Reviewerly/exHarmony huggingface dataset:
A Pruning Method Based on the Dissimilarity of Angle Among Channels and Filters

Convolutional Neural Network (CNN) is more and more widely used in various fileds, and its computation and memory-demand are also increasing significantly. In order to make it applicable to limited conditions such as embedded application, network compression comes out. Among them, researchers pay more attention to network pruning. In this paper, we encode the convolution network to obtain the similarity of different encoding nodes, and evaluate the connectivity-power among convolutional kernels on the basis of the similarity. Then impose different level of penalty according to different connectivity-power. Meanwhile, we propose Channel Pruning base on the Dissimilarity of Angle (DACP). Firstly, we train a sparse model by GL penalty, and impose an angle dissimilarity (AD) constraint on the channels and filters of convolutional network to obtain a more sparse structure. Eventually, the effectiveness of our method is demonstrated in the section of experiment. On CIFAR-10, we reduce 66.86% FLOPs on VGG-16 with 93.31% accuracy after pruning, where FLOPs represents the number of floating-point operations per second of the model. Moreover, on ResNet-32, we reduce FLOPs by 58.46%, which makes the accuracy after pruning reach 91.76%.
Information-Interaction Feature Pyramid Networks for Object Detection

Information interaction between multi-scale features is crucial for recognition systems detecting objects at different scales. In this paper, an Information-Interaction Feature Pyramid Network (IFPN) is proposed to enhance the power of the entire feature representations in a simple but efficient way. Specifically, to strengthen the longitudinal information interaction between multi-scale features, we establish a Bidirectional Information Pyramid Network, which significantly enhances all level features with reasonable localization and classification capabilities. Furthermore, Residual Information Branches are constructed to optimize the lateral information flow between the input and output neurons of the same middle pyramid levels. Taking Feature Pyramid Network (FPN) as the benchmark, by replacing Path Aggregation Network (PANet) with IFPN, our method achieves 3.5x and 1.6x Average Precision (AP) improvement in Faster R-CNN and YOLOX-Nano, respectively. With higher accuracy, IFPN uses 15% fewer GFLOPs than the Balanced Feature Pyramid (BFP) in YOLOX-Nano, achieving better speed and accuracy trade-offs. Furthermore, when IFPN replaces FPN, our method improves Mask R-CNN by 1.1% AP and RetinaNet by 1.0% AP, respectively, when using ResNet-50 as the backbone.
Device Behavior Identification in Encrypted Home Security Camera Traffic

Home security cameras have become one of the most popular IoT devices due to rigid demand and low cost. However, these devices have become a disaster area where security issues such as cyberattacks and privacy breaches often occur. Researchers and intruders often employ traffic behavior analyzing methods to mine vulnerabilities. Nevertheless, the content transmitted by the HSC device contains a lot of dynamic interference video traffic, so it is hard to mine the behavior information of the HSC device from it. In contrast, the HSC device's non-TLS one-way response packets carry more efficient behavior information. Therefore, we propose an approach to identify device behavior based on the features of one-way response packets in non-TLS traffic. Based on the functional characteristics of the HSC device, we have a more fine-grained type division of behaviors, including eight behaviors and five states. In addition, we propose an automatic labeling approach based on countercurrent and operation logs for the problem of tedious and inaccurate manual labeling. Based on the features of three attributes, we compared the recognition effects of nine classifiers on two datasets, the real-world dataset and the IMC 2019 payload public dataset. Finally, the CNN-based classifier can achieve the most desirable identification effect with an accuracy rate of 97.47%, a recall rate of 97.42%, and an F1 score of 97.4%. The results show that the proposed approach can accurately identify the behavior and state of HSC at a fine-grained level. Moreover, this work has a significant reference value for device anomalous behavior detection and threat awareness.
Knowledge Graph Completion by Multi-Channel Translating Embeddings

Knowledge graph completion (KGC) aims to perform link prediction to fill lost relations between entities by knowledge graph embedding (KGE). Translating embedding, as an efficient embedding method in KGE, is widely applied in numerous recent KGC models. However, these translating models may lack the ability to express various relation patterns and mapping properties for knowledge graphs (KGs). In this paper, a simple and well-performed translating model named TransC is proposed to express different relations. A multi-channel mechanism is defined firstly to constrain translating embeddings. Then a relation-aware transfer function is designed to break the expressive restriction and map triplets involving the same relation into a corresponding plane. We also mathematically prove that TransC is capable of expressing four popular relation patterns and all mapping properties. Finally, experimental results illustrate that TransC can efficiently represent the different relation patterns and properties and achieve better performance than state-of-the-art translating models.
Automated Drone Classification for Internet of Drones Based on a Hybrid Transformer Model

