Reviewerly/exHarmony huggingface dataset:
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining

To address the big data challenges, serverless multi-party collaborative training has recently attracted attention in the data mining community, since they can cut down the communications cost by avoiding the server node bottleneck. However, traditional serverless multi-party collaborative training algorithms were mainly designed for balanced data mining tasks and are intended to optimize accuracy (e.g., cross-entropy). The data distribution in many real-world applications is skewed and classifiers, which are trained to improve accuracy, perform poorly when applied to imbalanced data tasks since models could be significantly biased toward the primary class. Therefore, the Area Under Precision-Recall Curve (AUPRC) was introduced as an effective metric. Although multiple single-machine methods have been designed to train models for AUPRC maximization, the algorithm for multi-party collaborative training has never been studied. The change from the single-machine to the multi-party setting poses critical challenges. For example, existing single-machine-based AUPRC maximization algorithms maintain an inner state for local each data point, thus these methods are not applicable to large-scale multi-party collaborative training due to the dependence on each local data point.
Deep Weakly-supervised Anomaly Detection

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled. Since unlabeled instances are mostly normal, the relation prediction enforces a joint learning of anomaly-anomaly, anomaly-normal, and normal-normal pairwise discriminative patterns, respectively. PReNet can then detect any seen/unseen abnormalities that fit the learned pairwise abnormal patterns, or deviate from the normal patterns. Further, this pairwise approach also seamlessly and significantly augments the training anomaly data. Empirical results on 12 real-world datasets show that PReNet significantly outperforms nine competing methods in detecting seen and unseen anomalies. We also theoretically and empirically justify the robustness of our model w.r.t. anomaly contamination in the unlabeled data. The code is available at https://github.com/mala-lab/PReNet.
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval

Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model.However, existing negative sampling strategies suffer from the uninformative or false negative problem.In this work, we empirically show that according to the measured relevance scores, the negatives ranked around the positives are generally more informative and less likely to be false negatives.Intuitively, these negatives are not too hard (may be false negatives) or too easy (uninformative).They are the ambiguous negatives and need more attention during training.Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.Extensive experiments on four public and one industry datasets show the effectiveness of our approach.We made the code and models publicly available in https://github.com/microsoft/SimXNS.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices.We pose that the standardization is taking place in the wrong spot.Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances.We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other's work.GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.
Concadia: Towards Image-Based Text Generation with a Purpose

Current deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice.We argue that to close this gap, it is vital to distinguish descriptions from captions based on their distinct communicative roles.Descriptions focus on visual features and are meant to replace an image (often to increase accessibility), whereas captions appear alongside an image to supply additional information.To motivate this distinction and help people put it into practice, we introduce the publicly available Wikipedia-based dataset Concadia consisting of 96,918 images with corresponding English-language descriptions, captions, and surrounding context.Using insights from Concadia, models trained on it, and a preregistered human-subjects experiment with human-and model-generated texts, we characterize the commonalities and differences between descriptions and captions.In addition, we show that, for generating both descriptions and captions, it is useful to augment image-totext models with representations of the textual context in which the image appeared.
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative

Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics.Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., linguistic units of different granularity that combine syntax and semantics.As a first step towards assessing the compatibility of CxG with the syntactic and semantic knowledge demonstrated by state-ofthe-art pretrained language models (PLMs), we present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC).We conduct experiments examining the classification accuracy of a syntactic probe on the one hand and the models' behaviour in a semantic application task on the other, with BERT, RoBERTa, and DeBERTa as the example PLMs.Our results show that all three investigated PLMs are able to recognise the structure of the CC but fail to use its meaning.While human-like performance of PLMs on many NLP tasks has been alleged, this indicates that PLMs still suffer from substantial shortcomings in central domains of linguistic knowledge.
Opening up Minds with Argumentative Dialogues

Recent research on argumentative dialogues has focused on persuading people to take some action, changing their stance on the topic of discussion, or winning debates.In this work, we focus on argumentative dialogues that aim to open up (rather than change) people's minds to help them become more understanding to views that are unfamiliar or in opposition to their own convictions.To this end, we present a dataset of 183 argumentative dialogues about 3 controversial topics: veganism, Brexit and COVID-19 vaccination.The dialogues were collected using the Wizard of Oz approach, where wizards leverage a knowledge-base of arguments to converse with participants.Open-mindedness is measured before and after engaging in the dialogue using a questionnaire from the psychology literature, and success of the dialogue is measured as the change in the participant's stance towards those who hold opinions different to theirs.We evaluate two dialogue models: a Wikipedia-based and an argument-based model.We show that while both models perform closely in terms of opening up minds, the argument-based model is significantly better on other dialogue properties such as engagement and clarity.
Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

