Reviewerly/exHarmony huggingface dataset:
End-to-End Service Auction: A General Double Auction Mechanism for Edge Computing Services

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.
The development of the recommendatory resource for the adaptive learning of transport philosophy

The article examines the problems of transport philosophy’s adaptive learning considering individual characteristics of a student. The best solution for implementing adaptive learning material is to work with a personal teacher. However, it is worth to note that this approach has several limitations due to the inability to abandon mass education and switch to individual. The high cost and labor intensity are obvious. As a substitute for a teacher or support for his activities for an individual work regime information technology with adaptive learning function are actively used, which allow assessing strengths and weaknesses of the student, as well as consider his experience, interests and even a unique learning style, based on which the individual educational routes are built.
In Situ Wavelet Compression on Supercomputers for Post Hoc Exploration

Wavelet compression is a popular approach for reducing data size while maintaining high data integrity. This chapter considers how wavelet compression can be used for data visualization and post hoc exploration on supercomputers. There are three major parts in this chapter. The first part describes the basics of wavelet transforms, which are essential signal transformations in a wavelet compression pipeline, and how their properties can be used for data compression. The second part analyzes the efficacy of wavelet compression on scientific data, with a focus on analyses involving scientific visualizations. The third part evaluates how well wavelet compression fits in an in situ workflow on supercomputers. After reading this chapter, readers should have a high-level understanding of how wavelet compression works, as well as its efficacy for in situ compression and post hoc exploration.
Seismic Experimental Sites: Challenges and Opportunities

Seismic experimental sites, or natural laboratories for earthquake science, can be traced back to as early as mid-20th century. In recent years, these experimental sites are facing to newopportunities of development; while the solutions to tackle the long-standing challenges to their sustainability have some new developments. This chapter reviews the related challenges and opportunities.
GA-ANN Framework for Breast Cancer Classification Using NSGA-II

The Indian Medical Research Council records 1.5 lakh new cases of breast cancer in India, of which 70,000 occur each year. One of the main aspects of this problem is proper diagnosis at proper time In spite of various approaches suggested by researchers in this direction, their dependability on dataset, outliers, technology and transitory behaviour restricts their efficiency. The multi-object optimization approaches are extremely useful in dealing with this sporadic behaviour of cancer cells. In this paper, we use multi-target evolutionary hybrid system, combination of genetic algorithm and artificial neural network, to build a smart technology for the context neural propagation in the direction of breast cancer classification. The first stage focusses on development un-dominated genetic sorting algorithm (NSGA-II), and second stage hybridizes the enhanced algorithm to improve the conversion speed into the non-dominated front. The key idea of the proposed algorithm was to introduce a new technique to solve artificial neural architecture problems automatically. The scientific findings obtained by the proposed intelligent technology tested using the breast cancer dataset demonstrated the potential for enhancing the efficiency of the algorithm proposed with superior performance with 97.68% accuracy compared to individual artificial neural network approaches.
Boundary-Oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images

Background and Objective: Breast cancer is considered as the most prevalent cancer. Using ultrasound images is a momentous clinical diagnosis method to locate breast tumors. However, accurate segmentation of breast tumors remains an open problem due to ultrasound artifacts, low contrast and complicated tumor shapes in ultrasound images. To address this issue, we proposed a boundary-oriented network (BO-Net) for boosting the breast tumor segmentation in ultrasound images.Methods: The BO-Net boosts tumor segmentation performance from two perspectives. Firstly, a boundary-oriented module (BOM) was designed to capture the weak boundaries of breast tumors by learning additional breast tumor boundary maps. Second, we focus on enhanced feature extraction, which takes advantage of Atrous Spatial Pyramid Pooling (ASPP) module and Squeeze-and-Excitation (SE) block to obtain multi-scale and efficient feature information.Results: We evaluate our network on two public datasets: Dataset B and BUSI. For Dataset B, our network achieves 10.13mm in Average symmetric surface distance (ASSD), 80.27% in Dice coefficient (DC), 85.24% in Precision, and 80.42% in Recall. For BUSI dataset, our network achieves 74.37% in DC, 78.78% in Precision, and 74.64% in Recall.Conclusion: Experimental results show that BO-Net outperforms the state-of-the-art segmentation methods for breast tumor segmentation in ultrasound images. It demonstrates that focus on boundary and feature enhancement creates more efficient and robust breast tumor segmentation.
A Pre-Silicon Approach to Discovering Microarchitectural Vulnerabilities in Security Critical Applications

