Reviewerly/exHarmony huggingface dataset:
Constraint Optimization over Semirings

Interpretations of logical formulas over semirings (other than the Boolean semiring) have applications in various areas of computer science including logic, AI, databases, and security. Such interpretations provide richer information beyond the truth or falsity of a statement. Examples of such semirings include Viterbi semiring, min-max or access control semiring, tropical semiring, and fuzzy semiring. The present work investigates the complexity of constraint optimization problems over semirings. The generic optimization problem we study is the following: Given a propositional formula phi over n variable and a semiring (K,+, . ,0,1), find the maximum value over all possible interpretations of phi over K. This can be seen as a generalization of the well-known satisfiability problem (a propositional formula is satisfiable if and only if the maximum value over all interpretations/assignments over the Boolean semiring is 1). A related problem is to find an interpretation that achieves the maximum value. In this work, we first focus on these optimization problems over the Viterbi semiring, which we call optConfVal and optConf. We first show that for general propositional formulas in negation normal form, optConfVal and optConf are in FP^NP. We then investigate optConf when the input formula phi is represented in the conjunctive normal form. For CNF formulae, we first derive an upper bound on the value of optConf as a function of the number of maximum satisfiable clauses. In particular, we show that if r is the maximum number of satisfiable clauses in a CNF formula with m clauses, then its optConf value is at most 1/4^(m-r). Building on this we establish that optConf for CNF formulae is hard for the complexity class FP^NP[log]. We also design polynomial-time approximation algorithms and establish an inapproximability for optConfVal. We establish similar complexity results for these optimization problems over other semirings including tropical, fuzzy, and access control semirings.
Wasserstein Graph Distance Based on L1–Approximated Tree Edit Distance between Weisfeiler–Lehman Subtrees

The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on the consistency of the graph, which means that it is unable to detect slight structural differences. Consequently, this limits its ability to capture structural information, which also limits the performance of existing models that rely on the WL test. This limitation is particularly severe for traditional metrics defined by the WL test, which cannot precisely capture slight structural differences. In this paper, we propose a novel graph metric called the Wasserstein WL Subtree (WWLS) distance to address this problem. Our approach leverages the WL subtree as structural information for node neighborhoods and defines node metrics using the L1-approximated tree edit distance (L1-TED) between WL subtrees of nodes. Subsequently, we combine the Wasserstein distance and the L1-TED to define the WWLS distance, which can capture slight structural differences that may be difficult to detect using conventional metrics. We demonstrate that the proposed WWLS distance outperforms baselines in both metric validation and graph classification experiments.
ConvMatch: Rethinking Network Design for Two-View Correspondence Learning

Multilayer perceptron (MLP) has been widely used in two-view correspondence learning for only unordered correspondences provided, and it extracts deep features from individual correspondence effectively. However, the problem of lacking context information limits its performance and hence, many extra complex blocks are designed to capture such information in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage convolutional neural network (CNN) as the backbone to capture better context, thus avoiding the complex design of extra blocks. Specifically, with the observation that sparse motion vectors and dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Extensive experiments reveal that ConvMatch with a simple CNN backbone consistently outperforms state-of-the-arts including MLP-based methods for relative pose estimation and homography estimation, and shows promising generalization ability to different datasets and descriptors. Our code is publicly available at https://github.com/SuhZhang/ConvMatch.
PASS: Patch Automatic Skip Scheme for Efficient Real-Time Video Perception on Edge Devices

