Reviewerly/exHarmony huggingface dataset:
Students’ English Speaking Anxiety in Classroom Context

This research was designed to investigate students’ English speaking anxiety in a classroom context. There were 23 students from Unismuh Makassar who participated in this research by using the purposive sampling method. This mixed-methods study looked into the students' English-speaking anxiety and how they overcame it. A structured interview and a questionnaire adapted from the Horwitz et al. (1986) Foreign Language Classroom Anxiety Scale (FLCAS) were used to gather the data. According to the findings of the study, (1) the majority of English Education students at Unismuh Makassar experienced excessive anxiety, with 74.2% anxious, 21.7% anxious in the middle, and no one anxious in the low. (2) Based on the students' experiences, there were six strategies to deal with anxiety: keep silent, take a deep breath, relax with the circumstances, reiterate the lesson, avoid eye contact, and drink water. The finding indicated the excessive level of students’ anxiety and the strategies to cope with it. According to these findings, lecturers should be more aware of their students' worries in order to push them to speak up confidently and clearly in English class. Telling a joke, expressing gratitude, and making a pleasant gesture are all strategies to assist kids overcome their fear of public speaking. As a result, teachers are encouraged to be slightly innovative in developing new strategies to enable students to speak more effectively.
Correction of motion artifact in CL based on MAFusNet

Computed laminography (CL) is one of the best methods for nondestructive testing of plate-like objects. If the object and the detector move continually while the scanning is being done, the data acquisition efficiency of CL will be significantly increased. However, the projection images will contain motion artifact as a result. A multi-angle fusion network (MAFusNet) is presented in order to correct the motion artifact of CL projection images considering the properties of CL projection images. The multi-angle fusion module significantly increases the ability of MAFusNet to deblur by using data from nearby projection images, and the feature fusion module lessens information loss brought on by data flow between the encoders. In contrast to conventional deblurring networks, the MAFusNet network employs synthetic datasets for training and performed well on realistic data, proving the network’s outstanding generalization. The multi-angle fusion-based network has a significant improvement in the correction effect of CL motion artifact through ablation study and comparison with existing classical deblurring networks, and the synthetic training dataset can also significantly lower the training cost, which can effectively improve the quality and efficiency of CL imaging in industrial nondestructive testing.
Software Development for Comprehensive Assessment of English Online Teaching Quality in Universities Based on Data Mining

In the context of big data in education, the education industry is combined with information technology to form an online teaching model. In order to analyze the actual effectiveness of online teaching methods, a comprehensive evaluation model of online teaching quality is developed based on data mining. The model is divided into two parts: an improved K-modes algorithm to evaluate English teachers’ “teaching” and a feed-forward neural network to evaluate students’ “learning”. The improved K-modes algorithm cleansed, analyzed, and mined the teaching data, and improved the calculation of cosine similarity to establish a model for evaluating teachers’ teaching status, the neural network model has more excellent index results, where the average error is 0.98, within 1, so the neural network model has a smaller error result. The combined model has a strong feasibility for the comprehensive evaluation of English online teaching quality.
Traffic Sign Detection and Recognition System Using Improved YOLOV5s

Traffic sign detection is one of the most challenging tasks for autonomous vehicles, especially for the detection of different types of signs and real-time applications. Unmanned driving systems face a lot of problems to recognize traffic signs faster and more accurately. In this paper, we propose a model using improved YOLOv5 to detect Traffic signs and recognize them properly. Our dataset consists of 3500 pictures of the traffic sign and we have annotated all that pictures in YOLOv5 format. There is 39 classification of our dataset on all pictures based on the traffic sign. This system can be used for unmanned driving vehicles. Using this model a device can make which will help drivers who are driving a car. After implementation of our dataset with the help of improved YOLOv5, the output shows an accuracy of 86.75% in different conditions such as low light, cloudy, rainy, and sunny.
Characterization of random walks on space of unordered trees using efficient metric simulation

The simple random walk on $\mathbb{Z}^p$ shows two drastically different behaviours depending on the value of $p$: it is recurrent when $p\in\{1,2\}$ while it escapes (with a rate increasing with $p$) as soon as $p\geq3$. This classical example illustrates that the asymptotic properties of a random walk provides some information on the structure of its state space. This paper aims to explore analogous questions on space made up of combinatorial objects with no algebraic structure. We take as a model for this problem the space of unordered unlabeled rooted trees endowed with Zhang edit distance. To this end, it defines the canonical unbiased random walk on the space of trees and provides an efficient algorithm to evaluate its escape rate. Compared to Zhang algorithm, it is incremental and computes the edit distance along the random walk approximately 100 times faster on trees of size $500$ on average. The escape rate of the random walk on trees is precisely estimated using intensive numerical simulations, out of reasonable reach without the incremental algorithm.
Better virtual objects placement in real world through photogrammetry for object recognition and spatial anchoring

