Reviewerly/exHarmony huggingface dataset:
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding \texttt{condition} such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.
Method for Estimating Solar Energy Potential Based on Photogrammetry from Unmanned Aerial Vehicles

This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.
REVOLUTIONISED TECHNOLOGIES FOR MARKETING: THEORETICAL REVIEW WITH FOCUS ON ARTIFICIAL INTELLIGENCE

Information technology and its implications have shown significant developments in the last decades. The internet revolution and digital marketing have created a shrinking force on mass media advertising. They have formed the backbone of personalisation, marketing automation, neuromarketing, viral marketing, voice recognition, and conversion optimisation, and thus, Artificial Intelligence (AI) has risen as the phenomenon in marketing activities. Theoretical analysis of relevant researches on AI tools and future trends, presentation of conceptual framework and methodology design, and presentation of the research findings are the objectives of the research. This study has potential limitations such as the data collection process and access to the relevant literature. AI for marketing has already become a significant part of today’s competitive world, which utilising the marketing opportunities to obtain business goals and create breakthrough using AI. Hence, this paper aims to illustrate the theoretical review on the nexus between AI and marketing, which would lead to understanding the future scope of AI and its penetration in marketing activities.
Reconstructing and resizing 3D images from DICOM files

Current reconstruction of 3D images from DICOM (Digital Imaging and Communications in Medicine) files requires strict supervision for the reconstructed images to have the same metadata including slice thickness, spacing between slices, and image resolution. We propose an algorithm for reconstructing 3D images based on medical images in the DICOM format with varied metadata and resizing the 3D images while preserving the annotations. The 3D image resizing may facilitate processing because most current systems cannot handle the huge 3D image data sizes. After resizing the 3D images, the original annotations that can be used as ground truths to train and evaluate machine learning method are preserved by projection. Experimental results show that the proposed method can handle various DICOM files and correctly project annotations onto the resized image.
Outcomes of Visual Self-Expression in Virtual Reality on Psychosocial Well-Being With the Inclusion of a Fragrance Stimulus: A Pilot Mixed-Methods Study

In this pilot mixed-methods study, we examined the participants experiences of engaging in virtual drawing tasks and the impact of an olfactory stimulus (calming fragrance blend) on outcomes of affect, stress, self-efficacy, anxiety, creative agency, and well-being (satisfaction with life).This study used a parallel mixed-methods, simple block randomization design. The study participants included 24 healthy adults aged 18 to 54 years, including 18 women and six men. The participants completed two 1-h immersive virtual art making sessions and were randomly assigned to receive either a fragrance or a non-fragrance condition for the first session. Quantitative (standardized self-report measures) and qualitative (open-ended survey responses and virtual artwork) datasets were collected concurrently and integrated during data analysis.The quantitative results indicated that the fragrance condition demonstrated a significant reduction in negative affect (e.g., feeling hostile, jittery, upset, distressed, etc.), namely, reduced feelings of negativity when compared to the non-fragrance condition. A trend toward improvement in self-efficacy was also seen in the fragrance condition. No significant changes were found for fragrance or non-fragrance conditions for positive affect, anxiety, and creative agency. The qualitative findings included five themes related to art making experiences in virtual reality in both conditions: fun and joy; novelty of virtual media, experimentation, and play; relaxation and calm; learning curve; and physical discomfort and disorientation. Four themes were identified for virtual art content and visual qualities: nature imagery, references to memories and personal symbols, fantasy and play within imagery, and depiction of everyday objects.Overall, the participants reported positive responses to the novel virtual art making experiences which were further heightened by the inclusion of the fragrance stimulus for negative affect. These preliminary findings need to be replicated with larger sample sizes to confirm the outcomes and the trends that were seen in this pilot study. Further research is recommended to examine the differences between experiences of virtual and traditional art media and to examine different olfactory stimuli promoting focus and concentration.
Blockchain for automotive: An insight towards the IPFS blockchain-based auto insurance sector

The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk of error. The insurance sector faces a threat of failure due to losing and misconception of policies and information. We present a decentralized IPFS and blockchain-based framework for the auto insurance sector that regulates the activities in terms of insurance claims for automobiles and automates payments. This article also discusses how blockchain technology’s features can be useful for the decentralized autonomous vehicle’s ecosystem.
Self-Perceptions on Digital Competences for M-Learning and Education Sustainability: A Study with Teachers from Different Countries