Real-time video perception tasks are often challenging over the resource-constrained edge devices due to the concerns of accuracy drop and hardware overhead, where saving computations is the key to performance improvement. Existing methods either rely on domain-specific neural chips or priorly searched models, which require specialized optimization according to different task properties. In this work, we propose a general and task-independent Patch Automatic Skip Scheme (PASS), a novel end-to-end learning pipeline to support diverse video perception settings by decoupling acceleration and tasks. The gist is to capture the temporal similarity across video frames and skip the redundant computations at patch level, where the patch is a non-overlapping square block in visual. PASS equips each convolution layer with a learnable gate to selectively determine which patches could be safely skipped without degrading model accuracy. As to each layer, a desired gate needs to make flexible skip decisions based on intermediate features without any annotations, which cannot be achieved by conventional supervised learning paradigm. To address this challenge, we are the first to construct a tough self-supervisory procedure for optimizing these gates, which learns to extract contrastive representation, i.e., distinguishing similarity and difference, from frame sequence. These high-capacity gates can serve as a plug-and-play module for convolutional neural network (CNN) backbones to implement patch-skippable architectures, and automatically generate proper skip strategy to accelerate different video-based downstream tasks, e.g., outperforming the state-of-the-art MobileHumanPose (MHP) in 3D pose estimation and FairMOT in multiple object tracking, by up to 9.43 times and 12.19 times speedups, respectively. By directly processing the raw data of frames, PASS can generalize to real-time video streams on commodity edge devices, e.g., NVIDIA Jetson Nano, with efficient performance in realistic deployment.
Contrastive Attention Networks for Attribution of Early Modern Print

In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500--1800) English printed books. Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers in order to provide evidence of their origins. Until now, this work has been limited to manual investigations by analytical bibliographers. We present a Contrastive Attention-based Metric Learning approach to identify similar damage across character image pairs, which is sensitive to very subtle differences in glyph shapes, yet robust to various confounding sources of noise associated with digitized historical books. To overcome the scarce amount of supervised data, we design a random data synthesis procedure that aims to simulate bends, fractures, and inking variations induced by the early printing process. Our method successfully improves downstream damaged type-imprint matching among printed works from this period, as validated by in-domain human experts. The results of our approach on two important philosophical works from the Early Modern period demonstrate potential to extend the extant historical research about the origins and content of these books.
SRoUDA: Meta Self-Training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasingly more popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and fine-tuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy.
Cross-Modal Contrastive Learning for Domain Adaptation in 3D Semantic Segmentation

Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process of 3D data to some extent. A recent work named xMUDA leveraged multi-modal data to domain adaptation task of 3D semantic segmentation by mimicking the predictions between 2D and 3D modalities, and outperformed the previous single modality methods only using point clouds. Based on it, in this paper, we propose a novel cross-modal contrastive learning scheme to further improve the adaptation effects. By employing constraints from the correspondences between 2D pixel features and 3D point features, our method not only facilitates interaction between the two different modalities, but also boosts feature representations in both labeled source domain and unlabeled target domain. Meanwhile, to sufficiently utilize 2D context information for domain adaptation through cross-modal learning, we introduce a neighborhood feature aggregation module to enhance pixel features. The module employs neighborhood attention to aggregate nearby pixels in the 2D image, which relieves the mismatching between the two different modalities, arising from projecting relative sparse point cloud to dense image pixels. We evaluate our method on three unsupervised domain adaptation scenarios, including country-to-country, day-to-night, and dataset-to-dataset. Experimental results show that our approach outperforms existing methods, which demonstrates the effectiveness of the proposed method.
Less Is More Important: An Attention Module Guided by Probability Density Function for Convolutional Neural Networks

Attention modules, which adaptively weight and refine features according to the importance of the input, have become a critical technique to boost the capability of convolutional neural networks. However, most existing attention modules are heuristic without a sound interpretation, and thus, require empirical engineering to design structure and operators within the modules. To handle the above issue, based on our 'less is more important' observation, we propose an Attention Module guided by Probability Density Function (PDF), dubbed PdfAM, which enjoys a rational motivation and requires few empirical structure designs. Concretely, we observe that pixels with less occurrence are prone to be textural details or foreground objects with much importance to aid vision tasks. Thus, with PDF values adopted as a smooth and anti-noise alternative to the pixel occurrence frequency, we design our PdfAM by first estimating the PDF based on some distribution assumption, and then predicting a 3D attention map via applying a negative correlation between the attention weights and the estimated PDF values. Furthermore, we develop learnable PDF-rescale parameters so as to adaptively transform the estimated PDF and predict a customized negative correlation. Experiments show that our PdfAM consistently boosts various networks under both high- and low-level vision tasks, and also performs favorably against other attention modules in terms of accuracy and convergence.
Balancing Agents for Mining Imbalanced Multiclass Datasets – Performance Evaluation

