Reviewerly/exHarmony huggingface dataset:
Preregistration in diverse contexts: a preregistration template for the application of cognitive models

In recent years, open science practices have become increasingly popular in psychology and related sciences. These practices aim to increase rigour and transparency in science as a potential response to the challenges posed by the replication crisis. Many of these reforms—including the increasingly used preregistration —have been designed for purely experimental work that tests straightforward hypotheses with standard inferential statistical analyses, such as assessing whether an experimental manipulation has an effect on a variable of interest. But psychology is a diverse field of research. The somewhat narrow focus of the prevalent discussions surrounding and templates for preregistration has led to debates on how appropriate these reforms are for areas of research with more diverse hypotheses and more intricate methods of analysis, such as cognitive modelling research within mathematical psychology. Our article attempts to bridge the gap between open science and mathematical psychology, focusing on the type of cognitive modelling that Crüwell et al. (Crüwell S, Stefan AM, Evans NJ. 2019 Robust standards in cognitive science. Comput. Brain Behav. 2 , 255–265) labelled model application , where researchers apply a cognitive model as a measurement tool to test hypotheses about parameters of the cognitive model. Specifically, we (i) discuss several potential researcher degrees of freedom within model application, (ii) provide the first preregistration template for model application and (iii) provide an example of a preregistered model application using our preregistration template. More broadly, we hope that our discussions and concrete proposals constructively advance the mostly abstract current debate surrounding preregistration in cognitive modelling, and provide a guide for how preregistration templates may be developed in other diverse or intricate research contexts.
Laplacian pair-weight vector projection for semi-supervised learning

Semi-supervised learning is a new challenge that exploits the information of unlabeled instances. In this paper, we propose a novel Laplacian pair-weight vector projection (LapPVP) algorithm for semi-supervised classification . LapPVP consists of two stages: projection and classification. In the projection stage, LapPVP integrates a within-class scatter, a between-class scatter and a Laplacian regularization together and formulates a pair of optimization problems. The goal of LapPVP is to maximize the between-class scatter and minimize the within-class scatter on the basis of labeled data, and simultaneously maintain the geometric structure of labeled and unlabeled data in the projected subspace . The optimization problems of LapPVP are identical to generalized eigenvalue ones. Thus, it is easy to obtain the optimal solutions to LapPVP. In the classification stage, LapPVP adopts a minimal distance classifier to implement tasks in the projected subspace. The proposed LapPVP can find a better separability using the geometric information embedded in labeled and unlabeled instances and the discriminative information embedded in labeled instance. Experiments on artificial datasets and UCI datasets validate the feasibility and effectiveness of the proposed algorithm.
A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data

Detecting anomalies in time series data of hydrocarbon reservoir production is crucially important. Anomalies can result for different reasons: gross errors, system availability, human intervention, or abrupt changes in the series. They must be identified due to their potential to alter the series correlation, influence data-driven forecast, and affect classification results. We have developed a visual analytics approach based on an interactive visualization of time series data involving machine learning approaches for anomaly identification. Our methods rely upon a z-score normalization technique along with isolation forests. The methods leverage the prior probability of anomalies from a time-window, do not require labeled training data with normal and abnormal conditions, and incorporate specialist knowledge in the exploration process. We apply, evaluate, and discuss the methods’ capability using a benchmark data set (UNISIM–II–M-CO) and real field data in three visual exploration setups. The ground-truth annotations were done by human specialists and considered different interventions in the reservoir. Our methods detect approximately 95% of the human intervention anomalies, and about 82%–89% detection rate for other anomalies identified during data exploration.
Hardware in the Loop Topology for an Omnidirectional Mobile Robot Using Matlab in a Robot Operating System Environment