Microarchitectural vulnerabilities have become an increasingly effective attack vector. This is especially problematic for security critical applications, which handle sensitive data and may employ software-level hardening in order to thwart data leakage. These strategies rely on necessary assumptions about the underlying microarchitectural implementation, which may (and have proven to be) incorrect in some instances, leading to exploits. Consequently, devising early-stage design tools for reasoning about and verifying the correctness of high assurance applications with respect to a given hardware design is an increasingly important problem. This letter presents a principled dynamic testing methodology to reveal and analyze <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-dependent</i> microarchitectural behavior with the potential to violate assumptions and requirements of security critical software. A differential analysis is performed of the microarchitectural state space explored during register transfer-level (RTL) simulation to reveal internal activity which correlates to sensitive data used in computation. We demonstrate the utility of the proposed methodology through it's ability to identify secret data leakage from selected case studies with known vulnerabilities.
A New Skeleton-Neural DAG Learning Approach

Learning a Directed Acyclic Graph (DAG) structure from observational data plays an essential role in causal inference and machine learning. A recent advance in the area is that the DAG learning problem was formulated as a continuous optimization problem and this provides a new research line for leveraging powerful neural networks for DAG learning. Although several DAG learning algorithms using neural networks (called neural DAG learning) have been proposed, they still face some serious challenges, leading to unsatisfactory performance. To tackle this issue, in this paper, we combine ideas from local DAG learning and neural DAG learning techniques and propose a new Skeleton-Neural DAG (SN-DAG) structure learning algorithm. The SN-DAG algorithm first learns the structure skeleton of each variable using a local learning algorithm to initialize the weighted adjacency matrix, then employ multilayer perceptrons to optimize the weighted adjacency matrix for learning a DAG structure, and finally it corrects wrong edges in the learnt DAG using the learnt skeleton and v-structure identifying techniques. By conducting experiments on both synthetic and real datasets, experimental results validate the SN-DAG algorithm compared to traditional DAG learning algorithms and neural DAG learning methods.
Defect Identification Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion And Googlenet-Based Deep Learning Model

Carbon fiber sucker rods are widely used in oil production site due to their light weight, high strength and corrosion resistance, but there is still a lack of effective internal defect detection methods during production and installation. Aiming at the characteristics of irregular interface in the sucker rod, a novel defect identification method of carbon fiber sucker rod based on multi-sensor information fusion and GoogLeNet-based deep learning model is proposed to identify online the internal defects of carbon fiber sucker rod. First, the full coverage scan of the sucker rod in the cross-section was performed by a water-immersed ultrasonic array containing 32 probes, and the corresponding ultrasonic reflection signals was obtained. Then, a multi-sensor information fusion method was proposed to integrate amplitude and flight time of received ultrasonic reflection signals with the spatial angle information of each probe into defect images. Time signal waveforms of ultrasonic signals with different defects were mapped into different defect images, so that we can rely on deep learning models in the field of image recognition to identify those defects. Finally, A GoogLeNet-based deep learning model were trained to identify the image-based defect. The transfer learning method was adopted to enhance the convergence speed and generalization ability of the model for insufficient training samples. The testing results show that the overall defect identification accuracy of the trained GoogLeNet-based deep learning model was 99.72%, which can identify effectively four typical defects and no defects of carbon fiber sucker rods.
Multi-Object On-Line Tracking as an Ill-Posed Problem: Ensemble Deep Learning at the Edge for Spatial Re-identification