The growing number of drone serving in Smart Cities make it increasingly necessary to develop new technologies to assist traffic management in low-altitude airspace. The Internet of Drones (IoD) is a layered network providing efficient and safe airways for various drone inter-operable services. Automated Drone Classification (ADC) is a prerequisite in the IoD system that not only monitors flights and avoids collisions between drones but also detects malicious drone flights. In this work, we propose a Dual-stream Convolutional Transformer (DCT) model to carry out ADC tasks based on drone remote control signals. The DCT combines the self-attention mechanism and convolution to capture long-range dependencies of different frequencies/time steps and local invariant features. We evaluated the proposed method on two public datasets and compared the results with the baseline studies. The DCT model outperforms other baselines with the improvement of 3 % accuracy in multiclass classification and reaches 94.49 % and 98.73 % on two different datasets at 30 dB signal-to-noise ratio. Furthermore, the experiments also show the great potential of DCT model on out-of-distribution by the improvement of 7 % accuracy. The above results demonstrate that the proposed method is a promising solution for supporting the ADC task in reliable, fast, and inexpensive ways.
Private and Shared Feature Extractors Based on Hierarchical Neighbor Encoder for Adaptive Few-Shot Knowledge Graph Completion

While Knowledge Graphs (KGs) have been applied in many AI tasks, KGs are known for being incomplete with many missing facts. Previous works rely on a large number of training data for KG completion. However, there are often few entity pairs available for most relations in KGs. In this paper, we propose a Few-shot Knowledge Graph Completion (FKGC) model, named Private and Shared feature extractors based on Hierarchical neighbor encoder for Adaptive few-shot knowledge graph completion (PSHA). In the PSHA model, we first exploit the hierarchical attention mechanism to extract the inherent and valuable hidden information of the neighborhood surrounding the entity. Following that, we adopt a private feature extractor to extract the private features of relation information of the entity pairs, and then a shared feature extractor is used to extract the shared features of the entity pairs of the support set. In addition, an adaptive aggregator aggregates entity pairs of the support set about the query. We conduct experiments on the 2-shot and 5-shot of the NELL-One and CoDEx-S-One dataset. The experimental results show that the PSHA outperforms the existing FKGC models in both scenarios.
Skill-Oriented Hierarchical Structure for Deep Knowledge Tracing

Knowledge tracing (KT) which aims to trace stu-dents' knowledge state is an effective technique in intelligent tutoring systems. Although most KT models have exploited the question side information, plentiful hierarchical information between skills hasn't been well extracted for making more accurate predictions. In this paper, a novel model called Skill-oriented Hierarchical structure for Deep Knowledge Tracing (SHDKT) is proposed to discover the relations between questions, which are implicit in the hierarchical skill structure. SHDKT comprises three modules. First, The skill concurrency graph (SCG) is constructed by incorporating students' response infor-mation into the question-skill bipartite graph, which contains both sequence and co-occurrence relations between skills. Second, a hierarchical skill representation module (HSRM) is proposed to exploit the hierarchical information of skills based on the SCG. Finally, a question representation module (QRM) is presented by learning explicit and implicit interactions of question side infor-mation. Hence we can predict the student response accurately through question representation. Extensive experiments on the KT datasets validate the effectiveness of our model.
In-Batch Negatives' Enhanced Self-Supervised Learning

In contrastive self-supervised learning, such as Sim-CLR and MOCO, negative samples play an important role on the model's robustness. Instead of increasing the negatives' number, we promote the in-batch negatives' quality by adjusting their embedding. In-Batch Negatives' Enhanced Self-Supervised Learning (IBN-SSL) focus on negatives' quality by an importance-weighted algorithm and an online boundary. The importance-weighted negatives' quality algorithm decreases the negatives' relative locations from positive in projection space, which pushes the model to learn to distinguish positives from harder negatives. And the online boundary compresses negatives' vector space and keep negatives farther from positive. A better representation model can be leaned by IBN-SSL, and experiments show that it outperforms both RoBERTa and SimCLR in text classification task and similar sentence pair task.
DRL-TA: A Type-aware Task Scheduling and Load Balancing Method based on Deep Reinforcement Learning in Heterogeneous Computing Environment

