Reviewerly/exHarmony huggingface dataset:
Analysis on the Strategies of Cultivating Students' Pronunciation and Intonation Awareness in English Teaching in Higher Vocational Colleges in the Era of Big Data

With the continuous development of modern information technology, the world has entered the era of Internet + big data. Big data is profoundly changing people's thinking, production and lifestyle. English teaching in vocational colleges has also been affected by big data and changed. Especially in today's growing international exchanges, college students' English listening and speaking skills must keep pace with the times. Improving the pronunciation and intonation awareness of college students is an effective way to solve this problem. The typical problems of college students in pronunciation and intonation are poor pronunciation, incorrect intonation, and insufficient pronunciation and intonation. The formation of these problems is multifaceted, including the lack of students' awareness of pronunciation and intonation, and the lack of awareness of teachers' pronunciation and intonation. Teachers can adopt the following methods to improve students' pronunciation and intonation awareness: First, train a group of teachers with pronunciation and intonation awareness. Secondly, teachers must infiltrate the training of pronunciation and intonation in classroom teaching. Thirdly, teachers should combine pronunciation and intonation teaching with traditional English teaching methods. The author believes that by implementing these strategies, students can quickly improve their understanding of English pronunciation and intonation, and then help them master English faster and better.
BoundWarden: Thread-enforced spatial memory safety through compile-time transformations

This paper presents BoundWarden, a novel spatial memory safety enforcement approach that utilizes a combination of compile-time transformation and runtime concurrent monitoring techniques. The compiler extension component of BoundWarden transparently instruments source code of C programs with the code that allows the runtime component of BoundWarden to comprehensively detect and prevent buffer overflow and other out-of-bound errors in buffers on the stack, heap, as well as BSS and data segments of memory. To reduce runtime overhead of bound checking, the runtime component of BoundWarden leverages the ubiquity of multi-core processors by offloading most of the work to a dedicated bound checking thread, which is responsible for performing bound checking and managing metadata. To preserve memory layout and maintain compatibility with existing libraries and binaries, BoundWarden stores the base and the bound of buffers in a separated dedicated bound table. Experiments showed that the prototype of BoundWarden is effective at enforcing spatial memory safety by successfully passing all 850 tests of RIPE test suite, and 94% of NIST's SARD test suite 89, while the results from the Olden benchmark suite showed that on average BoundWarden introduced roughly 1.85x overhead, compared to the uninstrumented code.
Applications and Approaches for Texture Analysis and Their Modern Evolution

In this research work, we have presented a detailed study of various methods used for the texture analysis. These methods are classified as model-based methods and statistical-based methods. These methods are widely used in the field of remote sensing, document processing, automated inspection, and biomedical imaging applications. Texture plays a very prominent role in various applications related to image processing. These methods are discussed in brief in this research work.
AI and AR

This chapter presents some of the possibilities and approaches that are used in the application of AI (artificial intelligence) and AR (augmented reality) in the new learning environments. AI will add another dimension to distance learning or eLearning that in some cases already includes AR (augmented reality) virtual learning environments. Because of this advent in available technology and the impact it will have on learning, assessment of newly structured parameters and their impact on student outcomes is crucial when measuring student learning. For some of us there might be a concern about the domination of AI as seen in the movie The Terminator, but we can take ease in the notion that it is not only AI versus humans. A new version of human augmented intelligence (HI) is being developed as we speak.
Work in Progress: Information and Communications Technology (ICT) in Learning in Students of Elementary Education

The context of Information and Communications Technology (ICT) needs to be researched to know the impact on learning. Because of this, the purpose of the present paper is to identify the relationship between the use of ICT and the Learning. Through a convenience sampling, 133 sixth grade elementary students (between 10 and 13 years old) were selected from a public school (Lima-Peru). To evaluate the use of ICT, a Likert scale was applied and the learning was evaluated with the scores obtained in the different academic areas. The results showed that the use of ICTs was positively and significantly related to the areas of communication (r=.27**), religion (r=.27***), science and technology (r=.18*), demonstrating that the frequency of ICT use influences in learning; however, this influence is evident only in some subjects.
Safety-Guaranteed, Accelerated Learning in MDPs with Local Side Information

