Reviewerly/exHarmony huggingface dataset:
Multi-task Learning Model for Detecting Internet Slang Words with Two-Layer Annotation

The popularity of Internet slang words has become a trend. Some of these new words can be seen as out-of-vocabulary words, but some previously registered words have new uses. In the case of social media texts, Internet slang words with important meanings cannot be ignored by replacing them with unknown words, as is the usual practice. Definitions for Internet slang words can be divided into two main types: “new semantic words” and “new blended words,” which can then be subcategorized based on the richness of the word formations. By combining these definitions with the strong relevance of word formations, we propose a hierarchical, shared, multi-task learning method based on joint character and word embeddings to label sequentially the main types and subcategories of Internet slang words. In our experiments, our method demonstrated the second-best performance behind multi-task BERT in detecting the main types of Internet slang words, and performed best in detecting subcategories among the shared-language models tested. In addition, our model achieved an average 32.5% F1-score improvement in detecting main types and subcategories compared with single-task methods. We conclude that the use of two-layer annotation improves the performance of the models, and it facilitates better observations and detailed analyses of differences in detection models.
Biomedical Named Entity Recognition Based on MCBERT

In the biomedical field, the named entity recognition method using static word vectors to represent semantics has low accuracy and cannot completely represent the upper and lower semantic relationship of medical texts. Therefore, a model for biomedical named entity recognition is proposed, which combines the pre training language model MCBERT with CNN, BILSTM and CRF. First, MCBERT is used for semantic extraction to generate dynamic word vectors, then the word vectors are sent to CNN to extract local sequence features, and then LSTM is allowed to extract long-distance sequence features based on local sequence features. Finally, CRF is used to learn the pre and post dependencies of sentences, so as to add some constraints to ensure the correctness of the final prediction results. The average F1 value of the model on the ccks2019 evaluation task “medical entity recognition and attribute extraction data set for Chinese electronic medical records” has reached 82.8%. The experimental results show that the proposed model effectively improves the accuracy of biomedical named entity recognition.
No-Reference Video Quality Assessment by Deep Feature Maps Relations

Blind video quality assessment (BVQA) approaches are meant to analyze the quality of a distorted video perceived by viewers without knowing anything about the pristine video. Numerous deep network-based techniques have been introduced so far. These methods often pool the features obtained for each frame in different ways to generate a video representation and evaluate the quality. A novel approach is presented in this study for obtaining frame-level features to assess quality. In order to accomplish this, we explored the deep feature maps relations as useful information for video quality assessment. The Gram Matrix generated in each layer is analyzed and explored as higher-order quality features using pre-trained networks. The deep feature relations can be considered similar to the covariance matrix, which indicates the correlations between different feature maps. In fact, these correlations reflect the structural information of each frame. After features extraction and pooling, Using an SVR, a quality score is provided. The efficacy of the suggested features is demonstrated by empirical findings and comparable performance to the state-of-the-art techniques is offered.
iRED: Improving the DASH QoS by dropping packets in programmable data planes

Video services account for the largest share of all Internet traffic, demanding a network capable of supporting the requirements of delay-sensitive traffic. Fluctuations in network load can cause high delays in the queues of network routers, which tend to degrade the Quality of Service (QoS) for adaptive video streaming, such as Dynamic Adaptive Streaming over HTTP (DASH). This work is positioned in the scope of active management queues (AQM) to improve the QoS of a DASH service by means of dropping packets. One traditional AQM that adopts a packet drop policy is Random Early Detection (RED), developed to drain the flow in times of congestion and thus reduce queueing delay. We revisited and implemented a P4-based implementation of RED, named iRED (ingress RED), an algorithm capable of dropping packets at the ingress pipeline, an innovation compared to other AQM strategies based on dropping at the egress. iRED was evaluated in two scenarios. First, we compare iRED against state-of-art AQM algorithms employing egress packet dropping in terms of Round-Trip Time (RTT), throughput and their impact on resources usage. Our findings indicate that iRED outperforms existing P4-based approaches by approximately up to 2.5x in RTT and 0.75x in throughput for the given buffer sizes. Next, we compare iRED versus Tail Drop (TD) approach in an emulated programmable Content Delivery Network (CDN) employing DASH. Experiments indicate that the iRED improve the QoS by approximately 0.85x in terms of cached video available in the client’s buffer and 0.9x in Frames Per Second (FPS) played.
Transformer-based Sparse Encoder and Answer Decoder for Visual Question Answering

