Reviewerly/exHarmony huggingface dataset:
Discover Your Social Identity from What You Tweet: A Content Based Approach

An identity denotes the role an individual or a group plays in highly differentiated contemporary societies. In this paper, our goal is to classify Twitter users based on their role identities. We first collect a coarse-grained public figure dataset automatically, then manually label a more fine-grained identity dataset. We propose a hierarchical self-attention neural network for Twitter user role identity classification. Our experiments demonstrate that the proposed model significantly outperforms multiple baselines. We further propose a transfer learning scheme that improves our model’s performance by a large margin. Such transfer learning also greatly reduces the need for a large amount of human labeled data.
Classification Methods With Signal Approximation for Unknown Interference

There has been growing interest in the classification of interference types in communication systems, especially under large samples and unknown interference, which severely restrict anti-jamming performance of the system. In this paper, we present two signal approximation algorithms for classification restricted different conditions, and the transform learning label consistency (TLLC) is embedded into the evaluation owing to the imperfect performance for classification and feature library. First, the interference signals are converted into the signal feature space, and then the interference processing and feature extraction are conducted based on the Hilbert signal space theory. Second, the projection approximation (PA) for signal approximation is used to approximate the unknown interference, and the restricted projection property is demonstrated as well. Furthermore, in order to ease the restrictions, the sparse approximation (SA) for interference signals is demonstrated. Moreover, an unsupervised learning method and the unknown interference classifier are proposed based on the self-organizing map (SOM) neural network. Based on l1 minimization functions, we improve the accuracy of TLLC with sparse approximation, which is more suitable for general interference signals. Finally, the simulation results demonstrate that, compared with the traditional classification method, the proposed method improves the classification accuracy of known interference by 3.44%. In this case, the overall accuracy is close to that of the supervised learning method, and the speed of processing interference is increased by more than 10 times. When the SNR reaches 5 dB, the accuracy of unknown interference classification exceeds 94%. Finally, yet importantly, owing to the imperfect performance for classification and feature library at present, we acquire the final accuracy for them at 92.23% by intervening measures, and the time availability also has been obtained advantages on signal processing.
Debates: Does Information Theory Provide a New Paradigm for Earth Science? Sharper Predictions Using Occam's Digital Razor

Abstract Occam's Razor is a bedrock principle of science philosophy, stating that the simplest hypothesis (or model) is preferred, at any given level of model predictive performance. A modern restatement often attributed to Einstein explains, “Everything should be made as simple as possible, but not simpler.” Using principles from (algorithmic) information theory, both model descriptive performance and model complexity can be quantified in bits. This quantification yields a Pareto‐style trade‐off between model complexity (length of the model program in bits) and model performance (information loss in bits, or the missing information, needed to describe the original observations). Model complexity and performance can be collapsed to one single measure of lossless model size, which, when minimized, leads to optimal model complexity versus loss trade‐off for generalization and prediction. Our view puts both simple data‐driven and complex physical‐process‐based models on a continuum, in the sense that both describe patterns in observed data in compressed form, with different degrees of generality, model complexity, and descriptive performance. Information theory‐based assessment of compression performance with fair and meaningful accounting for model complexity will enable us to best compare and combine the strengths of physics knowledge and data‐driven modeling for a given problem, given the availability of data. “ Suppose we draw a set of points on paper in a totally random manner. I am saying it is possible to find a geometric line whose notation is constant and uniform, following a certain law, that will pass through all points, and in the same order they were drawn .” “ But if that law is strongly composed, the thing that conforms to it should be seen as irregular” Gottfried Wilhelm Leibniz, 1686: Discours de métaphysique V, VI (from French)
Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot

To be useful in everyday environments, robots must be able to identify and locate real-world objects. In recent years, video object segmentation has made significant progress on densely separating such objects from background in real and challenging videos. Building off of this progress, this paper addresses the problem of identifying generic objects and locating them in 3D using a mobile robot with an RGB camera. We achieve this by, first, introducing a video object segmentation-based approach to visual servo control and active perception and, second, developing a new Hadamard-Broyden update formulation. Our segmentation-based methods are simple but effective, and our update formulation lets a robot quickly learn the relationship between actuators and visual features without any camera calibration. We validate our approach in experiments by learning a variety of actuator-camera configurations on a mobile HSR robot, which subsequently identifies, locates, and grasps objects from the YCB dataset and tracks people and other dynamic articulated objects in real-time.
Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle

Unmanned Aerial Vehicle (UAV) spray has been used for efficient and adaptive pesticide applications with its low costs. However, droplet drift is the main problem for UAV spray and will induce pesticide waste and safety concerns. Droplet size and deposition distribution are both highly related to droplet drift and spray effect, which are determined by the nozzle. Therefore, it is necessary to propose an evaluating method for a specific UAV spray nozzles. In this paper, four machine learning methods (REGRESS, least squares support vector machines (LS-SVM), extreme learning machine, and radial basis function neural network (RBFNN)) were applied for quantitatively evaluating one type of UAV spray nozzle (TEEJET XR110015VS), and the case of twin nozzles was investigated. The results showed REGRESS and LS-SVM are good candidates for droplet size evaluation with the coefficient of determination in the calibration set above 0.9 and root means square errors of the prediction set around 2 µm. RBFNN achieved the best performance for the evaluation of deposition distribution and showed its potential for determining the droplet size of overlapping area. Overall, this study proved the accuracy and efficiency of using the machine learning method for UAV spray nozzle evaluation. Additionally, the study demonstrated the feasibility of using machine learning model to predict the droplet size in the overlapping area of twin nozzles.
Proposed Enhancement of A5/1 stream cipher

A5/1 used in GSM to encrypt the conversations. In this paper, proposed an enhancement of A5/1 is add fourth register will be increase the total security of proposed algorithm and apply a new filtration functions on each register to strengthen the linear combination function (XOR) with remain same the total number of bits of A5/1 are 64-bits. A5/1 is considered an efficient implementation of hardware. This proposed take efficiency implementation of hardware of A5/1 to produce an algorithm with more high security, also efficient in hardware implementation. The security of generated key will measure by using NIST tests and berlkamp- Massey algorithm.
Cooperative Initialization based Deep Neural Network Training

Researchers have proposed various activation functions. These activation functions help the deep network to learn non-linear behavior with a significant effect on training dynamics and task performance. The performance of these activations also depends on the initial state of the weight parameters, i.e., different initial state leads to a difference in the performance of a network. In this paper, we have proposed a cooperative initialization for training the deep network using ReLU activation function to improve the network performance. Our approach uses multiple activation functions in the initial few epochs for the update of all sets of weight parameters while training the network. These activation functions cooperate to overcome their drawbacks in the update of weight parameters, which in effect learn better "feature representation" and boost the network performance later. Cooperative initialization based training also helps in reducing the overfitting problem and does not increase the number of parameters, inference (test) time in the final model while improving the performance. Experiments show that our approach outperforms various baselines and, at the same time, performs well over various tasks such as classification and detection. The Top-1 classification accuracy of the model trained using our approach improves by 2.8% for VGG-16 and 2.1% for ResNet-56 on CIFAR-100 dataset.
Emerging trends: Reviewing the reviewers (again)

Abstract The ACL-2019 Business meeting ended with a discussion of reviewing. Conferences are experiencing a success catastrophe. They are becoming bigger and bigger, which is not only a sign of success but also a challenge (for reviewing and more). Various proposals for reducing submissions were discussed at the Business meeting. IMHO, the problem is not so much too many submissions, but rather, random reviewing. We cannot afford to do reviewing as badly as we do (because that leads to even more submissions). Negative feedback loops are effective. The reviewing process will improve over time if reviewers teach authors how to write better submissions, and authors teach reviewers how to write more constructive reviews. If you have received a not-ok (unhelpful/offensive) review, please help program committees improve by sharing your not-ok reviews on social media.
Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.
Plug-and-Play Rescaling Based Crowd Counting in Static Images

Crowd counting is a challenging problem especially in the presence of huge crowd diversity across images and complex cluttered crowd-like background regions, where most previous approaches do not generalize well and consequently produce either huge crowd underestimation or overestimation. To address these challenges, we propose a new image patch rescaling module (PRM) and three independent PRM employed crowd counting methods. The proposed frameworks use the PRM module to rescale the image regions (patches) that require special treatment, whereas the classification process helps in recognizing and discarding any cluttered crowd-like background regions which may result in overestimation. Experiments on three standard benchmarks and cross-dataset evaluation show that our approach outperforms the state-of-the-art models in the RMSE evaluation metric with an improvement up to 10.4%, and possesses superior generalization ability to new datasets.
Answering an open problem on t-norms for type-2 fuzzy sets

This paper proves that a binary operation ⋆ on [0, 1], ensuring that the binary operation ⋏ is a t-norm or ⋎ is a t-conorm, is a t-norm, where ⋏ and ⋎ are special convolution operations defined by(f⋏g)(x)=sup{f(y)★g(z):y▵z=x},(f⋎g)(x)=sup{f(y)★g(z):y▿z=x},for any f, g ∈ Map([0, 1], [0, 1]), where △ and ▽ are a continuous t-norm and a continuous t-conorm on [0, 1], answering negatively an open problem posed in [8]. Besides, some characteristics of t-norm and t-conorm are obtained in terms of the binary operations ⋏ and ⋎.
Conceptualising Sustainability Using a Cognitive Mapping Method

