Reviewerly/exHarmony huggingface dataset:
Stream sediment analysis for Lithium (Li) exploration in the Douro region (Portugal): A comparative study of the spatial interpolation and catchment basin approaches

Lithium (Li) was recently added to the list of critical raw materials by the European Union due to its significance for the green energy transition. Thus, the development of new toolchains to make Li exploration more economic and more effective is needed. Stream sediment analysis can play an important part in these new tool chains. In this work, two historical stream sediment datasets covering parts of the Fregeneda-Almendra pegmatite field in the Douro region (Portugal) were reprocessed considering two distinct approaches: spatial interpolation through inverse distance weighting (IDW) and the catchment basin approach using the concentration area (C-A) fractal analysis. The following objectives were delineated: (i) determine pathfinder elements for Li, considering relevant associations in the mineralization sources; (ii) compare the performance of both approaches; (iii) identify new target areas for Li. In the case of spatial interpolation, the highest Li values were associated to granites although the use of key elements allowed lithological discrimination and the delineation of target areas. In the catchment basin approach, fractal analysis proved to be effective in decreasing the number of areas of interest with high accuracy (>75%) when considering the previously mapped Li-pegmatites. One of the limitations identified was the number of anomalous basins related to the granites, despite the use of pathfinder elements allowing discriminating granite- from pegmatite-related Li anomalies. Comparing the two approaches, the spatial interpolation method is more adequate for the early stages of exploration (reconnaissance), while the catchment basin approach is more suited for prospect-scale exploration. Field validation of the results identified one pegmatite containing Li mineralization and three others with favorable signs for Li mineralization in the Douro region.
AI-driven customer relationship management for sustainable enterprise performance

Today, Artificial Intelligence (AI) is poised to take on Industry 4.0. Machines have conquered the human interface in complex board games like chess and Chinese games. AIcan now writes poetry, make predictions about decisions, interact with humans in real-time, analyze trillions of data points, and offer solutions in milliseconds. Customer Relationship Management (CRM) is one sector benefiting the most from AI. AI-driven customer relationship management (AI-CRM) technology is proposed in this article. Scholars developed a research method to evaluate product quality, performance, and commitment to AI-enabled technologies. The proposed model is designed with system-level architecture, process-level architecture, and mathematical models to correlate the input and output. A dataset was utilized to obtain primary data acquired by investigators using a survey technique. Data assessment revealed that customers are aware of AI-enabled technologies, that the technique under investigation is successful, and that customers are loyal to them.
Learning towards conversational AI: A survey

Recent years have witnessed a surge of interest in the field of open-domain dialogue. Thanks to the rapid development of social media, large dialogue corpus from the Internet builds up a fundamental premise for data-driven dialogue model. The breakthrough in neural network also brings new ideas to researchers in AI and NLP. A great number of new techniques and methods therefore came into being. In this paper, we review some of the most representative works in recent years and divide existing prevailing frameworks for a dialogue model into three categories. We further analyze the trend of development for open-domain dialogue and summarize the goal of an open-domain dialogue system in two aspects, informative and controllable. The methods we review in this paper are selected according to our unique perspectives and by no means complete. Rather, we hope this servery could benefit NLP community for future research in open-domain dialogue.
Model Predictive Path-Planning Controller With Potential Function for Emergency Collision Avoidance on Highway Driving

Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.
Reliable and Controllable Data Sharing Based on Blockchain

Nowadays, cloud-based data sharing becomes a notably fascinating service provided by the cloud platform due to its convenience and economy. Ciphertext-policy attribute-based encryption (CP-ABE) system is one of the best candidates for cloud-data sharing solutions because it can provide data encryption and fine-grained access control means for cloud storage systems. However, existing centralized schemes inevitably suffer from the limitations of a single point of failure, low reliability, and poor scalability. In addition, the management and maintenance of attributes in CP-ABE solutions often require higher costs. In this paper, we propose a reliable and controllable data sharing scheme based on blockchain, mapping the attributes in the CP-ABE scheme to attribute tokens. Users with the corresponding attribute token are granted access and decryption rights for encrypted data after paying the attribute token. The scheme is implemented based on the permissioned blockchain architecture Hyperledger Fabric, and a multi-channel Fabric deployment architecture and smart contracts are designed for data management and access control. By combining CP-ABE with blockchain, a common solution for data sharing that supports fine-grained data access control is realized, which is suitable for access control management of data sharing in a distributed environment. We further implemented a smart contract prototype on Fabric, and the experimental results show that our scheme is feasible.
SAT: 2D Semantics Assisted Training for 3D Visual Grounding

