Reviewerly/exHarmony huggingface dataset:
Hybrid Parallelism Based Communication Optimization for Edge Deep Learning

This paper focuses on the training of distributed deep learning in edge computing. First, we briefly describe the distributed training in the edge environment, and then analyze the impact of different communication methods on the training in the edge environment. Secondly, a description of the hybrid parallel model is given, and the problem of minimizing the average transmission time of data streams during training is proposed. The problem is solved based on the improved Gossip algorithm. Finally, the effectiveness of the algorithm is verified by simulation experiments.
Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters.
Dual-Alignment Based Generalized Zero-shot Learning for Human Activity Recognition

Generalized zero-shot activity recognition is a challenging problem, where the task is to recognize new activity classes that are unseen during the training phase, in addition to the seen activity classes. In generalized zero-shot learning, the bias towards seen classes will make the model misclassify the test sample from unseen classes. Distinguishing seen and unseen domains is mutually an effective yet challenging solution for Generalized zero-shot learning. We propose a dual-alignment method that leverages both feature and semantic modalities to distinguish seen and unseen classes. Specifically, we reconstruct two modalities by their latent representations of each other to achieve class-wise alignment. To distinguish between seen and unseen classes better, we also leverage instance-wise contrastive learning to achieve intra-class alignment and inter-class dispersion. Then, the unseen samples can be separated from the seen samples by the threshold from the center to the manifold boundary for each seen class. After that, different from previous work, which needs to retrain two expert classifiers, we only use the two networks involved in the training stage to classify the seen and unseen samples, respectively. We perform a comprehensive experimental evaluation on three activity recognition benchmark datasets. Experimental results show that the proposed model can significantly improve the results of GZSL.
t-PSI: Efficient Multi-party Private Set Intersection with Threshold

Multi-party privacy set intersection (MPSI) enables multiple parties to compute the intersection of their datasets without leaking data privacy. Among existing MPSI protocols, e.g., KMPRT-based protocols, oblivious evaluation of programmable pseudo-random function (OPPRF) or oblivious pseudo-random function (OPRF) is typically used to generate pseudo random numbers, requiring frequent online interactions. To mitigate such communication overheads, we propose the threshold Privacy Set Intersection (t-PSI) protocol for multi-party, leveraging the Shamir Secret Sharing (SSS) protocol and the masking construction mechanism. We propose two types of masking construction mechanisms: Polynomial and Garbled Bloom-filter. To the best of our knowledge, this is the first Secret Sharing-based MPSI protocol. With the significant reduction of inter-party communication, the t-PSI protocol is highly scalable and can handle datasets ranging from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> . Further, with built-in fault tolerance, t-PSI works even if some parties went offline during protocol execution. As shown in the experiments, for five parties with datasets of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> items each, the proposed t-PSI takes only 63 seconds, outperforming the state-of-the-art in [1].
Mutual Attention Feature Alignment In Cross-domain Detection

Domain shift exists among the data from different scenes, that is, the object detection model trained in a well-labeled domain has a poor performance in an unlabeled domain. Many works attempt to address this issue by incorporating domain adaptation (DA) techniques into object detection. However, these cross-domain detection works have not considered the different contributions from various features to domain alignment. In addition, most recent cross-domain detection methods use two-stage detector as the baseline model, which has low object detection efficiency. To solve these problems, this paper proposes the mutual attention feature alignment network (MAFA), which utilizes the mutual attention scores to highlight the features with high transferability and suppress the features with low transferability for domain alignment. After that, we successfully build a one-stage cross-domain detection framework. We use the MAFA to align the image-level feature and instance-level feature distributions on the YOLOv5s, which can significantly improve the model detection. At last, we experimentally validate our model with extensive experiments.
ProtoPLSTM: An Interpretable Deep Learning Approach for Wearable Fine-Grained Fall Detection

