Reviewerly/exHarmony huggingface dataset:
Function Names: Quantifying the Relationship Between Identifiers and Their Functionality to Improve Them

When students first learn to program, they often focus on functionality: does a program work? In an era where software volume and complexity increase exponentially, it is equally important that they learn to write programs with style so that they are readable and extendable. Writing quality code starts with the building blocks for any program, its functions. A carefully chosen name is vital for program maintainability and manageability. The identifier is the most portable and concise way to summarize what the function does. What makes for the right choice? And can we automatically assess the quality of function names? Using natural language processing, we were able to create a probabilistic model to evaluate their clarity. Using functionality encodings, we attempt to learn the relationship between functions in different programs to improve their names. We analyzed a total of 5,400 programs tackling five novice programming tasks submitted by over 1,000 students in CS1. We developed a software system to automate labor-intensive tasks, detect poor function names and recommend replacements. Our findings suggest that less than 2.5% of name substitutions have an adverse outcome, and in most cases, more than 50% result in an improvement.
Dual-level contrastive learning network for generalized zero-shot learning

Generalized zero-shot learning (GZSL) aims to utilize semantic information to recognize the seen and unseen samples, where unseen classes are unavailable during training. Though recent advances have been made by incorporating contrastive learning into GZSL, existing approaches still suffer from two limitations: (1) without considering fine-grained cluster structures, these models cannot guarantee the discriminability and semantic awareness of synthetic features; (2) classifiers tend to overfit the seen classes, as they only concentrate on the seen domain. To address these challenges, we propose a Dual-level Contrastive Learning Network (DCLN), in which intra-domain and cross-domain contrastive learning are seamlessly integrated into a unified learning model. Specifically, the former performs center-prototype contrasting to fully explore the discriminative structure knowledge, while the latter is proposed to effectively alleviate the overfitting problem by utilizing the semantic relationships between the seen and unseen domain. Finally, the experimental results on four benchmark datasets demonstrate the superiority of our DCLN over the state-of-the-art methods.
Analysis and Practice of Applying Oriented Teaching Method to Improve the Effect of College English Teaching

The Ministry of Education of my country released the first "national standard" in January 2018, clearly stating that it is necessary to attach importance to and improve the ability of undergraduates. Through the analysis of the research results in this field at home and abroad, this study finds that the thinking ability training strategies that can be provided in the previous research are relatively scattered, and there is a lack of effective training methods and guidance. Therefore, Dr. Wen Qiufang proposed the teaching method of "production-oriented approach." "Production-oriented approach" includes driving, promoting, and re-evaluation, in which the training content is integrated into each link. Therefore, this study combines training and Production-oriented Approach, and using this theoretical basis in the English teaching classroom can explore better. It provides a new way for the cultivation of college students' English thinking ability. In the actual classroom teaching, the production-oriented teaching method is used to teach English to the students during the research process. After the teaching course is over, the teaching data are collected and analyzed with SPSS 25.0 software, and the data results are displayed. It can be seen that applying the Production-oriented approach to English classroom teaching can indeed improve the level of domestic college English students and provide a reference approach and method for students' English training, and this method is effective and feasible.
Tick Box Design: A bounded and packageable co-design method for large workshops

We present Tick Box Design, a rapid co-design method for research and industry that allows users to gather many design ideas from large numbers of participants in a limited time whilst adhering to ethical principles around users understanding their contributions. The method is based on a design workshop model and can be packaged for delivery by remote teams making it well suited for distributed PD work. In this paper we describe an instance of the method in which 198 teenagers in one country, remotely contributed design ideas for a team in another country, across four rapid 60-minute workshops. In a systematic evaluation of the workshop, we take the needs of both sides into account, the teen participants, and the design team. We explore the participants’ ability to contribute ideas and the usefulness of these ideas to the design team. We show that the teenagers successfully participated in the activities and that the process delivered ideas that were useful to the design team. We discuss our evaluation in the context of ethical and useful participation of minors in HCI research and conclude that Tick Box Design is an efficient method that can be packaged for remote use and delivers value for designers and participants.
SFOC: A NOVEL MULTI-DIRECTIONAL AND MULTI-SCALE STRUCTURAL DESCRIPTOR FOR MULTIMODAL REMOTE SENSING IMAGE MATCHING

