Reviewerly/exHarmony huggingface dataset:
Spectrum Sensing and Signal Identification With Deep Learning Based on Spectral Correlation Function

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function (SCF) which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CASE1</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CASE2</monospace> . In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CASE1</monospace> , signals are jointly sensed and classified. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CASE2</monospace> , sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured over-the-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.
A brief survey on RGB-D semantic segmentation using deep learning

Semantic segmentation is referred to as a process of linking each pixel in an image to a class label. With this pragmatic technique, it is possible to recognize different objects in an RGB image based on the color and texture, and hence it becomes easier to evaluate. Recently, researchers could perform semantic segmentation pretty well in RGB images. However, the methods based on RGB image lack enough information to realize semantic segmentation of complex scenes. RGB-D semantic segmentation with depth information has been proved to achieve better segmentation results by a lot of experiments, but there is a lack of a comprehensive survey. In this paper, the main purpose is to offer a detailed review of RGB-D semantic segmentation according to the research progress in recent years. Specifically, recently updated RGB-D datasets will be focused on first, and problems on RGB-D semantic segmentation will be discussed. In the end, a comprehensive analysis is carried out on recent methods and their analysis of the semantic segmentation.
VDM-DA: Virtual Domain Modeling for Source Data-Free Domain Adaptation

Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods require the co-existence of source and target domain samples to reduce the distribution mismatch. However, access to the source domain samples may not always be feasible in real-world applications due to different problems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , storage, transmission, and privacy issues). In this work, we deal with the source data-free unsupervised domain adaptation problem and propose a novel approach referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA), in which the virtual domain acts as a bridge between the source and target domains. Specifically, based on the pre-trained source model, we generate the virtual domain samples by using an approximated Gaussian Mixture Model (GMM) in the feature space, such that the virtual domain maintains a similar distribution with the source domain without access to the original source data. Moreover, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning. In this way, we successfully achieve the goal of distribution alignment between the source and target domains when training deep networks without access to the source domain data. We conduct extensive experiments on four benchmark datasets for both 2D image-based and 3D point cloud-based cross-domain object recognition tasks, where the proposed method referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA) achieves the promising performance on all datasets.
Large graph convolutional network training with GPU-oriented data communication architecture

Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sampling the sparsely located neighboring nodes to obtain their features. Since real-world graphs often exceed the capacity of GPU memory, current GCN training systems keep the feature table in host memory and rely on the CPU to collect sparse features before sending them to the GPUs. This approach, however, puts tremendous pressure on host memory bandwidth and the CPU. This is because the CPU needs to (1) read sparse features from memory, (2) write features into memory as a dense format, and (3) transfer the features from memory to the GPUs. In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help. By removing the CPU gathering stage, our method significantly reduces the consumption of the host resources and data access latency. We further present two important techniques to achieve high host memory access efficiency by the GPU: (1) automatic data access address alignment to maximize PCIe packet efficiency, and (2) asynchronous zero-copy access and kernel execution to fully overlap data transfer with training. We incorporate our method into PyTorch and evaluate its effectiveness using several graphs with sizes up to 111 million nodes and 1.6 billion edges. In a multi-GPU training setup, our method is 65--92% faster than the conventional data transfer method, and can even match the performance of all-in-GPU-memory training for some graphs that fit in GPU memory.
Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting

Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, stand-alone, human-understandable queries with a pretrained sequence-to-sequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30% improvement in terms of NDCG@3 compared to the best submission of Text REtrieval Conference (TREC) Conversational Assistant Track (CAsT) 2019.
A social semiotic perspective on emoji: How emoji and language interact to make meaning in digital messages

This article presents a social semiotic analysis of emoji-language semiosis. Combining the theoretical architecture of Systemic Functional Linguistics and methodology of Multimodal Discourse Analysis, we propose an analytical framework that can identify how emoji make meaning both individually and in interaction with language. Using the web-based coding software WebAnno, we apply this framework to a dataset of text messages and social media posts. The results identify typical realisations of particular semiotic features by emoji as well as noteworthy dynamics in how emoji interact with language to realise meaning. We observe (1) how emoji and language jointly construing ideational meaning realise intermodal taxonomies (where hyper/hyponyms are distributed across modes) and particular fields of discourse (domains of experiential meaning), (2) how resources in one mode can serve to foreground particular regions of meaning potential in other modes, and (3) how attitudinal meaning realised by emoji appears to differ from the prosodic patterning of linguistic attitude.
Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning

Federated learning (FL) offers a promising paradigm that empowers numerous Internet of Things (IoT) devices to implement distributed learning on the premise of ensuring user privacy and data security. However, since FL adopts a synchronous distributed training mode, the heterogeneity of participating IoT devices and limited communication resources make FL encounter serious issues of low training efficiency in actual deployment. In this article, we propose an excellent FL policy for the heterogeneous IoT-edge FL system to improve distributed training efficiency. Specifically, first, by borrowing the idea of clustering, we explore an iterative self-organizing data analysis techniques algorithm (ISODATA)-based heterogeneous-aware client scheduling strategy to alleviate the issue of low training efficiency incurred by the heterogeneity of clients. Subsequently, to tackle the challenge of limited communication resources in FL, we first analyze the characteristics of the optimal resource block allocation solution theoretically and then introduce a mixed-integer linear programming (MILP)-based strategy to judiciously allocate resource blocks for scheduled clients. Comprehensive experimental results demonstrate that, compared with benchmarking strategies, our proposed FL policy can achieve up to 55.22% accuracy improvement in a relaxed time scenario, and attain up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.62\times $ </tex-math></inline-formula> acceleration for reaching the specific expected accuracy.
Anomaly detection based on a granular Markov model

Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets.
Mobile Computational Photography: A Tour

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography-the science and engineering of making great images from small-form-factor, mobile cameras. Modern algorithmic and computing advances, including machine learning, have changed the rules of photography, bringing to it new modes of capture, postprocessing, storage, and sharing. In this review, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution. At each step, we can draw naive parallels to the human visual system.
Optimal information disclosure strategies for a retail platform in the blockchain technology era

This paper considers a retail platform selling a product to consumers and voluntarily discloses product quality information by using blockchain technology (BCT). Consumers decide whether to deliberate before making purchase decisions. We develop an analytical model to explore the effects of information disclosure strategies on the retail platform’s pricing and consumers’ deliberation decisions. Some interesting results are obtained. First, regardless of the information disclosure strategy, consumers’ deliberation and purchasing decisions depend on the deliberation cost and product price. Consumers will only deliberate if the price is at the medium level and the deliberation cost is not too high. Second, when the disclosure cost is high, the retail platform either stimulates or prevents consumer deliberation; however, when the disclosure cost is at a medium interval, the retail platform’s pricing decision depends on its information disclosure strategy. Specifically, when disclosing quality information, the retail platform will implement the stimulated deliberation (SD) or prevented deliberation (PD) strategy and will never adopt the sold at a low price (SL) strategy; however, when quality information is withheld, the retail platform will only adopt the PD strategy. Third, we identify the conditions under which the retail platform should disclose quality information when facing different disclosure costs.
Partner-Assisted Learning for Few-Shot Image Classification

Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to train a feature extractor is still a challenge. In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples. We propose a two-stage training scheme, Partner-Assisted Learning (PAL), which first trains a Partner Encoder to model pair-wise similarities and extract features serving as soft-anchors, and then trains a Main Encoder by aligning its outputs with soft-anchors while attempting to maximize classification performance. Two alignment constraints from logit-level and feature-level are designed individually. For each few-shot task, we perform prototype classification. Our method consistently outperforms the state-of-the-art methods on four benchmarks. Detailed ablation studies of PAL are provided to justify the selection of each component involved in training.
Recognizing Emotion Cause in Conversations

We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON . Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause/effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset. Our transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches on our dataset. We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.
Interaction With Gaze, Gesture, and Speech in a Flexibly Configurable Augmented Reality System

