Reviewerly/exHarmony huggingface dataset:
A Multi-granularity Knowledge-enhanced Sequential Recommendation

Knowledge graph has been widely utilized to provide knowledge for the sequential recommendation. But their divergence produces new challenges to appropriately capture and represent items’ knowledge for recommendation. Additionally, the complex temporal features also increase the difficulty to model the user-item interaction sequences. The key issues remaining to be settled are how to mine adequate information from KG to capture items’ knowledge, and how to model the evolving user interests accurately. Facing the above problems, we propose a multi-task learning framework MLKT to learn the embedding of items and users’ dynamic preferences in both a knowledge-aware and time-aware way. Different from the previous works, we not only consider the timestamps of the interactions in the sequence, but also capture its evolution from multiple perspectives by designing a multi-scale temporal attention mechanism. Additionally, we represent each item’s features in different granularity by learning from neighbors and the category information in KG. Furthermore, to make the KG embeddings to be more suitable for the recommendation, a cross-component is designed to efficiently achieve the item information communication between KG and user-item interactions. Extensive experiments demonstrate the significant improvements of MLKT than the baseline methods on two recommendation tasks in three datasets.
Enhancing Adversarial Robustness for Deep Metric Learning through Adaptive Adversarial Strategy

Due to the security implications of adversarial vulnerability, it is essential to enhance the adversarial robustness of deep metric learning models. Existing defense approaches adopt Projected Gradient Decent (PGD) with handcrafted fixed attack strategies to generate adversarial triplets. They learn inefficiently from a weak adversary in order to avoid model collapse owing to extremely challenging adversarial triplets, thereby limiting the robustness of the deep metric model. In this paper, we propose a novel Adaptive Adversarial Strategy (AAS) for deep metric learning that can learn automatically to produce attack strategies for adversarial triplet generation of varying difficulties. We use a classical actor-critic network in the AAS framework, in which the actor network produces attack strategies to control adversarial triplet generation and the critic network utilizes adversarial triplets to enhance adversarial robustness. Comprehensive experiment results on two benchmark datasets indicate that our proposed adaptive adversarial strategy for deep metric learning overwhelmingly outperforms the most advanced defenses in terms of robustness as well as performance on benign triplets.
Multimodal Sentiment Analysis Based on Multiple Stacked Attention Mechanisms

Deciphering sentiments or emotions in face-to-face human interactions is an inherent capability of human intelligence, and thus a natural goal of artificial intelligence. The proliferation of multimedia data in video sites gives rise to multimodal sentiment analysis in various applications and research fields such as movie and product review, opinion polling, and affective computing. In order to improve the performance of multimodal sentiment analysis task, this paper proposes a novel neural network with multiple stacked attention mechanism (MSAM) on multimodal data containing texts, video, and audio at an utterance level. We conduct experiments using two benchmark datasets, namely CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus, and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Compared with a comprehensive set of state-of-the-art baselines, the evaluation results demonstrate the effectiveness of our proposed MSAM network.
Cross Domain on Snippets: BiLSTM-TextCNN based Vulnerability Detection with Domain Adaptation

Due to the ubiquity of computer software, software vulnerability detection(SVD) problem is essential to protect cyber system from attacks. Recently, deep learning-based vulnerability detection has achieved outstanding performance, relieving experts from tedious task of manually defining vulnerability features as well. However, its detection capability is compromised when facing with the scarcity of labeled data. One possible solution is to leverage training data with adequate labels from other domains, but the data distributions in different domains differ significantly. On the other hand, function level detection is too coarse-grained and not able to capture inter-procedure vulnerability patterns. In this paper, we propose a systematic Snippet-Oriented Cross-Domain Vulnerability Detection Framework with Domain Adaptation, which is the first time to detect cross-project vulnerabilities at a finer granularity than function. Firstly, we generate Code Snippets from 5 real-world projects and 3 types of CWE in NVD and SARD for cross-project and cross-type detection; Secondly, we propose an novel and effective approach to obtain deep features for domain adaptation; Finally, we employ the domain adaptation algorithm on these deep features to reduce the divergence between different domains and get the final result. Experimental results show that our framework outperforms other state-of-the-art approaches.
Multi-Information Filter Encoding Network for Multi-Label Text Classification

