Reviewerly/exHarmony huggingface dataset:
Robustness in machine learning explanations

The explainable AI literature contains multiple notions of what an explanation is and what desiderata explanations should satisfy. One implicit source of disagreement is how far the explanations should reflect real patterns in the data or the world. This disagreement underlies debates about other desiderata, such as how robust explanations are to slight perturbations in the input data. I argue that robustness is desirable to the extent that we're concerned about finding real patterns in the world. The import of real patterns differs according to the problem context. In some contexts, non-robust explanations can constitute a moral hazard. By being clear about the extent to which we care about capturing real patterns, we can also determine whether the Rashomon Effect is a boon or a bane.
Probability learning in mathematics using augmented reality: impact on student’s learning gains and attitudes

The development of Augmented Reality technologies has enabled students to learn in an environment that combines learning resources from the real and digital world. This paper integrates three mobile Augmented Reality-based applications into a series of mathematics lessons on probabilities in a junior high school. This paper aims to examine how mobile AR-based learning applications affect students’ learning gains through ANCOVA. Moreover, students’ attitudes towards the applications are discussed. The participants are 68 junior high school students. The experimental results show that the mobile AR-based applications would be helpful for students’ learning gains in the topic of probability. Students’ attitude towards the AR applications in this series of lessons was positive. Overall, it was found that mobile AR-based learning applications have positive effects on junior high students learning mathematics.
A Comparison of Semantic Similarity Methods for Maximum Human Interpretability

The inclusion of semantic information in any similarity measures improves the efficiency of the similarity measure and provides human interpretable results for further analysis. The similarity calculation method that focuses on features related to the text's words only, will give less accurate results. This paper presents three different methods that not only focus on the text's words but also incorporates semantic information of texts in their feature vector and computes semantic similarities. These methods are based on corpus-based and knowledge-based methods, which are: cosine similarity using tf-idf vectors, cosine similarity using word embedding and soft cosine similarity using word embedding. Among these three, cosine similarity using tf-idf vectors performed best in finding similarities between short news texts. The similar texts given by the method are easy to interpret and can be used directly in other information retrieval applications.
Deep Clustering With Variational Autoencoder

An autoencoder that learns a latent space in an unsupervised manner has many applications in signal processing. However, the latent space of an autoencoder does not pursue the same clustering goal as Kmeans or GMM. A recent work proposes to artificially re-align each point in the latent space of an autoencoder to its nearest class neighbors during training (Song et al. 2013). The resulting new latent space is found to be much more suitable for clustering, since clustering information is used. Inspired by previous works (Song et al. 2013), in this letter we propose several extensions to this technique. First, we propose a probabilistic approach to generalize Song's approach, such that Euclidean distance in the latent space is now represented by KL divergence. Second, as a consequence of this generalization we can now use probability distributions as inputs rather than points in the latent space. Third, we propose using Bayesian Gaussian mixture model for clustering in the latent space. We demonstrated our proposed method on digit recognition datasets, MNIST, USPS and SHVN as well as scene datasets, Scene15 and MIT67 with interesting findings.
Saliency Guided Self-Attention Network for Weakly and Semi-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) using only image-level labels can greatly reduce the annotation cost and therefore has attracted considerable research interest. However, its performance is still inferior to the fully supervised counterparts. To mitigate the performance gap, we propose a saliency guided self-attention network (SGAN) to address the WSSS problem. The introduced self-attention mechanism is able to capture rich and extensive contextual information but may mis-spread attentions to unexpected regions. In order to enable this mechanism to work effectively under weak supervision, we integrate class-agnostic saliency priors into the self-attention mechanism and utilize class-specific attention cues as an additional supervision for SGAN. Our SGAN is able to produce dense and accurate localization cues so that the segmentation performance is boosted. Moreover, by simply replacing the additional supervisions with partially labeled ground-truth, SGAN works effectively for semi-supervised semantic segmentation as well. Experiments on the PASCAL VOC 2012 and COCO datasets show that our approach outperforms all other state-of-the-art methods in both weakly and semi-supervised settings.
PlotTwist: A web app for plotting and annotating continuous data

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist
Sentiment Analysis of Social Media Network Using Random Forest Algorithm

Sentiment Analysis is the identification of sentiments or opinions from the given text. Social media generates large amount of sentiment loaded information in the form of reviews. Sentiment analysis is used to identify the customer's opinions from user reviews. Online purchasing have became more fashionable due to its varieties, low cost and immediate supply. In today's competitive ecommerce market ratings and reviews of various brand is used to understand how consumers really feel about the product. The feedback environment is developed to help the customers to buy the correct product and to guide the companies to enhance the features of product depending on the consumer's demand. The customer feels difficult to accurately find the review for a particular feature of a product that they intend to buy. Also, there is a mixture of positive and negative reviews. To avoid this confusion and to make this review system more transparent and user friendly, feature based opinion extraction is carried out. In this paper the rating from the online shopping website known as flipkart.com is analyzed, based on the aspects of the product the rating is classified as positive, neutral and negative. The proposed work is analyzed by using Machine Learning algorithm called Random Forest and simulated by using SPYDER. In our system the accuracy, precision, F-measure and recall is calculated for both Random Forest and Support Vector Machine (SVM) algorithm and then accuracy comparison is made these two algorithms. In which the Random Forest gives the best accuracy of 97% than the Support Vector Machine.
An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

