Reviewerly/exHarmony huggingface dataset:
Development of a Simplified Dynamic Teaching Tool for the Reciprocating Engine Mechanism

We developed a simplified dynamic teaching tool for the reciprocating engine mechanism. The tool can be utilized in an introductory lecture on engine systems and thermodynamics for undergraduate students and a scientific experiment for elementary, junior, and senior high school students. The tool comprises two main parts:1) an empty beverage can rocket that is used to show a combustion similar to that occurring in an internal combustion engine (ICE) of an automobile, and 2) a simple and inexpensive small cut-section-model of ICE to understand the crank mechanism. Because of its portability, it can be passed around to each student during the class. In addition, it can be used in real-time online lectures using a USB external webcam.
An Evaluation Set for Tibetan Sentences Similarity Computing

Sentence is not only the basic unit of natural language, but also the research object of Nature Language Process. Sentence similarity computing is the basic of text similarity computing, so sentence similarity evaluation set is an essential data set for similarity technology research. Only by establishing an appropriate evaluation set can we objectively evaluate the advantages and disadvantages of similarity computing methods. In order to objectively evaluate the performance of Tibetan sentence similarity, this paper designs the construction scheme of Tibetan sentence similarity evaluation set, and builds the evaluation set TSS_320 for evaluating Tibetan sentence similarity, based on the analysis of the construction method of English and Chinese sentence similarity evaluation set, also combined with the characteristics of Tibetan sentences. The validity of the evaluation set is verified by statistical methods.
Product Optimization in Stepwise Design

Stepwise design of programs is a divide-and-conquer strategy to control complexity in program modularization and theorems. It has been studied extensively in the last 30 years and has worked well, although it is not yet commonplace. This paper explores a new area of research, finding efficient products in colossal product spaces, that builds upon past work.
Construction of a multi-source heterogeneous hybrid platform for big data

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.
Blended Teaching Mode and Its Application for English Major Based on MOOC Platform

The blended teaching mode expands and enriches teaching content of English major, improves students' enthusiasm and initiative, and leads learners to deep learning. The blended teaching model for English major based on the MOOC platform, based on the information platform, breaks through the limitations of time and space; relying on the online platform to enhance the autonomy of learning; using mobile terminals as the medium to optimize the use of fragmented time. In order to improve the application effect of the MOOC platform, the architecture and functions of the MOOC platform have been designed; blended teaching model constructed by "front-end analysis, teaching design and teaching evaluation" has been constructed; the role of teachers has changed, and the concept of humanistic education should be reshaped.
A simulator for reinforcement learning training in the recommendation field

Deep reinforcement learning (DRL) is an unsupervised learning method, which has great commercial value in recommendation scenarios where it is difficult to collect available labeled data. Although very few researchers have begun to do research on the integration of deep reinforcement learning and recommendation methods, the development of these researches is slow because it is difficult to build an online training environment. Therefore, this paper proposes an environment (i.e. user) simulator for training reinforcement learning models in the recommendation field (RL-E Simulator). On the one hand, the simulator builds a user state generation model based on the Generative Adversarial Network (GAN). On the other hand, we propose an rating model based on attention mechanism, and realizes the reward of the simulator to the actions of DRL-based recommendation (i.e. agent). Experimental results show that the simulator provides a low-cost training environment for the DRL-based recommendation.
Research on the Knowledge Graph Construction for Flipped Classroom

Knowledge graph is a storage form that uses graph models to store entities and the relationships between entities and entities. We have built a knowledge graph for flipped classrooms to assist teaching work. In the process of constructing the knowledge graph and using the knowledge graph for recommendation, we designed a paragraph recommendation algorithm that comprehensive considering the student performance, content of course and user evaluation, which is used for personalized recommendation after the user enters the text. This algorithm sorts paragraphs based on the values of the Recopara. The user feedback mechanism in the algorithm can make the recommended paragraphs content better and better. Compared with the recommendation algorithm based on text content, the recommendation algorithm based on Recopara can better adapt to the needs of flipped classroom teaching and have been praised by the users.
RANSP: Ranking attention network for saliency prediction on omnidirectional images