Augmented reality is one of the technologies, which in recent years has been most in the spotlight for communities as diverse as researchers, industrial actors and gamers. A common need in almost any scenarios is to “register” the virtual world with the real one, so that the right virtual objects can be accurately placed in the user's view. Although positioning could be aided by global systems such as GPS, there are situations in which its accuracy or feasibility cannot be guaranteed. Indeed, a few sectors could be prevented from exploring augmented reality as a disrupting technology if this need cannot be adequately fulfilled. In this work, photogrammetry is investigated for scenarios in which a few static already known and well-defined real-world objects can be used for anchoring in a broader area. The goal is to create a solid and reliable augmented reality framework in terms of precise placement of objects with the aim of using it in contexts where other solutions lack the required accuracy. In particular, this work considers as the primary use case a solution developed using Microsoft Hololens 2 for the positioning of digital objects in the context of railway maintenance by exploiting the recognition of real objects in the environment through photogrammetry techniques. Indeed, only a precise positioning of the objects will allow the pervasive diffusion of this technology in sectors such as health, military and in any case in all those contexts where accuracy and reliability are essential elements for ensuring safety of operations.
The Novel Multi Source Method for the Randomness Extraction

Cryptography, statistical analysis, and numerical simulations are just a few of the areas where randomness is frequently applied. They are also a crucial resource in engineering and science. Usually, we have to supply unbiased, independent random bits for these applications. This raises the question of where these supposedly random bits can be found. Pseudorandom number generators are algorithms that produce seemingly random numbers but which are not actually random. When actual randomness is required, we employ true random number generators, which use unforeseen random events as their random source. Based on the inherent unpredictability of quantum measurements, quantum random number generators (QRNGs) produce actual random numbers. Sadly, due to classical noise, quantum randomness and classical randomness are invariably combined in practice. Also, randomness is frequently biased and correlated. The resulting raw bits sequence must be processed in order to provide output values of high quality that are as close to uniform distribution as is feasible. This calls for random extractors. We review numerous types of postprocessing as well as the randomness produced by quantum random number generators. The various randomness extractors are covered. Additionally, we employ information theoretically secure extractors and propose improved novel randomness extraction. Our new hybrid randomness extractor is based on a base of information-theoretically secure extractors. It uses multiple sources in the extraction process and can be changed based on our usage needs. Our extractor is resistant to quantum attacks, and the extraction process can be accelerated, say by means of parallelism.
A Distributed Multilevel Force-Directed Algorithm

The use of graph visualization approaches to present and analyze complex data is taking a leading role in conveying information and knowledge to users in many application domains. This creates the need of developing efficient and effective algorithms that automatically compute graph layouts. In this respect, force-directed algorithms are arguably among the most popular graph layout techniques. Aimed at leveraging the potential of modern distributed graph algorithms platforms, we present Multi-GiLA, the first multilevel force-directed graph visualization algorithm based on a vertex-centric computation paradigm. We implemented Multi-GiLA using the Apache Giraph platform. Experiments show that it can be successfully applied to compute high quality layouts of very large graphs on inexpensive cloud computing platforms.
Design and Implementation of SecPod, A Framework for Virtualization-Based Security Systems

The OS kernel is critical to the security of a computer system. Many systems have been proposed to improve its security. A fundamental weakness of those systems is that page tables, the data structures that control the memory protection, are not isolated from the vulnerable kernel, and thus subject to tampering. To address that, researchers have relied on virtualization for reliable kernel memory protection. Unfortunately, such memory protection requires to monitor every update to the guest's page tables. This fundamentally conflicts with the recent advances in the hardware virtualization support. In this paper, we present the design and implementation of SecPod, a practical and extensible framework for virtualization-based security systems that can provide both strong isolation and the compatibility with modern hardware. SecPod has two key techniques: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">paging delegation</i> delegates and audits the kernel's paging operations to a secure space; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">execution trapping</i> intercepts the (compromised) kernel's attempts to subvert SecPod by misusing privileged instructions. We have implemented a prototype of SecPod based on KVM. Our experiments show that SecPod is both effective and efficient.
Correlation Modeling and Resource Optimization for Cloud Service With Fault Recovery

