Reviewerly/exHarmony huggingface dataset:
Construction and Simulation of Online English Reading Model in Wireless Surface Acoustic Wave Sensor Environment Optimized by Particle Swarm Optimization

English reading is an important way to consolidate and expand English language knowledge, and it is also an important way to obtain information and understand British and American culture. Therefore, reading teaching has always been an important part of English education and teaching at all levels and types of schools, and college English teaching is no exception. College English teachers have been carrying out teaching reflections in their reading teaching practice and constantly exploring teaching modes and teaching directions that improve students’ reading engagement and reading comprehension ability. However, the current daily teaching of English reading still generally maintains the traditional teaching mode. The entire reading learning process is monotonous, boring, and stylized, and the ability to acquire and process information cannot be combined with language knowledge and language skills. This kind of teaching mode severely inhibited the college students’ involvement in English learning model. Based on the electromagnetic-polarization response expression in a uniformly polarized half-space, this paper transforms the problem of polarization parameter extraction into a minimum optimization problem and constructs a fitness function. A set of polarization parameters is selected to calculate the electromagnetic-polarization response under trapezoidal waves in a uniform half-space, and the basic particle swarm algorithm is used to extract single and multiple parameters, respectively. In this paper, by adding a window to the test data in the time domain, the multiplicative and additive interference in the test signal is suppressed, and the signal-to-noise ratio of the test result is improved. We use the platform built in this article to wirelessly test the temperature characteristics of the surface acoustic wave sensor. The research results identified eight cognitive attributes of English reading and successfully generated diagnostic information at the group and individual levels and finally formed graphics and textual diagnostic feedback. There is a certain correlation between students’ vocabulary mastery and English reading performance, which shows that the vocabulary teaching method can help students better understand the reading materials and improve their reading performance. Combining two student interviews and learning logs, it can be seen that students’ understanding and frequency of use of vocabulary knowledge have increased significantly after the action research. It is generally recognized that vocabulary has a positive effect on improving reading level and can be based on the recognition and understanding of vocabulary. The mastery of vocabulary can promote the improvement of college students’ English reading level to a certain extent. Learners should strengthen vocabulary learning and face up to the importance of vocabulary knowledge in English reading.
Construction of Lightweight Content key Distribution Framework for DRM systems

Digital content piracy has become a big challenge after the advancement in network and digital technologies. digital rights management (DRM) systems were introduced to regulate content distribution. Protocols are being designed to ensure secure and efficient communication in DRM systems. Both security and efficiency are important parameters and should travel together. During our analysis, we identified that much efficient content key delivery mechanism fails to achieve desired security parameters. Thus, it is required to work on efficient protocols design. We have worked on the design of the efficiency of the content delivery mechanism without diluting security. The proposed scheme presents an anonymous content key delivery mechanism. We analyze the security of the scheme. We verify the security against replay attacks and man-in-the-middle attacks using the formal verification simulation tool AVISPA, which indicates that the scheme is safe. The analysis of performance is done, which includes the computation cost at the user and server end. The comparative study of performance is also included to identify the improvement at efficiency side.
HNECV: Heterogeneous Network Embedding via Cloud Model and Variational Inference

Deep learning has been successfully used in heterogeneous network embedding. Although it shows excellent performance on preserving the structure and semantic characteristics of network while a large scale of training data is provided, it is still challenging to model complex structured representations that effectively perform on diverse network tasks. In this work, a new heterogeneous network embedding learning method is presented based on cloud model and variational inference, called HNECV. The model uses meta-path random walks to obtain structural information of original network which can capture abundant semantics of networks from different views. In addition, a novel framework is put forward to build an excellent embedding. We employ the forward cloud transformation algorithm to improve the sampling method of the variational autoencoder in its hidden space, and then a self-supervised learning module is constructed to guide the cluster of node vectors in the hidden space of variational autoencoder. Experimental results indicate that the proposed model can achieve better performance than those of state-of-the-art algorithms. Furthermore, HNECV shows better robustness and steadiness on different network tasks when different ratio of edges are disconnected at training.
A virtual binocular line-structured light measurement method based on a plane mirror