Abstract. Accurate matching of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, SAR, and rasterized maps) is still an ongoing challenge because of nonlinear radiometric differences (NRD) between these images. Considering that structural properties are preserved between multimodal images, this paper proposes a robust matching method based on multi-directional and multi-scale structural features, which consist of two critical steps. Firstly, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed to address severe NRD, which combines the first- and second-order gradient information by using the steerable filters to depict multi-directional and multi-scale structural features of images. Meanwhile, SFOC is further enhanced by performing the dilated Gaussian convolutions with different dilated rates on it, which can capture multi-level context structural features and improve the ability to resist noise. Then, a fast similarity measure, called Fast Normalized Cross-Correlation (Fast-NCCSFOC), is established to detect correspondences by a template matching scheme, which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. The performance of the proposed SFOC has been evaluated with many different kinds of multimodal RS images, and experimental results show its superior matching performance compared with the state-of-the-art methods.
One-Shot Object Detection in Heterogeneous Artwork Datasets

Christian archeologists face many challenges in understanding visual narration through artwork images. This understanding is essential to access underlying semantic in-formation. Therefore, narrative elements (objects) need to be labeled, compared, and contextualized by experts, which takes an enormous amount of time and effort. Our work aims to reduce labeling costs by using one-shot object detection to generate a labeled database from unannotated images. Novel object categories can be defined broadly and annotated using visual examples of narrative elements without training exclusively for such objects. In this work, we propose two ways of using contextual information as data augmentation to improve the detection performance. Furthermore, we introduce a multi-relation detector to our framework, which extracts global, local, and patch-based relations of the image. Additionally, we evaluate the use of contrastive learning. We use data from Christian archeology (CHA) and art history - IconArt-v2 (IA). Our context encoding approach improves the typical fine-tuning approach in terms of mean average precision (mAP) by about 3.5 % (4 %) at 0.25 intersection over union (IoU) for UnSeen categories, and 6 % (1.5 %) for Seen categories in CHA (IA). To the best of our knowledge, our work is the first to explore few shot object detection on heterogeneous artistic data by investigating evaluation methods and data augmentation strategies. We will release the code and models after acceptance of the work.
On the Proof Theory of Infinitary Modal Logic

Abstract The article deals with infinitary modal logic. We first discuss the difficulties related to the development of a satisfactory proof theory and then we show how to overcome these problems by introducing a labelled sequent calculus which is sound and complete with respect to Kripke semantics. We establish the structural properties of the system, namely admissibility of the structural rules and of the cut rule. Finally, we show how to embed common knowledge in the infinitary calculus and we discuss first-order extensions of infinitary modal logic.
Learning-based compression of visual objects for smart surveillance

Advanced video applications in smart environments (e.g., smart cities) bring different challenges associated with increasingly intelligent systems and demanding requirements in emerging fields such as urban surveillance, computer vision in industry, medicine and others. As a consequence, a huge amount of visual data is captured to be analyzed by task-algorithm driven machines. In this context, this paper proposes an efficient learning-based approach to compress relevant visual objects, captured in surveillance contexts and delivered for machine vision processing. An object-based compression scheme is devised, comprising multiple autoencoders, each one optimised to produce an efficient latent representation of a corresponding object class. The performance of the proposed approach is evaluated with two types of visual objects: persons and faces and two task-algorithms: class identification and object recognition, besides traditional image quality metrics like PSNR and VMAF. In comparison with the Versatile Video Coding (VVC) standard, the proposed approach achieves significantly better coding efficiency than the VVC, e.g., up to 46.7% BD-rate reduction. The accuracy of the machine vision tasks is also significantly higher when performed over visual objects compressed with the proposed scheme in comparison with the same tasks performed over the same visual objects compressed with the VVC. These results demonstrate that the learning-based approach proposed in this paper is a more efficient solution for compression of visual objects than standard encoding.
Research on Folk Handicraft Image Recognition Based on Neural Networks and Visual Saliency