Multi-label text classification (MLTC) is essential in natural language processing. For real applications, MLTC is challenging when class distribution is long-tailed, that is, a few of labels (head labels) have many documents while most labels (tail labels) have a few documents. In this paper, we propose a Multi-Information Filter Encoding Network (MIFEN) to address the issue of long-tailed distribution. Specifically, MIFEN constructs Multi-Information Filter Encoder to filter useless information and optimize the text space and label space. Then, MIFEN enriches the tail label-related features by extracting filtered text-specific label information for each document and highlights significant features of the corresponding text and label information to generate the final document representation. Experiment results have demonstrated that our MIFEN outperforms better than the state-of-the-art methods.
RZSR Randomly Initialized Zero-Shot Method for Blind Super-Resolution

When the unknown degradation is mixed with unknown blurry kernels, how to perform super-resolution operation is an open issue. The mean idea of the existing zero-shot and non-zero-shot methods is to estimate blurry kernel. The effects of these methods depend on the accuracy of the deduced blurry kernel. In this paper, we propose Randomly initialized Zero-Shot Super-Resolution (RZSR) training strategy. RZSR is a zero-shot training method and it allows the network to extract low-resolution image features and generate its counterpart high-resolution images under the interference of degradation algorithms. We further propose two model-agnostic modules which are Adaptive Information Extraction Module (AIEM) and knowledge dictionary. They respectively assist the network to extract features and well fit the data distribution of clear images. RZSR can be applied to any single image super-resolution and video super-resolution models. We prove the generalization ability and superiority of RZSR through a series of experiments.
Robust Preference Learning for Recommender Systems under Purchase Behavior Shifts

Learning user preferences by modeling historical purchase behaviors has significantly succeeded in existing recommender systems. Most use trained models to make predictions for users, and they assume that the training data samples and test data sample sets come from the same distribution. However, in practical applications, the distribution of users’ true preferences may be more complicated, and data drift can easily make the trained model invalid on the test dataset. In this case, to accurately model user preferences based on their historical behavior, two difficulties need to be addressed. First, it is difficult to model various purchase behavior shift situations due to their complexity. Second, inferring users’ true preferences from the complicated shifting cases is challenging. To solve the above problems, we build a robust recommender system to predict possible user purchase shifts and make recommendations for users. First, we propose a simulating strategy to cover possible scenarios when user purchase behavior shifts. Second, we build a novel voting framework to ensure the robustness of predicting results based on learned preferences. Extensive experiments were conducted, and the results demonstrate the outstanding performance of the proposed method on MovieLens-1M and LastFM datasets, providing at most 37.31% and 30.95% relative performance gains, respectively.
SimulE: A novel convolution-based model for knowledge graph embedding

Knowledge graph embedding technique is one of the mainstream methods to handle the link prediction task, which learns embedding representations for each entity and relation to predict missing links in knowledge graphs. In general, previous convolution-based models apply convolution filters on the reshaped input feature maps to extract expressive features. However, existing convolution-based models cannot extract the interaction information of entities and relations among the same and different dimensional entries simultaneously. To overcome this problem, we propose a novel convolution-based model (SimulE), which utilizes two paths simultaneously to capture the rich interaction information of entities and relations. One path uses 1D convolution filters on 2D reshaped input maps, which maintains the translation properties of the triplets and has the ability to extract interaction information of entities and relations among the same dimensional entries. Another path employs 3D convolution filters on the 3D reshaped input maps, which is suitable for capturing the interaction information of entities and relations among the different dimensional entries. Experimental results show that SimulE can effectively model complex relation types and achieve state-of-the-art performance in almost all metrics on three benchmark datasets. In particular, compared with baseline ConvE, SimulE outperforms it in MRR by 2.9%, 9.8% and 2.8% on FB15k-237, YAGO3-10 and DB100K respectively.
Tensions in design and participation processes: An ethnographic approach to the design, building and evaluation of a collective intelligence model

This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.
A Classification System of Career Guidance Questions Based on Natural Language Processing and Supervised Machine Learning Techniques

Questions classification is a crucial task in automatic question answering systems. In this paper, we present a system for classifying questions asked by students about higher studies and career choices based on their abilities and skills. The system uses natural language processing and machine learning techniques. We collected and labeled a dataset, preprocessed the textual content, and used the TF-IDF statistical representation to extract features. We implemented four supervised machine learning algorithms for classification: logistic regression (LR), decision tree (DT), K-nearest neighbors (KNN), and support vector machine (SVM). We also applied a feature selection technique to improve the system. The best accuracy achieved was 85.63% using the logistic regression classifier.
The Avatar Facial Expression Reenactment Method in the Metaverse based on Overall-Local Optical-Flow Estimation and Illumination Difference