In environments with uncertain dynamics, synthesis of optimal control policies mandates exploration. The applicability of classical learning algorithms to real-world problems is often limited by the number of time steps required for learning the environment model. Given some local side information about the differences in transition probabilities of the states, potentially obtained from the agent's onboard sensors, we generalize the idea of indirect sampling for accelerated learning to propose an algorithm that balances between exploration and exploitation. We formalize this idea by introducing the notion of the value of information in the context of a Markov decision process with unknown transition probabilities, as a measure of the expected improvement in the agent's current estimate of transition probabilities by taking a particular action. By exploiting available local side information and maximizing the estimated value of learned information at each time step, we accelerate the learning process and subsequent synthesis of the optimal control policy. Further, we define the notion of agent safety, a vital consideration for physical systems, in the context of our problem. Under certain assumptions, we provide guarantees on the safety of an agent exploring with our algorithm that exploits local side information. We illustrate agent safety and the improvement in learning speed using numerical experiments in the setting of a Mars rover, with data from onboard sensors acting as the local side information.
Digital Micrographs in Pathology

Digital image capture, storage, searching, retrieval, processing, and manipulation are surveyed. These are detailed by describing how to select suitable digital camera, perform digital photomicrography, and process digital images. Medical applications include education, telepathology aided by automated slide scanners, computer-assisted image analysis of immunohistochemical specimens, and quality assurance and control in histopathology. Image formats and commonly encountered limitations of digital photography and photomicrography are described. Documentation includes listings of suppliers of camera-to-microscope couplers, digital still cameras, slide scanners, and image-processing software. Signal-to-noise (S/N) ratio. Abbreviations of image formats are shown in Tab.2.
Research on integrated computer game algorithm for dots and boxes

Situation assessment and search are two key problems in computer game research. In general, as the game progresses, the difficulty of evaluating the situation of the game is significantly reduced, and the accuracy of the evaluation is significantly increased. Based on the famous chess game, this article proposes and implements a new scheme that combines the Monte Carlo tree search algorithm, the Alpha-Beta algorithm and the model based on the deep convolution neural network (CNN) to solve the computer game problem. This article first proposes a deep convolutional neural network model based on dots and boxes, including deep value network and deep strategy network, focusing on situation assessment and strategy recommendation, respectively. Then, using the Monte Carlo Tree Search (MCTS) algorithm as a framework, deep value network integrated MCTS algorithm and deep strategy network integrated MCTS algorithm are proposed. In both integrated models, Alpha-Beta complete search is used to truncate the Monte Carlo simulation process and improve simulation efficiency. Through competition with human players, the results show that the two integrated algorithm game systems have reached much higher intelligence level than ordinary humans in solving the problem of dots and boxes.
Memory-Augmented Dialogue State Tracker in Task-Oriented Dialogue System

Dialogue state tracker is a core component of task-oriented dialogue system, which tracks users' goals during interaction between users and systems. Though many models have been applied to task-oriented dialogue systems and made some progress, these models still have poor performance in multi-domain conversation. To improve memory ability of dialogue state tracker, we propose Mem-DST (Memory-augmented dialogue state tracker), which is based on memory networks. Experiments on Multiwoz show that our model perform well in multi-domain dialogue, as well as in single domain dialogue. Besides, it is an important progress that our model gets considerable joint accuracy in both scenes.
Hardware Implementation of Histogram-Based Algorithm for Image Enhancement

Image enhancement plays an important role in all fields of applications, like biomedical, satellite images, graphical applications and many others. In today’s world everything needs to work in real time, hence this study talks about the implementation of image enhancement technique. This technique uses hardware approach for analysing the quality of an image which gives less time and can be worked for parallel processing applications. As histogram of an image contains details such as pixel intensity, distribution of grey levels helps to understand and analyse the quality of an image. Here, we have proposed an efficient architecture for image enhancement technique and was simulated using Verilog HDL. This study outlines brightness enhancement and contrast stretching method which permits better visual appearance of an image. Also here we have proposed design for finding region of interest in an image by developing eight different grey levels which can be used in detecting object in an image as it is one of the major areas of research in biometric application.
Verification of stationary action trajectories via optimal control

A new optimal control based representation for stationary action trajectories is constructed by exploiting connections between semiconvexity, semiconcavity, and stationarity. This new representation is used to verify a known two-point boundary value problem characterization of stationary action.
Augmented Reality for Dies Alignment in Machine Setup

