Reviewerly/exHarmony huggingface dataset:
LSNet: Learned Sampling Network for 3D Object Detection from Point Clouds

The3D object detection of LiDAR point cloud data has generated widespread discussion and implementation in recent years. In this paper, we concentrate on exploring the sampling method of point-based 3D object detection in autonomous driving scenarios, a process which attempts to reduce expenditure by reaching sufficient accuracy using fewer selected points. FPS (farthest point sampling), the most used sampling method, works poorly in small sampling size cases, and, limited by the massive points, some newly proposed sampling methods using deep learning are not suitable for autonomous driving scenarios. To address these issues, we propose the learned sampling network (LSNet), a single-stage 3D object detection network containing an LS module that can sample important points through deep learning. This advanced approach can sample points with a task-specific focus while also being differentiable. Additionally, the LS module is streamlined for computational efficiency and transferability to replace more primitive sampling methods in other point-based networks. To reduce the issue of the high repetition rates of sampled points, a sampling loss algorithm was developed. The LS module was validated with the KITTI dataset and outperformed the other sampling methods, such as FPS and F-FPS (FPS based on feature distance). Finally, LSNet achieves acceptable accuracy with only 128 sampled points and shows promising results when the number of sampled points is small, yielding up to a 60% improvement against competing methods with eight sampled points.
A novel top-n recommendation method for multi-criteria collaborative filtering

Most online service providers utilize a recommender system to help their customers' decision making process by producing referrals. If a customer requests a suggestion for a specific item, the recommender systems produce predictions for it. On the other hand, it is also possible to create top-n lists containing the products that the customer might like the most. Recommender systems' outcomes depend on individuals' preferences which can be provided by considering a single criterion or multiple criteria about the services or products. Therefore, there must be methods to produce predictions and top-n lists for single and multiple-criteria datasets. Although the researchers introduced several algorithms on single criterion-based ratings for producing single predictions and top-n lists, there are only methods for producing referrals for a specific item on multi-criteria data. Accordingly, this paper proposes an intuitionistic fuzzy set-based top-n recommender system method with a novel neighborhood formation process for multi-criteria datasets. The proposed method consists of two crucial points: (i) Determining the relational structure between products; (ii) Investigating user tendencies, as well as their distinctive structures and rating distributions. The rating distribution and the relational structure between the products are determined with association rule mining and entropy measure, while the attitudes and tendencies of the users during the evaluation are analyzed with intuitionistic fuzzy sets. We also adopt a single-criterion top-n method to a multi-criteria recommender system, and we employ crisp ratings instead of fuzzy ones to compare the performance of the proposed method. The measurements of serendipity, diversity, and novelty are utilized to show how the experimental results are compelling. When the experiments' results are examined, it is concluded that our method can generate successful top-n lists.
A Novel Deep Learning Model for Accurate Prediction of Image Captions in Fashion Industry

As the need for automation in the IT sector is growing, several fashion companies are employing models that can create appropriate descriptions for product images. This will assist buyers to better understand the goods, resulting in increased sales for the apparel company. For creating the image descriptions, the researchers used a variety of feature extraction approaches, including convolution neural networks with several layers like VGG-16 and VGG-19. Once the image features are extracted using these convolution neural network (CNN) models, processing of text data is done using a recurrent neural network (RNN) that represents the input sequence of text as a fixed length output vector. Finally, both the vector outputs obtained from the digital image and its description are combined to train the image caption generator model. In this work, we put forward a smaller 5 layer convolution neural network (CNN-5) and compared it with transfer learning models like VGG-16 and VGG-19. The experiments were carried out on the Fashion MNIST dataset, which consists 70,000 gray scale images of size of 28x28 pixels. Each image is linked to one of ten labels (0-9) that represent ten different fashion items. We compared the performance of the proposed methodology as well as the state-of-the-art models using Bilingual Evaluation Understudy: BLEU-I, BLEU-2, BLEU-3 and BLEU-4 scores. The research demonstrates that a smaller layered convolution neural network can reach a similar degree of accuracy for the Fashion MNIST dataset as compared to state-of-the-art methods.
A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea

Photovoltaic (PV) power generation is considered a forward-looking industry. Nevertheless, solar energy is yet to become a direct source of electric power for mobile vehicles. Recently, there have been cases where solar panels were attached to the roof of trains to generate electricity. In this study, a method was devised to estimate the power generated by a solar train with panels. The solar irradiance on the roof of a moving train was calculated with respect to the location and time of the train, as well as the shadow effects of obstacles. The preprocessing of the input data required for calculating solar irradiation was executed through Geographic Information Systems, and finally, an algorithm for calculating solar irradiation and power generation was developed. With the algorithm-embodied Graphical User Interface, when spatial information of various routes is provided, the PV potential for each route can be calculated. Experimental calculations were conducted on the Gyeongbu line in Korea. During train operation, 122.15 MWh of power can be generated per year, with a reduction of 56 tons of CO2. The results of this preliminary evaluation are expected to accelerate the growth of the solar train industry.
DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning

Abstract We propose a deep spatiotemporal recurrent neural network, DeepShake, to project future shaking intensity directly from current ground-motion observations. DeepShake is a network-based forecasting model, able to predict future shaking intensity at all stations within a network given previously measured ground shaking. The model is not given any a priori knowledge of station locations; instead, it learns wave propagation amplitudes and delays solely from training data. We developed DeepShake with the 35,679 earthquakes from the 2019 Ridgecrest sequence. Tasked with alerting for modified Mercalli intensity (MMI) IV+ shaking on 3568 validation earthquakes at least 5 s in advance, DeepShake achieves an equal error rate of 11.4%. For the Mw 7.1 earthquake that hit Ridgecrest on 5 July 2019, DeepShake was able to provide targeted alerts to all stations inside the network 5 s prior to the arrival of MMI IV+ waveforms. DeepShake demonstrates that deep spatiotemporal neural networks can effectively provide one-step earthquake early warning with reasonable accuracy and latency.
Data-Driven Discovery of Stochastic Differential Equations

Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data

Tilt angle is a key parameter that affects solar photovoltaic (PV) power generation. Traditional empirical model based on latitude may fail to estimate the optimum tilt angle in regions with large climatic differences. In this study, we propose a framework that incorporates ground with satellite data to determine the optimum tilt angle at any location. We collect global solar radiation measurements of 133 stations in China and estimate the diffuse fraction through Boland–Ridley–Lauret model. Hourly PV power generation is modelled by considering photoelectric conversion process and PV system losses. The results show that the optimum tilt angles are highly correlated with latitude and diffuse fraction with an R of 0.86 and 0.80, respectively. Empirical models reconciled by diffuse fraction allow for spatial estimates of the optimum tilt angle from satellite data to reflect the climatic influence. Furthermore, we explore the benefit from periodically adjusting the tilt angle in China. PV panels fixed at the optimum tilt angle increase the annual power yield by 13.7% compared with horizontally fixed panels. Seasonal adjustment largely increases power yield in winter; additional yield gain of monthly adjustment is very limited; while tracking systems provide the option of achieving double or even triple gains.
Attention-based explainable friend link prediction with heterogeneous context information

Friend link prediction is an important research problem in recommender systems. Existing network embedding and knowledge embedding methods mainly consider the structural relationships of entities, ignoring the explanatory role of text contents. In this paper, we present an explainable friend link recommendation method that leverages direct and indirect similarity of user pairs via fusion embedding of heterogeneous context information. In social networks, while considering user content interests, first, a fusion user embedding method was developed by incorporating external knowledge semantics. Second, for a user pair, we calculate their direct similar relationship using fusion user embeddings. Additionally, based on intermediate neighbors, we compute their indirect similar relationship by using an attention mechanism, which explains neighbors' bootable and transitive influences for learning the social relationship of user pairs. Then, a hybrid personalized and neighbor attention model for friend link prediction was proposed by considering direct and indirect factors. Finally, experiments were conducted on the Sina Weibo datasets, which indicates that the proposed method effectively predicts the friends of users and provides a good interpretation for link prediction recommendation results.
New Benchmark for Household Garbage Image Recognition

Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification. Due to the ability to discover problem-specific features, deep learning and especially convolutional neural networks (CNNs) have been successfully and widely used for image representation learning. However, available and stable household garbage datasets are insufficient, which seriously limits the development of research and application. Besides, the state-of-the-art in the field of garbage image classification is not entirely clear. To solve this problem, in this study, we built a new open benchmark dataset for household garbage image classification by simulating different lightings, backgrounds, angles, and shapes. This dataset is named 30 classes of household garbage images (HGI-30), which contains 18 000 images of 30 household garbage classes. The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition. We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30, which serves as baseline results on this benchmark.
Performance of Parallel K-Means Algorithms in Java

K-means is a well-known clustering algorithm often used for its simplicity and potential efficiency. Its properties and limitations have been investigated by many works reported in the literature. K-means, though, suffers from computational problems when dealing with large datasets with many dimensions and great number of clusters. Therefore, many authors have proposed and experimented different techniques for the parallel execution of K-means. This paper describes a novel approach to parallel K-means which, today, is based on commodity multicore machines with shared memory. Two reference implementations in Java are developed and their performances are compared. The first one is structured according to a map/reduce schema that leverages the built-in multi-threaded concurrency automatically provided by Java to parallel streams. The second one, allocated on the available cores, exploits the parallel programming model of the Theatre actor system, which is control-based, totally lock-free, and purposely relies on threads as coarse-grain “programming-in-the-large” units. The experimental results confirm that some good execution performance can be achieved through the implicit and intuitive use of Java concurrency in parallel streams. However, better execution performance can be guaranteed by the modular Theatre implementation which proves more adequate for an exploitation of the computational resources.
Relation-based global-partial feature learning network for video-based person re-identification

Video-based person re-identification (Re-ID) aims to match the same pedestrian from the video sequences captured by non-overlapping cameras. It is the key to fully extracting abundant spatial and temporal information from the video frames in video-based Re-ID. In this paper, a novel Relation-Based Global-Partial Feature Learning framework is proposed to explore discriminative spatiotemporal features with the help of the global and partial relationship between frames. Specifically, we propose a Relation-Based Global Feature Learning Module (RGL) to obtain global references for generating features correlation maps between frames in the video sequence and determine the importance of frame-level features. As the supplementary of the global relation-based features, a Relation-Based Partial Feature Learning Module (RPL) is also proposed to obtain the relationship between partial features of the same spatial position in different frames to enhance the frame-level partial representation. Moreover, we design a multi-level training scheme to deeply supervise our model. Extensive experiments are conducted on three public video-based person Re-ID datasets, and the results indicate that our framework achieves state-of-the-art performance on three benchmarks.
sMARE: a new paradigm to evaluate and understand query performance prediction methods

Abstract Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP performance as a distribution instead of relying on point estimates. To obtain such distribution, we exploit the scaled Absolute Ranking Error (sARE) measure, and its mean the scaled Mean Absolute Ranking Error (sMARE). Our work demonstrates important statistical implications, and overcomes key limitations imposed by the currently used correlation-based point-estimate evaluation approaches. We also explore the potential benefits of using multiple query formulations and ANalysis Of VAriance (ANOVA) modeling in order to measure interactions between multiple factors. The resulting statistical analysis combined with a novel evaluation framework demonstrates the merits of modeling QPP performance as distributions, and enables detailed statistical ANOVA models for comparative analyses to be created.
MEANet: Multi-modal edge-aware network for light field salient object detection

