Reviewerly/exHarmony huggingface dataset:
Counterspeech

Abstract Counterspeech is communication that tries to counteract potential harm brought about by other speech. Theoretical interest in counterspeech partly derives from a libertarian ideal – as captured in the claim that the solution to bad speech is more speech – and partly from a recognition that well‐meaning attempts to counteract harm through speech can easily misfire or backfire. Here we survey recent work on the question of what makes counterspeech effective at remedying or preventing harm, in those cases where it is effective, as well as work investigating when and why there is a duty to engage in counterspeech. We suggest that the most fruitful area for philosophical inquiry on this topic, currently, relates to the questions about efficacy. Specifically, we argue that there is a need for better frameworks for conceptualizing the efficacy of counterspeech. Philosophers have collaborative work to do, alongside social scientists, in developing these frameworks.
Ranking-based contrastive loss for recommendation systems

The recommendation system is fundamental technology of the internet industry intended to solve the information overload problem in the big data era. Top-k recommendation is an important task in this field. It generally functions through the comparison of positive pairs and negative pairs based on Bayesian personalized ranking (BPR) loss. We find that the contrastive loss (CL) function used in contrastive learning is well-suited for top-k recommendation. However, there are two problems in the existing loss functions. First, all samples are treated the same, and hard samples are not considered. Second, all nonpositive samples are considered negative samples, which ignores the fact that they are unlabelled data containing items that users may like. Moreover, in our experiments, we find that when items are sorted by their similarities to the user, many negative items (or samples) appear before the positive items. We regard these negative items as hard samples and those at the top as potentially positive samples due to their high level of similarities with users. Therefore, we propose a ranking-based contrastive loss (RCL) function to exploit both hard samples and potentially positive samples. Experimental results demonstrate the effectiveness, broad applicability, and high training efficiency of the proposed RCL function. The code and data are available at https://github.com/haotangxjtu/RCL.
Local-global coordination with transformers for referring image segmentation

Referring image segmentation has sprung up benefiting from the outstanding performance of deep neural networks. However, most existing methods explore either local details or the global context of the scene without sufficiently modelling the coordination between them, leading to sub-optimal results. In this paper, we propose a transformer-based method to enforce the in-depth coordination between short- and long-range dependencies in both explicit and implicit fusion processes. Specifically, we design a Cross Modality Transformer (CMT) module with two successive blocks for explicitly integrating linguistic and visual features, which first locates the related visual region in a global view before concentrating on local patterns. Besides, a Hybrid Transformer Architecture (HTA) is utilized as a feature extractor in the encoding stage to capture global relationships and retain local cues. It can further aggregate the multi-modal features in an implicit manner. In the decoding stage, a Cross-level Information Integration module (CI2) is developed to gather information from adjacent levels by dual top-down paths, including a guided filtration path and a residual reservation path. Experimental results show that the proposed method outperforms the state-of-the-art methods on four RIS benchmarks.
Evaluating protection motivation based cybersecurity awareness training on Kirkpatrick's Model

Cybersecurity behavioral literature has a significant number of studies on training and awareness. However, there is lack of theoretical underpinnings in developing intervention to allow for positive behavioral change and evaluating them. The evaluation of theory based cybersecurity training warrants the use of program evaluation techniques. The protection motivation theory (PMT) was employed to understand the behavioral change after the implementation of cybersecurity training. The evaluation was done on three levels of Kirkpatrick's evaluation model – reaction, learning and behavior. A pre-post quasi experimental design was adopted in this research. A total of 154 undergraduate students from computing and digital arts backgrounds took part in the research. The results of the study showed that the PMT based training was effective in increasing the threat knowledge of the students along with the increase in information of countermeasure strategies. From the two components of the PMT, self-efficacy was found to be the significant predictor of the cybersecurity behavioral intention in both pre-test and post-test PMT models. The cybersecurity training increased the self-efficacy of the students significantly and contributed towards cybersecurity behavioral intention change. The findings of this study imply that in designing the cybersecurity trainings, educators should dominantly take into account the self-efficacy component of the PMT.
A Non-Iterative Reasoning Algorithm for Fuzzy Cognitive Maps based on Type 2 Fuzzy Sets

A Fuzzy Cognitive Map (FCM) is a causal knowledge graph connecting concepts using directional and weighted connections making it an effective approach for reasoning and decision making. However, the modelling and reasoning capabilities of a conventional FCM for real world problems in the presence of uncertain data is limited as it relies on Type 1 Fuzzy Sets (T1FSs). In this work, we extend the capability of FCMs for capturing greater uncertainties in the interrelations of the modelled concepts by introducing a new reasoning algorithm that uses Type 2 Fuzzy Sets based on z slices (zT2FSs) for the modelling of uncertain weights connecting FCM’s concepts. These Type 2 Fuzzy Sets are generated using interval valued data from surveyed participants and aggregated using the Interval Agreement Approach method. Our algorithm performs late defuzzification of the FCM’s values at the end of the reasoning process, preserving the uncertainty in values for as long as possible. The proposed algorithm is applied to the evaluation of the performance of modules of an undergraduate Mathematical programme. The results obtained show a greater correlation to domain experts’ subjective knowledge on the modules’ performance than both a corresponding FCM with weights modelled using T1FS and a statistical method currently used for evaluating the modules’ performance. Sensitivity analysis conducted demonstrates that the new algorithm effectively preserves the propagation of uncertainty captured from input data.
Automated broad transfer learning for cross-domain fault diagnosis