Various convolutional neural network (CNN)-based methods have shown the ability to boost the performance of saliency prediction on omnidirectional images (ODIs). However, these methods are limited by sub-optimal accuracy, because not all the features extracted by the CNN model are useful for the final fine-grained saliency prediction. Some features are redundant and may have negative impact on the final fine-grained saliency prediction. To tackle this problem, we propose a novel Ranking Attention Network for saliency prediction (RANSP) of head fixations on ODIs. Specifically, the part-guided attention (PA) module and channel-wise feature (CF) extraction module are integrated in a unified framework and are trained in an end-to-end manner for fine-grained saliency prediction. To better utilize the channel-wise feature maps, we further propose a new Ranking Attention Module (RAM), which automatically ranks and selects these feature maps based on scores for fine-grained saliency prediction. Extensive experiments and ablation studies are conducted to show the effectiveness of our method for saliency prediction on ODIs.
Predicted Distribution Density Estimation for Streaming Data

Recent growth in interest concerning streaming data has been forced by the expansion of systems successively providing current measurements and information, which enables their ongoing, consecutive analysis. The subject of this research is the determination of a density function characterizing potentially changeable distribution of streaming data. Stationary and nonstationary conditions, as well as both appearing alternately, are allowed. Within the distribution-free procedure investigated here, when the data stream becomes nonstationary, the procedure begins to be supported by a forecasting apparatus. Atypical elements are also detected, after which the meaning of those connected with new tendencies strengthens, while diminishing elements weaken. The final result is an effective procedure, ready for use without studies and laborious research.
An efficient ADMM-type algorithm for deep semi-nonnegative matrix factorization

Abstract In this paper, we focus on deep semi-nonnegative matrix factorization (DSemiNMF) which has a wider application in the real world than traditional NMF. We propose an efficient algorithm based on the classic alternating direction method of multipliers (ADMM) for DSemiNMF. By utilizing structures in DSemiNMF, we derive an efficient updating rule for updating subproblems according to its KKT conditions. Numerical experiments are conducted to compare the proposed algorithm with state-of-the-art deep semi-NMF algorithm. Results show that our algorithm performs better and the deep model indeed results in better clustering accuracy than single-layer model.
Training Model of Applied Talents in Universities in the Era of Big Data

In the era of big data, the amount of data is larger and the structure is more complex. It penetrates into all areas of human life and shows unprecedented vigor and vitality. In the era of big data, the main purpose of this article is to combine big data with the applied talent training model of local colleges and universities to establish a three-body applied talent training model. This paper mainly uses the survey method to explain the concept, characteristics and functions of big data, so as to promote the cultivation of talents. The experimental results of this paper show that in the era of big data, the employment rate of college students has increased by 24% by applying the talent training mode of colleges and universities, some intractable social problems have been solved, and the problem-solving ability has been improved by 12%.
STQ-SCS: An Efficient and Secure Scheme for Fine-Grained Spatial-Temporal Top- k Query in Fog-Based Mobile Sensor-Cloud Systems

With the emergence of the fog computing and the sensor-cloud computing paradigms, end users can retrieve the desired sensory data generated by any wireless sensor network (WSN) in a fog-based sensor-cloud system transparently. However, the fog nodes and the cloud servers may suffer from many kinds of attacks on the Internet and become semitrusted, which threatens the security of query processing in the system. In this paper, we investigated the problem of secure, fine-grained spatial-temporal Top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> query in fog-based mobile sensor-cloud systems (FMSCSs) and proposed a novel scheme named STQ-SCS to tackle the problem based on the virtual grid construction and the size-order encryption-binding techniques. STQ-SCS can preserve the privacy of the sensed data items and their scores and make end users verify the completeness of the query results of fine-grained spatial-temporal Top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> queries with a 100% successful rate even if the fog nodes and the cloud servers are not totally trustworthy. Besides the good security performance, simulation results indicate that STQ-SCS is also an efficient scheme that incurs a much lower communication cost than the state-of-the-art schemes on securing fine-grained spatial-temporal Top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> query in FMSCSs.
An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation

Abstract This paper proposes a new boundary uncertainty-based estimation method that has significantly higher accuracy, scalability, and applicability than our previously proposed boundary uncertainty estimation method. In our previous work, we introduced a new classifier evaluation metric that we termed “boundary uncertainty.” The name “boundary uncertainty” comes from evaluating the classifier based solely on measuring the equality between class posterior probabilities along the classifier boundary; satisfaction of such equality can be described as “uncertainty” along the classifier boundary. We also introduced a method to estimate this new evaluation metric. By focusing solely on the classifier boundary to evaluate its uncertainty, boundary uncertainty defines an easier estimation target that can be accurately estimated based directly on a finite training set without using a validation set. Regardless of the dataset, boundary uncertainty is defined between 0 and 1, where 1 indicates whether probability estimation for the Bayes error is achieved. We call our previous boundary uncertainty estimation method “Proposal 1” in order to contrast it with the new method introduced in this paper, which we call “Proposal 2.” Using Proposal 1, we performed successful classifier evaluation on real-world data and supported it with theoretical analysis. However, Proposal 1 suffered from accuracy, scalability, and applicability limitations owing to the difficulty of finding the location of a classifier boundary in a multidimensional sample space. The novelty of Proposal 2 is that it locally reformalizes boundary uncertainty in a single dimension that focuses on the classifier boundary. This convenient reduction with a focus toward the classifier boundary provides the new method’s significant improvements. In classifier evaluation experiments on Support Vector Machines (SVM) and MultiLayer Perceptron (MLP), we demonstrate that Proposal 2 offers a competitive classifier evaluation accuracy compared to a benchmark Cross Validation (CV) method as well as much higher scalability than both CV and Proposal 1.
Some Ethical, Legal, and Social Dimensions of Pandemic Response Technology

Shuangge Wen (SW): The test and trace systems in operation in many countries are a key strategy to lower the reproduction rate and control the virus, with technology enabling measures such as tracing of close contacts, notifying local alerts, and venue check-ins using QR codes. From your technological expertise, how do you see these systems working, and do you see any alternatives which should be explored?
Optimal Design of Maximum SNR for 64-QAM Channel Based on Imperialist Competitive Algorithm

QAM is a widely used signal modulation mode in recent years, the numerical noise is easy to appear in the signal transmission, which makes the code error and the information transmission error, resulting in the loss. Therefore, based on the analysis and summary of previous researching results, in order to increase the signal-to-noise ratio (SNR) of the channel, the position and probability of 64-QAM channel constellation points are reallocated. In this paper, combining with geometric shaping method (GS) and probability shaping method (PS), we design 64-QAM channel constellations satisfying Maxwell-Boltzmann distribution, Super-Gaussian distribution, and concentric circle position distribution by mainly using Imperial Competitive Algorithm (ICA).
Linear Classifiers Under Infinite Imbalance

Understanding Linear Classifiers in the Face of Severe Data Imbalance In “Linear Classifiers Under Infinite Imbalance,” Paul Glasserman and Mike Li tackle the challenge of binary classification when data are severely imbalanced—a common dilemma in fields like healthcare and finance. They build upon the work of Owen by examining the behavior of logistic regression and extending the analysis to a broader class of linear discriminant functions. Their key contribution is the proof of infinite-imbalance limits for these functions’ coefficient vectors, providing explicit expressions for these limits and distinguishing between classifiers with subexponential and exponential weight functions. This distinction allows for a better understanding of how to adjust classifiers in the context of extreme imbalance, ultimately leading to improved specificity or sensitivity in predictions. The authors also link their findings to the concepts of robustness and conservatism in classification decisions, offering insight into optimal classifier design against the most challenging alternatives. The practical implications of their theoretical work are illustrated through numerical examples and a credit risk case study, offering a new perspective on managing classification tasks in the face of infinite imbalance.
Variable-Length Metric Learning for Fast Image Retrieval

Learning a powerful distance metric is the key component of image retrieval. Recently, deep metric learning has been an active research topic for image retrieval. However, most existing metric learning approaches treat all the input images equally and learn all image embeddings at equal lengths. These methods ignore those easy examples that can be encoded as the shorter features, which is search-inefficient. We propose a simple but efficient variable-length metric learning method for search efficiency, in which the different query samples have different feature-length. First, we propose to learn the ranked (prioritized) list of features. The more distinguishing features, the higher the rank. We show that the proposed prioritized features can be used to perform fast retrieval in different feature-length configurations. Further, we propose an adaptive feature-length selection policy to determine the amount of feature-length for each query sample. Extensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed method can reduce computational costs without incurring a decrease in accuracy.
DeepEutaxy: Diversity in Weight Search Direction for Fixing Deep Learning Model Training Through Batch Prioritization

