Reviewerly/exHarmony huggingface dataset:
Over-the-Air Computation of Large-Scale Nomographic Functions in MapReduce Over the Edge Cloud Network

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Map</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shuffle</i> ; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reduce</i> . However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.
Actionness-Guided Transformer for Anchor-Free Temporal Action Localization

Temporal action localization, detecting actions in untrimmed videos, is widely studied by anchor-based approaches that first generate excessive action proposals, <i>i.e.</i>, temporal windows, then evaluate and classify these proposals. To reduce the number of action proposals, recent studies use an anchor-free approach that leverages each time point rather than a temporal window to represent an action instance. However, this point representation, usually modeled by temporal convolutions, may have the fixed and limited receptive field to detect an entire action. So we propose an Actionness-guided Transformer (Ag-Trans) model to learn representations for each point proposal. Ag-Trans first predicts the actionness, <i>i.e.</i>, time sequences of the action starting, continuing, and ending phases, then the corresponding action phase can be embedded to model the point representation. Experimental results show that the Ag-Trans model outperforms the CNN-based model under the same experiment settings, especially for long-duration actions.
A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.
Sarcasm Detection and Quantification in Arabic Tweets

The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user’s mentality and other factors. In addition to the aforementioned challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced when compared to English. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification (sarcastic vs. non-sarcastic) for the purpose of tackling the complex and user-dependent nature of the sarcastic text. The humanly annotated dataset will be available to the public for any usage.
Fast and Robust Online Handwritten Chinese Character Recognition With Deep Spatial and Contextual Information Fusion Network

Deep convolutional neuralnetworks have achieved fairly high accuracy for single online handwritten Chinese character recognition (SOLHCCR). However, in real application scenarios, users always write multiple characters to form a complete sentence, and previous contextual information holds significant potential for improving the accuracy, robustness and efficiency of recognition. In this work, we first propose a simple and straightforward model named the vanilla compositional network (VCN) by coupling convolutional neural network with a sequence modeling architecture (i.e., a recurrent neural network or Transformer), which exploits the handwritten character’s previous contextual information. Although VCN performs much better than the previous state-of-the-art SOLHCCR models, it is a two-stage architecture in nature. It suffers from high fragility when confronting with poorly written characters such as sloppy writing, and missing or broken strokes, due to relying heavily on contextual information. To improve the robustness of the OLHCCR model, we further propose a novel deep spatial & contextual information fusion network (DSCIFN). It utilizes an autoregresssive framework pre-trained on a large-scale sentence corpora as the backbone component, and highly integrates the spatial features of handwritten characters and their previous contextual information in a multi-layer fusion module. To verify the effectiveness of models, we reorganize a new form of online Chinese handwritten character with its previous context dataset, named OHCCC. Extensive experimental results demonstrate that DSCIFN achieves state-of-the-art performance and has increased strong robustness compared to VCN and previous SOLHCCR models. The in-depth empirical analysis and case study indicate that DSCIFN can significantly improve the efficiency of handwriting input because it does not need complete strokes to recognize a handwritten Chinese character precisely.
Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos

Extracting the underlying dynamics of objects in image sequences is one of the challenging problems in computer vision. Besides, dynamic mode decomposition (DMD) has recently attracted attention as a method for obtaining modal representations of nonlinear dynamics from general multivariate time-series data without explicit prior information about the dynamics. In this paper, we propose a convolutional autoencoder (CAE)-based DMD (CAE-DMD) to perform accurate modeling of underlying dynamics in videos. We develop a modified CAE model that encodes images to latent vectors and incorporated DMD on the latent vectors to extract DMD modes. These modes are split into background and foreground modes for foreground modeling in videos, or used for video classification tasks. And the latent vectors are mapped so as to recover the input image sequences through a decoder. We perform the network training in an end-to-end manner, i.e., by minimizing the mean square error between the original and reconstructed images. As a result, we obtain accurate extraction of underlying dynamic information in the videos. We empirically investigate the performance of CAE-DMD in two applications background foreground extraction and video classification on synthetic and publicly available datasets.
Fuzzy Logic Control for Avoiding Static Obstacle in Autonomous Vehicle Robot