3D visual grounding aims at grounding a natural language description about a 3D scene, usually represented in the form of 3D point clouds, to the targeted object region. Point clouds are sparse, noisy, and contain limited semantic information compared with 2D images. These inherent limitations make the 3D visual grounding problem more challenging. In this study, we propose 2D Semantics Assisted Training (SAT) that utilizes 2D image semantics in the training stage to ease point-cloud-language joint representation learning and assist 3D visual grounding. The main idea is to learn auxiliary alignments between rich, clean 2D object representations and the corresponding objects or mentioned entities in 3D scenes. SAT takes 2D object semantics, i.e., object label, image feature, and 2D geometric feature, as the extra input in training but does not require such inputs during inference. By effectively utilizing 2D semantics in training, our approach boosts the accuracy on the Nr3D dataset from 37.7% to 49.2%, which significantly surpasses the non-SAT baseline with the identical network architecture and inference input. Our approach outperforms the state of the art by large margins on multiple 3D visual grounding datasets, i.e., +10.4% absolute accuracy on Nr3D, +9.9% on Sr3D, and +5.6% on ScanRef.
Active Universal Domain Adaptation

Most unsupervised domain adaptation methods rely on rich prior knowledge about the source-target label set relationship, and they cannot recognize categories beyond the source classes, which limits their applicability in practical scenarios. This paper proposes a new paradigm for unsupervised domain adaptation, termed as Active Universal Domain Adaptation (AUDA), which removes all label set assumptions and aims for not only recognizing target samples from source classes but also inferring those from target-private classes by using active learning to annotate a small budget of target data. For AUDA, it is challenging to jointly adapt the model to the target domain and select informative target samples for annotations under a large domain gap and significant semantic shift. To address the problems, we propose an Active Universal Adaptation Network (AUAN). Specifically, we first introduce Adversarial and Diverse Curriculum Learning (ADCL), which progressively aligns source and target domains to classify whether target samples are from source classes. Then, we propose a Clustering Non-transferable Gradient Embedding (CNTGE) strategy, which utilizes the clues of transferability, diversity, and uncertainty to annotate target informative sample, making it possible to infer labels for target samples of target-private classes. Finally, we propose to jointly train ADCL and CNTGE with target supervision to promote domain adaptation and target-private class recognition. Extensive experiments demonstrate that the proposed AUDA model equipped with ADCL and CNTGE achieves significant results on four popular benchmarks.
Dynamic Dual Gating Neural Networks

In dynamic neural networks that adapt computations to different inputs, gating-based methods have demonstrated notable generality and applicability in trading-off the model complexity and accuracy. However, existing works only explore the redundancy from a single point of the network, limiting the performance. In this paper, we propose dual gating, a new dynamic computing method, to reduce the model complexity at run-time. For each convolutional block, dual gating identifies the informative features along two separate dimensions, spatial and channel. Specifically, the spatial gating module estimates which areas are essential, and the channel gating module predicts the salient channels that contribute more to the results. Then the computation of both unimportant regions and irrelevant channels can be skipped dynamically during inference. Extensive experiments on a variety of datasets demonstrate that our method can achieve higher accuracy under similar computing budgets compared with other dynamic execution methods. In particular, dynamic dual gating can provide 59.7% saving in computing of ResNet50 with 76.41% top-1 accuracy on ImageNet, which has advanced the state-of-the-art. Codes are available at https://github.com/lfr-0531/DGNet.
On Generating Transferable Targeted Perturbations

While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model’s decisions to a specific ‘targeted’ class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (TTP). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image ‘distribution’ with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains. Based on the proposed objective, we train a generator function that can adaptively synthesize perturbations specific to a given input. Our generative approach is in-dependent of the source or target domain labels, while consistently performs well against state-of-the-art methods on a wide range of attack settings. As an example, we achieve 32.63% target transferability from (an adversarially weak) VGG19 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BN</inf> to (a strong) WideResNet on ImageNet val. set, which is 4× higher than the previous best generative attack and 16× better than instance-specific iterative attack. Code is available at: https://github.com/Muzammal-Naseer/TTP.
Labor Market Soft Skills in the Context of Digitalization of the Economy

Economies globally have relied on the difficulties of labor market operations. Their survival is based on their ability to adapt to the realities of current technological and digital requirements. The purpose of this study was to assess the future needs of the labor market for soft skills acquired by employees in the context of digitalization of the economy in facing modern attributes of labor market requirements using secondary data. The arguments advanced in this paper are premised on the notion that the digitalization of the economy leads to an increase in employers’ need for employees to acquire soft skills. The study is grounded on the Goal-Setting Theory of Motivation, which presupposes that there is a critical link between the goals individuals or organizations set for themselves and their performance. The analysis of the selected studies revealed that soft skills are considered critical during job interviews and are becoming increasingly scarce. This study predicts a shortage of employees with soft skills, accompanied by an increase in employers’ need for flexible skills. Additionally, this research recommends that discussions should consider the implications of digital technologies for personnel management and holistic measures to safeguard organizational wellbeing and protect the labor market economy through digitalization.
Toward Computing Cross-Modality Symmetric Non-Rigid Medical Image Registration

