Reviewerly/exHarmony huggingface dataset:
Backtracking Reconstruction Network for Three-Dimensional Compressed Hyperspectral Imaging

Compressed sensing (CS) has been widely used in hyperspectral (HS) imaging to obtain hyperspectral data at a sub-Nyquist sampling rate, lifting the efficiency of data acquisition. Yet, reconstructing the acquired HS data via iterative algorithms is time consuming, which hinders the real-time application of compressed HS imaging. To alleviate this problem, this paper makes the first attempt to adopt convolutional neural networks (CNNs) to reconstruct three-dimensional compressed HS data by backtracking the entire imaging process, leading to a simple yet effective network, dubbed the backtracking reconstruction network (BTR-Net). Concretely, we leverage the divide-and-conquer method to divide the imaging process based on coded aperture tunable filter (CATF) spectral imager into steps, and build a subnetwork for each step to specialize in its reverse process. Consequently, BTR-Net introduces multiple built-in networks which performs spatial initialization, spatial enhancement, spectral initialization and spatial–spectral enhancement in an independent and sequential manner. Extensive experiments show that BTR-Net can reconstruct compressed HS data quickly and accurately, which outperforms leading iterative algorithms both quantitatively and visually, while having superior resistance to noise.
TERMS: textual emotion recognition in multidimensional space

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.
Performance-Aware Client and Quantization Level Selection Algorithm for Fast Federated Learning

In federated learning (FL), which clients are selected and which quantization levels are chosen for the deep model parameters have significant impacts on the learning time as well as the learning accuracy. In this paper, we formulate a joint optimization problem on the client and quantization level selections. As a low complexity solution to the formulated problem, we develop a performance-aware client and quantization level selection (PA-CQLS) algorithm where the FL server estimates the individual round times of clients based on their computing power and channel quality, and determines the most appropriate clients and quantization levels accordingly. Simulation results show that PA-CQLS can reduce the round time by up to 70% compared to conventional algorithms.
Automatic recognition of micronucleus by combining attention mechanism and AlexNet

Micronucleus (MN) is an abnormal fragment in a human cell caused by disorders in the mechanism regulating chromosome segregation. It can be used as a biomarker for genotoxicity, tumor risk, and tumor malignancy. The in vitro micronucleus assay is a commonly used method to detect micronucleus. However, it is time-consuming and the visual scoring can be inconsistent.To alleviate this issue, we proposed a computer-aided diagnosis method combining convolutional neural networks and visual attention for micronucleus recognition. The backbone of our model is AlexNet without any dense layers and it is pretrained on the ImageNet dataset. Two attention modules are applied to extract cell image features and generate attention maps highlighting the region of interest to improve the interpretability of the network. Given the problems in the data set, we leverage data augmentation and focal loss to alleviate the impact.Experiments show that the proposed network yields better performance with fewer parameters. The AP value, F1 value and AUC value reach 0.932, 0.811 and 0.995, respectively.In conclusion, the proposed network can effectively recognize micronucleus, and it can play an auxiliary role in clinical diagnosis by doctors.
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.
A high-order norm-product regularized multiple kernel learning framework for kernel optimization

Kernels offer an effective alternative to implicitly embed the original data into a higher or infinite-dimensional space in support vector machines. Kernel learning, which attempts to determine optimal kernel functions to evaluate relationships between data, has garnered increasing interest. Employing multiple kernels to enhance optimality and generalization is a promising direction. In this study, we focused on the parameter optimization problem of Hadamard kernel functions, which is a newly proposed kernel in machine learning. Motivated by the multiple kernel learning framework in optimizing kernel combinations and the intriguing properties that L4-norm possess, we proposed a high-order L4Lp(p⩾3) norm-product regularized multiple kernel learning framework to optimize the discrimination performance, where hinge, log, and square loss functions are detailed. We demonstrated that the Hadamard multiple kernel learning can effectively obtain the optimal performance while implicitly avoiding the parameter specification difficulty by optimizing the linear combination of Hadamard kernel functions over different kernel parameters. The effectiveness of the proposed approach was verified through experiments on several benchmark datasets. In addition, the high-order L4Lp(p⩾3) norm-product regularized multiple kernel learning framework can be used to optimize radial basis function kernels under different kernel parameters.
Dynamic Anchor: A Feature-Guided Anchor Strategy for Object Detection

