Reviewerly/exHarmony huggingface dataset:
Direct Measure Matching for Crowd Counting

Traditional crowd counting approaches usually use Gaussian assumption to generate pseudo density ground truth, which suffers from problems like inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a new measure-based counting approach to regress the predicted density maps to the scattered point-annotated ground truth directly. First, crowd counting is formulated as a measure matching problem. Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching. Third, we propose a self-supervised mechanism by devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an efficient optimization method is provided to minimize the overall loss function. Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++ and NWPU have validated the proposed method.
A Survey on Skill Identification From Online Job Ads

A changing job market, influenced by different factors such as globalization and demographic growth, urges close monitoring. The digitization of the job market has given the opportunity to researchers to better understand job market needs as job postings/ads become more accessible. However, such postings are submitted in unstructured text and need further processing to identify the required skills. The aim of this survey is to review current research on skill identification from job ads and to discuss possible future research directions. In this study, we systematically reviewed 108 research articles on the topic. In particular, we evaluated and classified the prior work aiming to identify the skill bases used for analyzing job market needs; the type of extracted skills; the skill identification methods; the studied sector and the skill identification granularity. Then, we categorized the existent applications and goals of skill identification. Finally, we present key challenges and discuss recent trends.
Interpretation of the Reflectance Spectra of Lithium (Li) Minerals and Pegmatites: A Case Study for Mineralogical and Lithological Identification in the Fregeneda-Almendra Area

Reflectance spectroscopy has been used to identify several deposit types. However, applications concerning lithium (Li)-pegmatites are still scarce. Reflectance spectroscopic studies complemented by microscopic and geochemical studies were employed in the Fregeneda–Almendra (Spain–Portugal) pegmatite field to analyze the spectral behavior of Li-minerals and field lithologies. The spectral similarity of the target class (Li-pegmatites) with other elements was also evaluated. Lepidolite was discriminated from other white micas and the remaining Li-minerals. No diagnostic feature of petalite and spodumene was identified, since their spectral curves are dominated by clays. Their presence was corroborated (by complementary techniques) in petalite relics and completely replaced crystals, although the clay-related absorption depths decrease with Li content. This implies that clays can be used as pathfinders only in areas where argillic alteration is not prevalent. All sampled lithologies present similar water and/or hydroxide features. The overall mineral assemblage is very distinct, with lepidolite, cookeite, and orthoclase exclusively identified in Li-pegmatite (being these minerals crucial targets for Li-pegmatite discrimination in real-life applications), while chlorite and biotite can occur in the remaining lithologies. Satellite data can be used to discriminate Li-pegmatites due to distinct reflectance magnitude and mineral assemblages, higher absorptions depths, and distinct Al–OH wavelength position. The potential use of multi- and hyperspectral data was evaluated; the main limitations and advantages were discussed. These new insights on the spectral behavior of Li-minerals and pegmatites may aid in new Li-pegmatite discoveries around the world.
Enhancing Chinese Character Representation With Lattice-Aligned Attention

Word–character lattice models have been proved to be effective for some Chinese natural language processing (NLP) tasks, in which word boundary information is fused into character sequences. However, due to the inherently unidirectional sequential nature, prior approaches have only learned sequential interactions of character–word instances but fail to capture fine-grained correlations in word–character spaces. In this article, we propose a lattice-aligned attention network (LAN) that aims to model dense interactions over word–character lattice structure for enhancing character representations. By carefully combining cross-lattice module, gated word–character semantic fusion unit, and self-lattice attention module, the network can explicitly capture fine-grained correlations across different spaces (e.g., word-to-character and character-to-character), thus significantly improving model performance. Experimental results on three Chinese NLP benchmark tasks demonstrate that LAN obtains state-of-the-art results compared to several competitive approaches.
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

As the world struggles with several compounded challenges caused by the COVID-19 pandemic in the health, economic, and social domains, timely access to disaggregated national and sub-national data are important to understand the emergent situation but it is difficult to obtain. The widespread usage of social networking sites, especially during mass convergence events, such as health emergencies, provides instant access to citizen-generated data offering rich information about public opinions, sentiments, and situational updates useful for authorities to gain insights. We offer a large-scale social sensing dataset comprising two billion multilingual tweets posted from 218 countries by 87 million users in 67 languages. We used state-of-the-art machine learning models to enrich the data with sentiment labels and named-entities. Additionally, a gender identification approach is proposed to segregate user gender. Furthermore, a geolocalization approach is devised to geotag tweets at country, state, county, and city granularities, enabling a myriad of data analysis tasks to understand real-world issues at national and sub-national levels. We believe this multilingual data with broader geographical and longer temporal coverage will be a cornerstone for researchers to study impacts of the ongoing global health catastrophe and to manage adverse consequences related to people’s health, livelihood, and social well-being.
A vision for contextualized evaluation of remote collaboration supported by AR