Developing a deep learning (DL) based software system is slow. One of the critical issues is to conduct many trials and errors in developing a DL model that usually serves as the major component of such a system. A major reason for this inefficiency is the progress of gradual reduction of the gap between the DL model under training and the ground truths. Prior techniques commonly focus on optimizing such errors after the errors have formed. They are insensitive to how a training dataset is provided to the DL model under training in batches, making their approaches nonproactive to deal with such errors. In this article, we propose DeepEutaxy, the first work to repair the model convergence problem from the batch prioritization perspective. Our key insight is that increasing the diversity (i.e., dissimilarity) of the corresponding weights of complex DL models before and after each training step can make the models learn faster and optimize the training errors quicker. DeepEutaxy first trains a DL model with several epochs for initialization. It then partitions and continually prioritizes the training batches for subsequent training epochs based on our novel notion of diversity between the pair of models before and after training on each batch, capturing the strength of the search direction to deal with training errors impacted by that batch. The experiment on six DL models over the MNIST and CIFAR-10 datasets shows that DeepEutaxy can accelerate the convergence of DL models on these two datasets with speedups of 1.75-8.45 and 2.67-15.15 times with respect to the training and test accuracies, respectively. DeepEutaxy can also be integrated into the existing techniques and compare favorably with the prior art in the experiment.
Grounding Plural Phrases: Countering Evaluation Biases by Individuation

Phrase grounding (PG) is a multimodal task that grounds language in images.PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric.This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric.We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box's component boxes.We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks.
Comparison of Swarm Intelligence Algorithms for Optimization Problems

Nowadays, optimization problems have been one of the most challenges for solving real-work problems. Because of the importance of practical problems in other fields, more and more intelligent search algorithms have been proposed. Each algorithm has both advantages and disadvantages. It is difficult to find a suitable algorithm to solve real problems. In this article, the comparison of four robust algorithms including two classical algorithms such as particle swarm optimization (PSO) and artificial bee colony (ABC), and two algorithms which are presented recently, i.e., cuckoo search (CS) and gray wolf optimizer (GWO). To make a diversity in comparison, the first ten benchmark test functions were chosen to compare items such as convergence rate and accuracy of the algorithms. Moreover, the high dimensions (n = 30) in the search space are also examined to evaluate the effectiveness of the algorithms in the variable dimension problems. Through the results achieved, an algorithm which is achieved a balance between the convergence rate, and the accuracy level is submitted.
Better Stereo Matching From Simple Yet Effective Wrangling of Deep Features

Cost volume plays a pivotal role in stereo matching. Most recent works focused on deep feature extraction and cost refinement for a more accurate cost volume. Unlike them, we probe from a different perspective: feature wrangling. We find that simple wrangling of deep features can effectively improve the construction of cost volume and thus the performance of stereo matching. Specifically, we develop two simple yet effective wrangling techniques of deep features, spatially a differentiable feature transformation and channel-wise a memory-economical feature expansion, for better cost construction. Exploiting the local ordering information provided by a differentiable rank transform, we achieve an enhancement of the search for correspondence; with the help of disparity division, our feature expansion allows for more features into the cost volume with no extra memory required. Equipped with these two feature wrangling techniques, our simple network can perform outstandingly on the widely used KITTI and Sceneflow datasets.
The Randomness of Input Data Spaces is an A Priori Predictor for Generalization

Over-parameterized models can perfectly learn various types of data distributions, however, generalization error is usually lower for real data in comparison to artificial data. This suggests that the properties of data distributions have an impact on generalization capability. This work focuses on the search space defined by the input data and assumes that the correlation between labels of neighboring input values influences generalization. If correlation is low, the randomness of the input data space is high leading to high generalization error. We suggest to measure the randomness of an input data space using Maurer’s universal. Results for synthetic classification tasks and common image classification benchmarks (MNIST, CIFAR10, and Microsoft’s cats vs. dogs data set) find a high correlation between the randomness of input data spaces and the generalization error of deep neural networks for binary classification problems.