The majority of modern object detectors rely on a set of pre-defined anchor boxes, which enhances detection performance dramatically. Nevertheless, the pre-defined anchor strategy suffers some drawbacks, especially the complex hyper-parameters of anchors, seriously affecting detection performance. In this paper, we propose a feature-guided anchor generation method named dynamic anchor. Dynamic anchor mainly includes two structures: the anchor generator and the feature enhancement module. The anchor generator leverages semantic features to predict optimized anchor shapes at the locations where the objects are likely to exist in the feature maps; by converting the predicted shape maps into location offsets, the feature enhancement module uses the high-quality anchors to improve detection performance. Compared with the hand-designed anchor scheme, dynamic anchor discards all pre-defined boxes and avoids complex hyper-parameters. In addition, only one anchor box is predicted for each location, which dramatically reduces calculation. With ResNet-50 and ResNet-101 as the backbone of the one-stage detector RetinaNet, dynamic anchor achieved 2.1 AP and 1.0 AP gains, respectively. The proposed dynamic anchor strategy can be easily integrated into the anchor-based detectors to replace the traditional pre-defined anchor scheme.
Text Sentiment Analysis Based on Transformer and Augmentation

With the development of Internet technology, social media platforms have become an indispensable part of people's lives, and social media have been integrated into people's life, study, and work. On various forums, such as Taobao and Weibo, a large number of people's footprints are left all the time. It is these chats, comments, and other remarks with people's emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results.
Effectiveness Evaluation of Evasion Attack on Encrypted Malicious Traffic Detection

With more and more TLS encrypted traffic on the Internet, an increasing amount of malware is using TLS to hide their tracks. The encrypted traffic makes the traditional malicious traffic detection methods invalid. Machine learning algorithms have become essential options for detecting encrypted malicious traffic. Recently, researchers found that machine learning algorithms have flaws, and threat actors can use some tricks to evade detection. But it remains an open question on how these machine learning-based encrypted malicious traffic detection algorithms perform in the face of evasion attacks.We explore the answer in this paper. We first define five mutation rules to generate adversarial examples. With these mutation rules, we can evaluate the ability of several detection algorithms to deal with evasion attacks when detecting encrypted malicious traffic. The encrypted malicious traffic collected for 12 months is used for experiments. Experiments show that modifying the destination port can reduce the detection rate of detection algorithms in feature space, except for random forest algorithms. Inserting junk data has minimal effect on these algorithms. Whether in the problem space or feature space, inserting useless cipher suites and simulating browser&#x2019;s traffic can significantly reduce the detection rate of these algorithms. When simulating browser&#x2019;s traffic, the random forest algorithm almost loses its usability. The same situation arises when SVM is faced with inserting useless cipher suites. Compared with inserting useless cipher suites, inserting useless extensions has a minor effect on these algorithms. Our findings will contribute to future research on encrypted malicious traffic detection.
Scalable Hardware Acceleration of Non-Maximum Suppression

Non-maximum Suppression (NMS) in one- and two-stage object detection deep neural networks (e.g., SSD and Faster-RCNN) is becoming the computation bottleneck. In this paper, we introduce a hardware acceleration for the scalable PSRR-MaxpoolNMS algorithm. Our architecture shows 75.0× and 305× speedups compared to the software implementation of the PSRR-MaxpoolNMS as well as the hardware implementations of GreedyNMS, respectively, while simultaneously achieving comparable Mean Average Precision (mAP) to software-based floating-point implementations. Our architecture is 13.4× faster than the state-of-the-art NMS one. Our accelerator supports both one- and two-stage detectors, while supporting very high input resolutions (i.e., FHD)—essential input size for better detection accuracy.
An evaluation of practitioners’ perceptions of a security risk assessment methodology in air traffic management projects

Cyber security is a key enabler for safe Air Traffic Management (ATM). This paper presents results from an empirical study, in which we have investigated and evaluated the use of the Security Risk Assessment Methodology for SESAR (SecRAM) in European ATM research and development projects. The study was performed with the intention to find and document common issues and aspects that could be improved in the methodology. The results from the study reveal that while most of the practitioners had a positive perception of the methodology itself, they were less satisfied with the process of applying it in their projects. Based on the results, we provide a number of recommendations, which aim to improve the security risk assessment process in the ATM domain.
Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learning

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the edge server (ES). The proposed scheme relies on non-coherent communication techniques for achieving distributed training by majority vote (MV). In this work, the votes, i.e., the signs of the local gradients, from the EDs are represented with the pulse-position modulation (PPM) symbols constructed with discrete Fourier transform (DFT)-spread orthogonal frequency division multiplexing (OFDM) (DFT-s-OFDM). By taking the delay spread and time-synchronization errors into account, the MV at the ES is obtained with an energy detector. Hence, the proposed scheme does not require CSI at the EDs and ES. We also prove the convergence of the distributed training when the MV is obtained with the proposed scheme under fading channel. Through simulations, we show that the proposed scheme provides a high test accuracy in fading channel while reducing the peak-to-mean envelope power ratio (PMEPR).
RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments

Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours.
Adaptive multi-scale transductive information propagation for few-shot learning

Few-shot learning aims to learn a classifier with more generalization capability from extremely limited labeled samples has drawn an increasing amount of attention in many areas. One typical work in this field is the transductive propagation network (TPN), which propagates labels by capturing the local geometry distribution information between data. However, TPN exploited only the manifold structure of feature extractor networks’ high-layer semantic features (single-scale) distribution, while neglecting their local geometry distribution in the low-layer semantic features. This paper presents the adaptive multi-scale transductive information propagation (AMTIP) model to address this problem. Specifically, we introduce the multi-scale feature extractor networks to simultaneously learn samples’ high-layer global semantic features and low-layer local semantic features. After acquiring the multi-scale features, we propose the adaptive multi-scale fusion networks to generate the adaptive semantic fusion features applying to different few-shot classification tasks. Finally, the adaptive semantic fusion features are applied to the label propagation model for few-shot image classification. Compared with the single-scale semantic features of TPN, AMTIP can better preserve the local geometry between the support set and query set data via the adaptive multi-scale semantic fusion features. Extensive experiments demonstrate the superiority of the proposed AMTIP compared to the state-of-the-art few-shot classification models.
Optimization of English Classroom Quality Evaluation Model with AHP

This work takes English classroom quality as the research object. Improving the quality of English classrooms is not only an unavoidable requirement for the current curriculum reform’s deepening and development, but also a necessary development trend of the evaluation system reform, and the call of the majority of classroom teaching position. The country invested financial, labor, and material resources, and students have also invested a lot of study time, but the English classroom effect is not satisfactory. The main issue is a lack of adequate evaluation of the teaching process as well as a scientific, fair, and practical technique for assessing classroom quality. This work combines AHP and BP networks to propose a method (AHP-IGA-BP) for evaluating the quality of English classrooms. The content of this work is as follows: (1) A hierarchical model and index system for English classroom quality evaluation based on AHP are constructed. Through the process of constructing a judgment matrix, single-level ranking, consistency check, and total-level ranking, the complete English classroom quality evaluation index weight is finally obtained, which realizes the transformation of subjective information into data with specific weight. (2) combine the original data with the aforementioned index weight data produced using AHP, as well as the neural network’s learning and training samples. Build a BP network structure to effectively evaluate the quality of English classrooms. (3) The BP network is optimized based on the improved GA algorithm.
QoE Models for Adaptive Streaming: A Comprehensive Evaluation

Adaptive streaming has become a key technology for various multimedia services, such as online learning, mobile streaming, Internet TV, etc. However, because of throughput fluctuations, video quality may be dramatically varying during a streaming session. In addition, stalling events may occur when segments do not reach the user device before their playback deadlines. It is well-known that quality variations and stalling events cause negative impacts on Quality of Experience (QoE). Therefore, a main challenge in adaptive streaming is how to evaluate the QoE of streaming sessions taking into account the influences of these factors. Thus far, many models have been proposed to tackle this issue. In addition, a lot of QoE databases have been publicly available. However, there have been no extensive evaluations of existing models using various databases. To fill this gap, in this study, we conduct an extensive evaluation of thirteen models on twelve databases with different characteristics of viewing devices, codecs, and session durations. Through experiment results, important findings are provided with regard to QoE prediction of streaming sessions. In addition, some suggestions on the effective employment of QoE models are presented. The findings and suggestions are expected to be useful for researchers and service providers to make QoE assessments and improvements of streaming solutions in adaptive streaming.
Learning the Compositional Domains for Generalized Zero-shot Learning

This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances from both seen and unseen classes at the test time. We propose a novel domain division method to solve G-ZSL. Some previous models with domain division operations only calibrate the confident prediction of source classes (W-SVM (Scheirer et al., 2014)) or take target-class instances as outliers (Socher et al., 2013). In contrast, we propose to directly estimate and fine-tune the decision boundary between the source and the target classes. Specifically, we put forward a framework that enables to learn compositional domains by splitting the instances into Source, Target, and Uncertain domains and perform recognition in each domain, where the uncertain domain contains instances whose labels cannot be confidently predicted. We use two statistical tools, namely, bootstrapping and Kolmogorov–Smirnov (K–S) Test, to learn the compositional domains for G-ZSL. We validate our method extensively on multiple G-ZSL benchmarks, on which it achieves state-of-the-art performances. The codes are available on https://github.com/hendrydong/demo_zsl_domain_division.