The paper deals with mining imbalanced multiclass datasets. The goal of the paper is to evaluate the performance of several balancing agents implemented by the authors. Agents have been constructed from 5 state-of-the-art classifiers designed originally for mining binary imbalanced datasets. To transform binary classifiers into multiclass ones, we use the one-versus-one (OVO) approach making use of the collective decision taken by the majority voting. The paper describes our approach and provides detailed description of the respective balancing agents. Their performance is evaluated in an extensive computational experiment. The experiment involved multiclass imbalanced datasets from the Keel imbalanced datasets repository. Experiment results allowed to select best performing balancing agents using statistical tools.
Fastformer: transformer-based fast reasoning framework

Video action recognition is a vital task in the field of computer vision. A great deal of redundant information is generated along with original video data in the process of depth computation. In order to solve this problem, most existing methods improve recognition speed at the cost of recognition accuracy. In this paper, we propose a new framework: Fastformer which is a transformer-based structure for fast inference video classification to further improve model inference speed while maintaining accuracy. To achieve the balance of speed and accuracy, we solve the inter-frame and intra-frame redundancy of video and design a new self-attention network, which uses the improved highway network to make the model realize the same function as the traditional self-attention module, while greatly reducing the amount of calculation and the number of required parameters. We conduct experiments to verify the effect of our model. Overall, Fastformer significantly outperforms existing vision transformers with regard to the speed versus accuracy trade-off. For example, at 76.4% Keyframes-400 accuracy, Fastformer is 28% faster than TimeSformer.
Advanced Undergraduate Computing Students' Perception of Software Quality Teaching: a Survey in the Brazilian Paraná State

Aspects of software quality (SQ), such as process and product metrics, and assessment techniques, can be taught to computing students during their undergraduate courses, however, there is no consensus on how. In Brazil, computing courses are structured as the Brazilian Computer Society suggests, still, researchers point out that there are few SQ subjects in these courses. This paper aims to analyze the perception of SQ concepts by advanced undergraduate students in the northwest of the Paraná state. We applied a survey and received ninety-nine answers. Our results show that most SQ concepts are taught, but the students feel they did not learn and are not able to apply them. We discuss and suggest guidelines to improve the understanding of SQ concepts.
Construction of Interactive Online Teaching System for English Speech Based on Web

Based on the increasingly close international exchange environment, English as an important language tool, has been promoting international friendly communication in every way.Therefore, the society has put forward stricter requirements for English professionals.However, at present, the traditional English teaching mode in colleges and universities can no longer meet the needs of the society and students.In order to ensure the high-quality development of talents, this paper uses FFmpeg as a decoding tool, combines streaming media technology with computer technology, and constructs a Webbased interactive online teaching system for English speeches.With Linux as the operating system, B/S architecture as the development framework, and Java Web application development framework to develop the system server, the functions of the system are detailed and perfect.The system will deploy the overall development in layers in MVC mode, so as to maintain the operating efficiency of the system and promote the further study and growth of high-quality English professionals.In the comprehensive evaluation part of the system, the analytic hierarchy process (AHP), which combines qualitative and quantitative analysis, is used to calculate, and the functional linear regression model is used as the statistical basis of the data, thus further optimizing the evaluation system of efficient English speech.
Request Strategies Pragmatic Study By Iraqi Elf University Learners