The solution of high dimensional function has always been a hot topic.In this paper, a novel algorithm based on Kernel Fuzzy C-means and dolphin swarm algorithm are proposed to solve highdimensional functions more accurately.First, to improve the global convergence ability of dolphin swarm algorithm, Kernel Fuzzy C-means is introduced into the algorithm, named as Kernel Fuzzy C-means dolphin swarm algorithm (KFCDSA); Second, the five typical high-dimensional functions are applied to test the performance of the combination of KFCDSA.Finally, some indicators are used to evaluate the performance of different meta-heuristic algorithms.The results show that: the performance of the proposed algorithm exceeds that of the dolphin swarm algorithm and some advanced metaheuristic algorithms considered for comparison based on five different evaluating indicators.Through the test results, it can be concluded that introducing Kernel Fuzzy C-means into dolphin swarm algorithm is an effective improvement and provides a possibility for obtaining global optimal solutions for high-dimensional functions.
Self-adaptive Multi-population Rao Algorithms for Engineering Design Optimization

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Deep learning-based intelligent face recognition in IoT-cloud environment

In recent years, the Internet-of-Things (IoT) technology is being used in many application areas such as healthcare, video surveillance, transportation etc. The massive adoption and growth of IoT in these areas are generating a massive amount of data. For example, IoT devices such as cameras are generating a huge amount of images when used in hospital surveillance scenarios. Here, face recognition is an important element that can be used for securing hospital facilities, emotion detection and sentiment analysis of patients, detecting patient fraud, and hospital traffic pattern analysis. Automatic and intelligent face recognition systems have high accuracy in a controlled environment; however, they have low accuracy in an uncontrolled environment. Also, the systems need to operate in real-time in many applications such as smart healthcare. This paper suggests a tree-based deep model for automatic face recognition in a cloud environment. The proposed deep model is computationally less expensive without compromising the accuracy. In the model, an input volume is split into several volumes, where a tree is constructed for each volume. A tree is defined by its branching factor and height. Each branch is represented by a residual function, which is constituted by a convolutional layer, a batch normalization, and a non-linear function. The proposed model is evaluated in various publicly available databases. A comparison of performance is also done with state-of-the-art deep models for face recognition. The results of the experiments demonstrate that the proposed model achieved accuracies of 98.65%, 99.19%, 95.84% on FEI, ORL, and LFW databases, respectively.
The challenges of using head mounted virtual reality in K-12 schools from a teacher perspective

Abstract The use of head mounted displays (HMDs) to experience virtual realities (VR) has become increasingly common. As this technology becomes more affordable, immersive and easier to use, it also becomes more serviceable in educational and training contexts. Even though the technology, content and feasibility for K-12 school purposes are still being developed, it is reasonable to expect that the call or ‘push’ to use HMD VR in K-12 schools will increase, especially as there is now a greater economic interest in the use of digital technologies in educational contexts. This article aims to inform the process of implementing HMD VR in K-12 contexts by researching the preconditions and challenges of use from a teacher perspective. It does this by analysing the organisational, institutional, contextual and practical challenges and opportunities in the implementation of HMD VR in K-12 school contexts. The data draws on (a) interviews, informal conversations and observations of teachers testing HMD VR and different VR applications in a Digital Learning Lab (DLL) and (b) data from a project involving upper secondary school history teachers discussing the planned implementation of HMD VR in their teaching and being in the DLL. The main findings are related to: (a) economy and technology, (b) initial learning barriers, (c) organisation and practical enactment for teaching and learning, (d) curricula, syllabuses and expected learning outcomes and (e) teachers’ competences, professional development and trust. The consequences for educational contexts and possible ways forward are also discussed.
Leading Conversational Search by Suggesting Useful Questions

This paper studies a new scenario in conversational search, conversational question suggestion, which leads search engine users to more engaging experiences by suggesting interesting, informative, and useful follow-up questions. We first establish a novel evaluation metric, usefulness, which goes beyond relevance and measures whether the suggestions provide valuable information for the next step of a user’s journey, and construct a public benchmark for useful question suggestion. Then we develop two suggestion systems, a BERT based ranker and a GPT-2 based generator, both trained with novel weak supervision signals that convey past users’ search behaviors in search sessions. The weak supervision signals help ground the suggestions to users’ information-seeking trajectories: we identify more coherent and informative sessions using encodings, and then weakly supervise our models to imitate how users transition to the next state of search. Our offline experiments demonstrate the crucial role our “next-turn” inductive training plays in improving usefulness over a strong online system. Our online A/B test in Bing shows that our more useful question suggestions receive 8% more user clicks than the previous system.
DeepTabStR: Deep Learning based Table Structure Recognition

