Reviewerly/exHarmony huggingface dataset:
Top-<i>k</i> Self-Adaptive Contrast Sequential Pattern Mining

For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, contrast pattern mining emerges to discover patterns with high-contrast rates between different categories. To date, existing contrast SPM methods face many challenges, including excessive parameter selection and inefficient occurrences counting. To tackle these issues, this article proposes a top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> self-adaptive contrast SPM, which adaptively adjusts the gap constraints to find top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> self-adaptive contrast patterns (SCPs) from positive and negative sequences. One of the key tasks of the mining problem is to calculate the support (the number of occurrences) of a pattern in each sequence. To support efficient counting, we store all occurrences of a pattern in a special array in a Nettree, an extended tree structure with multiple roots and multiple parents. We employ the array to calculate the occurrences of all its superpatterns with one-way scanning to avoid redundant calculation. Meanwhile, because the contrast SPM problem does not satisfy the Apriori property, we propose Zero and Less strategies to prune candidate patterns and a Contrast-first mining strategy to select patterns with the highest contrast rate as the prefix subpattern and calculate the contrast rate of all its superpatterns. Experiments validate the efficiency of the proposed algorithm and show that contrast patterns significantly outperform frequent patterns for sequence classification. The algorithms and datasets can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/SCP-Miner</uri> .
College Physical Education Course Management System Based on Internet of Things

With the continuous development of the Internet of Things technology, the latest information technology represented by the network, digital, and intelligence is changing people’s work, learning, and lifestyle and bringing new development space to education. The physical education curriculum management system in colleges and universities brings convenience to teachers and students, facilitates the centralized management of students by teachers, ensures the safety of students in physical education classes, and reduces the risk of injury to students in physical education classes. The innovative construction of college physical education curriculum is a systematic project, which aims to deepen educational reform, improve the physical condition of students, and enable students to experience advanced teaching methods. At the same time, university physical education teachers have a degree of higher education, provide information, and promote interaction with students. This improves the quality and ability of teachers and students themselves. The paper proposes related research on the design of college physical education curriculum management system based on the Internet of Things. Through the use of Internet of Things wireless channel technology and UWB indoor positioning technology, combined with the impact of the Internet of Things on cognition, the arrangement of physical education courses and student positioning and sign-in are designed, and the results of the system test are found. Compared with the previous system,. this system has improved the positioning and sign-in accuracy of physical education courses by 5–10%, and the efficiency of physical education courses has also increased by 2–5%.
An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

Internet of Things (IoT) is gradually adopted by many organizations to facilitate the information collection and sharing.In an organization, an IoT node usually can receive and send an email for event notification and reminder.However, unwanted and malicious emails are a big security challenge to IoT systems.For example, attackers may intrude a network by sending emails with phishing links.To mitigate this issue, email classification is an important solution with the aim of distinguishing legitimate and spam emails.Artificial intelligence especially machine learning is a major tool for helping detect malicious emails, but the performance might be fluctuant according to specific datasets.The previous research figured out that supervised learning could be acceptable in practice, and that practical evaluation and users' feedback are important.Motivated by these observations, we conduct an empirical study to validate the performance of common learning algorithms under three different environments for email classification.With over 900 users, our study results validate prior observations and indicate that LibSVM and SMO-SVM can achieve better performance than other selected algorithms.
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
The Multilingual TEDx Corpus for Speech Recognition and Translation

We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a collection of audio recordings from TEDx talks in 8 source languages. We segment transcripts into sentences and align them to the source-language audio and target-language translations. The corpus is released along with open-sourced code enabling extension to new talks and languages as they become available. Our corpus creation methodology can be applied to more languages than previous work, and creates multi-way parallel evaluation sets. We provide baselines in multiple ASR and ST settings, including multilingual models to improve translation performance for low-resource language pairs.
A virtual coaching model of professional development to increase teachers' digital learning competencies

Recent changes in the United States education system have accelerated the need for enhanced technology-based teaching and learning techniques (i.e., digital literacy) and support. Currently, digital literacy integration research focuses primarily on students. Teacher-focused research highlights teaching digital literacy versus using it to learn. Furthermore, little research assesses the effectiveness of professional development methods in providing digital literacy support to teachers. This mixed-methods experimental study addresses these concerns by creating a virtual coaching professional development model to increase teachers' Digital Learning Identity. With teachers demonstrating concerns over staying current with changing technology, study results indicate coaching as one innovative approach to professional development.
Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data