A virtual binocular line-structured light measurement method based on a plane mirror is proposed. A checkerboard target and its virtual image in a front coating plane mirror are captured by a camera in a single picture. The target is imaged in several positions to determine the spatial relationship between the camera and plane mirror. The centers of light stripes are matched with its mirror images by the vanishing point constraint between the checkerboard coplanar point and its corresponding mirrored point. By scanning the object on a moving stage, 3D point cloud data can be obtained. Compared with the traditional binocular line-structured light measurement system, the proposed method just needs a single camera, and two images can be obtained in one procedure. It improves the measurement speed, reduces the cost of measurement, and avoids measurement errors caused by non-strict synchronization between the left and right camera in binocular vision. Experimental results demonstrate good performance with the ability to reconstruction surfaces, and measurement accuracy (RMSE = 0.032 mm) can meet the industrial requirement.
Review on Recent Developments in the Mayfly Algorithm

In nature, every well-framed problem has a solution at its core. So it is very interesting to note that in recent times researchers have inclined themselves to nature’s intelligence. By mimicking, the behavior of nature they can reduce their hard complexity problems into simplified ones. In light of this, Mayfly Algorithm (MA) was recently proposed in 2020 based on the flight pattern and mating scheme of mayflies. In this paper, we will discuss and explore both sides of MA along with its applications in brief and then we will do the systematic and critical review of the recent developments and work done by the researchers to improve the original MA. Lastly, this paper will help the readers to gain insights into the future aspects of MA.
An Approach to Cost Minimization with EC2 Spot Instances Using VM Based Migration Policy

Amazon provides several different options for deploying workloads to AWS in the form of on-demand, reserved and spot instances. Spot instances come with steep discount offers but are susceptible to revocation and hence are not reliable. Using spot instances to run Workloads dramatically lowers computational cost, but we cannot guarantee the SLA requirements. In this paper, we have developed a novel technique that uses spot instances to minimize cost. It can also scale itself based on the workload and maintain SLA even during bulk preemption. Our system employs the following strategies. (i) Allocation of workloads based on a measure of spot score (represents the general trend of spot prices of a particular VM type over a predetermined duration of time), rather than just checking the spot price at a particular instant which may be high due to a temporary spike in traffic and may lead to an inefficient allocation. (ii) Migration of workloads triggered by certain resource utilization threshold conditions and spot price fluctuations over time. The proposed scheme is implemented and simulated on AWS spot instances with real demand traces from Google. The cost in our proposed method is significantly lower compared to the case when workloads run purely on on-demand instances or spot instances without VM migration. The results indicate that the proposed technique can reduce the cost of running large enterprise applications and compute-intensive workloads to a great extent.
E-Friend: A Logical-Based AI Agent System Chat-Bot for Emotional Well-Being and Mental Health

In this work, it is proposed the design of a Reasoning Logical Based Intelligent Agent System Chat-bot for Dialogue Composition (DC) named E-friend, which uses Logic Programming (LP) for reasoning tasks. The main contribution is the use of Knowledge Representation Reasoning with LP theories modelling the knowledge of the user agent (beliefs, intentions, and expectations) to reason, plan and to optimally solve the DC problem. Another contribution is the design of a system component that extends the theory of mind, for the user model, with emotions to detect if the user decepts to the system or to itself. This component has the aim to alert and inform the facilitator when E-friend detects possible deceit signals from the student. E-friend was designed to help first year university students to manage stress/anxiety to optimal well-being development and attempt the prevention of depression and addictions leading. Students can interact through a chat-bot (text-based questions and answers) to help the system learns from the user, at the same time the user learns from itself improving mental health well-being.
Design of Discrete Hopfield Neural Network Using a Single Flux Quantum Circuit

The superconductor single flux quantum (SFQ) logic family has been recognized as a promising candidate to resolve the energy consumption crisis in the post-Moore era, owing to its high switching speed and low power consumption. In the field of machine learning, where technology and computational requirements are growing rapidly (e.g., image recognition and natural language processing), there is great potential for the implementation of SFQ circuits. In this study, we investigate and implement a discrete Hopfield neural network (DHNN) using SFQ circuits. A DHNN is a binary neural network with less information than a standard full precision neural network; it also provides a higher processing speed. It is mainly used for pattern recognition and recovery. We designed the DHNN circuit with two patterns, each with eight elements. The circuit operates at the clock frequency of more than 50 GHz.
Crowd Counting Using Deep Learning in Edge Devices