Online tracking is a feature of many state-of-the-art object trackers. When learning online, the data is limited, so the tracker learns a sketch of the object’s features. For a tracker to successfully re-identify the same object in the future frames in many different contexts, including occlusions, the tracker has to keep meta-data over time. In multi-objective inferences, this can exponentially increase the costs and is an ill-posed problem. This paper introduces a model-based framework that combines an ensemble of offline pre-trained models cascaded with domain-specific context for spatial tracking. Our method is efficient in re-identifying objects detected by any camera detector as there is minimal online computation. The second model uses a cosine similarity ranking of the label detected by the first model to find its corresponding set of raw images from the domain training set. A high score means model one has previously seen the object, and a low score amounts to a new detection. By using a two-stage AI-trained ensemble at the edge device, we show that the proposed tracker can perform 10 times faster with its precise detection, and the reidentification at the second stage is accurate, avoiding ID flipping for longer durations on video streams.
Data Transmission in Clouds Using Heed and Energy-Efficient Routing Algorithm

Cloud Computing is a field of computer science in which user can access resources remotely through a browser. Cloud Computing increases the speed of accessing the services in very less cost without actually deploying them. It decreases the time from implementing the software to actually deploying it. Cloud Computing users can access resources on demand. Cloud provides the on-demand services, virtualization, and open source. The data owner can store and access the data from the cloud with the help of the cloud service provider. In large organizations, the data which is stored on the cloud is accessed by the many users of the same organization. In such a case, the problem of inconstancy may arise, because unauthorized user can delete or modify the data. If the two persons are accessing the same data at the same time, then read-write, write-read conflicts may arise. In this, I even have designed a wireless clump rule with the assistance node degree, stability issue, and weight and CH choice. In this work, there is data duplication and data redundancy problem that I have faced, and some other problems are the network lifetime problem due to the redundancy and transmission energy is lost, so there is energy consumption problem.
Supporting Early Childhood Development Remotely: Experimental Evidence from Sms Messages

Using a randomized field experiment in Costa Rica, we estimate the effect of providing parents of preschool students with a text message intervention containing information and activities to engage them with their children's learning process at home. After 15 weeks of intervention, the cognitive skills of children whose parents were assigned to the program was 0.11-0.12 standard deviations higher than the control group. We find suggestive evidence that the effect was driven by an increase in parent involvement through the proposed activities from the text message campaign.
Automated Evaluation of Short Answers: a Systematic Review

Automated short answer grading (ASAG) of free text responses is a field of study, wherein student's answer is evaluated considering baseline concepts required by question. It mainly concentrates on evaluating the content written by student, more than its grammatical form. In educational domain, assessment is indeed a tedious and time-consuming task. If in anyway the time utilized for this task is, then the instructor can focus more on teaching and learning activity and can help students in their overall growth. Many researchers are working in this field to provide a solution that can assign more accurate score to student response which are similar to the score assigned by human tutor. The goal of this paper is to provide insight in the field of ASAG domain by presenting concise review of existing ASAG research work. We have included the research work carried out using machine learning and deep learning approaches. We have also proposed our methodology to address this problem.
State-of-the-art: Implementation of augmented reality and virtual reality with the integration of 5G in the classroom

The advancement of technology in the education sector is playing a vital role by influencing, creating interest, and dragging the attention among the student community towards their studies. As the present world is running behind the technology, focusing on the online platforms for the teaching and learning process; the education sector will certainly need to adopt new trends and technologies into it to make the students understand the concepts more easily. As the previous researchers have demonstrated the drawbacks of implementation of technology in the education sector such as not promoting the usage of critical, analytical thinking, problem-solving and metacognition; The Augmented Reality (AR) and Virtual Reality (VR) have come into the force to overcome these difficulties. They proved their capability in making the teaching and learning process more active and effective. Implementing AR and VR in the classroom acts like a digital tool for the students and also the teachers to achieve educational goals as it enhances the understanding, critical thinking, visualization levels, and problem-solving skills. They also boost ups the imagination power in the students. To get accessed to AR and VR the present technology that is available (4G) is not meeting the requirements of it, integrating 5G with VR and AR would lead to effective implementation. Therefore, this paper discusses the research that has been conducted on AR and VR and the need to implement AR and VR with the integration of 5G in the classrooms.
Rigid and Elastic Registrations Benchmark on Re-stained Histologic Human Ileum Images