Task scheduling and load balancing in heterogeneous computing environments has been a challenge for long, especially when dealing with multiple types of task input batches. In this scenario, existing methods cannot take into account both the high efficiency of task processing and the full utilization of cluster resources. However, the rise of artificial intelligence methods provides a new way to solve this problem. In this paper, we design a type-aware task scheduling method based on deep reinforcement learning to tackle multiple types of tasks in heterogeneous computing environment. First, we adopt prioritized dueling double deep q-learning network to make action decisions for each batch of input tasks. Then we build a task type prediction neural network to predict the task type of the input task, and then use the Monte Carlo algorithm based on reward value to realize the load balancing of the scheduled cluster. To verify the effectiveness of our proposed method, we use a widely used dataset Alibaba cluster trace dataset for our experiments. Experimental results show that our proposed algorithm can significantly shorten the average makespan of task batches and achieve better load balancing effect compared with other existing solutions.
A Survey of Sentiment Analysis Based on Pretrained Language Models

Pretrained Language Models (PLMs) can be applied to downstream tasks with only fine-tuning, without learning the model from scratch. In particular, PLMs have been applied to Sentiment Analysis (SA), which detects, analyses, and extracts the polarity of the sentiment expressed in texts. To help researchers quickly grasp the state-of-art PLM-based SA, we survey PLM-based methods for mono-lingual and cross-lingual SA in this paper. Specifically, we brief these methods, compare their per-formance and point out the challenges for future research.
A Novel Efficient Unclick Behavior Modeling Framework for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is a crucial task in many applications, such as recommendation systems. It predicts a personalized click probability for each user-item pair. Recently, researchers have found that the performance of CTR prediction model can be significantly improved by considering user behavior sequence. However, most existing CTR prediction models take only implicit positive feedback (e.g., click) from user-item interactions as input. They do not consider the implicit negative feedback (e.g., unclick), resulting in incomprehensive user representations and sub-optimal model performance since they ignore the complete user behavior data. Furthermore, un click behavior usually contains heavy noise and greatly in-terferes with the accuracy of user interest modeling. To address these issues, we propose an efficient Unclick Behavior Modeling framework (UBM) to model the implicit negative feedback based on the click behavior modeling to learn users' complete and unbiased preferences for CTR prediction. Specifically, UBM utilizes a novel implicit negative feedback modeling to enrich user interest representations. Then, the Interest Denoising Layer and Explicit Interest Contrastive Layer are proposed, which use the representation of explicit positive feedback to distill accurate user interests from implicit negative feedback. Based on that, an Interest Extraction Layer is designed to effectively model and fuse with different types of user interest representations. Extensive experiments on real-world and large-scale datasets demonstrate that UBM significantly outperforms the state-of-the-art models, bringing 8.05% AUC lift.
Multi-Level Feature Aggregation Network for Full-Reference Image Quality Assessment

Image quality assessment (IQA) is an important task of computer vision. Most full-reference (FR) IQA methods do not reach desirable prediction performance. To address this issue, we propose a novel multi-level feature aggregation network (MLFAN) for FR-IQA. An important contribution is an effective multi-level feature aggregation network. This network utilizes a siamese network with vision transformer for multi-level feature extraction. It compares images at the multi-level perceptual feature differences by considering the relationship among color, texture and shape information, focuses more on the salient regions by an attention aggregator and scores images by a two-branch prediction head. Another important contribution is a novel loss function. This loss function jointly utilizes Mean Square Error, KL divergence and rank order of quality scores to provide stable training. It makes the proposed MLFAN-IQA method effectively learn perceptual quality of images. Experiments are done to test IQA performance of the proposed MLFAN-IQA method. Comparisons show that the proposed MLFAN-IQA method outperforms some state-of-the-art FR-IQA methods on the datatsets of conventional distorted images. Moreover, the proposed MLFAN-IQA method also reaches comparable performance on the dataset of GAN-based synthetic distorted images.
Argumentation-Based Adversarial Regression with Multiple Learners

Despite the extensive benefits of machine learning techniques in practice, several studies demonstrated that many approaches are vulnerable to attacks. These attacks generate adversarial data to manipulate learning models that result ambiguous decisions. In this paper, we propose a hybrid-reasoning framework that combines data-driven and non-monotonic reasoning, specifically formal argumentation and adversarial regression with multiple learners, to deal with ambiguous predictions of predictive models. The introduced hybrid-reasoning framework ensures three significant benefits. It (i) provides an argumentation-based aggregation function for combining multiple learners, (ii) reduces the effort to resolve conflicts in predictions, and (iii) cost-effective and robust training in adversarial regression solutions. To illustrate the introduced framework, we consider a benchmark of resource traces obtained from Yahoo's service cluster for anomaly detection under adversarial settings. The experimental analysis shows 3% more accuracy in prediction under argumentation-based adversarial settings.
Deep Learning Methods for Animal Counting in Camera Trap Images