Existing models have obtained satisfactory results on whether users have fallen or not. However, in our lives, due to the unpredictability of falls, the binary fall detection models cannot identify different patterns of falls and thus cannot take corresponding protective measures. Therefore, the recognition of more subtle falls has become an urgent challenge. Moreover, the complexity of fall detection models makes it difficult to explain the decision process. In this paper, we propose a new interpretable fine-grained fall detection network, called ProtoPLSTM. The model consists of three main modules: CNN-LSTM encoder backbone network, contextual enhancement module, and ProtoPNet-based network. The first module learns a multi-sensor feature representation through a carefully designed embedding network. The contextual enhancement module expands the receptive field to capture more discriminative features through contextual information and then mines the inter-class differences and intra-class associations of different kinds of falls. The last module globally explains why a fall is detected as some kind of fall from the prototype’s perspective. Our experiments on SisFall, a publicly available dataset with the largest number of fall classes, demonstrate that the proposed method can outperform state-of-the-art methods while having interpretability. The visualization and qualitative analysis explained the important factors and decision-making processes for the occurrence of different kinds of falls from a prototypical perspective.
Blockchainizing the Wordle Game in Advanced Metaverse Realms Using Smart Wearables

6G will enable not only the intelligentization but also the blockchainization of future mobile networks based on decentralized Web 3.0 technologies. A variety of fundamental technologies need to be integrated in 6G in order to drive the implementation of the next Internet, referred to as the Metaverse, through virtual and augmented reality (VR/AR). In addition to incentivizing transactions, the Metaverse should provide gamified experiences, given that gamification will encompass the activity and story around emerging Web 3.0 blockchain technologies. In this paper, we focus on the Wordle game, which was developed during Covid-19 lockdowns and became a worldwide Internet phenomenon. In this paper, we adopt the original Wordle game to the Metaverse, including but not limited to VR and AR. Specifically, we design and experimentally investigate advanced cognitive cues for playing the Wordle game in the eight different experience realms of the so-called Multiverse, the anticipated successor of the Metaverse, using state-of-the-art smart wearables. In addition, we develop a blockchainized version of the game that allows remote experts to play-to-earn tokens and cooperate with local players by providing them with cognitive assistance via smart wearables.
Research on Chinese Short Text Classification Based on Prefix-vector Attention Template and Probabilistic Answer Set

When people tend to communicate in the network, a large amount of text data is generated. These text data have become an important way for people to obtain information. Nowadays, text classification through prompt learning has also become the focus of people’s research. However, the effect of prompt learning is not ideal due to the immobilization of the prompt template and the single label words. In response to the above problems, this paper proposes a method of constructing a prefix vector attention template (PAP) and a probabilistic answer set. The prefix space vector is added in front of the input text sentence as a template for prompt learning, and using the Attention mechanism to connect the template with the input sentence to construct the input of the model sentence. For each label word, an answer space set is constructed, and the probability that the text belongs to this category is obtained in the form of the answer set probability. The experimental results show that the PAP + probabilistic answer set algorithm proposed in this paper is significantly better than other benchmark models in the text classification task, and can achieve better results.
Encoding and Storing Only Once: The Road to CMAF Adoption

The spectacular growth in video streaming consumption over the past decade has relied on Hypertext Transfer Protocol (HTTP) adaptive streaming (HAS), implemented by a variety of standards linked to several competing technological ecosystems. To serve this fragmented market and reach a heterogeneous set of target devices, operators have to decline their services into many flavors, leading to a complex video delivery infrastructure with many redundancies. As an extensible format for encoding and packaging, the Common Media Application Format (CMAF) standard emerged in 2016 from a collaborative industry effort to simplify and streamline HTTP-based streaming media, bringing the promise of encoding and storing the media only once, as an additional step toward video streaming sustainability. From the standard publication to its fulfillment of promises, CMAF is gradually overcoming a set of technological and economic challenges. This article details the primary considerations for its implementation, including device rollout, encryption scheme and low-latency support, and also evaluates the infrastructure footprint reduction for a typical operator.
Flow-Based User Click Identification in Encrypted Web Traffic