The current international landscape shows that the most common alternative for the continuity of formative learning processes during the coronavirus pandemic has been the use the of e-learning to support children’s learning in environments outside of school. This forced change in teaching methods has consolidated the recognition that the digital skills of teachers are a relevant factor for the sustainability of education, both during the pandemic and in a future post-pandemic period or in other emergencies. In this sense, the objective of this study carried out between May and September 2020 was to determine the perceptions of 427 teachers from 15 countries about their digital competences in working with m-learning in primary education using a Montessori approach. The results of the questionnaire showed that teachers perceive their digital competences as inert and not very effective for innovation compared with the subsistence of traditional pedagogical practices, to deal with unpredictable situations or to generate differentiated adaptations for an inclusive education. The results of this study also serve as empirical support for establishing four training dimensions that can be considered priorities for the construction and implementation of a teacher training model that contributes to the sustainable development of education.
Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine Framework

Many approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-means clustering algorithm for image segmentation and co-training algorithm to enlarge the sample sets was used to classify the agricultural planting structure at large-scale areas. Data sets collected from a small-scale area within the Hetao Irrigation District (HID) at the upper reaches of the Yellow River basin were used to evaluate the SS-ELM framework. The results of the SS-ELM algorithm were compared with those of the random forest (RF), ELM, support vector machine (SVM) and semi-supervised support vector machine (S-SVM) algorithms. Then the SS-ELM algorithm was applied to analyze the complex planting structure of HID in 1986–2010 by comparing the remote sensing estimated results with the statistical data. In the small-scale case, the SS-ELM algorithm performed better than the RF, ELM, SVM, and S-SVM algorithms. For the SS-ELM algorithm, the average overall accuracy (OA) was in a range of 83.00–92.17%. On the contrary, for the other four algorithms, their average OA values ranged from 56.97% to 92.84%. Whereas, in the classification of planting structure in HID, the SS-ELM algorithm had an excellent performance in classification accuracy and computational efficiency for three major planting crops including maize, wheat, and sunflowers. The estimated areas by using the SS-ELM algorithm based on the remote sensing images were consistent with the statistical data, and their difference was within a range of 3–25%. This implied that the SS-ELM framework could be served as an effective method for the classification of complex planting structures with relatively fast training, good generalization, universal approximation capability, and reasonable learning accuracy.
ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them

This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task.The purpose of the TC task was to identify an applied propaganda technique given propaganda text fragment.The goal of SI task was to find specific text fragments which contain at least one propaganda technique.Both of the developed solutions used semi-supervised learning technique of selftraining.Interestingly, although CRF is barely used with transformer-based language models, the SI task was approached with RoBERTa-CRF architecture.An ensemble of RoBERTa-based models was proposed for the TC task, with one of them making use of Span CLS layers we introduce in the present paper.In addition to describing the submitted systems, an impact of architectural decisions and training schemes is investigated along with remarks regarding training models of the same or better quality with lower computational budget.Finally, the results of error analysis are presented.
Diverse Knowledge Distillation for End-to-End Person Search

Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. The former views person search as two independent tasks and achieves dominant results using separately trained person detection and re-identification (Re-ID) models. The latter performs person search in an end-to-end fashion. Although the end-to-end approaches yield higher inference efficiency, they largely lag behind those two-step counterparts in terms of accuracy. In this paper, we argue that the gap between the two kinds of methods is mainly caused by the Re-ID sub-networks of end-to-end methods. To this end, we propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck. We also design a spatial-invariant augmentation to assist model to be invariant to inaccurate detection results. Experimental results on the CUHK-SYSU and PRW datasets demonstrate the superiority of our method against existing approaches -- it achieves on par accuracy with state-of-the-art two-step methods while maintaining high efficiency due to the single joint model. Code is available at: https://git.io/DKD-PersonSearch.
Don’t take “nswvtnvakgxpm” for an answer –The surprising vulnerability of automatic content scoring systems to adversarial input

Automatic content scoring systems are widely used on short answer tasks to save human effort.However, the use of these systems can invite cheating strategies, such as students writing irrelevant answers in the hopes of gaining at least partial credit.We generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and show that even simple methods lead to a surprising decrease in content scoring performance.As an extreme example, up to 60% of adversarial answers generated from random shuffling of words in real answers are accepted by a state-of-the-art scoring system.In addition to analyzing the vulnerabilities of content scoring systems, we examine countermeasures such as adversarial training and show that these measures improve system robustness against adversarial answers considerably but do not suffice to completely solve the problem.
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning

In order to improve the performance of a deep neural network, the activation function is an important aspect that we must research continuously, that is why we have expanded the research in this direction. We introduced a novel P-Swish activation function (Parametric Swish), which is able to bring performance improvements on object classification tasks using datasets such as CIFAR-10, CIFAR-100, but we will see that we also used datasets for Natural Language Processing (NLP). To test it, we used several types of architectures, including LeNet-5, Network in Network (NiN), and ResNet34 compared to popular activation functions such as sigmoid, ReLU, Swish, and our proposals. In particular, the P-Swish function facilitates fast network training, which makes it suitable for the Transfer Learning technique.
Reinforcement learning based energy-efficient internet-of-things video transmission

The video transmission in the Internet-of-Things (IoT) system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services. In this paper, we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference, in which the base station controls the transmission action of the IoT device including the encoding rate, the modulation and coding scheme, and the transmit power. A reinforcement learning algorithm state-action-reward-state-action is applied to choose the transmission action based on the observed state (the queue length of the buffer, the channel gain, the previous bit error rate, and the previous packet loss rate) without knowledge of the transmission channel model at the transmitter and the receiver. We also propose a deep reinforcement learning based energy-efficient IoT video transmission scheme that uses a deep neural network to approximate Q value to further accelerate the learning process involved in choosing the optimal transmission action and improve the video transmission performance. Moreover, both the performance bounds of the proposed schemes and the computational complexity are theoretically derived. Simulation results show that the proposed schemes can increase the peak signal-to-noise ratio and decrease the packet loss rate, the delay, and the energy consumption relative to the benchmark scheme.
Shallow Graph Convolutional Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model's abilities to exploit the global and semantic discriminative information due to the limits of receptive fields. Furthermore, the fixed graph size would cause many redundancies in the representation of actions, which is inefficient for the model. The redundancies could also hinder the model from focusing on beneficial features. To address those issues, we proposed a plug-and-play channel adaptive merging module (CAMM) specific for the human skeleton graph, which can merge the vertices from the same part of the skeleton graph adaptively and efficiently. The merge weights are different across the channels, so every channel has its flexibility to integrate the joints. Then, we build a novel shallow graph convolutional network (SGCN) based on the module, which achieves state-of-the-art performance with less computational cost. Experimental results on NTU-RGB+D and Kinetics-Skeleton illustrates the superiority of our methods.
Modelling count data via copulas

Copula models have been widely used to model the dependence between continuous random variables, but modelling count data via copulas has recently become popular in the statistics literature. Spearman's rho is an appropriate and effective tool to measure the degree of dependence between two random variables. In this paper, we derive the population version of Spearman's rho via copulas when both random variables are discrete. The closed-form expressions of the Spearman correlation are obtained for some copulas with different marginal distributions. We derive the upper and lower bounds of Spearman's rho for Bernoulli marginals. The proposed Spearman's rho correlations are compared with their corresponding Kendall's tau values and their functional relationships are characterized in some special cases. An extensive simulation study is conducted to demonstrate the validity of our theoretical results. Finally, we propose a bivariate copula regression model to analyse the count data of a cervical cancer dataset.
Multi-UAV trajectory planning using gradient-based sequence minimal optimization

Multi-UAV system is widely used in surveillance, search and rescue, and industrial inspection. Multi-UAV trajectory planning is crucial for the multi-UAV system, but multi-UAV trajectory planning often needs to consider many constraints, such as trajectory smoothness, obstacle collisions, mutual collisions, dynamic limits, time-consuming, and trajectory length. It is a challenge to balance these constraints while considering computational performance. This paper proposes a novel multi-UAV trajectory planning method to solve the challenge. This method uses time segmentation instead of traditional waypoint segmentation to establish a trajectory optimization model based on the unified time interval, which simplifies the calculation of cost functions. At the same time, virtual segments are introduced to adapt to the trajectory length of different UAVs to reduce the total arrival time. Nonlinear constraints are cast into cost functions and a gradient-based sequential minimal optimization (GB-SMO) algorithm is proposed to minimize the cost function, which decouples the constraint of the mutual collisions in each iteration to save the planning time. Experiments are performed on a multi-UAV system to prove the effectiveness of the proposed method. Results show that this method has good performance in obstacle-rich environments and is efficient for a large number of UAVs.
Incremental face clustering with optimal summary learning via graph convolutional network

In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering; thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data distribution via supervised learning. Moreover, an efficient framework to cluster previous summaries with new data is explored. Although learning summaries from original data costs more than those from previous clusters, the entire framework consumes just a little bit more time because clustering current data and generating summaries for new data share most of the calculations. Experiments show that the proposed approach significantly outperforms the existing incremental face clustering methods, as evidenced by the improvement of average F-score from 0.644 to 0.762. Compared with state-of-the-art static face clustering methods, our method can yield comparable accuracy while consuming much less time.