To implement a metaverse exhibition interaction system, the instability problem of high-quality avatar facial reenactment must be considered. How to void identity limitations and eliminate artifacts are key challenges for avatar reenactment. It also lacks the support of the application system it is implemented in. We propose a metaverse system architecture oriented to emotional interaction. And we propose a novel method for avatar expression reenactment named Overall-Local Feature Warping Fusion Model based on Optical-Flow field prediction. We solve the identity limitation by overall optical-flow estimation and local optical-flow estimation and eliminate artifacts by illumination consistency. We compare with the mainstream optical flow face reenactment methods and outperform them in identity similarity, structural similarity, and facial action unit recognition ratio. We experimentally compared our method improves by an average improvement of 3.79%. And we also implement our method in the metaverse exhibition system. Although we satisfy most of the interaction scenarios, our method is still insufficient in some side-face cases.
Social Media's Toxic Comments Detection Using Artificial Intelligence Techniques

Cyberbullying takes its place in social media and has increased throughout the past few years. The damage that cyberbullying has on the users is undeniable they get attacked either on their appearances, ethnicities, religions, and even their thoughts and personal opinion. The attack causes these users anxiety, depression, low self-esteem, and in the worst scenarios suicide. These harmful actions toward the users drive researchers to identify and detect cyberbullying to fight it. Unfortunately, most of the previous approaches were on English texts, hardly any on other languages. This paper presents a cyberbullying detection system in the Moroccan dialect on an Instagram-collected dataset. The experiment results gave accuracies of around 77% to 91% from both the ML and DL algorithms. The LSTM model gave the best outcome by 91.24% outperforming the ML models.
Using graph embeddings to improve the performance of embedding based recommender Systems

This paper discusses the use of node2vec graph embeddings to improve the performance of wide and deep recommenders by substituting the embedding layer with graph embeddings to leverage its representational power without major investments in migrating from current recommender systems. First, recommender systems importance in modern day platforms is discussed. Then, the magnitude of investment needed to deploy a recommender system into a production environment is discussed and finally, wide and deep recommenders and their importance in industry is outlined along with our experiments and results in improving its performance using graph embeddings,
Machine Learning Based Study of QoE Metrics in Twitch.tv Live Streaming

Video streaming generates most network traffic in today’s Internet. For that reason, video on demand streaming is researched heavily in recent years, and many traffic monitoring mechanisms, flow and stream models, and models to predict the user perceived quality are well established. However, the quickly growing live streaming sector is not considered in these Quality of Experience models and not even the relation between network traffic and playback quality has been studied so far, forming a gap in literature. For that reason, we investigate Twitch.tv streaming as one of the largest live streaming platforms based on a large dataset and investigate the possibility to predict live streaming quality based on uplink request information. We apply approaches that are well studied for on demand streaming to predict quality changes, playback quality, and video interruption events as the most important metrics impairing user perceived quality. In this context, we answer whether these models are suitable for live streaming, if small changes are sufficient for satisfactory prediction results, or if fundamental changes and new models are required.
Uplink-based Live Session Model for Stalling Prediction in Video Streaming

Today, video streaming is responsible for about 50 % of all Internet traffic worldwide. To cope with this massive amount of video streaming data, a major concern of network providers is the development of efficient traffic monitoring and management techniques. However, fast and efficient monitoring which leads to intelligent management decisions is becoming highly resource intense and complex, due to the steady increase of the number of streamed videos and the quality of the streamed content. Considering HTTP adaptive streaming applications, we present a simple machine learning free, uplink request based approach to estimate drops in the video playback butter. These drops are the first indicator leading to quality impairment events like downwards quality changes or stalling. With our approach, instead of analyzing thousands of encrypted packets in the network, we only need to consider one single packet every 5 s-10s on average, depending on the video chunk size and independently of the played resolution. Nevertheless, we are able to detect nearly all stalling events or consider a trade-off between stalling detection recall and false positives. Our approach can be implemented completely moving average based, thus not requiring any parameter setup or other expert knowledge. Due to its simplicity, it can be deployed on any access point to collect streaming quality information that is useful for active network management and intelligent resource provisioning but also in a data center to analyze a massive number of parallel video flows.
Combining Autonomic and Intent-Based Networking