Camera traps are widely used to monitor the biodiversity and population density of animal species. Camera trap images are usually taken in bursts, and the animal counting problem for a sequence of camera trap images is also an important part of evaluating animal population density. In this paper, two new animal counting methods based on Microsoft MegaDetector V 4 have been proposed. FilterDetector uses different filters with bounding box ensemble algorithms to achieve more accurate bounding box detection. DLEDetector is an ensemble method that uses two base deep learning models to correct and enhance the detection result of MegaDetector. Our experimental results in iWildCam 2022 competition test dataset show that both methods outperformed the best method in iWildCam 2021 and the baseline method based on MegaDetector V 4 in iWildCam 2022 competition by 9.09% and 6.44%, respectively, and ranked first and third in the competition.
An Edge Computing-Based Paltform of Railway Signalling System On-site Digital Smart Test

The railway and urban rail signalling system is a safety-related system with extremely high safety integrity level requirements. The completeness and correctness of the on-site test after installation are indispensable for the guarantee of the safety function of the signalling system. At current the signalling system site test and the post installation check all been done by the site test engineers manually, which may not well recorded digital evidence, and the efficiency is low. Somehow even if the test engineer is not competent and they are not performing the test rightly or even they are not performing the site test but only directly marked in the test record. The paper shows an edge computing-based platform which can perform the railway signalling system on-site test which can perform the on-site test and record the site results and logs automatically. All the on-site test and test records can restore and show for the safety assessment and quality assessment to make sure the safety integrity of the railway signalling system is fulfilled the requirement.
An Improved Multi-object Tracking Algorithm for Autonomous Driving Based on DeepSORT

Multi-object tracking (MOT) is one of the most important tasks of the vision perception module of autonomous driving, which provides crucial inputs for the downstream task modules such as motion prediction, planning and control. However, how to balance the efficiency and accuracy of MOT in autonomous driving scenario, has always been a great challenge. To address this issue, we try to improve the classical DeepSORT model which follows the Track-By-Detection paradigm from three perspectives of object detection, feature extraction and data association, so as to further improve its tracking accuracy while retaining its online, light-weight and high-efficiency advantages. First, a single-stage detection model Yolo-v5 is adopted to replace the two-stage model Faster R-CNN used in DeepSORT, which leads to significant improvement of detection efficiency and accuracy. Second, specified light-weight ShuffleNet-v2 in stead of time-consuming DenseNet is used to extract appearance feature from detected object bounding boxes to further enhance the efficiency of MOT. Finally, an efficient and accurate data association algorithm integrating cascade matching and IoU matching is proposed. Based on the cost matrix jointly constructed by appearance features, motion and shape, effective tracking of multiple objects is realized. A large number of experiments have been conducted to compare our improved MOT model proposed and the DeepSORT model in terms of many evaluation metrics, such as IDs, IDF1, MOTA, MOTP, etc. Moreover, the sensitivity of some hyper-parameters that have important influence on MOT efficiency is also analyzed. Experimental results show that the efficiency and accuracy of MOT can be effectively balanced by keeping the lightweight and efficient characteristics of the original DeepSORT model and further improving the performance of its three key components, i.e. object detection, feature extraction and data association.
A Generative Adversarial Active Learning Method for Effective Outlier Detection

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.
Social Media Event Summarization using Neural Networks

Micro blogging sites have become extremely popular, with a huge number of tweets being posted daily on a broad assortment of subjects. Recent studies have exposed that a significant part of these tweets are regarding “events”, and the discovery of new events in the tweet-stream has pulled in a lot of attention. In the Natural Language Processing (NLP) field, automatic text summarization is viewed as an extremely tricky problem. The content substance on the internet, specifically, is rising at an outstanding level. The capacity to decode through such immense volumes of data, to facilitate mine the require knowledge, is a foremost undertaking and needs an automatic system to serve with the present warehouse of data. A decent text summarization framework should comprehend the entire content, reorganize information, and create coherent, useful and amazingly tiny summaries to pass on the significant information of the innovative content. In this article, an inventive content summarization framework has been built, which combines RNN with LSTM and encoder-decoder structure. Our framework is assessed on the LCSTS dataset, which is an excellent corpus of Chinese short content summarization dataset created from “SinaWeibo”. Empirical outcomes exhibit the adequacy of the proposed framework, which outperforms all the existing approaches. Particularly, the proposed framework enhances the semantic consistency by 5% in terms of human assessment.