Most of the state-of-the-art action recognition methods focus on offline learning, where the samples of all types of actions need to be provided at once. Here, we address continual learning of action recognition, where various types of new actions are continuously learned over time. This task is quite challenging, owing to the catastrophic forgetting problem stemming from the discrepancies between the previously learned actions and current new actions to be learned. Therefore, we propose Else-Net, a novel Elastic Semantic Network with multiple learning blocks to learn diversified human actions over time. Specifically, our Else-Net is able to automatically search and update the most relevant learning blocks w.r.t. the current new action, or explore new blocks to store new knowledge, preserving the unmatched ones to retain the knowledge of previously learned actions and alleviates forgetting when learning new actions. Moreover, even though different human actions may vary to a large extent as a whole, their local body parts can still share many homogeneous features. Inspired by this, our proposed Else-Net mines the shared knowledge of the decomposed human body parts from different actions, which benefits continual learning of actions. Experiments show that the proposed approach enables effective continual action recognition and achieves promising performance on two large-scale action recognition datasets.
Clustered Vehicular Federated Learning: Process and Optimization

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML model among edge devices on their distributed datasets while keeping data locally. While FL requires less communication compared to classical distributed learning, it remains hard to scale for large models. In vehicular networks, FL must be adapted to the limited communication resources, the mobility of the edge nodes, and the statistical heterogeneity of data distributions. Indeed, a judicious utilization of the communication resources alongside new perceptive learning-oriented methods are vital. To this end, we propose a new architecture for vehicular FL and corresponding learning and scheduling processes. The architecture utilizes vehicular-to-vehicular(V2V) resources to bypass the communication bottleneck where clusters of vehicles train models simultaneously and only the aggregate of each cluster is sent to the multi-access edge (MEC) server. The cluster formation is adapted for single and multi-task learning, and takes into account both communication and learning aspects. We show through simulations that the proposed process is capable of improving the learning accuracy in several non-independent and-identically-distributed (non-i.i.d) and unbalanced datasets distributions, under mobility constraints, in comparison to standard FL.
Automatic detection of Long Method and God Class code smells through neural source code embeddings

Code smells are structures in code that often harm its quality. Manually detecting code smells is challenging, so researchers proposed many automatic detectors. Traditional code smell detectors employ metric-based heuristics, but researchers have recently adopted a Machine-Learning (ML) based approach. This paper compares the performance of multiple ML-based code smell detection models against multiple metric-based heuristics for detection of God Class and Long Method code smells. We assess the effectiveness of different source code representations for ML: we evaluate the effectiveness of traditionally used code metrics against code embeddings (code2vec, code2seq, and CuBERT). This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. This approach helped us leverage the power of transfer learning – our study is the first to explore whether the knowledge mined from code understanding models can be transferred to code smell detection. A secondary contribution of our research is the systematic evaluation of the effectiveness of code smell detection approaches on the same large-scale, manually labeled MLCQ dataset. Almost every study that proposes a detection approach tests this approach on the dataset unique for the study. Consequently, we cannot directly compare the reported performances to derive the best-performing approach.
Reachable Distance Function for KNN Classification

Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable distance values in real applications. This can lead to incorrect measure of the affinity between data points. This paper proposes a reachable distance function for KNN classification. The reachable distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the reachable distance between data points includes their class center distance and real distance. Its shape looks like &#x201C;Z,&#x201D; and we also call it a Z distance function. In this way, the affinity between data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the reachable distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification.
A Blockchain-Based IoT-Enabled E-Waste Tracking and Tracing System for Smart Cities

Recycling of electronic waste is a rapidly growing global issue that requires proper monitoring and tracing of electronic devices and the business transactions between the stakeholders. The majority of current systems that manage electronic devices throughout their supply chain stages are centralized and lack data transparency, immutability, and security. Specifically, such systems are incapable of handling problems like comprehensive coverage of the life cycle of e-products, access control for maintaining data security, reputation-aware selection of stakeholders, and large amounts of data generated during various stages of the supply chain processes. In this paper, we propose a blockchain-based IoT-enabled system for monitoring all post-production business processes, activities, and operations performed on an electronic device. The system is supported by five smart contracts that record the actions of users on the immutable distributed ledger that aid in ensuring that the business processes carried out by the participants are transparent, traceable, and secure. To store large files, such as images of e-waste materials, products, and licenses for stakeholders, we have integrated our system with a distributed storage system. The proposed system is tested on Ethereum blockchain to check the gas consumption of the functions of the smart contracts. The cost and security analysis shows that the proposed system is viable.
FOSTER: Feature Boosting and Compression for Class-Incremental Learning