Multimodal interaction has become a recent research focus since it offers better user experience in augmented reality (AR) systems. However, most existing works only combine two modalities at a time, e.g., gesture and speech. Multimodal interactive system integrating gaze cue has rarely been investigated. In this article, we propose a multimodal interactive system that integrates gaze, gesture, and speech in a flexibly configurable AR system. Our lightweight head-mounted device supports accurate gaze tracking, hand gesture recognition, and speech recognition simultaneously. The system can be easily configured into various modality combinations, which enables us to investigate the effects of different interaction techniques. We evaluate the efficiency of these modalities using two tasks: the lamp brightness adjustment task and the cube manipulation task. We also collect subjective feedback when using such systems. The experimental results demonstrate that the <i>Gaze+Gesture+Speech</i> modality is superior in terms of efficiency, and the <i>Gesture+Speech</i> modality is more preferred by users. Our system opens the pathway toward a multimodal interactive AR system that enables flexible configuration.
A novel proof of useful work for a blockchain storing transportation transactions

Proof-of-Work (PoW) is a common mechanism used to validate peer-to-peer transactions and maintain highly secured immutability of the blockchain. However, this mechanism has been criticized due to its inefficient use of computing resources and its limited usefulness. In this paper, we propose the Proof-of-Useful-Work (PoUW) as an alternative mechanism for transaction validation that puts the squandered computing resources to beneficial use. The main premise is to replace the mathematical puzzle, which constitutes a fundamental part of the Proof-of-Work mechanism, with NP-hard optimization problems whose solutions benefit the participants of the blockchain. We demonstrate its usefulness in the context of transportation. Accordingly, PoUW-based blockchain not only tracks, manages and validates transactions, but also optimizes transportation requests profiting its ecosystem. We describe the framework of the proposed PoUW along with the associated optimization model and the miner’s reward mechanism.
VCANet: Vanishing-Point-Guided Context-Aware Network for Small Road Object Detection

Advanced deep learning technology has made great progress in generic object detection of autonomous driving, yet it is still challenging to detect small road hazards in a long distance owing to lack of large-scale small-object datasets and dedicated methods. This work addresses the challenge from two aspects. Firstly, a self-collected long-distance road object dataset (TJ-LDRO) is introduced, which consists of 109,337 images and is the largest dataset so far for the small road object detection research. Secondly, a vanishing-point-guided context-aware network (VCANet) is proposed, which utilizes the vanishing point prediction block and the context-aware center detection block to obtain semantic information. The multi-scale feature fusion pipeline and the upsampling block in VCANet are introduced to enhance the region of interest (ROI) feature. The experimental results with TJ-LDRO dataset show that the proposed method achieves better performance than the representative generic object detection methods. This work fills a critical capability gap in small road hazards detection for high-speed autonomous vehicles.
Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition

While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, e.g., the "hat" attribute and the "boots" attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the "hat" attribute should be consistent, whether it is a baseball cap, beret, or helmet. To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute. Specifically, we first propose a spatial consistency regularization to focus on reliable and stable attribute-related regions. Based on the precise attribute locations, we further propose a semantic consistency regularization to extract intrinsic and discriminative semantic features. We conduct extensive experiments on popular benchmarks including PA100K, RAP, and PETA. Results show that the proposed method performs favorably against state- of-the-art methods without increasing parameters.
Generating Failing Test Suites for Quantum Programs With Search

Testing quantum programs requires systematic, automated, and intelligent methods due to their inherent complexity, such as their superposition and entanglement. To this end, we present a search-based approach, called Quantum Search-Based Testing (QuSBT), for automatically generating test suites of a given size depending on available testing budget, with the aim of maximizing the number of failing test cases in the test suite. QuSBT consists of definitions of the problem encoding, failure types, test assessment with statistical tests, fitness function, and test case generation with a Genetic Algorithm (GA). To empirically evaluate QuSBT, we compared it with Random Search (RS) by testing six quantum programs. We assessed the effectiveness of QuSBT and RS with 30 carefully designed faulty versions of the six quantum programs. Results show that QuSBT provides a viable solution for testing quantum programs, and achieved a significant improvement over RS in 87% of the faulty programs, and no significant difference in the rest of 13% of the faulty programs.