Many different interpretations of the meaning of sustainability has the potential to cause dilution of meaning, as well as ambiguity and confusion, and a reduction in the use of sustainability as a relevant global objective. In this research, we examine these different interpretations using a cognitive mapping method, where 12 sustainability experts provide their own unique conceptualisations of sustainability. Our research objective is to develop a conceptual tool that explains differences in these many sustainability meanings. The experts’ conceptualisations reveal worldviews that are predominantly anthropocentric, but which also contain principles consistent with the sustaincentrism paradigm. We observe that each expert’s worldview, together with the assumed primary objective of sustainability, and the type of actions recommended to achieve sustainability, are central to understanding the diversity of sustainability conceptualisations. This insight leads to the development of a framework that infers sustainability can be conceptualised to consist of three distinct layers consisting of a core objective, or what is to be sustained; a worldview that underpins the conceptualisation; and actions aimed at achieving sustainability. By clarifying the multiple layers contained within sustainability conceptualisations, competing and contested versions of sustainability can be clarified within a common theoretical framework. The sustainability framework should also assist educators and students navigate through the multiplicity of sustainability meanings, and enable new context-specific definitions to be developed, such as those used in business and government.
Real-time vehicle distance estimation using single view geometry

Distance estimation is required for advanced driver assistance systems (ADAS) as well as self-driving cars. It is crucial for obstacle avoidance, tailgating detection and accident prevention. Currently, radars and lidars are primarily used for this purpose which are either expensive or offer poor resolution. Deep learning based depth or distance estimation techniques require huge amount of data and ensuring domain invariance is a challenge. Therefore, in this paper, we propose a single view geometric approach which is lightweight and uses geometric features of the road lane markings for distance estimation that integrates well with the lane and vehicle detection modules of an existing ADAS. Our system introduces novelty on two fronts: (1) it uses cross-ratios of lane boundaries to estimate horizon (2) it determines an Inverse Perspective Mapping (IPM) and camera height from a known lane width and the detected horizon. Distances of the vehicles on the road are then calculated by back projecting image point to a ray intersecting the reconstructed road plane. For evaluation, we used lidar data as ground truth and compare the performance of our algorithm with radar as well as the state-of-the-art deep learning based monocular depth prediction algorithms. The results on three public datasets (Kitti, nuScenes and Lyft level 5) showed that the proposed system maintains a consistent RMSE between 6.10 to 7.31. It outperforms other algorithms on two of the datasets while on KITTI it falls behind one (supervised) deep learning method. Furthermore, it is computationally inexpensive and is data-domain invariant.
An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.
Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation

In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye.Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images.The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks.In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR-HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images.The novel learning technique is called cumulative histogram-based learning and color difference learning.The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images.
3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space

Estimating 3D hand pose from a single RGB image is a challenging task because of its ill-posed nature (i.e., depth ambiguity). Recently, various generative approaches have been proposed to predict the 3D joints of an RGB hand image by learning a unified latent space between two modalities (i.e., RGB image and 3D joints). However, projecting multi-modal data (i.e., RGB images and 3D joints) into a unified latent space is difficult as the modality-specific features usually interfere the learning of the optimal latent space. Hence in this paper, we propose to disentangle the latent space into two sub-latent spaces: modality- specific latent space and pose-specific latent space for 3D hand pose estimation. Our proposed method, namely Disentangled Cross-Modal Latent Space (DCMLS), consists of two variational autoencoder networks and auxiliary components which connect the two VAEs to align underlying hand poses and transfer modality-specific context from RGB to 3D. For the hand pose latent space, we align it with the two modalities by using a cross-modal discriminator with an adversarial learning strategy. For the context latent space, we learn a context translator to gain access to the cross-modal context. Experimental results on two widely used public benchmark datasets RHD and STB demonstrate that our proposed DCMLS method is able to clearly outperform the state-of-the-art ones on single image based 3D hand pose estimation.
Elliptic Curve Cryptography for Wireless Sensor Networks Using the Number Theoretic Transform

We implement elliptic curve cryptography on the MSP430 which is a commonly used microcontroller in wireless sensor network nodes. We use the number theoretic transform to perform finite field multiplication and squaring as required in elliptic curve scalar point multiplication. We take advantage of the fast Fourier transform for the first time in the literature to speed up the number theoretic transform for an efficient realization of elliptic curve cryptography. Our implementation achieves elliptic curve scalar point multiplication in only 0.65 s and 1.31 s for multiplication of fixed and random points, respectively, and has similar or better timing performance compared to previous works in the literature.
Achieving high utilization of flowlet-based load balancing in data center networks

Modern data center networks provide multiple paths between any host pairs. Load balancing traffic across these paths is critical to achieve low latency and high throughput. Despite prior solutions show that flowlet-based solutions are powerful in achieving load balancing in asymmetric topology, they suffer from the congestion mismatch problem in rerouting flowlets across different paths. That is, due to lacking the explicit congestion feedback, existing flowlet-based solutions are difficult to utilize bandwidth resource efficiently, which may result in low link utilization and packet loss. In this paper, we propose a congestion-aware load balancing scheme named CAF to eliminate the problem of congestion mismatch. The basic idea behind CAF is that whenever the sender perceives flowlet switching, it proactively sends probe packets to measure available bandwidth and uses the measurement result to properly set the congestion window, avoiding the unnecessary under-utilization and packet loss. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that CAF reduces average flow completion time by up to 86% compared with the state-of-the-art mechanisms.