Abstract Forging is one of the material deformation processing techniques. While the operation cycle is short and efficient, the machine setup process is generally long and time consuming. One of the difficulties in operation is position alignment procedure of the equipment during the die changing process. Due to the size and shape complexity of the machine and equipment, the operators are not able to clearly see the important parts – e.g. guided pins, holes, and sub-plate which need to be moved to finely adjust the alignment. Augmented Reality (henceforth AR) is used to solve this problem by rendering virtual objects and guides associated to those parts over the real equipment. Thus, the operators can monitor the position of the parts during the alignment process through the virtual objects. This AR system is developed based on ARcore SDK together with Unity on the Android platform. The experiment was conducted in real factory environment.
Protocol Switching Mechanism for Countering Radiometric Signature Exploitation

Manufacturing variations introduce features that distinguish radio transceivers even those of the same vendor. Such distinction is often referred to as radiometric signature and is found to be useful in conducting device authentication and crime forensics. Yet, radiometric signatures could constitute a privacy threat. Particularly, in the realm of wireless networks, an adversary may exploit RF fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show that RF fingerprinting could be a major tool for the adversary to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. We further develop a novel countermeasure to degrade the adversary's ability in exploiting RF fingerprinting. The proposed countermeasure is based on switching among preset communication protocols and employs adversarial machine learning to select the protocol for a transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.
Navigation of a Quadratic Potential with Star Obstacles

The Rimon-Koditscheck potential is known to be a navigation function for sufficiently curved worlds. With global information, a diffeomorphism can be constructed to convert complex star worlds into sphere worlds so that the point agent can follow the negative gradient of the navigation function potential. Without global information, convergence guarantees only exist for sufficiently curved worlds or ellipsoidal worlds. This work closes the gap between local information and global information by extending convergence guarantees with local information to star worlds in general. Convergence to the goal and obstacle avoidance is established from every initial position in the free space. Results are numerically verified with a discretized version of the proposed dynamic flow.
Application of Computer Digital Simulation Technology in the Research of Martial arts Action Structure Image

The reference of Martial arts action structure to assist the artificial correction is poor, for this problem, in this paper, a new method of Martial arts action structure analysis and reconstruction based on computer numerical simulation is proposed. Firstly, three-dimensional visual image information acquisition of martial arts is carried out, and adaptive threshold decomposition and wavelet analysis are used to perform noise reduction pretreatment. The original domain feature point library is formed by the original separation of the domain feature points of the martial arts action structure image. Then, the edge contour feature extraction method is used to extract the contour features of the martial arts in the image, input into the expert system of the orthodontics, carry on the visual analysis and correction, and realize the Martial arts action structure analysis and the feature reconstruction identification. The results of computer simulation show that this method can be used to reconstruct the martial arts structure and improve the quantitative analysis ability of martial arts. The signal-to-noise ratio of the output image is high, and the recognition performance of the image processing and motion is better.
Invariant-EKF Design for a Unicycle Robot under Linear Disturbances

We consider a nonlinear estimation problem where a unicycle vehicle moves with unknown disturbances generated from linear time-invariant systems. The vehicle measures its position to estimate its state and disturbance information simultaneously. We show that this system is invariant under the action of a Lie group and design an Invariant Extended Kalman Filter (IEKF). We propose a first-order approximation of the noise covariance in the invariant frame. Through Monte-Carlo simulations, we demonstrate that the first-order approximation improves the performance of the IEKF and that the IEKF yields superior transient performance over the standard EKF.
Ensembled Tricks for Instance Segmentation

Computer Vision has attracted more and more attention with the fast development of deep learning. The instance segmentation area, which extends the Object detection, can better help us comprehend the surrounding environments. In this paper, we ensembled the tricks that can strengthen the model performance for instance segmentation. We do the ablation experiments for the MS-COCO datasets and LVIS datasets. The results demonstrate that the selected tricks can greatly boost the performance. With our tricks, our model achieves the 7th on the LVIS Challenge Track for ICCV 2019 workshop.
An Improved Fuzzy Algorithmic Approach Applying on Medical Image to Improve the Contrast