Energy-efficient cloud computing has recently attracted much attention, where not only performance but also energy consumption are important metrics to be considered for designing rational resource scheduling strategies. Most of existing approaches for achieving energy efficient computing focus on connecting these two metrics and balancing the tradeoff between them, which however is inadequate because another important factor reliability is not considered. In fact, both virtual machine (VM) failures and server failures inevitably interrupt execution of a cloud service, and eventually result in spending more time and consuming more energy on completing the cloud service. Therefore, reliability significantly affects service performance and energy consumption, and thus they should not be handled separately. Connecting these correlated metrics is essential for making more precise evaluation and further for developing rational cloud resource scheduling strategies. In this paper, we present a correlated modeling approach applying Semi-Markov models, the Laplace-Stieltjes transform (LST), a Bayesian approach to analyze reliability-performance (R-P) and reliability-energy (R-E) correlations for cloud services using a retrying fault recovery mechanism. A recursive method is also proposed for modeling the correlations for cloud services using a check-pointing fault recovery mechanism. The proposed correlation models can be used to calculate the expected service time and energy consumption for completing a cloud service. Moreover, the models can contribute to analyzing the expected performance-energy tradeoff. We formulate the expected performance-energy optimization problem by describing performance and energy consumption metrics as functions of assigned CPU frequencies. Finally, we use a derivation approach to determine Pareto optimal solutions for the formulated optimization problem. Illustrative examples are provided.
Beyond saliency: Understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation

Despite the tremendous achievements of deep convolutional neural networks (CNNs) in many computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein. Our proposed method starts out with a layer-wise relevance propagation (LRP) step which estimates a pixel-wise relevance map over the input image. Following, we construct a context-aware saliency map, SR map, from the LRP-generated map which predicts areas close to the foci of attention instead of isolated pixels that LRP reveals. In human visual system, information of regions is more important than of pixels in recognition. Consequently, our proposed approach closely simulates human recognition. Experimental results using the ILSVRC2012 validation dataset in conjunction with two well-established deep CNN models, AlexNet and VGG-16, clearly demonstrate that our proposed approach concisely identifies not only key pixels but also attention areas that contribute to the underlying neural network's comprehension of the given images. As such, our proposed SR map constitutes a convenient visual interface which unveils the visual attention of the network and reveals which type of objects the model has learned to recognize after training. The source code is available at https://github.com/Hey1Li/Salient-Relevance-Propagation.
Reference-Frame-Independent Quantum Key Distribution Using Fewer States

Reference-frame-independent quantum key distribution (RFI QKD) protocol can reduce the requirement on the alignment of reference frames in practical systems. However, comparing with the Bennett-Brassard (BB84) QKD protocol, the main drawback of RFI QKD is that Alice needs to prepare six encoding states in the three mutually unbiased bases (X, Y, and Z), and Bob also needs to measures the quantum state with such three bases. Here, we show that the RFI QKD protocol can be secured in the case where Alice sends fewer states. In particular, we find that transmitting three states (two eigenstates of the Z basis and one of the eigenstates in the X basis) is sufficient to obtain the comparable secret key rates and the covered distances, even when the security against coherent attacks with statistical fluctuations of finite-key size is considered. Finally, a proof-of-principle experiment based on time-bin encoding is demonstrated to show the feasibility of our scheme, and its merit to simplify the experimental setup.
Statistical analysis of lake sediment geochemical data for understanding surface geological factors and processes: An example from Amazonian upland lakes, Brazil

Statistical evaluation applied to geochemical data of upland lake sediments and their catchment basins rocks from Serra dos Carajás was used to identify geochemical signatures associated with underlying processes, sediment provenances, and source-sink relationship. The lakes are Violão, Amendoim and Três Irmãs - TI1, TI2 and TI3. A centred log-ratio transformation (clr) was used prior to multivariate analyses in order to eliminate closure issues in compositional data. Due to the similarity between δ15N values and organic sources (mainly from C3 plants), the three lakes were clustered together. Violão Lake receives largest organic contribution from autochthonous sources, such as siliceous sponge spicules and algae, except for it shallower portion (WNW extension), which is more similar to TI2, having low δ15N values that are similar to the isotopic signature of upland swamps. The upper continental crust (UCC) normalization pattern shows that sediments are mainly enriched in Fe, P and Se, which is closely related to the catchment lithology. The distribution of elements in TI2 is significantly different from the other lakes, because it is dominated by organic carbon, while the other lakes are a mix of detritus and organic carbon. Factor Analysis (FA) using clr-transformed data distinguishes several geochemical assemblages in the sediments, with the major detritic groups being similar to catchment basin laterites: the Ti-Zr-Hf-Nb-Y-HREEs group corresponds to resistant minerals which remained stable during lateritization; the LREEs group reflects mobilization and reprecipitation by REE bearing minerals; and the Al-V-Cr-Sc association reflects metavolcanic rock. The Fe-P-Mo-As-Zn cluster in the sediments is attributed to Fe-oxyhydroxide precipitation, while TOC-SO3-Hg-Se group is controlled by organic matter. Principal Component Analysis (PCA) further indicates that detritic lake sediments are not directly derived from the parent rocks, but from weathered crusts, mainly ferruginous laterites and soils, which is consistent with their elements ratios.
Composite Description Based on Salient Contours and Color Information for CBIR Tasks