A mineral prospectivity map (MPM) focusing on gold mineralization in the Larder Lake region of Northern Ontario, Canada, has been produced in this study. We have used the Random Forest (RF) algorithm to use 32 predictor maps integrating geophysical, geochemical, and geological datasets from various sources that represent vectors to gold mineralization. It is evident from the efficiency of classification curves that MPMs generated are robust. The unsupervised algorithms, K-means and principal component analysis (PCA) were used to investigate and visualize the clustering nature of large geochemical and geophysical datasets. We used RQ-mode PCA to compute variable and object loadings simultaneously, which allows the displays of observations and the variables at the same scale. PCA biplots of the Larder Lake geochemical data show that Au is strongly correlated with W, S, Pb and K, but inversely correlated with Fe, Mn, Co, Mg, Ca, and Ni. The known gold mineralization locations were well classified by RF with the accuracy of 95.63 %. Furthermore, partial least squares-discriminant analysis (PLS-DA) model combines 3D geophysical clusters and geochemical compositions, which indicates the Au-rich areas are characterized with low to mid resistivity – low susceptibility properties. We conclude that the Larder Lake-Cadillac deformation zone (LLCDZ) is relatively more fertile than the Lincoln-Nipissing shear zone (LNSZ) with respect to gold mineralization due to deeper penetrating faults. The intersection of the LLCDZ and network of high-angle NE-trending cross faults acts as key conduits for gold endowments in the Larder Lake area. This study innovatively combined multivariate geological, geochemical, and geophysical datasets via machine learning algorithms, which improves identification of geochemical anomalies and interpretation of spatial features associated with gold mineralization.
Deep Learning-Based Cancer Detection Technique

The time is now for deep learning (DL)-dependent analysis of healthcare images to move from the realm of exploratory research projects to that of translational ones, and eventually into clinical practise. This process has been sped up by developments in data availability, DL methods, and computer power over the last decade. As a result of this experience, the authors now know more about the potential benefits and drawbacks of incorporating DL into clinical treatment, two factors that, in the authors' opinion, will propel progress in this area over the next several years. The most significant of these difficulties are the widespread need of strength of commonly utilized DL training approaches to various pervasive pathological properties of healthcare images and storages, the need of an properly digitised environment in hospitals, and the need of sufficient open datasets on which DL approaches may be trained and tested.
Diverse Adaptive Bulk Search: a Framework for Solving QUBO Problems on Multiple GPUs

Quadratic Unconstrained Binary Optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. As many NP-hard problems can be reduced to QUBO problems, considerable research has gone into developing QUBO solvers running on various computing platforms such as quantum devices, ASICs, FPGAs, GPUs, and optical fibers. This paper presents a framework called Diverse Adaptive Bulk Search (DABS), which has the potential to find optimal solutions of many types of QUBO problems. Our DABS solver employs a genetic algorithm-based search algorithm featuring three diverse strategies: multiple search algorithms, multiple genetic operations, and multiple solution pools. During the execution of the solver, search algorithms and genetic operations that succeeded in finding good solutions are automatically selected to obtain better solutions. Moreover, search algorithms traverse between different solution pools to find good solutions. We have implemented our DABS solver to run on multiple GPUs. Experimental evaluations using eight NVIDIA A100 GPUs confirm that our DABS solver succeeds in finding optimal or potentially optimal solutions for three types of QUBO problems.
EGIA: An External Gradient Inversion Attack in Federated Learning

Federated learning (FL) has achieved state-of-the-art performance in distributed learning tasks with privacy requirements. However, it has been discovered that FL is vulnerable to adversarial attacks. The typical gradient inversion attacks primarily focus on attempting to obtain the client’s private input in a white-box manner, where the adversary is assumed to be either the client or the server. However, if both the clients and the server are honest and fully trusted, is the FL secure? In this paper, we propose a novel method called External Gradient Inversion Attack (EGIA) in the grey-box settings. Specifically, we concentrate on the point that public-shared gradients in FL are always transmitted through the intermediary nodes, which has been widely ignored. On this basis, we demonstrate that an external adversary can reconstruct the private input using gradients even if both the clients and the server are honest and fully trusted. We also provide a comprehensive theoretical analysis of the black-box attack scenario in which the adversary has only the gradients. We perform extensive experiments on multiple real-world datasets to test the effectiveness of EGIA. The outcomes of our experiments validate that the EGIA method is highly effective.
Mineral prospectivity mapping over the Gomoa Area of Ghana's southern Kibi-Winneba belt using support vector machine and naive bayes