Identifying user clicks from passive traces is the fundamental task for user behavior analysis, which is important for understanding modern web usage and providing optimized personalized service. With the widespread adoption of encryption protocols, identifying user clicks in encrypted web traffic becomes more and more challenging. Existing studies on user click identification mainly focus on information extracted from request packets, which makes them difficult to apply over encrypted traffic and large network. In this paper, we propose a novel flow-based method for user click identification in encrypted web traffic. We observe that the flows directly driven by user clicks have uniform communication patterns for classification. Thus, we turn user click identification into a flow classification problem and apply a GNN-based classifier to learn flow patterns accurately. We collect two real-world traffic datasets and conduct experiments to demonstrate the effectiveness of the proposed method. Experimental results show that our proposed model outperforms the existing baseline methods in terms of performance and stability.
MLRNet: Towards Real-Time Crowd Counting With Mobile-Based Lightweight Framework

The trade-off between efficiency and generalization has been a challenging problem in crowd counting. In this work, we are devoted to suggesting an application-oriented solution to practical device deployment for crowd counting in resource-constrained scenes. In order to optimize the application computation complexity, this paper proposes a counting architecture, called Mobile Lightweight Refine Net (MLRNet), which is targeted for the high-effectiveness of neural networks in real-time crowd counting tasks on light-scale servers or intelligent devices. In the proposed framework, MobileNetV3 is used as the front-end which enables the model of an efficient feature extraction ability with low resource use. Besides, the back-end introduces the design of Light-weight RefineNet, which maintains the overall counting accuracy while compressing the computing costs. Extensive experiments on four mainstream datasets have demonstrated the availability of our proposed network. Particularly, compared with the baseline method, MLRNet achieves higher accuracy while only using around 1/3 of the FLOPS and parameters on three public crowd counting datasets.
Joint Optimization of Coverage and Reliability for Application Placement in Mobile Edge Computing

Mobile edge computing (MEC) provides a new distributed computing paradigm that overcomes the inability of cloud computing to offer low end-to-end latency. In a MEC environment, app vendors can deliver lower-latency services to mobile app users by placing applications on edge servers in close proximity to app users. From an app vendor's perspective, an optimal edge application placement strategy under a budget ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ) constraint aims to place application instances on <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge servers within a specific area to maximize user coverage. However, edge servers may be subject to failure due to multiple reasons, e.g., hardware faults, software exceptions, cyber-attacks, etc. App users served by failed edge servers need to access applications from remote cloud servers if they cannot access any other edge servers. This impacts app users’ quality of experience significantly. Thus, app vendors need to consider the reliability of the edge server network when choosing edge servers for placing their application instances. We make the first attempt in this paper to tackle this problem of joint optimization of coverage and reliability for edge application placement (EAP-CR). We formulate this problem as a constrained optimization problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness theoretically. Then, we propose an optimal approach to find the optimal solutions with the integer programming technique, an approximation approach is also proposed to find approximate solutions for large-scale EAP-CR problems. We evaluate EAP- <inline-formula><tex-math notation="LaTeX">$OPT$</tex-math></inline-formula> and EAP- <inline-formula><tex-math notation="LaTeX">$APX$</tex-math></inline-formula> against three relevant approaches through experiments conducted on a widely-used real-world data set and a synthetic data set. The results demonstrate that our proposed approaches can solve the EAP-CR problem effectively and efficiently.
Investigating Translation Concepts in Machine Translation

Machine translation (MT) has caused profound transformations in the field of translation on various levels, from translation processes and workflows to the translation profession and the status of human translation in the digital age. MT-induced transformations have also led to increased interest in MT on the part of translation studies (TS) in recent years. However, TS and computational linguistics (CL) – the scientific field and main driver of the innovations behind MT – have so far hardly participated in a cross-disciplinary discourse. Translaboration as a blended concept can serve as a gateway for the exchange of theories and concepts between TS and CL, thus contributing to a holistic view of MT. The investigation of translation concepts in MT can serve as a starting point for a translaboration between TS and CL.
Sentiment analysis of microblogs with rich emoticons