How to identify quickly the images of folk arts and crafts works has become a difficult problem of cultural heritage value mining. Therefore, combined with the image recognition technology can improve the accuracy of the identification of folk arts and crafts works. This paper improves the ITTI significant model based on the method of linear addition of significant maps with the same proportion. Firstly, Bayesian model and Gaussian model are used to extract the probability distribution of image feature vector; secondly, the k-means algorithm is used to identify image accuracy extraction work, and finally ALOI database is used to test judgment image recognition accuracy; experimental results found that the improved technology does help to improve the folk arts and handicraft image recognition accuracy.
Plane-based grid map: A robot-centric mapping algorithm for wheel-legged rover motion planning in unstructured terrain environments

Active suspension rover is gaining more and more attention. Among them, the wheel-legged rover has outstanding performance. In order to fully utilize the rover’s capabilities and improve the autonomy in planetary exploration, this paper addresses a plane-based grid (PBG) mapping method for its motion planning in unstructured terrain environments. First, the kinematics of the TAWL rover and each leg’s dynamic are presented to analyze the physical characteristics. Second, the PBG map is constructed based on the rover’s capabilities. It can be directly used during motion planning. This mapping framework utilizes a sensor model to predict whether the grid is occupied or free. Then, a plane cluster algorithm is proposed to classify grid cells, which incorporates two machine learning algorithms. Thus, the flat areas are clustered from the unstructured terrains. With the PBG map and the motion planning framework, the wheel-legged rover can simplify the motion planning process and better utilize the driving efficiency and the legs’ adaptability. Third, the proposed mapping algorithm is tested on a depth camera. Robotic experiments are carried out, demonstrating our framework which allows the rover to safely combine the wheeled motion with legged motion in autonomous navigation.
Artificial intelligence and big data driven digital media design

Traditional digital media system can not complete the format conversion and video transcoding of massive image and video information at the same time, which leads to long time of information processing and loss of data storage. Therefore, a digital media system driven by artificial intelligence and big data is designed. Using FastDFS design of digital media data management module. Design digital media image, video conversion module and digital media resource data dictionary library. Develop image plug-ins based on the MapInfo platform. Design video plug-in, introduce virtual reality technology to retrieve image information, call video source, create CvCapture object. Design system software functions and digital media information acquisition algorithm. Intelligent artificial pixel feature acquisition technology is used to collect 3D visual information of digital media and design its pseudo-code. Compared with the traditional system, the information processing time of the designed system is shorter, and it takes 11.555 ms when there are more information objects. The experimental results show that the designed system can complete more complete storage of data.
The educational opportunities of Ukrainian children at the time of the Russian invasion: perspectives from teachers

The global Covid-19 pandemic has passed. This process required greater attention and care to the specifics of the organization of distance learning and the resolution of problems that arose for participants in the educational process. By late 2021 face-to-face teaching was returning but Russian aggression against Ukraine interrupted the comparatively carefree life of children and hindered access to kindergartens and schools which required a further focus on remote education. The paper outlines the peculiarities of the organization of distance learning under martial law in Ukraine and presents the problems of the organization of training during a time of conflict as well as the challenges presented by Internet platforms for the organization of the distance learning approaches recommended by the Ministry of Education of Ukraine. The aim of the research was theoretical analysis and generalization of literature on research problems, pedagogical observations, surveys, methods of mathematical statistics. The article provides one of the first analyses of the difficulties faced during this period of immense disruption based on empirical data and explains to what extent Ukrainian teachers under martial law have the possibility to organize distance teaching, what problems they face, what is the psychological state of children.
Reasoning Algorithms on Feature Modeling—A Systematic Mapping Study

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review.
A Bayesian Network Model for Supporting School Managers Decisions in the Pandemic Era

Due to the dramatic health situation caused by the COVID-19 pandemic, in Italy the emergency remote teaching lasted longer than in other countries. The mandatory teaching modalities have required digital transformation processes in a framework where digital-divide is one of the limitations to school modernization. We believe that the experience can promote a deeper formatting of organizational process. The paper shows results of a multitarget research carried out during the Italian lockdown aiming at animating the debate around school from multi-actors perspectives and at supporting policies. The paper aims at showing the potentiality of a multivariate statistical method as a tool supporting school managers in identifying those challenges they have to face to improve the setting up of internal processes. The main result is a model supporting the decision making process at orienting school managers strategies.
Online nonparametric monitoring of heterogeneous data streams with partial observations based on Thompson sampling