Abundant light field cues have been demonstrated helpful to boost performance of salient object detection (SOD) in challenging scenarios. However, the majority of existing deep light field SOD models focus on exploring spatial interrelations across focal slices and seldom consider boundary accuracy of salient objects, which inevitability limits the detection performance. Meanwhile, in addition to focal stacks, several other data forms/modalities can be derived simultaneously from the light field. Therefore existing UNet-like strategies widely adopted for a single modality may not be very suitable for this task. To address these issues, we propose a novel multi-modal edge-aware network for light field SOD, named MEANet. MEANet has two innovative components elaborately designed for this task, i.e., the cross-modal compensation (CMC) module and the multi-modal edge supervision (MES) module. CMC utilizes the attention mechanism to explore cross-modal complementarities and overcome the information loss of focal stack cues, whereas MES generates explicit object edges and edge features in order to progressively refine regional features to achieve edge-aware detection. Comprehensive evaluations on four benchmark datasets show that MEANet outperforms state-of-the-art light field and RGB-D/RGB SOD models, generating saliency maps with fine-grained accurate object boundaries effectively from multiple data inputs. The code and models are publicly available at https://github.com/jiangyao-scu/MEANet.
Emotional conversation generation with heterogeneous graph neural network

The successful emotional conversation system depends on sufficient perception and appropriate expression of emotions. In a real-life conversation, humans firstly instinctively perceive emotions from multi-source information, including the emotion flow hidden in dialogue history, facial expressions, audio, and personalities of speakers. Then, they convey suitable emotions according to their personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, in this paper, we propose a heterogeneous graph-based model for emotional conversation generation. Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. Secondly, we employ an Emotion-Personality-Aware Decoder to generate a response relevant to the conversation context as well as with appropriate emotions, through taking the encoded graph representations, the predicted emotions by the encoder and the personality of the current speaker as inputs. Experiments on both automatic and human evaluation show that our method can effectively perceive emotions from multi-source knowledge and generate a satisfactory response. Furthermore, based on the up-to-date text generator BART, our model still can achieve consistent improvement, which significantly outperforms some existing state-of-the-art models.
Sequential Recommendation with Multiple Contrast Signals

Sequential recommendation has become a trending research topic for its capability to capture dynamic user intents based on historical interaction sequence. To train a sequential recommendation model, it is a common practice to optimize the next-item recommendation task with a pairwise ranking loss. In this paper, we revisit this typical training method from the perspective of contrastive learning and find it can be taken as a specialized contrastive learning task conceptually and mathematically, named context-target contrast . Further, to leverage other self-supervised signals in user interaction sequences, we propose another contrastive learning task to encourage sequences after augmentation, as well as sequences with the same target item, to have similar representations, called context-context contrast . A general framework, ContraRec, is designed to unify the two kinds of contrast signals, leading to a holistic joint-learning framework for sequential recommendation with different contrastive learning tasks. Besides, various sequential recommendation methods (e.g., GRU4Rec, Caser, and BERT4Rec) can be easily integrated as the base sequence encoder in our ContraRec framework. Extensive experiments on three public datasets demonstrate that ContraRec achieves superior performance compared to state-of-the-art sequential recommendation methods.
Demystifying disinformation shadow economies: fake news work models in Indonesia and the Philippines

Engaging with the special issue theme of media freedom 'from below,' this article contributes ethnographically grounded and comparative research of two democratic Southeast Asian countries dealing with urgent threats to media freedom and democracy: Indonesia and the Philippines. Our research identifies the main disinformation work models in Southeast Asia, most notably through the use of buzzers (Indonesia) and trolls (the Philippines). Our research examines the increasingly gray area between trolls, buzzers and disinformation, and their increasing relationship to political elites. By explaining recent practices of political disinformation campaigns and journalist harassment, we aim to deepen understanding as to how these campaigns are organized in order to prevent them in the future. This article ultimately calls for a critical collaboration with diverse stakeholders in countering 'fake news' by examining four dominant disinformation work models.