Visual Question Answering (VQA) is all about understanding images and questions. Existing Transformer-based methods achieve excellent performance by associating questions with image region objects and directly using a special classification token for answer prediction. However, answering a question only needs to focus on some specific keywords and image regions, while excessively computing the attention of questions and image region objects will introduce unnecessary noise. Meanwhile, the information from these two modalities cannot be fully utilized when directly using the classification token to predict the answer. To this end, we propose a Transformer-based Sparse Encoder and Answer Decoder (SEAD) model for visual question answering, in which a two-stream sparse Transformer module based on co-attention is built to enhance the most relevant visual features and textual descriptions inter-modality. Furthermore, a single-step answer decoder is proposed to fully exploit the information of both modalities in the answer prediction stage, and a strategy is designed that fully utilizes the ground truth to correct the visual relevance scores in the decoder to focus on salient objects in the image. Our model performs magnificently, as shown by experiment results on the VQA v2.0 benchmark dataset.
Demo: Blockchain-based Inter-Provider Agreements for 6G Networks

With their transparency and smart contract features, blockchain and Distributed Ledger Technologies (DLT) can pave the way toward a decentralized marketplace for multi-administrative domains. An open and decentralized marketplace can aid 6G use cases such as inter-provider agreements, where multi-administrative domains can lease or buy resources to meet the needs of consumers. These agreements between multi-administrative domains can be performed using blockchain, increasing trust and transparency for Service Level Agreement (SLA) management, penalty, or billing. In this demonstration, we present the use of the Ethereum smart contract for a use case where the consumer can buy resources from a provider. However, Ethereum suffers from high transaction costs and latency; therefore, we aim to leverage IOTA Tangle to reduce the cost of transactions on the Ethereum blockchain and minimize the latency. In particular, we show the process of domains registered on an Ethereum blockchain network, consumers selecting a service from the list of available services to buy, and finally, transferring the agreed amount after the services are delivered.
Fast and Interpretable Deep Learning Pipeline for Breast Cancer Recognition

Breast cancer is one of the main causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. In this paper, we propose a fast and interpretable deep learning-based pipeline for automatic detection of the metastatic tissues in breast histopathological images. Firstly, the proposed pipeline uses multiple preprocessing and data augmentation techniques to reduce over-fitting. Then, the proposed pipeline employs one - cycle policy technique in the pre-trained convolutional neural networks model in shallow and deep fine-tuning phases to find the optimal values. Finally, gradient-weighted class activation mapping (Grad-CAM) technique is utilized to produce a coarse localization map of the important regions in the image. Experiments on the PatchCamelyon dataset demonstrate the superior classification performance of the proposed method over the state-of-the-art.
Real-Time Vehicle Detection and Classification in UAV imagery Using Improved YOLOv5

Recently, Unmanned Aerial Vehicles (UAV) have become useful for various applications. Real-time vehicle detection and classification is an essential task in some real-world applications, such as UAV-based traffic surveillance or disaster management. However, vehicles appear too small in aerial UAV imagery, reducing detection accuracy. While modern UAVs are capable of recording high-resolution videos with higher spatial information, increasing input size reduces inference speed. So balancing the accuracy and inference time is an existing challenge that needs to be addressed. This paper addresses this challenge by proposing an improved YOLOv5 single-stage object detector model, making it more suitable for detecting small objects in high-resolution images. At the same time, we modified the network width and depth to make it suitable for real-time applications that require high inference speed. Experiments conducted on VisDrone and CARPK datasets confirm that compared to baseline YOLOv5 models, the proposed model has 3.7% higher mAP50 and 6.1 FPS higher inference speed on VisDrone dataset. At the same time, its size is 44.6 MB less than YOLOv5L. These results confirm the efficacy of the proposed modifications applied on YOLOv5 to balance accuracy and inference time.
Coverage Technology of Autonomous Mobile Mapping Robots

The coverage technique is one of the essential applications of autonomous mobile mapping robots. There are various approaches for coverage depending on the model (model/non-model), robot systems (single/multi), and its purpose (patrol/cleaning). Coverage components include viewpoint generation and path planning approaches, which are described as CPP research work. Particularly, in surveillance systems, coverage techniques, such as spanning tree, cyclic coverage, and area-based coverage, are reviewed specifically, which can be expanded for multi-robot systems. In addition, required coverage techniques according to conditions for intelligent surveillance systems are summarized. Lastly, several issues on coverage, specifically cyclic coverage, are described and considered.
SeqMask: Behavior Extraction Over Cyber Threat Intelligence Via Multi-Instance Learning