Remote collaboration using Augmented Reality (AR) has potential to support physically distributed team-members that need to achieve a common goal by increasing knowledge retention, improving understanding and awareness of the problem and its context. In this vein, the path to achieve usable, realistic and impactful solutions must entail an explicit understanding regarding how remote collaboration occurs through AR and how it may help contribute to a more effective work effort. Thus, characterization and evaluation of the collaborative process is paramount, but a particularly challenging endeavor, due to the multitude of aspects that define the collaboration effort. In this context, the work presented here contributes with a critical analysis, discussing current evaluation efforts, identifying limitations and opportunities. Then, we outline a conceptual framework to support researchers in conducting evaluations in a more structured manner. To instrument this vision, an evaluation toolkit is proposed to support contextual data collection and analysis in such scenarios and obtain an additional perspective on selected dimensions of collaboration. We illustrate the usefulness and versatility of the toolkit through a case study on remote maintenance, comparing two distinct methods: sharing of video and AR-based annotations. Last, we discuss the results obtained, showing the proposed vision allows to have an additional level of insights to better understand what happened, eliciting a more complete characterization of the work effort.
Predicted Squared Error: A Criterion for Automatic Model Selection

Whereas conventional empirical modeling techniques require an assumed model structure, new procedures have been developed which generate the model structure as well as the model coefficients from a data base. These procedures include the GMDH and PNETTR algorithms for creating polynomial networks. Key to any automatic procedure for generating models is the criterion for ranking different model structures and selecting the best.
AI-enabled photonic smart garment for movement analysis

Smart textiles are novel solutions for remote healthcare monitoring which involve non-invasive sensors-integrated clothing. Polymer optical fiber (POF) sensors have attractive features for smart textile technology, and combined with Artificial Intelligence (AI) algorithms increase the potential of intelligent decision-making. This paper presents the development of a fully portable photonic smart garment with 30 multiplexed POF sensors combined with AI algorithms to evaluate the system ability on the activity classification of multiple subjects. Six daily activities are evaluated: standing, sitting, squatting, up-and-down arms, walking and running. A k-nearest neighbors classifier is employed and results from 10 trials of all volunteers presented an accuracy of 94.00 (0.14)%. To achieve an optimal amount of sensors, the principal component analysis is used for one volunteer and results showed an accuracy of 98.14 (0.31)% using 10 sensors, 1.82% lower than using 30 sensors. Cadence and breathing rate were estimated and compared to the data from an inertial measurement unit located on the garment back and the highest error was 2.22%. Shoulder flexion/extension was also evaluated. The proposed approach presented feasibility for activity recognition and movement-related parameters extraction, leading to a system fully optimized, including the number of sensors and wireless communication, for Healthcare 4.0.
Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the subsequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.
Empirical evidence on the efficiency of backward contact tracing in COVID-19

Abstract Standard contact tracing practice for COVID-19 is to identify persons exposed to an infected person during the contagious period, assumed to start two days before symptom onset or diagnosis. In the first large cohort study on backward contact tracing for COVID-19, we extended the contact tracing window by 5 days, aiming to identify the source of the infection and persons infected by the same source. The risk of infection amongst these additional contacts was similar to contacts exposed during the standard tracing window and significantly higher than symptomatic individuals in a control group, leading to 42% more cases identified as direct contacts of an index case. Compared to standard practice, backward traced contacts required fewer tests and shorter quarantine. However, they were identified later in their infectious cycle if infected. Our results support implementing backward contact tracing when rigorous suppression of viral transmission is warranted.
Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method

Industrial big data technology has become one of the important driving forces to intelligent manufacturing in the steel industry. In this study, the characteristics of data in steel production are analyzed and an industrial big data platform for steeling process is developed to extract the quality-related parameters. A data-driven approach to construct prediction intervals (PIs) of mechanical performances for hot-rolling strips is proposed to represent the uncertainty and reliability of the prediction results. The proposed method employs a new manifold visualization method, SLISEMAP, to reduce the feature dimensions with interpretability, utilizes lower upper bound estimation (LUBE) method to obtain the PIs, in which the broad learning system (BLS) is used as the basic training network model and the artificial bee colony (ABC) algorithm is applied to optimize the weighting parameters of BLS under the LUBE framework. A hot-rolling steel coil dataset consisting of 39 variables and 1335 coil samples is used to validate the proposed method. Two Delta-based approaches, namely back propagation neural network (BPNN) and extreme learning machine (ELM); and three LUBE-based approaches, namely ABC-BPNN, ABC-ELM, and ABC-support vector regression (SVR) are compared with the proposed method. Results show that the proposed ABC-BLS in LUBE is effective and efficient in constructing the PIs with a higher coverage probability and a narrower interval width. • Develop an industrial big data platform for steeling production. • Apply the SLISEMAP method to reduce the feature dimension with interpretability. • Combine the broad learning system with ABC algorithm to construct PIs of mechanical performances under LUBE framework. • Data from a practical large hot rolling mill is used to evaluate the performance of the proposed approach.
A Systematic Review of Drone Based Road Traffic Monitoring System