Sentiment analysis for social media can help to explore deeper insight into the attitudes, opinions, and emotions behind the posts. Existing work usually analyze the emoticons and texts of the posts separately, and ignore the impact of emoticons on the emotional polarity of texts. As a result, the polarity of the posts could be marked inaccurately in the scenarios where the polarity of the texts relies on the contextual information of the emoticons. To address this issue, we propose a model, WnhBert-Bi-LSTM, for microblog sentiment analysis. The model trains phrase and emoticon embedding on a large-scale corpus composed of 280,000 Chinese microblogs, and uses the self-attention mechanism to evaluate the impact of emoticons on the overall emotional polarity. By converting emoticons into tractable features, the emoticons can be analyzed jointly with the texts to explore their feature interaction. Evaluations on 8,965 sina microblog posts show that the accuracy of our model is 3.19% higher than the baseline models. In addition, we constructed and open-sourced a new emoticon label corpus with more widely used words and more comprehensive emoticon data than the existing corpus.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling

Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discrete snapshots of the temporal networks and modeling all events with equal importance. This paper proposes Significant Ties Graph Neural Networks (STGNN), a novel framework that captures and describes significant ties. To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors’ information and adaptively obtain the significance of node pairs. Experimental results on four real networks demonstrate the effectiveness of the proposed framework.
A Lightweight Locally Repairable Code-based Storage Architecture for Blockchains

The blockchain system requires every node to preserve a complete copy of data arbitrarily, which exerts tremendous storage pressure on nodes. Some researchers applied the erasure code to reduce storage redundancy. However, code storage schemes have the problem of inefficient data communication while verifying transactions and downloading data. To solve this problem, this paper proposes a lightweight locally repairable code (LRC) storage scheme inspired by the idea of slice strategy from privacy computing. Firstly, partitioning each block into distinct transaction slices substantially reduces the amount of transmitted data required to verify a transaction. Secondly, our scheme can recover single-point data with fewer code data slices by local nodes and with less network communication overhead. At last, we analyze the performance of our scheme from theoretical perspectives and examine the storage performance and computation efficiency of our scheme from experimental perspectives. Results suggest that our scheme can effectively reduce the storage overhead while also decreasing the network communication overhead and improving the data reading efficiency.
NormSoftmax: Normalizing the Input of Softmax to Accelerate and Stabilize Training

Softmax is a basic function that normalizes a vector to a probability distribution and is widely used in machine learning, most notably in cross-entropy loss function and dot product attention operations. However, the optimization of softmax-based models is sensitive to the input statistics change. We observe that the input of softmax changes significantly during the initial training stage, causing slow and unstable convergence when training the model from scratch. To remedy the optimization difficulty of softmax, we propose a simple yet effective substitution, named NormSoftmax, where the input vector is first normalized to unit variance and then fed to the standard softmax function. Similar to other existing normalization layers in machine learning models, NormSoftmax can stabilize and accelerate the training process, and also increase the robustness of the training procedure against hyperparameters. Experiments on Transformer-based models and convolutional neural networks validate that our proposed NormSoftmax is an effective plug-and-play module to stabilize and speed up the optimization of neural networks with cross-entropy loss or dot-product attention operations.
A Simple and Effective Method for RGB-T Salient Object Detection

Salient object detection aims to segment the prominent targets in the image which will be applied to intelligent traffic and transportation. Segmenting targets in complex scenes using only RGB images is very challenging, therefore, some researchers acquire thermal infrared images as auxiliary information to segment objects. The task that utilizes both color information and thermal infrared information for salient object detection is called RGB-T SOD. However, recent studies of RGB-T SOD have not fully exploited the information in the different modalities or ignored the problem of positional information during denoise process. In this paper, we first extract the features of both modalities using two ConvNeXt backbones with shared parameters that can model long-range information and local information. Then the redundant information in the auxiliary modality is suppressed by using a feature purification module that both eliminate the noise and retain the positional information of the features before feature fusion. In addition, we introduce a feature guide module to enhance the representation of spatial detail information in the features. Through the perfect cooperation of the three parts, our proposed method shows outstanding performance and outperforms the state-of-the-art methods on three publicly available RGB-T SOD datasets.