Crowd counting is required for many situations and has historically been undertaken using approximate (manual) estimations and measures. Deep learning allows to improve this situation. Modern crowd counting models are commonly based on pixel-wise density maps using deep convolutional neural networks (CNNs) comprising tens of millions of parameters. These models require high-performance GPUs for training and subsequent usage and inference. As such, these models are difficult to deliver to edge devices that have limited computing resources such as surveillance cameras, mobile phones and Internet of Things (IoT)-type devices. This paper proposes a new method to tackle this issue based on three key components: feature fusion, Bayesian Loss [19] and datasets utilising bounding-box annotations to increase the efficiency of the crowd counting task. Experiments show that the proposed method can not only provide accuracy close to the latest state-of-the-art deep learning models, but support real-time inference in edge devices offering limited computational capacity.
The influence of variable anisotropic search parameters on implicitly-modelled volumes and estimated contained metal in a structurally-complex gold deposit

Modelling and subsequent estimation of an orebody are necessary for the financial assessment of exploration and mining projects. Potentially exploitable resources are increasingly deep and structurally complex, necessitating better-constrained geological models. Where these prospects and deposits occur in a structurally-complex terrane, it is best-practice to undertake a rigorous structural analysis and to incorporate this into the resultant 3D model. A review of a random selection of Competent Person reports, on projects situated in the Birimian of West Africa, indicates that there is little to no structural analysis incorporated into the great majority of their underlying 3D models, despite their situation in a structurally-complex terrane. A sub-suite of data from a Birimian-style Au deposit was subjected to numerous modelling iterations wherein several parameters were systematically and incrementally applied across a series of implicit models. This produced a range of mineralized volumes, which vary significantly from the geometry of the ground-truthed volume that has been established by pit mapping, structural analysis, mining and ongoing reconciliation. Comparison of these varying parameters and their resultant volumes to the ground-truthed volume demonstrates volume inflation of 18% to 40%. This volume inflation is also reflected in contained Au ounces, which were determined for a sub-suite of the implicit models. Notable outliers reflect parameters that produce shapes that are similar to subvertical, tabular mineralized zones, which is a geometry that repeatedly appears in MRE reports on structurally-complex Au - and other precious metal - deposits and prospects. Such geometries are produced by explicit modelling using cross-sections, very often with the erroneous cognitive bias of connecting grade intervals at high angles between drillholes. Exaggerated continuity, interconnectivity and an inflation of volume in these simplistic mineralized zones translate into an overestimation of contained Au ounces, which is reflected in historical surveys of mine development and reconciliation.
GDPR Compliant Audit Log Management System with Blockchain

Audit log files can contain both personal data and system actions. Therefore, storing audit log files in accordance with the General Data Protection Regulation (GDPR) has become a legal obligation. In this paper, we propose a GDPR-compliant log storage system called Solid Log Chain (SLC). The goal of Solid Log Chain is to store audit logs and ensure the confidentiality and immutability of log data while meeting GDPR obligations. We have developed SLC by combining existing technologies with blockchain and using it in innovative ways. We have developed a data structure that allows only personal data to be deleted without destroying data integrity. Solid Log Chain is intended to be an alternative to expensive hardware-based solutions. We describe the design concept and architecture of the SLC and evaluate its performance in terms of latency and payload size.
An Entity Event Deduplication Method Based on Connected Subgraph

The news data from different sources has a high degree of repetition. How to accurately get the most timely news is the primary task of text processing and analysis. Therefore, we propose an entity event deduplication method based on a connected subgraph to address the high repetition rate of multisource events. In this paper, according to the correlation of events and development factors of events, we extract relevant features and improve the accuracy of the deduplication method. The final experimental evaluation shows that the proposed method has higher accuracy than the current deduplication methods, and can significantly improve the repeat detection rate of the entity event within the effective time.
Large-Scale Closed High-Utility Itemset Mining