Abstract Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%.
CRBFT: An Optimized Blockchain Algorithm for Edge-Based IoT System

In this article, a credit identity ring optimization blockchain algorithm (CRBFT) based on practical Byzantine fault tolerance (PBFT) is proposed to address the security problem for the network of Internet of Things (IoT). The contributions of this work are given as follows. First, combined with the edge computing architecture, CRBFT introduces the improved credit grading protocol and the improved ring signature scheme into the blockchain network, rewards or punishes the nodes through their behavior, changes the way of selecting the primary node, and ensures that the identity of the primary node will not be disclosed, so as to prevent the primary node from adaptive attack by Byzantine nodes, improve the reliability of the nodes participating in the consensus, and enhance the security of the system. Second, in order to reduce the communication traffic among nodes, CRBFT optimizes the consensus protocol, which not only reduces the time complexity of the traffic in the consensus stage from O ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}^{{2}}$ </tex-math></inline-formula> ) to O ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> ) but also improves the consensus efficiency of the system while ensuring the system security. Finally, CRBFT aims to measure the performance of CRBFT. This article is simulated and tested in three aspects: throughput (transactions per second), consistency delay, and communication overhead. Performance evaluation shows that CRBFT achieves lower communication overhead than PBFT. When the number of nodes is the same, CRBFT can obtain higher TPS and lower consistency delay. The results show that compared with PBFT, CRBFT can provide very competitive results.
Unsupervised video anomaly detection based on multi-timescale trajectory prediction

Video anomaly detection refers to the identification of events that differ from normal behavior. Most of the commonly used methods currently use the reconstruction method based on appearance features. However, appearance features are unstructured signals that are sensitive to noise, and the redundant information contained will also increase the burden of distinguishing signals from noise during training. Reconstruction methods try to minimize the reconstruction error of the training data but cannot guarantee that the reconstruction error of anomalous events is large. From this, we propose an unsupervised video anomaly detection algorithm based on multi-timescale trajectory prediction. We use the object tracking network to detect and track pedestrians in the scene and send them to the multi-timescale trajectory prediction and velocity calculation modules for training. Due to the different motion durations, we add a multi-timescale mechanism to predict pedestrian trajectories and introduce step signals in digital signal processing for trajectory subsequence segmentation. During the testing of abnormal videos, the irregular motion behavior of pedestrians cannot be predicted by the normal model and will result in higher trajectory outliers. Similarly, the velocity calculation module constrains and calculates pedestrian velocities at different camera views, and events that differ from normal velocity can be detected under the dual constraints of space and motion (time). Compared with state-of-the-art methods and other anomalous event detection methods, the proposed model has certain advantages in frame-level AUC.
Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation

Over the past few years, personalized recommendation systems aim to address the problem of information overload to help users achieve useful information and make quick decisions. Recently, due to the benefits of effective representation learning and no labeled data requirements, autoencoder-based models have commonly been used in recommendation systems. Nonetheless, auxiliary information that can effectively enlarge the feature space is always scarce. Moreover, most existing methods ignore the hidden relations between extended features, which significantly affects the recommendation accuracy. To handle these problems, we propose a Multi-Feature extension method via a Semi-AutoEncoder for personalized recommendation (MFSAE). First, we extract auxiliary information from DBpedia as feature extensions of items. Second, we leverage the LSI model to learn hidden relations on top of item features and embed them into low-dimensional feature vectors. Finally, the resulting feature vectors, combined with the original rating matrix and side information, are fed into a semi-autoencoder for recommendation prediction. We ran comprehensive experiments on the MovieLens datasets. The results demonstrate the effectiveness of MFSAE compared to state-of-the-art methods.
Test scenario generation for feature-based context-oriented software systems

Feature-based context-oriented programming reconciles ideas from context-oriented programming, feature modelling and dynamic software product lines. It offers a programming language, architecture, tools and methodology to develop software systems consisting of contexts and features that can become active at run-time to offer the most appropriate behaviour depending on the actual context of use. Due to their high run-time adaptivity, dedicated tool support to test such systems is needed. Building upon a pairwise combinatorial interaction testing approach from the domain of software product lines, we implement an algorithm to generate automatically a small set of relevant test scenarios, ordered to minimise the number of context activations between tests. We also explore how the generated scenarios can be enhanced incrementally when the software evolves, and how useful the proposed testing approach is in practice.
RSU Cluster Deployment and Collaboration Storage of IoV Based Blockchain