With the rapid advancement of sensor technology driven by Internet-of-Things-enabled applications, tremendous amounts of measurements of heterogeneous data streams are frequently acquired for online process monitoring. Such massive data, involving a large number of data streams with high sampling frequency, incur high costs on data collection, transmission, and analysis in practice. As a result, the resource constraint often restricts the data observability to only a subset of data streams at each data acquisition time, posing significant challenges in many online monitoring applications. Unfortunately, existing methods do not provide a general framework for monitoring heterogeneous data streams with partial observations. In this article, we propose a nonparametric monitoring and sampling algorithm to quickly detect abnormalities occurring to heterogeneous data streams. In particular, an approximation framework is incorporated with an antirank-based CUSUM procedure to collectively estimate the underlying status of all data streams based on partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. Theoretical justification of the proposed algorithm is also investigated. Both simulations and case studies are conducted to demonstrate the superiority of the proposed method.
Philippine Currency Counterfeit Detector using Image Processing

Image processing is the utilization of a computer to process digital images through an algorithm. This method can also be used in detecting the security features of banknotes to tell if the banknote is authentic or counterfeit. Using algorithms such as Canny Edge Detection, Hough Line Transform, Optical Character Recognition and K-Means Clustering to detect Philippine currency banknote level 1 security features such as watermark, asymmetrical serial number, see-through print, and security thread. Canny Edge Detection would be used to detect edges and curves found on the banknotes. Hough Line Transform would be used to detect straight lines found on the banknotes. Optical Character Recognition (OCR) would be used to distinguish text found on the banknotes. K-means clustering would be used to detect color ranges used by the banknotes by means of vector quantization. The tests yield a result of 100% for 200-peso banknotes, 97.50% for 500-peso and 1000-peso banknotes, 96.67% for 50-peso banknotes 91.82% for 20-peso banknotes, and 91.67% for 100-peso banknotes, which has a mean average of 95.86% which is significantly higher than the average accuracy from previous studies of 86.27%.
Designing of Chinese Ancient Poetry App Based on iOS Platform Augmented Reality and Machine Learning

An application program named Poetry in Painting integrating the listening, speaking, reading, painting, and testing skills were designed based on the technology of augmented reality and machine learning in the iOS mobile operating system, and also combined with Chinese traditional culture and ancient poetry. The app is full of attraction and creativity, which can not only cultivate users’ academic literacy, but also cultivate their imagination and creativity.
MO-QoE: Video QoE using multi-feature fusion based Optimized Learning Models

The escalating demand for video content and streaming services has made it a predominant medium of exchanging information in the modern era. Videos are processed, compressed, and streamed over dynamic wireless channels having limited bandwidth. This introduces video impairments that deteriorate the video quality. Humans are the targeted observers of multimedia content. Improving viewing experience requires human intervened subjective assessment of video quality; which is relatively cumbersome and resource intensive. Devising alternatives that can estimate subjective quality scores are essential to evaluate Quality-of-Experience (QoE) in multimedia applications and services. The features (objective quality metrics and impairment factors) provide a measure of artifacts and content characteristics that influence observers viewing experience. We have proposed MO-QoE, a framework to predict video QoE using Multi-Feature Fusion (MFF) based Optimized Learning Models (OLMs). The OLMs are the optimized neural network models designed for QoE prediction. We make use of Adaptive Moment estimation and Batch Gradient Descent algorithms that update the values of weight and biases of the learning models to their optimal values. The inputs to our model is based on MFF scheme, that eliminates distortion specific biased performance of any individual feature. The resultant non-linear regression score from the model is obtained after fusion of scores from multiple features. The QoE prediction using the MFF-based OLMs has been tested on publicly available databases. The prediction performance/accuracy is improved by ≈2 to 3 times as compared to the existing models.
Multi-User Eye-Tracking

The human gaze characteristics provide informative cues on human behavior during various activities. Using traditional eye trackers, assessing gaze characteristics in the wild requires a dedicated device per participant and therefore is not feasible for large-scale experiments. In this study, we propose a commodity hardware-based multi-user eye-tracking system. We leverage the recent advancements in Deep Neural Networks and large-scale datasets for implementing our system. Our preliminary studies provide promising results for multi-user eye-tracking on commodity hardware, providing a cost-effective solution for large-scale studies.