This paper introduces a novel image descriptor for content based image retrieval tasks that integrates contour and color information into a compact vector. Loosely inspired by the human visual system and its mechanisms in efficiently identifying visual saliency, operations are performed on a fixed lattice of discrete positions by a set of edge detecting kernels that calculate region derivatives at different scales and orientation. The description method utilizes a weighted edge histogram where bins are populated on the premise of whether the regions contain edges belonging to the salient contours, while the discriminative power is further enhanced by integrating regional quantized color information. The proposed technique is both efficient and adaptive to the specifics of each depiction, while it does not need any training data to adjust parameters. Experimental evaluation conducted on seven benchmarking datasets against 13 well known global descriptors along with SIFT, SURF implementations (both in VLAD and BOVW), highlight the effectiveness and efficiency of the proposed descriptor.
ANFIS model for the prediction of generated electricity of photovoltaic modules

The fact that conventional energy sources are exhaustive and limited are increasingly encouraging research in the field of alternative and renewable energy sources. The electricity generated by solar photovoltaic modules and panels occupies an ever greater percentage in total electricity production, so it is clear that photovoltaic systems are increasingly integrating with the existing electricity network into one system or functioning as autonomous systems. The aim of the research is to create a model based on the principles of the fuzzy logic and artificial neural networks that will perform the task of predicting the maximum energy of photovoltaic modules as accurately as possible. The prediction should facilitate work in planning production and consumption, system management, economic analysis. The most important methods used in the research are modeling and simulation. Input and output variables are selected and in the ANFIS (Adaptive Neuro Fuzzy Inference System) model a set of their values is presented. Based on them it comes to the function of dependency. The prediction rating of the created model was performed on a separate data set for testing and a model with the lowest average test error value was selected. The performance of the model was compared with the mathematical model through sensitivity analysis, which led to the conclusion that the ANFIS model gives more accurate results.
Time evolution of quantum-memory-assisted entropic uncertainty relation and quantum correlations under dissipative environment

By assuming the effect of dissipative environment and the Hamiltonian XY-model and external magnetic field, the time evolution of the entropic uncertainty relation (EUR) in the presence of quantum-memory and the dynamics of quantum correlations (QC) are investigated. It is shown that EUR and QC may evolve in different ways during the time. However, they can behave similarly when the time tends to infinity. The effects of external magnetic field and anisotropic parameter are different on the dynamics of EUR and QC.
Max-Plus Operators Applied to Filter Selection and Model Pruning in Neural Networks

Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks. Besides showing that they can be easily implemented with modern machine learning frameworks, we confirm and extend the observation that a Max-plus layer can be used to select important filters and reduce redundancy in its previous layer, without incurring performance loss. Experimental results demonstrate that the filter selection strategy enabled by a Max-plus layer is highly efficient and robust, through which we successfully performed model pruning on two neural network architectures. We also point out that there is a close connection between Maxout networks and our pruned Max-plus networks by comparing their respective characteristics. The code for reproducing our experiments is available online (for code release, please visit https://github.com/yunxiangzhang. ).
Entanglement transfer in a noisy cavity network with parity-deformed fields

We investigate the effects of parity-deformed fields on the dynamics of entanglement transfer to distant noninteracting atomic qubits. These qubits are embedded in two distant lossy cavities connected by a leaky short-length fiber (or additional cavity). The process is studied within a single-excitation subspace, the parity-deformed cavity photons allowing the introduction of static local classical fields, which function as a control. The mechanism of state transfer is analyzed in comparison to the uncontrolled case. We find that the transfer evolution exhibits an asymmetry with respect to atom-field detuning, being sensitive to the sign of the detuning. Under a linear interaction controlled by the local classical fields, we show that the entanglement distribution can be both amplified and preserved against the noise. These results motivate developments towards the implementation or simulation of the purely theoretical model employing parity-deformed fields.