An information fusion architecture for large-scale integration of closed high-utility patterns from several distributed databases is presented in this paper. The generic and standard composite model is then initialized to group transactions based on their relevant correlation, which will help assure the accuracy and completeness of our fusion model. The technique uses the well-known MapReduce architecture to handle large-scale datasets for information fusion and integration. According to the results of examination, the developed model is well-suited for managing big databases with little memory consumption.
Structural Parameter Space Exploration for Reinforcement Learning via a Matrix Variate Distribution

The trade-off between exploration and exploitation is essential for reinforcement learning, where an agent needs to be aware of when to explore for high reward policies and when to exploit the optimal policy known so far. Parameter space exploration provides an elegant solution. As one of the principal methods, injecting noise into the model parameters greatly improves exploration. However, directly stretching the parameters of the neural network into a vector and generating noise for this vector ignore the structural information of the model. In this paper, we aim to incorporate spatial information into weight matrices and propose matrix-variate noise exploration, which exploits the structural weight uncertainty brought by matrix variate noise to enhance the stochasticity of the agent. Indeed, we construct a bridge between the matrix noise exploration and probabilistic neural networks, which theoretically explains the improved performance of parameter space exploration. Extensive experiments have shown that matrix variate noise exploration outperforms fully factorized noisy exploration on most Atari tasks and Super Mario Bros tasks and is competitive to the state-of-the-art methods.
Decision Support System for Breast Cancer Detection Using Biomarker Indicators

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.
FPGA-based Implementation of SHA-256 with Improvement of Throughput using Unfolding Transformation

Security has grown in importance as a study issue in recent years. Several cryptographic algorithms have been created to increase the performance of these information-protecting methods. One of the cryptography categories is a hash function. This paper proposes the implementation of the SHA-256 (Secure Hash Algorithm-256) hash function. The unfolding transformation approach was presented in this study to enhance the throughput of the SHA-256 design. The unfolding method is employed in the hash function by producing the hash value output based on modifying the SHA-256 structure. In this unfolding method, SHA-256 decreases the number of clock cycles required for traditional architecture by a factor of two, from 64 to 34 because of the delay. To put it another way, one cycle of the SHA-256 design can generate up to four parallel inputs for the output. As a result, the throughput of the SHA-256 design can be improved by reducing the number of cycles by 16 cycles. ModelSim was used to validate the output simulations created in Verilog code. The SHA-256 hash function factor four hardware implementation was successfully tested using the Altera DE2-115 FPGA board. According to timing simulation findings, the suggested unfolding hash function with factor four provides the most significant throughput of around 4196.30 Mbps. In contrast, the suggested unfolding with factor two surpassed the classic SHA-256 design in terms of maximum frequency. As a result, the throughput of SHA-256 increases 13.7% compared to unfolding factor two and 58.1% improvement from the conventional design of SHA-256 design.
Predictive model of breast cancer lymph node metastasis based on deep learning (E-Transformer)

Breast cancer cell lymph node metastasis is an important criterion to define the early and middle stages of breast cancer with biopsy required, and so with bad patient experience. Traditional image-aided diagnosis is inefficient and ineffective in manually extracting features and combining image features. The emerging image-assisted diagnosis based on deep learning uses convolutional neural networks to automatically segment lesions, extract image features, and automatically combine features to classify cancers through full-connected layers or machine learning, which provides new ideas for clinicians' diagnosis and treatment plans. However, whether cancer cells have lymph node metastasis or not has little difference in Molybdenum mammography of breast, and it is difficult to distinguish, which belongs to the problem of fine-grained image classification. If two images are randomly selected, even the most experienced doctors in the radiology department cannot directly judge whether the cancer cells have metastasized in the lymph nodes from the mammography images. This paper proposes a breast cancer lymph node metastasis prediction model based on deep learning, named E-Transformer, which solves the problem of fine-grained classification of breast cancer lymph node metastasis. The model uses EfficientNet for coarse-grained feature extraction, adds Transformer-encoder to introduce an attention mechanism to enhance the model's fine-grained feature processing capabilities and finally uses Lightgbm for feature combination and two categories. Tests on the unpublished mammography data set of 391 breast cancer patients found that the auc of this model is 0.9643, the ace is 0.9681, and the f1-score is 0.9669. The reliability and accuracy of the model are in the first echelon of the current study.