This paper describes a new non-rigid approach to register images from same- and cross-imaging modalities such as magnetic resonance imaging, computed tomography, and 3D rotational angiography. The deformation is a key challenge in medical image registration. We have proposed a diffeomorphism-based method to tackle this problem using an optimized framework. A non stationary velocity field is used to minimize the effect of forces that are derived from the image gradients. Furthermore, we propose a similarity energy function, based on the gray scale distribution, to limit the fluctuations while approaching the local minima. The proposed method is evaluated on both private and public datasets; the results show that the values of mean square error (MSE), normalized cross-correlation (NCC), structural similarity (SS), mutual information (MI), feature similarity index (FSIM), and mean absolute error (MAE) are 1.3136, 0.9962, 0.9897, 0.883, 0.9922, and 1.52± 2.09, respectively. Both qualitative and quantitative evaluation show promising registration accuracy reflecting the potential of the proposed method.
A Style and Semantic Memory Mechanism for Domain Generalization

Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic in-variance across domains. Meanwhile, the inherent intra-domain style invariance is usually underappreciated and put on the shelf. In this paper, we reveal that leveraging intra-domain style invariance is also of pivotal importance in improving the efficiency of domain generalization. We verify that it is critical for the network to be informative on what domain features are invariant and shared among in-stances, so that the network sharpens its understanding and improves its semantic discriminative ability. Correspondingly, we also propose a novel “jury” mechanism, which is particularly effective in learning useful semantic feature commonalities among domains. Our complete model called STEAM can be interpreted as a novel probabilistic graphical model, for which the implementation requires convenient constructions of two kinds of memory banks: semantic feature bank and style feature bank. Empirical results show that our proposed framework surpasses the state-of-the-art methods by clear margins.
T<sub>k</sub>ML-AP: Adversarial Attacks to Top-k Multi-Label Learning

Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine. However, the vulnerabilities of such algorithms with regards to dedicated adversarial perturbation attacks have not been extensively studied previously. In this work, we develop methods to create adversarial perturbations that can be used to attack top-k multi-label learning-based image annotation systems (T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> ML-AP). Our methods explicitly consider the top-k ranking relation and are based on novel loss functions. Experimental evaluations on large-scale benchmark datasets including PASCAL VOC and MS COCO demonstrate the effectiveness of our methods in reducing the performance of state-of-the-art top-k multi-label learning methods, under both untargeted and targeted attacks.
The power of emotions: Leveraging user generated content for customer experience management

Customer experience management (CEM) in the social media age finds itself needing to adapt to a rapidly changing digital environment and hence there is a need for innovative digital data analytical solutions. Drawing on an action case study of a large global automotive manufacturer, this study presents a digital innovation for enhanced emotion analytics on user generated content (UGC) and behaviour (UGB), to improve consumer insights for CEM. The digital innovation captures customer experience in real time, enabling measurement of a wide range of discrete emotions on the studied social media platform, which goes beyond traditional tools that capture positive or negative sentiment only. During the digital intervention, a substantial number of inauthentic and bot like behaviours was revealed, unbeknown to the case organisation. These accounts were found to be posting and amplifying highly emotional and potentially damaging content surrounding the case brand and its products. The study illustrates how emotion in the context of customer experience should go beyond typical categorisations, given the complexity of human emotion, while a distinction between bot and authentic users is imperative for CEM.
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark U-Net trained on real data (DSC 0.901; bAVD 0.294) for intracranial vessels. In conclusion, our solution generates realistic 3D TOF-MRA patches and labels for brain vessel segmentation. We demonstrate the benefit of using mixed precision for computational efficiency resulting in the best-performing GAN-architecture. Our work paves the way towards sharing of labeled 3D medical data which would increase generalizability of deep learning models for clinical use.
FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms that can provide complementary information for user-interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this article, we propose a federated native ad CTR prediction method named FedCTR, which can learn user-interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy and differential privacy to the local and aggregated user embeddings, respectively, for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.
Zero-shot Natural Language Video Localization

Understanding videos to localize moments with natural language often requires large expensive annotated video regions paired with language queries. To eliminate the annotation costs, we make a first attempt to train a natural language video localization model in zero-shot manner. Inspired by unsupervised image captioning setup, we merely require random text corpora, unlabeled video collections, and an off-the-shelf object detector to train a model. With the unpaired data, we propose to generate pseudo-supervision of candidate temporal regions and corresponding query sentences, and develop a simple NLVL model to train with the pseudo-supervision. Our empirical validations show that the proposed pseudo-supervised method outperforms several baseline approaches and a number of methods using stronger supervision on Charades-STA and ActivityNet-Captions.