The symmetry of the omnidirectional robot motion abilities around its central vertical axis is an important advantage regarding its driveability for the flexible interoperation with fixed conveyor systems. The paper illustrates a Hardware in the Loop architectural approach for integrated development of an Ominidirectional Mobile Robot that is designed to serve in a dynamic logistic environment. Such logistic environments require complex algorithms for autonomous navigation between different warehouse locations, that can be efficiently developed using Robot Operating System nodes. Implementing path planning nodes benefits from using Matlab-Simulink, which provides a large selection of algorithms that are easily integrated and customized. The proposed solution is deployed for validation on a NVIDIA Jetson Nano, the embedded computer hosted locally on the robot, that runs the autonomous navigation software. The proposed solution permits the live connection to the omnidirectional prototype platform, allowing to deploy algorithms and acquire data for debugging the location, path planning and the mapping information during real time autonomous navigation experiments, very useful in validating different strategies.
Discover Micro-Influencers for Brands via Better Understanding

With the rapid development of the influencer marketing industry in recent years, the cooperation between brands and micro-influencers on marketing has achieved much attention. As a key sub-task of influencer marketing, micro-influencer recommendation is gaining momentum. However, in influencer marketing campaigns, it is not enough to only consider marketing effectiveness. Towards this end, we propose a concept-based micro-influencer ranking framework, to address the problems of marketing effectiveness and self-development needs for the task of micro-influencer recommendation. Marketing effectiveness is improved by concept-based social media account representation and a micro-influencer ranking function. We conduct social media account representation from the perspective of historical activities and marketing direction. And two adaptive learned metrics, endorsement effect score and micro-influencer influence score, are defined to learn the micro-influencer ranking function. To meet self-development needs, we design a bi-directional concept attention mechanism to focus on brands’ and micro-influencers’ marketing direction over social media concepts. Interpretable concept-based parameters are utilized to help brands and micro-influencers make marketing decisions. Extensive experiments conducted on a real-world dataset demonstrate the advantage of our proposed method compared with the state-of-the-art methods.
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost

Transformer architecture achieves great success in abundant natural language processing tasks.The over-parameterization of the Transformer model has motivated plenty of works to alleviate its overfitting for superior performances.With some explorations, we find simple techniques such as dropout, can greatly boost model performance with a careful design.Therefore, in this paper, we integrate different dropout techniques into the training of Transformer models.Specifically, we propose an approach named UniDrop to unite three different dropout techniques from fine-grain to coarse-grain, i.e., feature dropout, structure dropout, and data dropout.Theoretically, we demonstrate that these three dropouts play different roles from regularization perspectives.Empirically, we conduct experiments on both neural machine translation and text classification benchmark datasets.Extensive results indicate that Transformer with UniDrop can achieve around 1.5 BLEU improvement on IWSLT14 translation tasks, and better accuracy for the classification even using strong pre-trained RoBERTa as backbone.
Incremental learning strategies for credit cards fraud detection

Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quickly become outdated and require sensitive data storage. The evolving nature of bank fraud enshrines the importance of having up-to-date models, and sensitive data retention makes companies vulnerable to infringements of the European General Data Protection Regulation. For these reasons, evaluating incremental learning strategies is recommended. This paper designs and evaluates incremental learning solutions for real-world fraud detection systems. The aim is to demonstrate the competitiveness of incremental learning over conventional batch approaches and, consequently, improve its accuracy employing ensemble learning, diversity and transfer learning. An experimental analysis is conducted on a full-scale case study including five months of e-commerce transactions and made available by our industry partner, Worldline.
Improving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatures

Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for several years, but it is still fraught with technical challenges. One such challenge is the imbalanced nature of most remotely sensed data. The asymmetric class distribution impacts negatively the performance of classifiers and adds a new source of error to the production of these maps. In this paper, we address the imbalanced learning problem, by using K-means and the Synthetic Minority Oversampling Technique (SMOTE) as an improved oversampling algorithm. K-means SMOTE improves the quality of newly created artificial data by addressing both the between-class imbalance, as traditional oversamplers do, but also the within-class imbalance, avoiding the generation of noisy data while effectively overcoming data imbalance. The performance of K-means SMOTE is compared to three popular oversampling methods (Random Oversampling, SMOTE and Borderline-SMOTE) using seven remote sensing benchmark datasets, three classifiers (Logistic Regression, K-Nearest Neighbors and Random Forest Classifier) and three evaluation metrics using a five-fold cross-validation approach with three different initialization seeds. The statistical analysis of the results show that the proposed method consistently outperforms the remaining oversamplers producing higher quality land cover classifications. These results suggest that LULC data can benefit significantly from the use of more sophisticated oversamplers as spectral signatures for the same class can vary according to geographical distribution.
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries

In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS).Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding timeline for each story.To achieve this, we propose a novel unsupervised summarization framework based on the two-stage affinity propagation process.We also introduce a quantitative evaluation measure for MTLS based on the previous TLS evaluation methods.Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can provide better results than TLS.
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels

Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages.However, most existing resources with annotation of idiomaticity include ratings only at type level.This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level.We compiled 8,725 and 5,091 token level annotations for English and Portuguese, respectively, which are strongly correlated with the corresponding scores obtained at type level.The NCTTI dataset is used to explore how vector space models reflect the variability of idiomaticity across sentences.Several experiments using state-of-the-art contextualised models suggest that their representations are not capturing the noun compounds idiomaticity as human annotators.This new multilingual resource also contains suggestions for paraphrases of the noun compounds both at type and token levels, with uses for lexical substitution or disambiguation in context.
User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing

Deep Learning techniques have been successfully applied to solve many Artificial Intelligence (AI) applications problems.However, owing to topologies with many hidden layers, Deep Neural Networks (DNNs) have high computational complexity, which makes their deployment difficult in contexts highly constrained by requirements such as performance, real-time processing, or energy efficiency.Numerous hardware/software optimization techniques using GPUs, ASICs, and reconfigurable computing (i.e, FPGAs), have been proposed in the literature.With FPGAs, very specialized architectures have been developed to provide an optimal balance between high-speed and low power.However, when targeting edge computing, user requirements and hardware constraints must be efficiently met.Therefore, in this work, we only focus on reconfigurable embedded systems based on the Xilinx ZYNQ SoC and popular DNNs that can be implemented on Embedded Edge improving performance per watt while maintaining accuracy.In this context, we propose an automated framework for the implementation of hardware-accelerated DNN architectures.This framework provides an end-to-end solution that facilitates the efficient deployment of topologies on FPGAs by combining custom hardware scalability with optimization strategies.Cutting-edge comparisons and experimental results demonstrate that the architectures developed by our framework offer the best compromise between performance, energy consumption, and system costs.For instance, the low power (0.266W) DNN topologies generated for the MNIST database achieved a high throughput of 3,626 FPS.
A new perceptual hashing method for verification and identity classification of occluded faces

Recently, research communities on Computer Vision and biometrics have shown a lot of interest in face verification and classification methods. Fighting against Child Sexual Exploitation Material (CSEM) is one of the applications that might benefit most from these advances. In CSEM, discriminative parts of the face, i.e. mostly the eyes, are often occluded to make the victim identification more difficult. Most of the current face recognition methods are not able to handle such kind of occlusions. To overcome this problem, we present One-Shot Frequency Dominant Neighborhood Structure (OSF-DNS), a new perceptual hashing method that shows advantages on two scenarios: (a) occluded face verification, i.e., matching occluded faces with their non-occluded versions, and (b) face classification, i.e., getting the identity of an occluded face by means of a classifier trained with the non-occluded faces using the perceptual hash codes as feature vectors. We have compared the face verification performance of OSF-DNS with three perceptual hashing methods and with the features obtained from five deep learning techniques, using the occluded versions of six different facial datasets. The proposed method achieves accuracies between 69.24% and 99.46% depending on the dataset, and always higher than the compared methods. For the face classification task, we compared the performance of OSF-DNS with the features obtained by four deep learning techniques. Experimental results on LFW and CFPW datasets showed that the proposed hashing method outperformed the results obtained with deep features with an accuracy up to 89.53%. • Victims in Child Sexual Exploitation Material (CSEM) often present the eye region occluded. • Deep learning face recognition models fail when the eye region is occluded. • We present a perceptual hashing method called OSF-DNS. • OSF-DNS is robust against eye occlusion for face verification and classification. • OSF-DNS outperforms other perceptual hashing and state-of-the-art face recognition methods.
Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER

Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encoding to integrate word-character lattice information, but fail to capture fine-grained correlations in word-character spaces. In this paper, we propose DCSAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. By carefully combining cross-lattice and self-lattice attention modules with gated word-character semantic fusion unit, 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. Experiments on four Chinese NER datasets show that DCSAN obtains stateof-the-art results as well as efficiency compared to several competitive approaches.
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform

Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings. Among all stereo matching algorithms, Efficient Large-scale Stereo (ELAS) offers one of the best tradeoffs between efficiency and accuracy. However, due to the inherent iterative process and unpredictable memory access pattern, ELAS can only run at 1.5-3 fps on high-end CPUs and difficult to achieve real-time performance on low-power platforms. In this paper, we propose an energy-efficient architecture for real-time ELAS-based stereo matching on FPGA platform. Moreover, the original computational-intensive and irregular triangulation module is reformed in a regular manner with points interpolation, which is much more hardware-friendly. optimizations, including memory management, parallelism, and pipelining, are further utilized to reduce memory footprint and improve throughput. Compared with Intel i7 CPU and the state-of-the-art $\mathrm{C}\mathrm{P}\mathrm{U}+$FPGA implementation, our FPGA realization achieves up to $ 38.4\times$ and $ 3.32\times$ frame rate improvement, and up to $ 27.1\times$ and $ 1.13\times$ energy efficiency improvement, respectively.
Reordering Examples Helps during Priming-based Few-Shot Learning

The ability to learn from limited data, or fewshot learning, is a desirable and often critical requirement for NLP systems.While many existing methods do poorly at learning from a handful of examples, large pretrained language models have recently been shown to be efficient few-shot learners.One approach to few-shot learning, which does not require finetuning of model parameters, is to augment the language model's input with priming text which is typically constructed using task specific descriptions and examples.In this work, we further explore priming-based few-shot learning, with focus on using examples as prompts.We show that presenting examples in the right order is key for generalization.We introduce PERO (Prompting with Examples in the Right Order), where we formulate few-shot learning as search over the set of permutations of the training examples.We show that PERO can learn to generalize efficiently using as few as 10 examples, in contrast to existing approaches.While the newline token is a natural choice for separating the examples in the prompt, we show that learning a new separator token can potentially provide further gains in performance.We demonstrate the effectiveness of the proposed method on the tasks of sentiment classification, natural language inference and fact retrieval.Finally, we analyze the learned prompts to reveal novel insights, including the idea that two training examples in the right order alone can provide competitive performance for sentiment classification and natural language inference.
A new weighted fuzzy C-means clustering for workload monitoring in cloud datacenter platforms

The rapid growth in virtualization solutions has driven the widespread adoption of cloud computing paradigms among various industries and applications. This has led to a growing need for XaaS solutions and equipment to enable teleworking. To meet this need, cloud operators and datacenters have to overtake several challenges related to continuity, the quality of services provided, data security, and anomaly detection issues. Mainly, anomaly detection methods play a critical role in detecting virtual machines' abnormal behaviours that can potentially violate service level agreements established with users. Unsupervised machine learning techniques are among the most commonly used technologies for implementing anomaly detection systems. This paper introduces a novel clustering approach for analyzing virtual machine behaviour while running workloads in a system based on resource usage details (such as CPU utilization and downtime events). The proposed algorithm is inspired by the intuitive mechanism of flocking birds in nature to form reasonable clusters. Each starling movement's direction depends on self-information and information provided by other close starlings during the flight. Analogically, after associating a weight with each data sample to guide the formation of meaningful groups, each data element determines its next position in the feature space based on its current position and surroundings. Based on a realistic dataset and clustering validity indices, the experimental evaluation shows that the new weighted fuzzy c-means algorithm provides interesting results and outperforms the corresponding standard algorithm (weighted fuzzy c-means).