Geospatial modeling of mineral prospective regions is essential, owing to its significant contribution towards the development and economic gains of many mineral-endowed countries including, Ghana. Thus, the primary objective of this study is to delineate mineral potential zones in the Gomoa Area of Ghana's southern Kibi-Winneba belt in order to supplement mineral resources in Ghana's existing mineral prospective zones. To achieve the aforementioned objective, researchers generated predictive models characterising gold mineralisation prospects within the study area by employing machine learning techniques comprising support vector machines (SVM) and naive bayes (NB) classifiers on mineral-related conditioning factors. These mineral-related factors (geoscientific thematic layers) were sourced from geophysical, remote sensing, and geological datasets. The resulting mineral prospective models (MPM) produced based on SVM and NB classifiers were exhibited in binary classes (prospective and non-prospective zones). Regions delineated as prospective zones within the study area were, respectively estimated to cover an area extent of 181.62 km2 and 296.02 km2 for the SVM-derived MPM and NB-derived MPM and analogously characterise 22.07% and 35.97% of the study area. The ability of these two models to predict was determined using the area under the receiver operating characteristic curve (AUC). The AUC scores obtained for the SVM-derived MPM and the NB-derived MPM were, respectively, 0.90 and 0.83. Outputs of the AUC scores generally indicate that the two models produced have good accuracy, although the SVM-derived MPM performed better than that of the NB-derived MPM. Thus, the machine learning-based mineral prospectivity models produced in this study are worthy outputs to guide the planning of detailed mineral exploration surveys within the study area.
A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model's high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target's maneuvering turn. Second, an improved Sage-Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.
TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning

Multivariate data analysis, as an important research topic in the field of machine learning, focuses on how to utilize the intrinsic connection between feature variables and target variables. However, in the face of complex multivariate prediction environments, existing single prediction models often fail to obtain ideal results. Meanwhile, existing ensemble prediction models are not always adapted to certain complex data. Moreover, the randomness in the clustering process cannot guarantee the clustering accuracy. Therefore, to improve the model's prediction accuracy and ability to adapt to complex data and reduce the impact of randomness on clustering accuracy, this paper designs a multivariate prediction model utilizing three-way clustering (TWC) and ensemble learning, which is named the TWC-EL model. First, the initial division of the sample set is realized by k-means clustering algorithm, and further the sample set is divided again via the k-means clustering algorithm to solve the problem of clustering accuracy. Then, the results of clustering twice are combined according to the difference in the number of intersection points and the distance from the samples to the center point of each cluster, and the core and fringe regions of each cluster in the initial clustering results are obtained, forming a new TWC method. Next, based on the correlation between the regions, the obtained core and fringe regions are classified into low-correlation, medium-correlation and high-correlation regions, and an ensemble prediction model is designed by combining the advantages of the Elman neural network model, the Extreme Learning Machine (ELM) model and the back propagation neural network (BPNN) model. Finally, the experimental analysis results exhibit that the constructed TWC-EL model is efficient and feasible, and points out the excellent performance compared with the existing prediction models. The validity of the TWC method and the ensemble prediction model in the proposed TWC-EL model are verified by experiments, respectively.
Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Internet of Things (IoT) devices for the home have made a lot of people's lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study's findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models' generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.
Multimodal Federated Learning: A Survey

Federated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non-identical distribution (non-IID) caused by a data distribution imbalance related to data labels and data amount. In real-world applications, data are usually described by multiple modalities. However, to the best of our knowledge, only a handful of studies have been conducted to improve system performance utilizing multimodal data. In this survey paper, we identify the significance of this emerging research topic of multimodal federated learning (MFL) and present a literature review on the state-of-art MFL methods. Furthermore, we categorize multimodal federated learning into congruent and incongruent multimodal federated learning based on whether all clients possess the same modal combinations. We investigate the feasible application tasks and related benchmarks for MFL. Lastly, we summarize the promising directions and fundamental challenges in this field for future research.
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning

Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learning for taxi-demand prediction that allows multiple parties to train a machine learning model on their own data while keeping the data private and secure. This can enable organizations to build models on data they otherwise would not be able to access. Evaluation with real-world data collected from 16 taxi service providers in Japan over a period of six months showed that the proposed system can predict the demand level accurately within 1% error compared to a single model trained with integrated data.