The Internet of Vehicles is increasingly becoming an indispensable platform to make interactions among vehicles, humans, and roadside infrastructures, and it is continuing to evolve with improvements on its application scenarios and service robustness. Academia and industry have also been concerned with the security issue of Internet of Vehicles (IoV). The Blockchain technology provides a new solution to improve security of the IoV, and it has drawn increased attention. Blockchain technology deals with the network model, identity authentication, trust management, and access control. However, there are insufficient studies on strategy of nodes deployment in the existing BIoV (Blockchain-based IoV). Based on the principle of partition, this paper studies the Blockchain-based IoV model in which the vehicle network system consists of vehicles and Road Side Unit (RSU) nodes. The Blockchain network is formed by the RSU nodes. By optimizing the LEACH algorithm, we partition RSUs and select cluster heads in BIoV, which has good scalability while maintaining a reasonable scale of Blockchain. Simulation results indicated that the improved-LEACH algorithm (I-LEACH) is more effective than the LEACH, DEEC, and the improved-DEEC algorithms (I-DEEC) with respect to the network life cycle and data transmission. Additionally, in order to reduce dependence of the storage resources from BIoV, we designed an intra-cluster collaborative storage scheme by adopting three algorithms for comparison—the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and the model quantum genetic algorithm (QGA). It revealed that the intra-cluster collaborative storage scheme is effective to ease the bottleneck of storage space demanded in BIoV, and verified by the simulation experiments.
Heuristic position allocation methods for forming multiple UAV formations

It is a common action that the unmanned aerial vehicles (UAVs) change the formation during the flight to realize different goals and address an emergency. In this process, the UAVs should determine their positions in the new formation to avoid the collision and reduce the flight time. In this paper, the position allocation problem for multiple UAV formations is studied, and the problem is solved considering the different requirements on offline and online cases. In the offline case, the trajectories of UAVs when forming new formations are calculated by the consensus-based trajectory planning (CBTP) method, in which the transient process is introduced to avoid the collision among UAVs. Then a hybrid genetic and simulated annealing (HGSA) algorithm, which utilizes the framework of genetic algorithm (GA) and the operators in simulated annealing (SA) algorithm, is proposed to obtain the optimal position allocation scheme. The CBTP method is treated as a part of the HGSA algorithm to calculate the optimization index for a specific position allocation scheme. In the online case, a two-step minimal cost increase strategy (MCIS)-based method is developed and the UAVs in each new formation and the allocated position of UAV in the new formation are determined successively. Simulation results demonstrate that the CBTP method can generate the safe trajectories for the UAVs when forming new formations, and the optimal position allocation scheme can be obtained by the HGSA algorithm. The HGSA algorithm performs better than other similar algorithms especially in the complicated situation. The MCIS-based method can output the partially optimized position allocation scheme at short notice, but the real trajectories of UAVs are not considered in the online case to reduce the computation time.
Combining BERT with TCN-BiGRU for enhancing Arabic aspect category detection

Aspect-based sentiment analysis (ABSA) is a challenging task of sentiment analysis that aims at extracting the discussed aspects and identifying the sentiment corresponding to each aspect. We can distinguish three main ABSA tasks: aspect term extraction, aspect category detection (ACD), and aspect sentiment classification. Most Arabic ABSA research has relied on rule-based or machine learning-based methods, with little attention to deep learning techniques. Moreover, most existing Arabic deep learning models are initialized using context-free word embedding models, which cannot handle polysemy. Therefore, this paper aims at overcoming the limitations mentioned above by exploiting the contextualized embeddings from pre-trained language models, specifically the BERT model. Besides, we combine BERT with a temporal convolutional network and a bidirectional gated recurrent unit network in order to enhance the extracted semantic and contextual features. The evaluation results show that the proposed method has outperformed the baseline and other models by achieving an F1-score of 84.58% for the Arabic ACD task. Furthermore, a set of methods are examined to handle the class imbalance in the used dataset. Data augmentation based on back-translation has shown its effectiveness through enhancing the first results by an overall improvement of more than 3% in terms of F1-score.