Reviewerly/exHarmony huggingface dataset:
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the communication cost incurred in communicating model updates from the edge devices. In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device. We first characterize the training error due to such channel noise in OTA-FL by establishing a fundamental lower bound for general functions with Lipschitz-continuous gradients. Then, by introducing an adaptive transceiver power scaling scheme, we propose an over-the-air federated learning algorithm with joint adaptive computation and power adjustment (ACPA-OTA-FL). We provide the convergence analysis for ACPA-OTA-FL in training with non-convex objective functions and heterogeneous data. We show that the convergence rate of ACPA-OTA-FL matches that of FL with noise-free communications.
Emotion Recognition for Partial Faces Using a Feature Vector Technique

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.
Discovering association rules from big graphs

This paper tackles two challenges to discovery of graph rules. Existing discovery methods often (a) return an excessive number of rules, and (b) do not scale with large graphs given the intractability of the discovery problem. We propose an application-driven strategy to cut back rules and data that are irrelevant to users' interests, by training a machine learning (ML) model to identify data pertaining to a given application. Moreover, we introduce a sampling method to reduce a big graph G to a set H of small sample graphs. Given expected support and recall bounds, the method is able to deduce samples in H and mine rules from H to satisfy the bounds in the entire G . As proof of concept, we develop an algorithm to discover Graph Association Rules (GARs), which are a combination of graph patterns and attribute dependencies, and may embed ML classifiers as predicates. We show that the algorithm is parallelly scalable, i.e. , it guarantees to reduce runtime when more machines are used. We experimentally verify that the method is able to discover rules with recall above 91% when using sample ratio 10%, with speedup of 61 times.
Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.
Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

The trajectory planning for Unmanned aerial vehicles (UAVs) in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, extended hierarchical graph, to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward exploration and backward navigation algorithm, matrix alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to $$t+1$$ . This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.
Interpretability, Reproducibility, and Replicability [From the Guest Editors]

Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions.
Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy

Dealing with real-world reinforcement learning (RL) tasks, we shall be aware that the environment may have sudden changes. We expect that a robust policy is able to handle such changes and adapt to the new environment rapidly. Context-based meta reinforcement learning aims at learning environment adaptable policies. These methods adopt a context encoder to perceive the environment on-the-fly, following which a contextual policy makes environment adaptive decisions according to the context. However, previous methods show lagged and unstable context extraction, which are hard to handle sudden changes well. This paper proposes an environment sensitive contextual policy learning (ESCP) approach, in order to improve both the sensitivity and the robustness of context encoding. ESCP is composed of three key components: variance minimization that forces a rapid and stable encoding of the environment context, relational matrix determinant maximization that avoids trivial solutions, and a history-truncated recurrent neural network model that avoids old memory interference. We use a grid-world task and 5 locomotion controlling tasks with changing parameters to empirically assess our algorithm. Experiment results show that in environments with both in-distribution and out-of-distribution parameter changes, ESCP can not only better recover the environment encoding, but also adapt more rapidly to the post-change environment (10x faster in the grid-world) while the return performance is kept or improved, compared with state-of-the-art meta RL methods.
Organization of Student-Centered learning within the Professional Training of a future teacher in a Digital Environment

In recent years, student-centered learning has undergone significant changes influenced by the introduction of the competency-based approach to the digital learning environment. The new approach places a teacher at the center of the educational process taking into account professional competencies and personal interests of educators to foster the improvements of methodological, organizational, and technological support of personalized learning. The sample (N = 36) consists of the 3rd year students of University (KazNPU). The research methodology uses the following metrics to test the psychological and emotional characteristics of students: Communication Skills Assessments and Analysis of Cultural and Value Orientations. The study revealed a background of discomfort with the current system of education - about half of the respondents experience psychological and emotional stress due to the current education format. In addition, after the implementation of the program it was found that the SCL configuration model, applied to distance learning, had a significant impact on students. Statistical analysis of the chi-square indices of general fit (χ2 = 122.77) shows a good fit to the set of data points. The results can be used by educators and in future research to analyze the feasibility and advantages of digital educational processes in other geographical regions.
DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation

Citing and describing related literature are crucial to scientific writing. Many existing approaches show encouraging performance in citation recommendation, but are unable to accomplish the more challenging and onerous task of citation text generation. In this paper, we propose a novel disentangled representation based model DisenCite to automatically generate the citation text through integrating paper text and citation graph. A key novelty of our method compared with existing approaches is to generate context-specific citation text, empowering the generation of different types of citations for the same paper. In particular, we first build and make available a graph enhanced contextual citation dataset (GCite) with 25K edges in different types characterized by citation contained sections over 4.8K research papers. Based on this dataset, we encode each paper according to both textual contexts and structure information in the heterogeneous citation graph. The resulted paper representations are then disentangled by the mutual information regularization between this paper and its neighbors in graph. Extensive experiments demonstrate the superior performance of our method comparing to state-of-the-art approaches. We further conduct ablation and case studies to reassure that the improvement of our method comes from generating the context-specific citation through incorporating the citation graph.
Semantic Self-Segmentation for Abstractive Summarization of Long Documents in Low-Resource Regimes

The quadratic memory complexity of transformers prevents long document summarization in low computational resource scenarios. State-of-the-art models need to apply input truncation, thus discarding and ignoring potential summary-relevant contents, leading to a performance drop. Furthermore, this loss is generally destructive for semantic text analytics in high-impact domains such as the legal one. In this paper, we propose a novel semantic self-segmentation (Se3) approach for long document summarization to address the critical problems of low-resource regimes, namely to process inputs longer than the GPU memory capacity and produce accurate summaries despite the availability of only a few dozens of training instances. Se3 segments a long input into semantically coherent chunks, allowing transformers to summarize very long documents without truncation by summarizing each chunk and concatenating the results. Experimental outcomes show the approach significantly improves the performance of abstractive summarization transformers, even with just a dozen of labeled data, achieving new state-of-the-art results on two legal datasets of different domains and contents. Finally, we report ablation studies to evaluate each contribution of the components of our method to the performance gain.
Multilingual Code Snippets Training for Program Translation

Program translation aims to translate source code from one programming language to another. It is particularly useful in applications such as multiple-platform adaptation and legacy code migration. Traditional rule-based program translation methods usually rely on meticulous manual rule-crafting, which is costly both in terms of time and effort. Recently, neural network based methods have been developed to address this problem. However, the absence of high-quality parallel code data is one of the main bottlenecks which impedes the development of program translation models. In this paper, we introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages. The dataset is parallel at the level of code snippets, which provides much more fine-grained alignments between different languages than the existing translation datasets. We also propose a new program translation model that leverages multilingual snippet denoising auto-encoding and Multilingual Snippet Translation (MuST) pre-training. Extensive experiments show that the multilingual snippet training is effective in improving program translation performance, especially for low-resource languages. Moreover, our training method shows good generalizability and consistently improves the translation performance of a number of baseline models. The proposed model outperforms the baselines on both snippet-level and program-level translation, and achieves state-of-the-art performance on CodeXGLUE translation task. The code, data, and appendix for this paper can be found at https://github.com/reddy-lab-code-research/MuST-CoST.
Action-Aware Embedding Enhancement for Image-Text Retrieval

Image-text retrieval plays a central role in bridging vision and language, which aims to reduce the semantic discrepancy between images and texts. Most of existing works rely on refined words and objects representation through the data-oriented method to capture the word-object cooccurrence. Such approaches are prone to ignore the asymmetric action relation between images and texts, that is, the text has explicit action representation (i.e., verb phrase) while the image only contains implicit action information. In this paper, we propose Action-aware Memory-Enhanced embedding (AME) method for image-text retrieval, which aims to emphasize the action information when mapping the images and texts into a shared embedding space. Specifically, we integrate action prediction along with an action-aware memory bank to enrich the image and text features with action-similar text features. The effectiveness of our proposed AME method is verified by comprehensive experimental results on two benchmark datasets.
AdaptivePose: Human Parts as Adaptive Points

Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency. Towards a compact and efficient pipeline for multi-person pose estimation task, in this paper, we propose to represent the human parts as points and present a novel body representation, which leverages an adaptive point set including the human center and seven human-part related points to represent the human instance in a more fine-grained manner. The novel representation is more capable of capturing the various pose deformation and adaptively factorizes the long-range center-to-joint displacement thus delivers a single-stage differentiable network to more precisely regress multi-person pose, termed as AdaptivePose. For inference, our proposed network eliminates the grouping as well as refinements and only needs a single-step disentangling process to form multi-person pose. Without any bells and whistles, we achieve the best speed-accuracy trade-offs of 67.4% AP / 29.4 fps with DLA-34 and 71.3% AP / 9.1 fps with HRNet-W48 on COCO test-dev dataset.
Two-Stage Octave Residual Network for End-to-End Image Compression

Octave Convolution (OctConv) is a generic convolutional unit that has already achieved good performances in many computer vision tasks. Recent studies also have shown the potential of applying the OctConv in end-to-end image compression. However, considering the characteristic of image compression task, current works of OctConv may limit the performance of the image compression network due to the loss of spatial information caused by the sampling operations of inter-frequency communication. Besides, the correlation between multi-frequency latents produced by OctConv is not utilized in current architectures. In this paper, to address these problems, we propose a novel Two-stage Octave Residual (ToRes) block which strips the sampling operation from OctConv to strengthen the capability of preserving useful information. Moreover, to capture the redundancy between the multi-frequency latents, a context transfer module is designed. The results show that both ToRes block and the incorporation of context transfer module help to improve the Rate-Distortion performance, and the combination of these two strategies makes our model achieve the state-of-the-art performance and outperform the latest compression standard Versatile Video Coding (VVC) in terms of both PSNR and MS-SSIM.
Signals Recognition by CNN Based on Attention Mechanism

Automatic modulation recognition is a key technology in non-collaborative communication. However, it is affected by complex electromagnetic environments, leading to low recognition accuracy. To address this problem, this paper develops a ResNext signal recognition model based on an attention mechanism. Firstly, a channel, including additive Gaussian white noise (AWGN), Rician multipath fading, and clock offset, is created to simulate the complex electromagnetic environment, and transmission-impaired modulated signals with various signal-to-noise ratios (SNRs) are synthesized as a dataset. Secondly, using parallel stacked residual blocks of the same topology, instead of the residual blocks of ResNet, and introducing the attention layer (CBAM), the types of feature extraction are enriched without significantly increasing the parameter order of magnitude and avoiding the over-fitting phenomenon caused by depth deepening. The results show that the signal recognition method, based on the improved neural network framework, outperformed other deep learning methods, and the recognition rate obtained of 10 different modulation types of signals was above 90% at SNRs greater than 0 dB. The proposed signal recognition method achieved accurate recognition in complex electromagnetic environments.
Trusted Execution Environments: Applications and Organizational Challenges

A lack of trust in the providers is still a major barrier to cloud computing adoption – especially when sensitive data is involved. While current privacy-enhancing technologies, such as homomorphic encryption, can increase security, they come with a considerable performance overhead. As an alternative Trusted Executing Environment (TEE) provides trust guarantees for code execution in the cloud similar to transport layer security for data transport or advanced encryption standard algorithms for data storage. Cloud infrastructure providers like Amazon, Google, and Microsoft introduced TEEs as part of their infrastructure offerings. This review will shed light on the different technological options of TEEs, as well as give insight into organizational issues regarding their usage.
Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning

Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a “device-edge-cloud” collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the problem of offloading power line inspection tasks is classified as an optimization problem to obtain the minimum delay under the constraints of edge server computing and communication resources. Finally, the problem is constructed as a Markov decision, and a deep Q-network (DQN) is used to obtain the minimum delay of the system. In addition, an experience replay mechanism and a greedy algorithm are introduced in the learning process to improve the offloading accuracy. The experimental results show that the proposed offloading strategy in this paper saves 54%, 37% and 26% of the task completion time, respectively, compared with local offloading, cloud offloading and random offloading. It effectively reduces the UAV inspection delay and improves the transmission line inspection efficiency.
KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks.