Reviewerly/exHarmony huggingface dataset:
Adaptive-Gravity: A Defense Against Adversarial Samples

This paper presents a novel model training solution, denoted as Adaptive-Gravity, for enhancing the robustness of deep neural network classifiers against adversarial examples. We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid. We use the centroid associated with each cluster to derive an anti-gravity force that pushes the centroids of different classes away from one another during network training. Then we customized an objective function that aims to concentrate each class’s features toward their corresponding new centroid, which has been obtained by anti-gravity force. This methodology results in a larger separation between different masses and reduces the spread of features around each centroid. As a result, the samples are pushed away from the space that adversarial examples could be mapped to, effectively increasing the degree of perturbation needed for making an adversarial example. We have implemented this training solution as an iterative method consisting of four steps at each iteration: 1) centroid extraction, 2) anti-gravity force calculation, 3) centroid relocation, and 4) gravity training. Gravity’s efficiency is evaluated by measuring the corresponding fooling rates against various attack models, including FGSM, MIM, BIM, and PGD using LeNet and ResNet110 networks, benchmarked against MNIST and CIFAR10 classification problems. Test results show that Gravity not only functions as a powerful instrument to robustify a model against state-of-the-art adversarial attacks but also effectively improves the model training accuracy.
Three-Dimensional Object Segmentation and Labeling Algorithm Using Contour and Distance Information

Object segmentation and object labeling are important techniques in the field of image processing. Because object segmentation techniques developed using two-dimensional images may cause segmentation errors for overlapping objects, this paper proposes a three-dimensional object segmentation and labeling algorithm that combines the segmentation and labeling functions using contour and distance information for static images. The proposed algorithm can segment and label the object without relying on the dynamic information of consecutive images and without obtaining the characteristics of the segmented objects in advance. The algorithm can also effectively segment and label complex overlapping objects and estimate the object’s distance and size according to the labeling contour information. In this paper, a self-made image capture system is developed to capture test images and the actual distance and size of the objects are also measured using measuring tools. The measured data is used as a reference for the estimated data of the proposed algorithm. The experimental results show that the proposed algorithm can effectively segment and label the complex overlapping objects, obtain the estimated distance and size of each object, and satisfy the detection requirements of objects at a long-range in outdoor scenes.
VPint: value propagation-based spatial interpolation

Given the common problem of missing data in real-world applications from various fields, such as remote sensing, ecology and meteorology, the interpolation of missing spatial and spatio-temporal data can be of tremendous value. Existing methods for spatial interpolation, most notably Gaussian processes and spatial autoregressive models, tend to suffer from (a) a trade-off between modelling local or global spatial interaction, (b) the assumption there is only one possible path between two points, and (c) the assumption of homogeneity of intermediate locations between points. Addressing these issues, we propose a value propagation-based spatial interpolation method called VPint, inspired by Markov reward processes (MRPs), and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant. Both these interpolation variants operate locally, while implicitly accounting for global spatial relationships in the entire system through recursion. We evaluated our proposed methods by comparing the mean absolute error, root mean squared error, peak signal-to-noise ratio and structural similarity of interpolated grid cells to those of 8 common baselines. Our analysis involved detailed experiments on a synthetic and two real-world datasets, as well as experiments on convergence and scalability. Empirical results demonstrate the competitive advantage of VPint on randomly missing data, where it performed better than baselines in terms of mean absolute error and structural similarity, as well as spatially clustered missing data, where it performed best on 2 out of 3 datasets.
A Named Entity Recognition Model Based on Entity Trigger Reinforcement Learning

Named entity recognition is a practical approach to automatically identifying named entities in text and data. Towards the vast amount of data generated in our daily life, Artificial Intelligence (AI) with economical but powerful computing resources are inevitably becoming the most appropriate method for name entities classification. However, the results of currently popular methods may also lack the aiming super high accuracy to specific data and the interests of the subscribers. This paper proposes a named entity recognition model based on entity trigger reinforcement learning for automatic Chinese recognition. Unlike existing named entity recognition methods, the proposed method can support multiple inputs. The accuracy proof and performance evaluation show that the proposed method is provable robotic in entity categories classification and efficient in practice.
Investigation of Stateful Microservice Availability During Failover

