Reviewerly/exHarmony huggingface dataset:
An Effective Deep Learning Architecture Combination for Tissue Microarray Spots Classification of H&amp;E Stained Colorectal Images

Tissue microarray (TMA) assessment of histomorphological biomarkers contributes to more accurate prediction of outcome of patients with colorectal cancer (CRC), a common disease. Unfortunately, a typical problem with the use of TMAs is that the material contained in each TMA spot changes as the TMA block is cut repeatedly. A reclassification of the content within each spot would be necessary for accurate biomarker evaluation. The major challenges to this end however lie in the high heterogeneity of TMA quality and of tissue characterization in structure, size, appearance and tissue type. In this work, we propose an end-to-end framework using deep learning for TMA spot classification into three classes: tumor, normal epithelium and other tissue types. It includes a detection of TMA spots in an image, an extraction of overlapping tiles from each TMA spot image and a classification integrated into two effective deep learning architectures: convolutional neural network (CNN) and Capsule Network with the prior information of intended tissue type. A set of digitized H&E stained images from 410 CRC patients with clinicopathological information is used for the validation of the proposed method. We show experimentally that our approach brings state-of-the-art performances for several relevant CRC H&E tissue classification and that these results are promising for use in clinical practice.
Surface Navigation Target Detection and Recognition based on SSD

In order to enhance the perception ability of unmanned ship's surface environment and solve the problem that the target can not be effectively classified and recognized in radar or sensor-based target detection method, this paper collects and manufactures the data set of surface target detection independently, uses machine learning algorithm to detect and recognize multi-size and multi-type targets in ship image, the default target box parameters are initialized by clustering algorithm, which improves the accuracy of the training model in small target detection rate and target box location. The experimental results show that the mAP of the improved model has been upgraded, which can more accurately accomplish the task of multi-target, multi-type and multi-scale surface object detection and recognition, and provide more abundant and effective surface environment information for the automatic driving of unmanned vehicle.
Activity Detection in Untrimmed Videos Using Chunk-based Classifiers

Activity detection in untrimmed videos - the process of detecting and localizing human activities in potentially long videos - is a challenging problem in computer vision. We propose an algorithm which is based on the proposition that despite the differences between activity classification and detection, a strong classifier can still be used to achieve state-of-the-art performance in detection by breaking the video into multiple overlapping chunks and classifying each individually. We further introduce two new auxiliary tasks which we call chunk inclusion and localization. The outputs of these tasks, when carefully applied, can be used to dramatically improve performance. We call our method Chunk Aggregation. It is straight-forward to implement and use, and is agnostic to the backbone activity classification architecture used. We also demonstrate the effectiveness of chunk association by presenting results and a series of ablation experiments on the THUMOS'14 and ActEV datasets.
Cursor Click Modality in an Accelerometer-Based Computer Access Device

The purpose of this study is to investigate the effects of different cursor click modalities in an alternative computer access device using accelerometry from head tilt to control cursor movement. Eighteen healthy adults performed a target acquisition task using the device with five different cursor click modalities, while maintaining cursor movement control via accelerometry. Three dwell-based click modalities with dwell times of 0.5 s, 1.0 s, and 1.5 s were tested. Two surface electromyography-based click modalities - with the sensor placed next to the eye for a blink and above the eyebrow for a brow raise - were tested. Performance was evaluated using metrics of target selection accuracy, path efficiency, target selection time, and user effort. Surface electromyography-based click modalities were as fast as the shortest dwell time and as accurate as the longest dwell time, and also minimized user effort. Three of the four performance metrics were not affected by sensor location. Future studies will investigate if these results are similar in individuals with neuromuscular disorders.
Guidelines for Instructional Design for Teaching in a Blended Learning Course

The design of training actions in blended learning mode raises doubts among teachers. Questions such as the choice of learning method, the choice of platform, the set of resources and activities of which they are composed, among others, generate uncertainty in the design of effective educational practices. This text is intended as a guide to clarify some of the basic aspects that must be taken into account when planning the design of training practices with an online component.
Hedge Your Bets: Optimizing Long-term Cloud Costs by Mixing VM Purchasing Options

Cloud platforms offer the same VMs under many purchasing options that specify different costs and time commitments, such as on-demand, reserved, sustained-use, scheduled reserve, transient, and spot block. In general, the stronger the commitment, i.e., longer and less flexible, the lower the price. However, longer and less flexible time commitments can increase cloud costs for users if future workloads cannot utilize the VMs they committed to buying. Large cloud customers often find it challenging to choose the right mix of purchasing options to reduce their long-term costs, while retaining the ability to adjust capacity up and down in response to workload variations.To address the problem, we design policies to optimize long-term cloud costs by selecting a mix of VM purchasing options based on short- and long-term expectations of workload utilization. We consider a batch trace spanning 4 years from a large shared cluster for a major state University system that includes 14k cores and 60 million job submissions, and evaluate how these jobs could be judiciously executed using cloud servers using our approach. Our results show that our policies incur a cost within 41% of an optimistic optimal offline approach, and 50% less than solely using on-demand VMs.
Joint Tracking and Identification Based on Constrained Joint Decision and Estimation