The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this phenomenon, whereas most of them either fall into the stability-plasticity dilemma or take too much computation or storage overhead. Inspired by the gradient boosting algorithm to gradually fit the residuals between the target model and the previous ensemble model, we propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively. Specifically, we first dynamically expand new modules to fit the residuals between the target and the output of the original model. Next, we remove redundant parameters and feature dimensions through an effective distillation strategy to maintain the single backbone model. We validate our method FOSTER on CIFAR-100 and ImageNet-100/1000 under different settings. Experimental results show that our method achieves state-of-the-art performance. Code is available at https://github.com/G-U-N/ECCV22-FOSTER .
Accelerating Convolutional Neural Networks by Removing Interspatial and Interkernel Redundancies

Recently, the high computational resource demands of convolutional neural networks (CNNs) have hindered a wide range of their applications. To solve this problem, many previous works attempted to reduce the redundant calculations during the evaluation of CNNs. However, these works mainly focused on either interspatial or interkernel redundancy. In this paper, we further accelerate existing CNNs by removing both types of redundancies. First, we convert interspatial redundancy into interkernel redundancy by decomposing one convolutional layer to one block that we design. Then, we adopt rank-selection and pruning methods to remove the interkernel redundancy. The rank-selection method, which considerably reduces manpower, contributes to determining the number of kernels to be pruned in the pruning method. We apply a layer-wise training algorithm rather than the traditional end-to-end training to overcome the difficulty of convergence. Finally, we fine-tune the entire network to achieve better performance. Our method is applied on three widely used datasets of an image classification task. We achieve better results in terms of accuracy and compression rate compared with previous state-of-the-art methods.
An efficient approach to 3D path planning

Since the environmental complexity of a 3D environment significantly increases as compared to a 2D environment, previous studies on 3D path planning have mostly focused on efficiency in generating a 3D path. In this context, the present study proposes a method, COSPS (Critical Obstacles and Surrounding Point Set), which can lower the complexity for efficient 3D path planning by determining a subset of obstacles in terms of their importance for planning a path, and by finding a subset of grid points that can fully surround those obstacles in 3D. Due to this reduction of obstacles and points, a path can be efficiently generated, as a graph is constructed with a smaller number of points, rather than with the total number of points in 3D. To observe the effect of the proposed method on 3D path planning as compared to well-known methods that consider all obstacles and points, the simulation experiment was conducted for multiple 3D maps with different sizes, shapes, and distribution of obstacles. The results demonstrated that the proposed method can generate a feasible path and significantly enhance the efficiency of 3D path planning without compromising the quality of the solution in terms of path length.
A Novel Scheme Based on the Diffusion to Edge Detection

A novel scheme of edge detection based on the physical law of diffusion is presented in this paper. Though the most current studies are using data based methods such as deep neural networks, these methods on machine learning need big data of labeled ground truth as well as a large amount of resources for training. On the other hand, the widely used traditional methods are based on the gradient of the grayscale or color of images with using different sorts of mathematical tools to accomplish the mission. Instead of treating the outline of an object in an image as a kind of gradient of grayscale or color, our scheme deals with the edge detection as a character of an energy diffusing in the space of media such as charge-coupled device. By using the characteristic function of diffusion, the information of the energy will be extracted. The scheme preserves the structural information of images very well. Because it comes from the inhere law of images' physical property, it has a unified mathematical framework for images' edge detection under different conditions, for example, multiscales, diferent light conditions, and so on. Moreover, it has low computational complexity.
Online Meta Adaptation for Fast Video Object Segmentation

Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed.
W-MetaPath2Vec: The topic-driven meta-path-based model for large-scaled content-based heterogeneous information network representation learning

Recently, heterogeneous network representation learning has attracted a lot of attentions due to its potential applications. Our works in this paper are concentrated on how to leverage the output of network representation learning by combining with the topic similarity between nodes in content-based heterogeneous information network (CHIN). These unique challenges come from the shortage of topic similarity evaluation between text-based nodes which limit the accuracy of the similarity search as well other network mining tasks. Moreover, the massive sizes of current real-world network also raises challenges for traditional standalone-based heterogeneous network analysis models. Different from previous network representation learning models, such as: Node2Vec or Metapath2Vec, our proposed W-MethPath2Vec model uses the topic-driven meta-path-based random walk mechanism for generating heterogeneous neighborhood of nodes as the learning features. Then, these learning nodes' features are used to train the learning model which is used for solving various heterogeneous network mining tasks such as: node similarity search, clustering, classification, link prediction, etc. The W-MethPath2Vec model enables the simultaneous modeling of structural and topic correlations between nodes in heterogeneous networks. Moreover, the W-MethPath2Vec model is implemented in the Apache Spark-based distributed framework which enables the capability of handling large-scaled networks. We tested our W-MethPath2Vec model with the previous state-of-the-art approaches in the real-world datasets to demonstrate the effectiveness of our proposed model.