Smart manufacturing system pursues automated modeling algorithms for industrial applications in dynamic environments. The prevalent deep transfer learning (DTL) has achieved promising results in cross-domain fault diagnosis. However, most DTL algorithms are dataset- and domain-specific. They require hyperparameter optimization (HPO) calling for prior knowledge to accomplish a promising prediction performance. This dilemma persists when new data from different domains arrive. An automated broad-transfer learning algorithm (AutoBTL) is proposed to improve predictive modeling for cross-domain tasks. AutoBTL includes three components, a broad classifier, an active estimator, and a hyperparameter optimizer for solving the HPO problem in cross-domain fault diagnosis. At each iteration, AutoBTL initiates a gated broad architecture assigned by the optimizer for target prediction. Then, an active estimator samples the target data with reliable pseudo labels for domain adaptation and performance evaluation. Finally, the optimizer updates a surrogate model and optimizes the hyperparameter space. These steps get repeated until satisfactory model is generated. AutoBTL involves a transductive joint validation strategy, which significantly improves the performance of the existing HPO algorithms in cross-domain tasks. The performance of the AutoBTL algorithm is validated with three benchmark datasets, including 14 cross-domain tasks. The computational results have demonstrated the accuracy and efficiency of the proposed algorithm over the widely used predictive modeling algorithms.
Harnessing the potential of trace data and linguistic analysis to predict learner performance in a multi‐text writing task

Abstract Background Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives We explored the viability of using product and process features to develop machine learning classifiers that identify low‐ and high‐performing essays in a multi‐text writing task. Methods We examined learning processes and essay submissions of 163 graduate students working on an authentic multi‐text writing assignment. We utilised learners' trace data to obtain process features and state‐of‐the‐art natural language processing methods to obtain product features for our classifiers. Results and Conclusions Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re‐reading and planning) features as important predictors of writing quality. Major Takeaways The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi‐text writing task to help them do well in the task.
Dissimilate-and-assimilate strategy for video anomaly detection and localization

Unsupervised anomaly detection in videos is a challenging task owing to the remarkable generalization capacity of the deep convolutional autoencoders and the complex nature of anomalous events. In this study, we introduce a dissimilate-and-assimilate strategy to learn essential patterns of multilevel latent representations of normal spatial and temporal information. To obtain the core normality of the appearance and motion samples over multiple layers of the network, our proposed method diversifies the latent patterns of normal spatial and temporal data to make the out-of-distribution samples discrete (dissimilation) and integrates the latent features of two different samples into a single sample using a feature attention mechanism for robust optimization (assimilation). Based on the learned representations, the network generates convincing predictions of the normal frame, even if it receives abnormal samples after training. That is, the anomalous objects in a series of frames can be detected with significant reconstruction errors, thus leading to better detection and precise localization performance. To verify the effectiveness of the proposed method, we quantify the preciseness of anomaly localization using the outside-inside error ratio along with the traditional area under the curve (AUC) metric to measure the detection performance on the USCD Pedestrian 2, CHUK Avenue and ShanghaiTech Campus datasets.
RETRACTED: An optimization based feature extraction and machine learning techniques for named entity identification

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to a PubPeer post, which states that this article contains tortured phrases, as detailed here: PubPeer - An optimization based feature extraction and machine learnin... The journal requested that the authors provide an explanation to these concerns, as well as raw data and evidence of collaboration between authors. The authors did not respond. The Editor-in-Chief reviewed the case and decided to retract the article.
Ontology-based approach to real-time risk management and cyber-situational awareness

The requirement of continuous risk assessment and management is attracting growing attention because of the need of keeping risk under control. Over the years, companies are dealing with a growing number of malicious actions coming from heterogeneous sources, so risk management must be dynamic in real-time to define action strategies and validate the effectiveness of the safeguards in place. This exposure makes it imperative to use sensor-based systems to detect anomalies or to have an updated catalog of vulnerabilities to understand the situation in which the system finds itself and its level of risk. Such a wealth of heterogeneous information has led to the use of ontologies to organize data, as they allow the extraction of new concepts and behaviors, for instance, measuring the risk level of a system or generating metrics for decision support systems. This paper presents an ontology to describe different types of anomalies, merged with previously developed models for Cyber-Threat Intelligence, becoming a proposal to define real-time risk management in a converged secure environment with physical and logical elements, using these ontologies and SPARQL Rules to infer knowledge and calculate dynamically the risk level of the system.
Learning generalized visual odometry using position-aware optical flow and geometric bundle adjustment