As a critical component of traffic safety, violation detection and treatment can effectively reduce illegal driving so as to ensure the safety of our lives. In this system, both road-constrained target tracking and identification are involved, which are coupled. This is a constrained joint tracking and identification (CJTI) problem, and good solutions require addressing the two problems jointly. The recently proposed joint decision and estimation (JDE) framework and the conditional JDE (CJDE) approach have been proved to be superior for such problems involving coupled decision (identification) and estimation (tracking). Inspired by this, for CJTI of ground targets, this paper proposes a novel constrained CJDE approach. First, a hybrid CJTI system incorporating target dynamics, class characteristics and road constraints is formulated. Based on this, we propose a constrained CJDE risk converting a constrained tracking cost and a constrained identification risk into one framework. An optimal constrained CJDE solution containing an estimator and a decider is derived. Simulation results verify the effectiveness of the proposed constrained CJDE method. They show that by fully utilizing the constraint information and the coupling between tracking and identification, the constrained CJDE approach outperforms traditional two-step methods in joint performance.
Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider.As such, many users select services based on description alone.An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications.The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data.However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models.We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google.Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model.We test this under a number of cross-application and cross-cloud scenarios.
Numerical approach for the evolution of spin-boson systems and its application to the Buck–Sukumar model

We study the evolution properties of spin-boson systems by a systematic numerical iteration approach, which performs well in the whole coupling regime. This approach evaluates a set of coefficients in the formal expression of the time-dependent Schrodinger equation by expanding the initial state in Fock space. This set of coefficients is unique for the spin-boson Hamiltonian studied, allowing one to calculate the time evolution from different initial states. To complement our numerical calculations, we apply the method to the Buck–Sukumar model. We find that when the ground-state energy of the model is unbounded and no ground state exists in a certain parameter space, the time evolution of the physical quantities is naturally unstable.
Subspace code constructions

We improve on the lower bound of the maximum number of planes of $$\mathrm{PG}(8,q)$$ mutually intersecting in at most one point leading to the following lower bound: $$\mathcal A_q(9, 4; 3) \ge q^{12}+2q^8+2q^7+q^6+q^5+q^4+1$$. We also construct two new non–equivalent $$(6, (q^3-1)(q^2+q+1), 4; 3)_q$$–constant dimension subspace orbit–codes.
Coupling Principled Refinement with Bi-Directional Deep Estimation for Robust Deformable 3D Medical Image Registration

Deformable 3D medical image registration is challenging due to the complicated transformations between image pairs. Traditional approaches estimate deformation fields by optimizing a task-guided energy embedded with physical priors, achieving high accuracy while suffering from expensive computational loads for the iterative optimization. Recently, deep networks, encoding the information underlying data examples, render fast predictions but severely dependent on training data and have limited flexibility. In this study, we develop a paradigm integrating the principled prior into a bidirectional deep estimation process. Inheriting from the merits of both domain knowledge and deep representation, our approach achieves a more efficient and stable estimation of deformation fields than the state-of-the-art, especially when the testing pairs exhibit great variations with the training.
A TextCNN Based Approach for Multi-label Text Classification of Power Fault Data

With the increasing number of power terminal devices in power systems, fault diagnosis based on textual fault data has become a challenging task. Classification technology plays an important role in power system fault diagnosis. In this paper, we propose a TextCNN based approach to multi-label text classification for analyzing fault text data of power terminal devices. First, the main category labels of faults and failure were constructed. The dataset was preprocessed for feature representation. Next, we adopted the TextCNN based model for multi-label classification of power fault texts. The experimental results show that the proposed method is better than the typical multi-label classification models for power fault text multi-label classification. The constructed label categories were also experimentally evaluated, and these labels reflect the main types of power faults and failure.
The effectiveness of learning using social media to improve student’s communication skills in fluids’ topics

Abstract This research aims at measuring the effectiveness of learning set using social media in improving students’ communication skills in the topic of fluids. This is experimental design research using One Group Pretest-Posttest, using a Project-based Learning model. This research is a part of Research and Development (R &amp; D); the ADDIE learning set developed to consist of syllabus, lesson plan, materials, LKPD , test, and evaluation instruments. This research takes place in MTs. Negeri 1 Kota Gorontalo , using 25 students as the sample. The result of the data analysis shows that the average communication skill indicates a significant difference; the N-Gain is in high category of 0.72. Therefore, it can be concluded that the learning set using social media is effective to improve students’ communication skills in the topic of fluids.
The use of smartphone on learning activities: Systematic review

Abstract This article aims to analyze the use of smartphone technology in education field, with a focus on exploring the effects of smartphone and its software application use in learning activities. This study was conducted using the method of systematic literature review through 3 stages, namely: searching related journals through an online database of several publishers, analyzing the search results journals and dividing them into 3 groups according to the research objectives, and describing the results of the analysis in the form of scientific and logical arguments. From the results of the study it can be concluded that smartphone is significantly useful in student-centered learning, can be used in various disciplines, and at all levels of education, and there are various smartphone applications that can be used for learning needs.
Remote Pair Programming