Deployment of light weight of containers enables a high degree of flexibility and velocity. However, not all benefits of containers apply to stateful microservices. While the maintainability of stateful micro services running in orchestrated container environments is high, it makes a more prominent impact on availability compared to stateless microservices. A more precise SLO and availability budget can be set when knowing the impact of maintenance on the availability. This research aims to investigate stateful microservice availability rate during failover, and to identify factors that make a significant impact on availability in an orchestrated container environment. An experiment was performed to evaluate how load intensity, load balancer type, request length, and connection type impact the availability of a database cluster during failover.
CCTV Latent Representations for Reducing Accident Response Times

Emergency Medical Services’ response times to accidents are crucial to saving lives in vehicle accidents. Using deep learning to instantly detect accidents in public cameras and automatically alerting authorities could help this issue. However, this would require a large set of data on public cameras to train on, but this type of data hardly exists in a usable form. Current deep learning approaches to vehicle accidents typically use first-person cameras, which are not helpful for reducing response time as we do not have access to these cameras at all times. Also, public cameras such as closed-circuit television (CCTV) pick up a much larger amount of street activity than private cameras. Thus, we create a video dataset from live closed-circuit television, so we have access to the cameras at all times. We annotate the videos with metadata to help with future trend prediction as well as give further information for each video, as they are unlabeled. We create an unsupervised learning model to train on this video dataset, and visualize latent space representations of this data in order to cluster different types of street activity and pinpoint vehicle accidents. 1
Improve vulnerability prediction performance using self-attention mechanism and convolutional neural network

With the vigorous development of the Internet, the number of commonly used software has also increased rapidly. The security and reliability of software have become important challenges that researchers must deal with. Fuzzing is a way of detecting vulnerabilities by providing unintended inputs to the target software and observing the final running results. During these years, fuzzing has proven its effectiveness in software security testing. But a large number of fuzzing tools rely solely on runtime information while testing software. Achieving static vulnerability prediction for programs in advance can greatly improve the efficiency of fuzzing. Vulnerability prediction aims to obtain the possibility of vulnerabilities in different parts of the program. The existing vulnerability prediction methods are relatively simple for feature extraction between basic blocks. We design a novel model combining self-attention mechanism and convolutional neural networks, which can extract and integrate the internal information of functions. Compared with the state-of-the-art V-Fuzz, our recall can be improved by 9.7 percentage points, and the accuracies of K-100~K-1000 can be higher than 90%.
A Lightweight Human Action Classification Method for Green IoT Sport Applications

This paper proposes a lightweight human action classification method for Green Internet of Things (IoT) sport applications. This method classifies the human motion data collected by wearables or other IoT devices with energy-efficient techniques, by enabling a small number of sample training and incremental classification to achieve the purpose of energy-efficient. To lessen the complexity of the model and reduce the number of samples required for parameter estimation, we propose a shared Hidden Conditional Random Field (sHCRF) model. The sHCRF model adds a shared-classification layer structure to reduce the parameter computation. In the experiments, the classification accuracy of the sHCRF model is above 95%. This paper introduces an incremental learning method based on knowledge distillation. The new model suppresses the forgetting of existing classification knowledge while fitting new data to learn new classification knowledge. In the incremental scenarios, the classification accuracy of the sHCRF model is above 70%. The experimental results show that this method can lightly implement convenient and fast automatic classification of action acquisition.
Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis

The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance.
Inconsistency Measurement for Logical Agents

The literature on inconsistency management hosts a wide array of measures of inconsistency. In this paper, we argue that these measures are not particularly suitable for measuring the inconsistency of the knowledge base of a logical agent. Logical agents are typically limited in their ability to discover inconsistency. Moreover, what is usually at stake is the effect inconsistency may have on the agent's behavior. To that end, we address the issue of practical measures of incon-sistency for logical agents by (i) developing a general notion of decidable, resource-bounded entailment and (ii) studying inconsistency measurement in the context of an assumption-based reason-maintenance system which keeps track of the premises underlying various conclusions made by the agent. This allows us to develop inconsistency measures for logical agents with limited reasoning capabilities and limited ability to discover inconsistency.
Defending Against Adversarial Attacks on Time- series with Selective Classification