This paper presents a novel method for the analysis of tabular structures in document images using the potential of deformable convolutional networks. In order to assess the suitability of the model to the task of table structure recognition, most of the prior methods have been tested on the smaller ICDAR-13 table structure recognition dataset comprising of just 156 tables. We curated a new image-based table structure recognition dataset, TabStructDB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , comprising of 1081 tables densely labeled with row and column information. Instead of collecting new images for this purpose, we leveraged the famous Page-Object Detection dataset from ICDAR-17, and added structural information for all the tabular regions present in the dataset. This new publicly available dataset will enable the development of more sophisticated table structure recognition techniques in the future. We performed extensive evaluation on the two datasets (ICDAR-13 and TabStructDB) including cross-dataset testing in order to evaluate the efficacy of the proposed approach. We achieved state-of-the-art results with deformable models on ICDAR-13 with an average F-Measure of 92.98% (89.42% for rows and 96.55% for columns) and report baseline results on TabStructDB for guiding future research efforts with an F-Measure of 93.72% (91.26% for rows and 95.59% for columns). Despite promising results, structural analysis of tables with arbitrary layouts is still far from achievable at this point.
A comprehensive model of information security factors for decision-makers

Decision-making in the context of organizational information security is highly dependent on various information. For information security managers, not only relevant information has to be clarified but also their interdependencies have to be taken into account. Thus, the purpose of this research is to develop a comprehensive model of relevant management success factors (MSF) for organizational information security. First, a literature survey with an open-axial-selective analysis of 136 articles was performed to identify factors influencing information security. These factors were categorized into 12 areas: physical security, vulnerability, infrastructure, awareness, access control, risk, resources, organizational factors, CIA, continuity, security management, compliance & policy. Second, an interview series with 19 experts from the industry was used to evaluate the relevance of these factors in practice and explore interdependencies between them. Third, a comprehensive model was developed. The model shows that there are key-security-indicators, which directly impact the security-status of an organization while other indicators are only indirectly connected. Based on these results, information security managers should be aware of direct and indirect MSFs to make appropriate decisions.
Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCC-LSTM Model Based on Short Term Multivariate Historical Datasets

Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.
Dynamic consent management for clinical trials via private blockchain technology

Clinical trials (CTs) are essential for the advancement of medical research, paving the way for the development and adoption of new treatments, and contributing to the evolution of healthcare. An essential factor for the success of a CT is the appropriate management of its participants and their personal data. According to the current regulations, collecting and using personal data from participants must comply with rigorous standards. Therefore, healthcare institutes need to obtain freely given, specific, informed, and unambiguous consent before being able to collect the data. Some of the major limitations of the current technological solutions are the lack of control over the granularity of consent grants, as well as the difficulty of handling dynamic changes of consent over time. In this paper, we present SCoDES, an approach for trusted and decentralized management of dynamic consent in clinical trials, based on blockchain technology (BCT). The usage of blockchain provides a set of features that allow maintaining consent information with trust guarantees while avoiding the need for a dedicated or centralized third trusted party. We provide a full implementation of SCoDES, made available as a self-contained infrastructure, with the possibility to interact with external services, and using hyperledger as a blockchain framework.
A novel automation-assisted cervical cancer reading method based on convolutional neural network

While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell images with size of 4000 × 3000 and complex cellular situation corresponding to 10 categories. Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean average precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the average precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, providing a new idea for future development of computer-assisted reading systems in clinical cervical screening.
Path planning for asteroid hopping rovers with pre-trained deep reinforcement learning architectures

Asteroid surface exploration is challenging due to complex terrain topology and irregular gravity field. A hopping rover is considered as a promising mobility solution to explore the surface of small celestial bodies. Conventional path planning tasks, such as traversing a given map to reach a known target, may become particularly challenging for hopping rovers if the terrain displays sufficiently complex 3-D structures. As an alternative to traditional path-planning approaches, this work explores the possibility of applying deep reinforcement learning (DRL) to plan the path of a hopping rover across a highly irregular surface. The 3-D terrain of the asteroid surface is converted into a level matrix, which is used as an input of the reinforcement learning algorithm. A deep reinforcement learning architecture with good convergence and stability properties is presented to solve the rover path-planning problem. Numerical simulations are performed to validate the effectiveness and robustness of the proposed method with applications to two different types of 3-D terrains.