In the fields of technology and industrial automation, robots are now widely used to help reduce human labor. A robot is a mechanical system that can perform complex actions automatically based on computer programmed. In order to build an automated robot, navigation is one of the main problems that still to be solved until now. Many researchers have been use various methods to improve the navigation system of autonomous robots. This research attempts to implement a fuzzy method in an autonomous robot, which aims to maintain the distance between the robot and the obstacle, so that the robot can avoid the obstacle smoothly. In this study, we used three HCSR04 ultrasonic sensors as an input that installed on the front of the robot, and the Arduino MEGA 2560 as a microcontroller. The experiment has been done using case study where the distance of obstacle to the right sensor, middle sensor, and left sensor is 5 cm, 8 cm, and 17 cm respectively. The output of fuzzy is the speed of the PWM motor. Based on the results, the simulation shows the speed of right and left PWM motor is 167 and 33.4 respectively. Meanwhile, the experiment show that the right side PWM motor is 166.61 and the left side motor PWM is 33.89. According to this result, we can conclude that the fuzzy logic control was successfully implemented in the robot to avoid obstacles, the performance value of the right motor is 99.998%, and the performance value of the left motor is 99.086%. It can be seen that the output comparison between the simulation with the realization in the robot is not much different, it’s indicate that the realization of fuzzy logic in the robot to avoid obstacles is successful.
Deep Embedded K-Means Clustering

Recently, deep clustering methods have gained momentum because of the high representational power of deep neural networks (DNNs) such as autoencoder. The key idea is that representation learning and clustering can reinforce each other: Good representations lead to good clustering while good clustering provides good supervisory signals to representation learning. Critical questions include: 1) How to optimize representation learning and clustering? 2) Should the reconstruction loss of autoencoder be considered always? In this paper, we propose DEKM (for Deep Embedded K-Means) to answer these two questions. Since the embedding space generated by autoencoder may have no obvious cluster structures, we propose to further transform the embedding space to a new space that reveals the cluster-structure information. This is achieved by an orthonormal transformation matrix, which contains the eigenvectors of the within-class scatter matrix of K-means. The eigenvalues indicate the importance of the eigenvectors’ contributions to the cluster-structure information in the new space. Our goal is to increase the cluster-structure information. To this end, we discard the decoder and propose a greedy method to optimize the representation. Representation learning and clustering are alternately optimized by DEKM. Experimental results on the real-world datasets demonstrate that DEKM achieves state-of-the-art performance.
Head-Motion-Aware Viewport Margins for Improving User Experience in Immersive Video

Viewport-dependent delivery (VDD) is a technique to save network resources during the transmission of immersive videos. However, it results in a non-zero motion-to-high-quality delay (MTHQD), which is the delta time from the moment where the current viewport has at least one low-quality tile to when all the tiles in the new viewport are rendered in high quality. MTHQD is an important metric in the evaluation of the VDD systems. This paper improves an earlier concept called viewport margins by introducing head-motion awareness. The primary benefit of this improvement is the reduction (up to 64%) in the average MTHQD.
Image Noise Level Estimation by Employing Chi-Square Distribution

Image denoising is a hot topic in computer vision. Most prior image denoising methods reduce noise with the empirical noise level, resulting in noise removal uncleanly or structure smoothness. The main reason is that the noise level is not precisely and adaptively estimated. To cope with this problem, this paper proposes a novel noise estimation scheme based on Chi-square distribution, including: First, the noisy image is divided into image blocks through a sliding window. Then, the flat blocks are selected from the image blocks by using the gradient feature. Next, the initial noise level is estimated by chi-square distribution on flat blocks. Finally, the stable noise level is final obtained by an iterative strategy. Experimental results validate that the proposed noise level estimation strategy is effective and is even superior to the state-of-the-arts.
Spatiotemporal Vision Transformer for Short Time Weather Forecasting

Weather forecasting is a critical research area that may lead to serious consequences, if not done accurately. In order to manage the impact of adverse weather effects short time forecasting is employed, since it can be highly accurate to predict a short time, e.g., hours, into the future. In this work, we tackle the challenge of short time weather forecasting using a novel approach based on a modified UNet based model. Specifically, all convolution-based building blocks were replaced by 3D shifted window transformers in both encoder and decoder branches. Shifted window transformers greatly reduce computational complexity requirements, a major constraint of self-attention, without sacrificing performance. To support the pre-training of the transformer-based model a carefully crafted augmentation scheme was proposed. The model was tested on the IEEE Big Data Weather4cast Competition data, which requires the prediction of 8 hours ahead frames (4 per hour) from an hourly weather product sequence. We show the importance of including other weather products in encouraging spatial generalization, while this may not be optimal for temporal generalization. The model demonstrates highly competitive performance on both the validation and test datasets. The code is available online at https://github.com/bojesomo/Weather4Cast2021-SwinUNet3D
Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and Beyond

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.
A Compositional Deadlock Detector for Android Java