This study explaining pragmatic consciousness EFL learners depending strategies of request, using in acquisition pragmatic capability in&nbsp; the academic besides&nbsp; the social communication. The research offered by a public programmer leading to the prioritizes&nbsp; are in need of&nbsp; English learners to improve the capability of&nbsp; using request in a successful way in social besides the academic communications. This research targeted for illustrating the strategies essential role to develop the awareness learners' pragmatic&nbsp; of undergraduate Iraqi EFL learners. Besides, lots of learners of English language&nbsp; failing to provide pragmatic capability of the way of&nbsp; using request by associating their meanings utterances, deliberate the language users' intention. A developed studies on the strategies effectiveness to increase students 'pragmatic consciousness in EFL teaching. Discussing improving&nbsp; language learners’ pragmatic capability significance considered one of the important needs of the successful communication. focusing on requests speech act being speech act most regularly studying. The current research showing an intermediate level Iraq EFL learners tending of using conventional indirect strategies for making requests&nbsp; besides using different structures depending the internal besides external modifications for making their requests more polite, yet request strategies choice in the formal conditions not different to a large extent from that informal contexts used .
Practice of Laboratory Extracurricular Open Management Based on Mobile Internet Technology

In order to solve the problems of laboratory construction and management, this paper puts forward the practice of laboratory extracurricular open management based on mobile Internet technology.Design and complete an intelligent open laboratory system by using mobile Internet and Internet of Things technology.The bottom layer of the system is composed of wireless sensor network nodes and coordinators.The mobile terminal sends different instructions to each sensor through the network control coordinator to realize the control of laboratory terminal equipment.The system is easy to operate and improves the intelligence of laboratory management.
Research on the Application of C Programming Course Blended Teaching Based on "PBL+FC" Model

In consideration of the problems in the teaching of C programming courses, guided by the common problems that students encounter in the C programming courses, based on analyzing the characteristics of flipped classroom, a teaching and learning model based on 'PBL+FC' are constructed to achieve the better learning levels.The model is an integration of three concepts: (1) transfer knowledge before class; (2) internalize knowledge in class; (3) consolidate and summarize after class.In the study, the implementation of the model is introduced in detail and tested by two flipped classes (experimental class and control class) in School of Big Data, Baoshan university.The experiment results show that the flipped classroom based on 'PBL+FC' makes students leading roles of study, and concluded that the flipped classroom teaching of C Language Programming has a good effect on the improvement of students' grades and quality.
Twitter Spam Detection Using Different Machine Learning Techniques

Vehicle reconciliation strategies have been involved a few times in spam channels to incorporate approaching/active messages, for example, spam and spam bunches. This technique expresses that each bunch contains little miniature groups, and each miniature group is dispersed. Notwithstanding, this thought ought not be trifled with, and the miniature group might have a lopsided dispersion. To build the respectability of the main strategy for appropriating the Internet class, we suggest supplanting the Euclidean space with a succession of models that incorporate into the miniature bunch connected with the circulation. Here, the Naïve Bayes (INB) classification has been carried out to carry out miniature bunches across the line. While these INBs can decide the distance and limits of micro clusters, Euclidean space considers the overall worth of the group and misdirects the bigger micro cluster. In this report, DenStream is upheld by a committed framework called INB DenStream. To represent the presentation of INB-DenStream, current techniques, like DenStream, StreamKM + + , and CluStream, have been utilized in the Twitter chronicle, and their exhibition has been estimated as far as quality, honesty as a general rule, memory as a rule, F1 measurements., markers, and complex estimations. The relatively close outcomes show that our techniques outflank its rivals in the set figures.
Research on Innovative Teaching Mode of Education Informatization Based on Artificial Intelligence Algorithm

With the rapid development of artificial intelligence technology and the continuous expansion of computer application fields, the traditional education model can no longer meet the needs of today's society for talents.Intelligence as a new teaching method provides the possibility to realize the comprehensive information ability and the characteristics of autonomy and open collaboration that students need to have in the learning process.Intelligent teaching mode has become the inevitable trend of future education development.In the context of the new era, we build a digital learning environment based on AI algorithms that is efficient and feasible and can effectively guide teachers in their practical work and improve the overall level of the school, innovate and reform the teaching mode, make the traditional education informatization method more suitable for modern development needs, and thus improve students' learning efficiency and classroom participation.
Sign Language Recognition using Deep Learning through LSTM and CNN

This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%.