disastrous, ultimately producing a teacher-proof syllabus that deskilled and disempowered teachers thereby leading to tissue-rejection and its ultimate failure to effectively address key expectations. The research concludes that previous explanations on the success and/or failure of curriculum reforms were rather incomplete for they sidestepped teachers’ perceptions in explaining the development and implementation of the reforms in the first place.
Applications of Clustering Techniques in Data Mining: A Comparative Study

In modern scientific research, data analyses are often used as a popular tool across computer science, communication science, and biological science. Clustering plays a significant role in the reference composition of data analysis. Clustering, recognized as an essential issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for further understanding. Among many clustering algorithms, “more than 100 clustering algorithms known” because of its simplicity and rapid convergence, the K-means clustering algorithm is commonly used. This paper explains the different applications, literature, challenges, methodologies, considerations of clustering methods, and related key objectives to implement clustering with big data. Also, presents one of the most common clustering technique for identification of data patterns by performing an analysis of sample data.
Collisional–radiative modelling of a helium microwave plasma in a resonant cavity

A collisional–radiative model is proposed to describe the behaviour of a helium plasma sustained in a resonant cavity at atmospheric pressure. A set of rate constants is proposed at 2450 K. Indeed, most of the available data in the literature are reliable below 500 K. It is shown that the time post-discharge is mainly controlled by ambipolar diffusion during the first ten µs, next by electron-assisted recombination of helium ions and finally by chemionization from the excited state. This post-discharge lasts for hundreds of µs. It is mainly due to the slow recombination of electrons together with chemionization. Then, at steady state in CW mode, the electron temperature is found to be lower than 1 eV since low reduced fields are needed to sustain the discharge.
Effect of application of glucose, cellulose, and rice straw on nitrogen fixation (acetylene reduction and soil-nitrogen components) in anaerobic soil

The use of the plastic net bag method for the measurement of the decomposition rate of rice straw in the reduced layer enabled to demonstrate that the organic matter of rice straw consisted of at least two components, one rapidly decomposable (half-life, 17 days) and the other slowly decomposable (half-life, 58 days). Stimulation by rice straw of N2-fixation under anaerobic (under N2 gas) conditions was compared with that by cellulose and glucose. The peak of acetylene reduction activity (ARA) in the soil to which glucose had been added occurred on the 1st day of incubation. When cellulose or straw was added at 1%, the ARA peak occurred after 3 to 4 days. Peak height induced by cellulose was 3 times larger than that by straw. After 2 weeks of incubation, the activity was negligible. Addition of glucose and cellulose increased the number of colonies of anaerobic bacteria grown on nitrogen-free medium. The levels of hydrolyzable-N and α-amino acid-N increased by glucose or cellulose addition, presumably due.
A critical note on extrapolated helium pair potentials

It is difficult, if not impossible, to extrapolate the helium pair potential to the limit of a complete basis to within the accuracy needed to improve significantly on existing, directly computed potentials. Even though the basis-set convergence of calculations in a correlation-consistent basis with cardinal number X is dominated by the X−3 term, it is important to account for energy terms that converge more rapidly than ∝X−3. The electron-correlation contribution to the potential will be overestimated noticeably when these terms are not properly taken into account.
Applications of Video forensics

: Forensic video analysis is the scientific examination, contrast and/or evaluation of video in criminal matters. This definition became created with the aid of accredited video forensic corporations around the United States and across the world including LEVA, NATIA in addition to IAI. The video forensic method should be executed in a forensic lab this is prepared with the ideal gear and follows satisfactory practice protocols a good way to process the video recording with integrity and accuracy. Different edges related to video forensics and variables influencing crime scene investigation are discussed. In this paper, we are also emphasizing on the video forensics applications, as well as information contained in forensics report.
Shying Away from Sex: Examining Fear of Negative Evaluation and Body Esteem as Mediators in the Associations Between Shyness and Sexuality

Shying Away from Sex: Examining Fear of Negative Evaluation and Body Esteem as Mediators in the Associations Between Shyness and Sexuality Carlie Grace Palmer School of Family Life, BYU Master of Science Researchers have examined how shyness influences broad aspects of relationships (i.e., quality, satisfaction); however, little is known about how shyness impacts sexuality, specifically, within committed relationships. This study examined associations between shyness and sexual frequency and sexual satisfaction within committed young adult relationships. Potential explanations for these associations (i.e., fear of negative evaluation and body esteem, consecutively) were also examined. Participants included 3,670 individuals (61.6% female) ages 30-35 from the United States who were in committed dating or marital relationships. Results revealed that shyness was negatively associated with sexual frequency and satisfaction among men and women. In addition, fear of negative evaluation via body esteem mediated these associations. The discussion considers the implications of shyness and the associated processes and sexual outcomes in young adulthood.