We develop a static deadlock analysis for commercial Android Java applications, of sizes in the tens of millions of LoC, under active development at Facebook.The analysis runs primarily at code-review time, on only the modified code and its dependents; we aim at reporting to developers in under 15 minutes.To detect deadlocks in this setting, we first model the real language as an abstract language with balanced re-entrant locks, nondeterministic iteration and branching, and non-recursive procedure calls.We show that the existence of a deadlock in this abstract language is equivalent to a certain condition over the sets of critical pairs of each program thread; these record, for all possible executions of the thread, which locks are currently held at the point when a fresh lock is acquired.Since the critical pairs of any program thread is finite and computable, the deadlock detection problem for our language is decidable, and in NP.We then leverage these results to develop an open-source implementation of our analysis adapted to deal with real Java code.The core of the implementation is an algorithm which computes critical pairs in a compositional, abstract interpretation style, running in quasi-exponential time.Our analyser is built in the INFER verification framework and has been in industrial deployment for over two years; it has seen over two hundred fixed deadlock reports with a report fix rate of ∼54%.
Features Application of Sensor Networks for Measurement of Raw Cotton Bunts

Currently, wireless sensor networks are widely used in all industries including manufacturing, construction, agriculture, hydrometeorology, industrial, medical, warehousing, plant security, and other systems. The flexibility of wireless sensor networks, interoperability with various technologies, quick and easy acquisition of measured values as well as early warning and early detection of possible disasters and data transmission rates are used in many areas. This article describes the special features of temperature measurement and control in warehouses, the structure of sensor networks and data processing via wireless sensor networks.
Evaluating Data‐type Heterogeneity in Interactive Visual Analyses with Parallel Axes

Abstract The application of parallel axes for the interactive visual analysis of multidimensional data is a widely used concept. While multidimensional data sets are commonly heterogeneous in nature, i.e. data items contain both numerical and categorical (including ordinal) attribute values, the use of parallel axes often assumes either numerical or categorical attributes. While Parallel Coordinates and their large variety of extensions focus on numerical data, Parallel Sets and related methods focus on categorical attributes. While both concepts allow for displaying heterogeneous data, no clear strategies have been defined for representing categories in Parallel Coordinates or discretization of continuous ranges in Parallel Sets. In practice, type conversion as a pre‐processing step can be used as well as coordinated views of numerical and categorical data visualizations. We evaluate traditional and state‐of‐the‐art approaches with respect to the interplay of categorical and numerical dimensions for querying probability‐based events. We also compare against a heterogeneous Parallel Coordinates/Parallel Set approach with a novel interface between categorical and numerical axes . We show that approaches for mapping categorical data to numerical axis representations can lead to lower accuracy in answering probability‐based questions and higher response times than hybrid approaches in multiple‐event scenarios.
Choosing Units of Analysis in Temporal Discourse

Despite the promise of quantitative ethnographic approaches for visualizing the trajectories of change over time (temporal analysis) further work is needed to develop strategies for accurately representing phenomena. This holds especially true for identifying the relational context of discourse, which includes the creation of time units that group lines of data for the purpose of interpretation. While in-depth interpretive review of discourse may serve as the ‘gold standard’ for identification of thematic time units, this approach is tedious and may not be appropriate for larger datasets. Incremental approaches, such as creating a new unit for every ten lines of chronological data, are functional for larger datasets, but may lack nuance. This work introduces the Knowledge Building Discourse Explorer (KBDeX), which computationally identifies relational units using socio-semantic network analysis, allowing for the identification of time units based on characteristics of the discourse that can be systematically applied to larger datasets. To examine the utility of each approach, epistemic networks of COVID-19 press releases from seven countries were created with time units derived from the incremental and computational approaches, which were then compared to the interpretive approach. Results indicated that KBDeX and incremental network means were closer to the ‘gold standard’ interpretive approach in some instances. Two countries’ trajectories are examined in greater depth to understand when each approach might be most appropriate. The work concludes with a discussion of the affordances and constraints of each approach, and contexts in which they may be useful.
Extension of Restricted Equivalence Functions and Similarity Measures for Type-2 Fuzzy Sets

In this work, we generalize the notion of restricted equivalence function for type-2 fuzzy sets, leading to the notion of extended restricted equivalence functions. We also study how under suitable conditions, these new functions recover the standard axioms for restricted equivalence functions in the real setting. Extended restricted equivalence functions allow us to compare any two general type-2 fuzzy sets and to generate a similarity measure for type-2 fuzzy sets. The result of this similarity is a fuzzy set on the same referential set (i.e., domain) as the considered type-2 fuzzy set. The latter is crucial for applications such as explainable AI and decision-making, as it enables an intuitive interpretation of the similarity within the domain-specific context of the fuzzy sets. We show how this measure can be used to compare type-2 fuzzy sets with different membership functions in such a way that the uncertainty linked to type-2 fuzzy sets is not lost. This is achieved by generating a fuzzy set rather than a single numerical value. Furthermore, we also show how to obtain a numerical value for discrete referential sets.