Recent visual odometry (VO) methods incorporating geometric algorithm into deep-learning architecture have shown outstanding performance on the challenging monocular VO task. Despite encouraging results are shown, previous methods ignore the requirement of generalization capability under noisy environment and various scenes. To address this challenging issue, this work first proposes a novel optical flow network (PANet). Compared with previous methods that predict optical flow as a direct regression task, our PANet computes optical flow by predicting it into the discrete position space with optical flow probability volume, and then converting it to optical flow. Next, we improve the bundle adjustment module to fit the self-supervised training pipeline by introducing multiple sampling, ego-motion initialization, dynamic damping factor adjustment, and Jacobi matrix weighting. In addition, a novel normalized photometric loss function is advanced to improve the depth estimation accuracy. The experiments show that the proposed system not only achieves comparable performance with other state-of-the-art self-supervised learning-based methods on the KITTI dataset, but also significantly improves the generalization capability compared with geometry-based, learning-based and hybrid VO systems on the noisy KITTI and the challenging outdoor (KAIST) scenes.
Single stage architecture for improved accuracy real-time object detection on mobile devices

YOLOv4-tiny is one of the most representative lightweight one-stage object detection algorithms. In this paper, we propose Mini-YOLOv4-tiny, an improved lightweight one-stage object detector based on the YOLOv4-tiny. Typical compression techniques address both memory optimization and computational complexity, but compromise model accuracy. For model compression and speedup, we selectively cut the width of the last convolutional layers. To increase model performance, we propose several improvements with minimal impact on memory and inference time. First, we replace the Leaky-RELU activation function with Mish and fine-tune the data augmentation parameters. To enlarge the receptive field of the network, we propose a modified Spatial Pyramid Pooling module with a reduced number of convolutional blocks and filters, thus adding fewer Floating Point Operations (FLOPs) during the inference process. We performed experiments on the PASCAL VOC and MS COCO datasets and evaluated the inference speed on NVidia Jetson Nano and Raspberry PI 4 (model B). The experimental results show that our methods achieve 72.1% [email protected] and 23.4% [email protected] [0.5:0.95] on the PASCAL VOC and MS COCO datasets, achieving state-of-the-art results among lightweight object detectors. Compared with YOLOv4-tiny on MS COCO, our method has 37% fewer parameters and requires 19% fewer FLOPs, while improving the Intersection over Union (IoU) by 4.02% and the average precision (interval 0.50:0.95) by 2.8%. On PASCAL VOC, Mini-YOLOv4-tiny with an input size of 288 improves [email protected] by 0.3%, while requiring 61% fewer FLOPS and running almost twice as fast.
An ensemble hierarchical clustering algorithm based on merits at cluster and partition levels

Ensemble clustering has emerged as a combination of several basic clustering algorithms to achieve high quality final clustering. However, this technique is challenging due to the complexities in primary clusters such as overlapping, vagueness, instability and uncertainty. Typically, ensemble clustering uses all the primary clusters into partitions for consensus, where the merits of a cluster or a partition can be considered to improve the quality of the consensus. In general, the robustness of a partition may be poorly measured, while having some high-quality clusters. Inspired by the evaluation of cluster and partition, this paper proposes an ensemble hierarchical clustering algorithm based on the cluster consensus selection approach. Here, the selection of a subset of primary clusters from partitions based on their merit level is emphasized. Merit level is defined using the development of Normalized Mutual Information measure. Clusters of basic clustering algorithms that satisfy the predefined threshold of this measure are selected to participate in the final consensus. In addition, the consensus of the selected primary clusters to create the final clusters is performed based on the clusters clustering technique. In this technique, the selected primary clusters are re-clustered to create hyper-clusters. Finally, the final clusters are formed by assigning instances to hyper-clusters with the highest similarity. Here, an innovative criterion based on merit and cluster size for defining similarity is presented. The performance of the proposed algorithm has been proven by extensive experiments on real-world datasets from the UCI repository compared to state-of-the-art algorithms such as CPDM, ENMI, IDEA, CFTLC and SSCEN.
Full-body pose estimation for excavators based on data fusion of multiple onboard sensors

To reduce machine-related accidents on sites, automatically monitoring the full-body poses of operating heavy machines is crucial. Conventional pose estimation systems relying on homogeneous sensors are vulnerable to negative environmental impacts, leading to inaccurate and unstable estimation of machine states. Hence, a full-body pose estimation framework is proposed for excavators, with a data fusion strategy to utilize different types of onboard sensors for enhanced accuracy and robustness. Specifically, a non-invasive onboard visual-inertial sensor system is designed for data fusion. Then, through competitive and complementary data fusion, the keypoints describing the full-body poses of the excavator are tracked in 3D space. Especially, an EKF-based localization algorithm is developed for optimized multi-keypoint tracking, which is verified to improve the accuracy and robustness of pose estimation by a real-world excavator case study. The proposed sensor-fusion method can effectively improve operational safety, by accurately monitoring the motion of heavy machines operating on construction sites.
Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Abstract Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination ( R 2 ) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R 2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R 2 = 0.9931) and (RMSE = 0.6019 mD, R 2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available.