Despite their many advantages, deep learning models are known for their poor performance on input data that is dissimilar to the training data. With adversarial attacks the input data is intentionally perturbed to test the model's robustness. Low robustness of deep learning models prohibits their usage in safety critical applications. This study suggests to counteract adversarial attacks with a combination of adversarial training and selective classification. While adversarial training is a state-of-the- art approach to increase the robustness against adversarial attacks, selective classification is thus far mainly used to improve the model performance independent of targeted attacks. It identifies malicious samples and neglects them instead of conducting false predictions. This work shows that combining both approaches leads to a more performant defense against adversarial attacks. Four different methods for selective time-series classification regarding their impact on defense against different white- and black-box attacks are evaluated: Softmax Respone, VAE Reconstruction Loss, Selective Net and a Joint Model, the latter being a combination of a VAE and a classifier developed by the authors of this work in a previous paper. The study reveals the superiority of the Joint Model approach for strong white-box attacks.
Artificial fine-tuning tasks for yes/no question answering

Abstract Current research in yes/no question answering (QA) focuses on transfer learning techniques and transformer-based models. Models trained on large corpora are fine-tuned on tasks similar to yes/no QA, and then the captured knowledge is transferred for solving the yes/no QA task. Most previous studies use existing similar tasks, such as natural language inference or extractive QA, for the fine-tuning step. This paper follows a different perspective, hypothesizing that an artificial yes/no task can transfer useful knowledge for improving the performance of yes/no QA. We introduce three such tasks for this purpose, by adapting three corresponding existing tasks: candidate answer validation, sentiment classification, and lexical simplification. Furthermore, we experimented with three different variations of the BERT model (BERT base, RoBERTa, and ALBERT). The results show that our hypothesis holds true for all artificial tasks, despite the small size of the corresponding datasets that are used for the fine-tuning process, the differences between these tasks, the decisions that we made to adapt the original ones, and the tasks’ simplicity. This gives an alternative perspective on how to deal with the yes/no QA problem, that is more creative, and at the same time more flexible, as it can exploit multiple other existing tasks and corresponding datasets to improve yes/no QA models.
Moving Object Tracking Based On Feedback Particle Filter Technique

This paper proposed a new method for moving object tracking based on the feedback particle filter technique. The Gain function and innovation error are the key factors of feedback particle filter (FPF). The FPF performs very well in non-linear tracking estimation although it received much attention from researchers in this field. A robust and effective color-based target tracking algorithm using a feedback particle filter is proposed which is very well applicable in the field of visual target tracking. The experiment has been performed on real time and recorded videos. This proposed model perform quite efficient result in tracking moving objects. This paper concludes that proposed technique perform efficient tracking as compare to conventional technique.
Assessing the range of cognitive processes in the Hong Kong Diploma of Secondary Education Examination (HKDSE)’s English language reading literacy test

Abstract This study examines the range of cognitive processes assessed in the English language reading literacy test as part of the Hong Kong Diploma of Secondary Education Examination (HKDSE), the secondary school exit test in Hong Kong. Prior studies have suggested that higher order cognitive processes are often undermined in high-stakes tests and classrooms, due to what is called the “washback effect.” This prompts the following hypothesis: despite its top-performing education system, Hong Kong has failed to provide a versatile collection of test items that involve complex reasoning. To investigate the cognitive demands of the test, as well as their relationship with student performance, this study maps the test items with reference to the cognitive levels assessed using in-depth document analysis. ANOVA is used to statistically determine differences in accuracy rates to supplement the analysis throughout. Results indicate that the test has placed an overwhelming emphasis on lower order cognitive processes over the past decade (2012–2019) and that items assessing higher order cognitive skills are, as expected, met with statistically significantly poorer performance in the test. Implications for future revision of the test and curriculum policy are discussed.
A Novel Attention Mechanism for Scene Text Detection

In recent years, segmentation-based methods have been developed rapidly in scene text detection, but there is still a challenge in detecting text with irregular shapes and large size variations. To stress the issue, this paper proposes a novel multi-scale spatial attention fusion network based on backbone (MSAFNet). The network includes a backbone and multi-scale spatial attention fusion (MSAF). The MSAF can enhance the feature extraction of small scale text and increase the feature extraction of large scale text. Experimental results on ICDAR2015 depict that the proposed method is more effective than the most advanced text detection methods.
Effect of Edutech Awareness on Learning Immersion of Two-year Hair and Beauty Majors