Registration of images from re-stained tissue sections is an initial step in generating ground truth image data for machine learning applications in histopathology. In this paper, we focused on evaluating existing feature-based and intensity-based registration methods using regions of interest (ROIs) extracted from whole slide images (n = 25) of human ileum that was first stained with hematoxylin and eosin (H&E) and then re-stained with immunohistochemistry (IHC). Elastic and moving least squares deformation models with rigid, affine and similarity feature matching were compared with intensity-based methods utilizing an optimizer to find rigid and affine transformation parameters. Corresponding color H&E and IHC ROIs were registered through gray-level luminance and deconvoluted hematoxylin images. Our goal was to identify methods that can yield a high number of correctly registered ROIs and low median (MTRE) and average (ATRE) target registration errors. Based on the benchmark landmarks (n = 5020) placed across the ROIs, the elastic deformation model with rigid matching and the intensity-based rigid registrations on color-deconvoluted hematoxylin channels yielded the highest (86%, 100%) rates of correctly registered ROIs. For these two methods, the MTRE was 2.00 and 2.12 pixels ( $$0.982$$ $$\,\upmu $$ m, $$1.04$$ $$\,\upmu $$ m), and ATRE was 3.14 and 4.0 pixels (1.54 $$\,\upmu $$ m, 1.964 $$\,\upmu $$ m), respectively. Although the intensity-based rigid registration was the slowest of all methods tested, it may be more practical in use due to the highest rate of correctly registered ROIs and the second-best MTRE. The WSIs and ROIs with landmarks that we prepared can be valuable in benchmarking other image registration approaches.
Research on Intelligent Construction Technology of Information-driven Power Grid Security Situation Knowledge Graph

In order to cope with the increasingly complex power grid security operation situation, this article designs a framework for the construction of knowledge graphs in the power field and analyses its key technologies for the comprehensive control and precise analysis of the grid security situation, to realize the framework construction, situation analysis, and intelligent reasoning based on the knowledge graph. And smart feedback. Finally, the application of future knowledge graphs in the security situation of smart grids is discussed in detail, which is of great significance for improving the intelligent level of the security situation of power grids.
Certified Verification of Relational Properties

The use of function contracts to specify the behavior of functions often remains limited to the scope of a single function call. Relational properties link several function calls together within a single specification. They can express more advanced properties of a given function, such as non-interference, continuity, or monotonicity. They can also relate calls to different functions, for instance, to show that an optimized implementation is equivalent to its original counterpart. However, relational properties cannot be expressed and verified directly in the traditional setting of modular deductive verification. Self-composition has been proposed to overcome this limitation, but it requires complex transformations and additional separation hypotheses for real-life languages with pointers. We propose a novel approach that is not based on code transformation and avoids those drawbacks. It directly applies a verification condition generator to produce logical formulas that must be verified to ensure a given relational property. The approach has been fully formalized and proved sound in the Coq proof assistant.
Study of User Behavior When Using a List of Predicted Words

In this article, we present a study on the user's gaze tracking during a text input task on a soft keyboard associated with a word prediction list. The purpose of this study is to investigate user strategies for using the prediction list. When does he watch it? How frequently? Does he look at the whole list? our first results shows that the user only looks at the top of the list. Therefore, it does not use the prediction list optimally and enters more characters on the soft keyboard. Moreover, typing on the soft keyboard is also penalized by the presence of the list next to the keyboard because the user looks at it without necessarily using it. This sub-optimal use of the list coupled with slower input on the soft keyboard therefore results in a slower average input speed on the soft keyboard associated with a list.
Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model

Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This negatively influences the performance of the prediction algorithms. Including out-of-distribution data in the unlabeled dataset can lead to varying degrees of performance degradation, or even improvement, by using a distance to measure how out-of-distribution a piece of data is. This work aims to propose an approach that allows estimating the predictive uncertainty of supervised algorithms, improving the behaviour when atypical samples are presented to the distribution of the dataset. In particular, we have used this approach to mammograms X-ray images applied to binary classification tasks. The proposal makes use of Feature Density, which consists of estimating the density of features from the calculation of a histogram. The obtained results report slight differences when different neural network architectures and uncertainty estimators are used.