Noise led from nodes of medical imaging system and underexposure or overexposure will make the image be in a low contrast. In this paper we try to enhance the image contrast with a method of digital image processing. We present an improved fuzzy algorithmic approach on the basis of the conventional Pas. S. K algorithm. Processed by this improved algorithm, the imperceptible information can be avoided to lose when the planar space of original image is transformed to the fuzzy space. And special experiment has been carried out to demonstrate the effect of this approach applying on the X-ray roentgenoscopy image of animal carotid, which is obtained by a homemade instrument and convert to digital signal with an image-collecting card. We find that both images processed with the conventional algorithm and the improved algorithm can provide more details than the original one. While percent of the zero gray pixels of the image processed with the conventional algorithm is much more (to reach 12.3%) than that with the improved algorithm (6.2%) by analyzing with Visual C++ image process program, indicating a more loss of information caused by the forcible process of making some image elements equal to zero. Thus it can be concluded that the improved algorithm seems to be more distinct than the conventional one.
Hybrid Routing: Towards Resilient Routing in Anonymous Communication Networks

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.
LASF—A Lightweight Authentication Scheme for Fog-Assisted IoT Network

Internet of things widens the scope of communication by connecting the physical objects to the Internet. These physical objects are vulnerable to various malicious activities, thus strong security features are required in IoT devices. Low power resource constrained-IoT devices limit the use of computational complex algorithm. In this paper, a lightweight authentication scheme has been proposed for fog-assisted IoT network to authenticate IoT devices at low computation cost. It uses three-way handshake with challenge response mechanism to verify the authenticity of the participating device. The performance is evaluated by using IFogSim tool kit and MATLAB, which shows that the proposed scheme is authenticating the user devices at low computational cost and storage utilization. It takes less handshake duration and average response time between the authenticating devices and the fog devices to improve the quality of service.
Semi‐supervised long short‐term memory for human action recognition

In real human action recognition task, it is a common phenomenon that there are many unlabelled samples and few labelled samples. How to make good use of unlabelled samples to improve the generalisation ability of models is the focus of semi-supervised learning research. In this study, the authors present two semi-supervised methods based on long short-term memory (LSTM) to learn discriminative hidden features. One is the LSTM ladder network, the other is the Symmetrical LSTM network. By them unlabelled samples can be used automatically to improve learning performance without relying on external interaction. Both on the NTU-RGB + D dataset and the Kinetics dataset, their methods achieve >10 and 5% improvements, separately.
AutoTrain: An Efficient Auto-training System for Small-scale Image Classification

Machine learning has become the most promising research field. However, the involved models usually require complex, tedious and expensive manual intervention. The automated machine learning technology plays a significant role in mitigating this issue. However, the current studies ignore the importance of automation in data preprocessing. In this paper, we propose an efficient automatic training system, AutoTrain, to solve small-scale image classification problems. First, we design sample equalization in data augmentation to improve the performance of training on uneven data. Then, a Bayesian optimization-based strategy controller is introduced to rapidly find the strategy applied in data augmentation. Additionally, we present a dynamic adjustment model to fit tasks with different scales and complexity. Finally, experimental results show that the AutoTrain's training speed is about 3 times faster on average than the conventional methods. And the avergae accuracy of AutoTrain has 2% improved to the conventional methods.
General Frameworks for Designing Arithmetic Components for Residue Number Systems

In many previous works, researchers have proposed Residue-based arithmetic components for the two classical moduli sets $$(2^{p}, 2^p-1, 2^p+1)$$ , and $$(2^{p}, 2^p-1, 2^{p-1}-1)$$ , where p is a positive integer. These components included reverse converters, sign identifiers, and scalers. In this paper, we are widening the umbrella of these two sets to be $$(2^{k}, 2^p-1, 2^p+1)$$ , and $$(2^{k}, 2^p-1, 2^{p-1}-1)$$ , where k is a positive integers such that $$0<k\le 2p $$ . The classical moduli sets are special cases of these expanded sets when $$p=k$$ . This paper introduces multiplicative inverses for these expanded moduli sets. The introduced multiplicative inverses will ease the process of designing residue-based arithmetic components. This paper also proposes general frameworks for designing reverse converters, sign identifiers, comparators, and scalers. Additionally, this work expands the options available for a designer willing to design a RNS processor based on these enhanced moduli sets.