The purpose of this study is to investigate the effect of edutech recognition on learning immersion, and to present basic data that can be helpful in preparing a plan to improve the learning immersion of hair major students. Data collection for this study was conducted from January 2 to February 1, 2022, targeting men and women majoring in beauty with two years of experience in using Edutech in Seoul and Gyeonggi Province. For statistical analysis, frequency analysis, factor analysis, correlation analysis, and regression analysis were used. The study results are as follows. Edutech perception showed statistically significant results in learning engagement, purpose experience, specific feedback, time distortion, and challenge concentration. Hair/beauty majors' perception of Edutech is an important variable for learning immersion. Therefore, if the learning goal is set in a strategic direction to increase the learning immersion of the hair beauty major and to strengthen the hair beauty major's competency, the learning performance will improve and the academic achievement will increase. Although this study was conducted with limited research themes and variables, it is expected that comprehensive and practical research taking into account various variables will continue in future studies.
MLI: A Multi-level Inference Mechanism for User Attributes in Social Networks

In the social network, each user has attributes for self-description called user attributes, which are semantically hierarchical. Attribute inference has become an essential way for social platforms to realize user classifications and targeted recommendations. Most existing approaches mainly focus on the flat inference problem neglecting the semantic hierarchy of user attributes, which will cause serious inconsistency in multi-level tasks. In this article, we propose a multi-level model MLI, where information propagation part collects attribute information by mining the global graph structure, and the attribute correction part realizes the mutual correction between different levels of attributes. Further, we put forward the concept of generalized semantic tree, a way of representing the hierarchical structure of user attributes, whose nodes are allowed to have multiple parent nodes unlike the regular tree. Both regular and generalized semantic trees are commonly used in practice, and can be handled by our model. Besides, by making the inference start from sub-networks with sufficient attribute information, we design a “Ripple” algorithm to improve the efficiency and effectiveness of our model. For evaluation purposes, we conduct extensive verification experiments on DBLP datasets. The experimental results show the superior effect of MLI, compared with the state-of-the-art methods.
Methods of Improving and Optimizing English Education in Colleges and Universities Assisted by Microvideo Technology

English teaching in general higher education is faced with great challenges. English teachers have to face a group of students from different regions, who have received different English teaching methods during middle school, and their English proficiency is uneven. At present, the teaching methods of most colleges and universities are backward, unable to make full use of modern information technology, and students have low interest in learning, poor independent learning ability, and low learning effect, especially in English. In order to comprehensively improve students’ English level, English teaching methods in colleges and universities should be completely different from the traditional teaching methods in middle schools. This paper sorts out trial research, questionnaire survey, and data analysis method and combines some problems existing in college English teaching and the characteristics of microvideo, such as short, concise, sound, and text, to apply microvideo as a teaching resource in college English grammar teaching. A trial group was set up to study the effectiveness of microvideo teaching. No students in the trial group disliked the microvideo teaching and the average score of students in the trial group was 8.992 points higher than the average score of students in the traditional group. The results show that microvideo teaching can not only increase students’ interest in learning English but also improve their English language skills.
Engaging the Disengaged: Implementing a No-Tech Policy After Years of Adding Tech to the Classroom

After a decade of adding technology to the classroom, students asking for a laptop ban sent me on a journey of discovery. After a literature review of existing research and a semester of a no-tech policy, I found less tech, not more increases student engagement and learning. Despite more than a dozen studies over the last decade detailing the negative learning effects of laptops in the classroom, the majority of faculty believe that laptop use in class increases learning. I highlight the research findings, explain my experience with the new policy, and provide suggestions on how to attempt your own.
Detection of small objects based on feature fusion

In order to solve the problem of poor detect effectiveness of small target objects in the process of algorithm, a feature fusion method for Faster R-CNN has been proposed. This method fully fuses the deep and shallow feature information, which well improves the detection model for small objects. Meanwhile, in order to better detect small objects, oversampling is used to preprocess the data, and the corresponding hyperparameter values of the Faster R-CNN model are adjusted. From the experimental results, it is easy to see that the detection accuracy is improved by 7.6%, and for small target objects Bottle, Plant, Cow and Boat is improved by 13.9%, 11.2%, 6.7% and 9.5%, respectively. The detection effect of this model has been substantially improved.