Pair programming is routinely used in industry and taught in face-to-face programming classes. Research indicates that it improves self-confidence and students' programming, communication and team working skills. We investigate the technology, barriers, and soft-skill benefits for distance-learning students who pair program with a remote partner online. In one study, students watched two tutors pair programming and then performed a pair programming task remotely with a student partner. Students felt significantly more positive with the latter compared to the former. As universities strive to provide a good student experience during a pandemic, these findings highlight the value of active remote pair programming using standard online communication tools.
A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer

In this paper a comparative study between linear classification and non-linear classification is performed for the prediction of diagnosis of breast cancer. The most crucial factor for any cancer patient is the diagnosis, if the cancer is detected early it can drastically increase the estimated life expectancy of the patient after the treatment. We propose an Artificial Neural Network based classifier in comparison with linear machine learning algorithms like Support Vector Machine, Logistic Regression and Random Forest based classifiers. The paper presents the details of implementation and the respective results for all the above mentioned classifiers. The comparative study then showed us that SVM had a better performance in terms of sensitivity but overall ANN performed better than the conventional linear classifiers.
Investigation of Dimension Reduction Methods by Multi-Objective Optimization for Electromagnetic Device with GP

Recently, demand of use-oriented electromagnetic devices is increasing, and studies that designing IPMSM, internal rotor structure of which is flexible by Multi-objective Genetic Programing (MOGP) are conducted. However, many objective functions are had to consider, so analysis result is high-dimensional data, and it's difficult to grasp characteristics of models. In this paper, the reserch purpose is visualizing the high-dimensional result data by applying dimension reduction.
Early prediction for mode anomaly in generative adversarial network training: An empirical study

Mode anomaly (MA for short) significantly blocks the application of generative adversarial networks (GANs). Although diverse metrics have been proposed to measure the MA, and a lot of efforts have been made to resolve the MA, none of them gives a quantitative definition for MA detection. Moreover, very few studies concentrate on the early-stage prediction of MA. In this paper, we make the first effort to this field with a systematic empirical study. To this end, we first give a fine-grained definition where the MA is categorized into three typical sub-patterns. Afterwards, traditional MA metrics are studied with extensive experiments on numbers of representative combinations of subjects (including 13 GANs and 3 datasets) to explore their sensitivity for the MA across different training steps. We find that in most of cases, the MA can be reasonably predicted in very early training stage through our sensitivity studies. Under the insight, we propose a novel prediction strategy using conception of “anomaly sign”. The evaluation results on diverse experimental subjects demonstrate the feasibility and high accuracy for the early prediction of MA. We also discuss the prediction efficiency, as well as analyze the prediction effectiveness from human perception.
Reliability Evaluation of Public Security Face Recognition System Based on Continuous Bayesian Network

For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.
Why and How Your Traceability Should Evolve: Insights From an Automotive Supplier

Most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts. We present how traceability can be evolved in the development lifecycle, from early elicitation of traceability needs to the implementation of mature traceability strategies.
Sequential Bayesian Filtering with Particle Smoother for Improving Frequency Estimation in Frequency Domain Approach

In signal processing, frequency estimation is one of the most important tasks for enormous number of applications. Particle filtering has been implemented intensively for the purpose of frequency estimation and the results were found to be excellent for many cases, under different time-frequency representation of the signal and different particle filter implementations. This work presents an enhancement of frequency estimation by using a particle smoother (backward particle filter) to enhance the frequency estimates as compared to the forward particle filter (PF). Calculated from the short-time Fourier transforms (STFTs), the signal was analyzed in the frequency domain, acting as a measurement model of the PF framework. Simulation results exhibit the advantage of the particle smoother over the forward PF. Demonstrated via the frequency estimates from both filters, particle smoother delivers better tracking results than the forward PF under low noise levels.
Tensor-Based Low-Rank and Sparse Prior Information Constraints for Hyperspectral Image Denoising

Hyperspectral data have been widely used in various fields due to its rich spectral and spatial information in recent years. Yet, hyperspectral images are always tainted by a variety of mixed noises. These noises seriously limit the accuracy of subsequent applications. To remove noise, this paper, based on low-rank tensor decomposition, combined with non-local self-similar prior information, proposes a tensor-based non-local low-rank denoising model, where non-local self-similarity uses mainly spatial correlation while low-rank tensor decomposition method uses mainly spectral correlation between bands. Traditional tensor-based methods are commonly NP-hard to compute and are sensitive to sparse noise. However, the method proposed in this paper can separate efficiently the low-rank clean image from Gaussian noise and sparse noise (pulses, deadlines, stripes, speckle, etc.) by using a new tensor singular value decomposition (T-SVD) and tensor nuclear norm (TNN). The NP-hard task was also achieved well by the alternating direction multiplier method. Due to the full use of spectral and spatial information of the data, Gaussian noise and sparse noise can be effectively removed. The effectiveness of our algorithm was verified through experiments using simulated and real data.