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.
Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval

Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics. Most of existing cross-modal retrieval approaches ignore the usage of scene text information and directly adding this information may lead to performance degradation in scene text free scenarios. To address this issue, we propose a full transformer architecture to unify these cross-modal retrieval scenarios in a single Vision and Scene Text Aggregation framework (ViSTA). Specifically, ViSTA utilizes transformer blocks to directly encode image patches and fuse scene text embedding to learn an aggregated visual representation for cross-modal retrieval. To tackle the modality missing problem of scene text, we propose a novel fusion token based transformer aggregation approach to exchange the necessary scene text information only through the fusion token and concentrate on the most important features in each modality. To further strengthen the visual modality, we develop dual contrastive learning losses to embed both image-text pairs and fusion-text pairs into a common cross-modal space. Compared to existing methods, ViSTA enables to aggregate relevant scene text semantics with visual appearance, and hence improve results under both scene text free and scene text aware scenarios. Experimental results show that ViSTA outperforms other methods by at least 8.4% at Recall@ 1 for scene text aware retrieval task. Compared with state-of-the-art scene text free retrieval methods, ViSTA can achieve better accuracy on Flicker30K and MSCOCO while running at least three times faster during the inference stage, which validates the effectiveness of the proposed framework.
Crypt-DAC: Cryptographically Enforced Dynamic Access Control in the Cloud

Enabling cryptographically enforced access controls for data hosted in untrusted cloud is attractive for many users and organizations.However, designing efficient cryptographically enforced dynamic access control system in the cloud is still challenging.In this paper, we propose Crypt-DAC, a system that provides practical cryptographic enforcement of dynamic access control.Crypt-DAC revokes access permissions by delegating the cloud to update encrypted data.In Crypt-DAC, a file is encrypted by a symmetric key list which records a file key and a sequence of revocation keys.In each revocation, a dedicated administrator uploads a new revocation key to the cloud and requests it to encrypt the file with a new layer of encryption and update the encrypted key list accordingly.Crypt-DAC proposes three key techniques to constrain the size of key list and encryption layers.As a result, Crypt-DAC enforces dynamic access control that provides efficiency, as it does not require expensive decryption/reencryption and uploading/re-uploading of large data at the administrator side, and security, as it immediately revokes access permissions.We use formalization framework and system implementation to demonstrate the security and efficiency of our construction.
Group recommender systems: A multi-agent solution

Providing recommendations to groups of users has become a promising research area, since many items tend to be consumed by groups of people. Various techniques have been developed aiming at making recommendations to a group as a whole. Most works use aggregation techniques to combine preferences, recommendations or profiles. However, satisfying all group members in an even way still remains as a challenge. To deal with this problem, we propose an extension of a multi-agent approach based on negotiation techniques for group recommendation. In the approach, we use the multilateral Monotonic Concession Protocol (MCP) to combine individual recommendations into a group recommendation. In this work, we extend the MCP protocol to allow users to personalize the behavior of the agents. This extension was evaluated in two different domains (movies and points of interest) with satisfactory results. We compared our approach against different baselines, namely: a preference aggregation algorithm, a recommendation aggregation algorithm, and a simple one-step negotiation. The results show evidence that, when using our negotiation approach, users in the groups are more uniformly satisfied than with traditional aggregation approaches.
A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot

The development of a navigation system for autonomous robotic sailing is a particularly challenging task since the sailboat robot uses unpredictable wind forces for its propulsion besides working in a highly nonlinear and harsh environment, the water. Toward solving the problems that appear in this kind of environment, we propose a navigation system which allows the sailboat to reach any desired target points in its working environment. This navigation system consists of a low-level heading controller and a short-term path planner for situations against the wind. For the low-level heading controller, a gain-scheduling proportional-integral (GS-PI) controller is shown to better describe the nonlinearities inherent to the sailboat movement. The gain-scheduling-PI consists of a table that contains the best control parameters that are learned/defined for a particular maneuver and perform the scheduling according to each situation. The idea is to design specialized controllers which meet the specific control objectives of each application. For achieving short-term path-planned targets, a new approach for optimization of the tacking maneuvering to reach targets against the wind is also proposed. This method takes into account two tacking parameters: the side distance available for the maneuvering and the desired sailboat heading when tacking. An optimization method based on genetic algorithm is used in order to find satisfactory upwind paths. Results of various experiments verify the validity and robustness of the developed methods and navigation system.