Autonomic Networking has long been a topic for standardization at the IETF. The first phase of standardization was mostly concerned with providing infrastructural foundations: Autonomic and secure enrollment of devices into a network domain and establishment of an autonomic control plane to support secure communication between autonomic service agents, on top of which actual autonomic functionality could subsequently be built. At the same time, Intent-Based Networking has been emerging as a new management paradigm, concerned with managing networks by defining outcomes without specifying how they might be achieved, which could in turn require autonomic functionality. In this position paper we outline ways in which Autonomic and Intent-Based Networking can complement one another and explore the role that interoperability and standards can play in this space. From this, we outline gaps and identify opportunities in current IETF standardization and offer our conclusions regarding which directions IETF standardization may take in the future.
Improve Video Conferencing Quality with Deep Reinforcement Learning

Many studies have applied machine learning to bitrate control to increase Quality of Experience (QoE) of video streaming services in highly dynamic networks. However, their solutions mainly focused on HTTP adaptive streaming with one-to-one connections. This paper studies video conferencing applications where multi-party, full-duplex communication happens among participants. In particular, we propose Muno, a Deep Reinforcement Learning (DRL)-based bandwidth prediction framework for multi-party video conferencing systems. Muno learns and predicts an appropriate bitrate for each connection in a multi-party conferencing call. We trained Muno to maximize the QoE of individual connections by constructing a feedback loop between a media server and DRL servers. Our experimental results show that Muno achieves a higher video streaming rate and lower delay compared to state-of-the-art rulebased algorithms.
Knowledge-Aided Sea Clutter Suppression Based on Echo State Network

Sea clutter suppression is critical in the detection of slow and small target. This paper studies the knowledge-aided sea clutter suppression based on echo state network (ESN). Aiming at the problem that the sea clutter suppression based on time series by ESN has poor suppression effect under the condition of high sea state and low signal-to-clutter ratio (SCR), We propose the knowledge-aided sea clutter suppression methods based on frequency spectrum and power spectrum by ESN. Sea clutter suppression is achieved by learning the sea clutter characteristics under the prior knowledge through ESN. We use the measured datasets of Ice Multiparameter Imaging X-Band (IPIX) radar for experimental verification, and the results show that the proposed methods can effectively suppress the sea clutter in different sea states and even detect the target with lower SCR.
Crowd Behaviour Recognition Methods in Video Images

With the development of the information age and the acceleration of the process of urban intelligence, the analysis and recognition of video crowd behaviour has become a hot spot in computer vision research. Crowd behaviour recognition provides an effective technical means for video surveillance and video retrieval in public places. The aim of this paper is to investigate methods for crowd behaviour recognition in video images. The full paper begins by describing the current state of research and the research significance of this topic, arguing that the recognition of crowd behaviour is an essential component of intelligent monitoring and development. Secondly, an improved Vibe foreground detection model is proposed, and finally a behaviour recognition method based on anchor point selection with the KLT feature point extraction algorithm is proposed, showing that the algorithm can accurately estimate the number of people in the surveillance area.
The Smart Performance Development of Geographic Routing in a Reliable Communication

Geographic routing is a routing method used in communication networks that is effective, dependable, and resilient. It is based on the actual locations of the network nodes. The source and destination nodes' geographic coordinates are used to determine the route, taking into consideration any obstacles or other nodes along the path. Geographic routing is especially useful in sensor networks where the nodes have limited capabilities. Geographic routing works by using the geographic coordinates of the nodes to select the shortest and most reliable path. The route is then determined by taking into account any obstacles or other nodes along the path. This ensures that the route is reliable and secure. The main advantages of geographic routing are its scalability, robustness, and reliability. The geographic routing is reliable. It is able to maintain a reliable and secure connection even in the presence of environmental changes. This is because the route is determined based on the geographic coordinates of the nodes, which are not affected by the environment. The geographic routing is an efficient, reliable and robust routing technique used in communication networks. It is based on the actual locations of the network nodes and can keep a safe and stable connection even when there are barriers or other nodes in the way. Additionally, it is very trustworthy, robust, and scalable.