Reviewerly/exHarmony huggingface dataset:
Exploring for Possible Effect of Persuasive Strategy Implementation Choices: Towards Tailoring Persuasive Technologies

Persuasive strategies have been implemented in various ways in persuasive systems to motivate behaviour change. However, there is no empirical evidence regarding whether or not the effectiveness of persuasive systems varies based on implementation choices and why. To address this gap, we conduct a large-scale study involving 568 participants to investigate the perceived persuasiveness or effectiveness of different implementations of each strategy in the same system. We focused on six (6) popular strategies from the Persuasive Systems Design (PSD) model: self-monitoring, suggestion, reward, cooperation, social role, and normative influence. Our results show that the perceived persuasiveness of distinct implementations of the same strategy varies. For instance, people perceive Social Role strategy implemented as communication between a user and a "human" therapist as significantly more persuasive than implementing the strategy as communication between a user and a "non-human" therapist. Also, implementing Suggestion strategy as a list of suggestions/tips accessible via a menu is perceived as significantly more persuasive than contextual pop-up tips. We perform thematic analysis of qualitative comments from participants to examine the reasons for their implementation choices (why). We also offer practical design guidelines for tailoring persuasive systems based on our findings.
Analysis of Visual Modeling Tools Development for Complex Production Systems

The paper reviews the software tools for visual system analysis of complex production systems in the context of digital industry transformation. The main trends in the visual modeling development of complex systems and the problems of their implementation are outlined. The groups of tools for visual system analysis of complex systems are heterogeneous. There are diagrammatic, iconic, and parametric visual models. The analyzed tools differ in the depth of coverage of the studied processes, their detailing, and the integration of blocks of different levels of expertise into one model. Based on the results of a comparative analysis of individual classes of visual modeling tools, recommendations for their use are given. The market analysis of visual instruments is based on the Interest Index. According to the data, in Russia, there is a growing interest in solutions that focus on the modeling of active systems and opportunities to integrate the modeling of business processes with the modeling of information system components. One of the major trends in the development and implementation of modern visual business process modeling solutions is the use of simulation modeling as an integral component. Based on this, it is concluded that the visual model of the business process in digital transformation should be considered a digital twin. The analysis of the dynamics of the Interest Index allows stating the prevalence of ARIS-based tool solutions in the market. UML-based solutions in demand increased their share, but not significantly, indicating the conservatism and a certain constancy of the potential group of its users.
Clustered Hierarchical Anomaly and Outlier Detection Algorithms

Anomaly and outlier detection is a long-standing problem in machine learning. In some cases, anomaly detection is easy, such as when data are drawn from well-characterized distributions such as the Gaussian. However, when data occupy high-dimensional spaces, anomaly detection becomes more difficult. We present CLAM (Clustered Learning of Approximate Manifolds), a manifold mapping technique in any metric space. CLAM begins with a fast hierarchical clustering technique and then induces a graph from the cluster tree, based on overlapping clusters as selected using several geometric and topological features. Using these graphs, we implement CHAODA (Clustered Hierarchical Anomaly and Outlier Detection Algorithms), exploring various properties of the graphs and their constituent clusters to find outliers. CHAODA employs a form of transfer learning based on a training set of datasets, and applies this knowledge to a separate test set of datasets of different cardinalities, dimensionalities, and domains. On 24 publicly available datasets, we compare CHAODA (by measure of ROC AUC) to a variety of state-of-the-art unsupervised anomaly-detection algorithms. Six of the datasets are used for training. CHAODA outperforms other approaches on 16 of the remaining 18 datasets. CLAM and CHAODA scale to large, high-dimensional "big data" anomaly-detection problems, and generalize across datasets and distance functions. Source code to CLAM and CHAODA are freely available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Software Modernization: Theoretical Framework

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“Bildung” as a Forgotten Aspect of Algorithmic Technologies

Developers, coders, and designers of learning software have a significant influence on the understanding of education and learning in the expanding Global Educational Industry (GEI) as they introduce proprietary digital technologies and data science to formal and informal education. The chapter focuses on this kind of knowledge production and its implications for the understanding of teaching and learning practices. We explore the question how sense-making within algorithmic systems can be described in contrast to human sense-making as generally found in education. This concerns especially the relation of knowledge, learning, education (in the sense of “Bildung”) and time. Throughout all life and developmental stages, humans are addressed by algorithmic systems in the same way, leaving out specific pedagogical values concerning lifetime and biography. By integrating digital software in the education process, established routines are questioned and changed. For example, algorithmic systems work with temporal immediacy – the systems answer immediately, but also provoke immediate reactions. Against this background, it stands in contrast to an understanding of learning and “Bildung”, which inevitably takes time. The contribution calls attention to how digital systems challenge education and force educational routines into dilemmatic situations, such as setting a pedagogical relationship between solicitousness and data security. Digital systems are arguably shaping, in the long run, the next generation of adults as functionally appropriate complying participants and users of the algorithmic systems designed by the GEI.
Towards Monocular Neural Facial Depth Estimation: Past, Present, and Future

This article contains all of the information needed to conduct a study on monocular facial depth estimation problems.A brief literature review and applications on facial depth map research were offered first, followed by a comprehensive evaluation of publicly available facial depth datasets and widely used loss functions.The key properties and characteristics of each facial depth map dataset are described and evaluated.Furthermore, facial depth maps loss functions are briefly discussed, which will make it easier to train neural facial depth models on a variety of datasets for both short-and long-range depth maps.The network's design and components are essential, but its effectiveness is largely determined by how it is trained, which necessitates a large dataset and a suitable loss function.Implementation details of how neural depth networks work and their corresponding evaluation matrices are presented and explained.In addition, an SoA neural model for facial depth estimation is proposed, along with a detailed comparison evaluation and, where feasible, direct comparison of facial depth estimation methods to serve as a foundation for a proposed model that is utilized.The model employed shows better performance compared with current state-of-the-art methods when tested across four datasets.The new loss function used in the proposed method helps the network to learn the facial regions resulting in an accurate depth prediction.The network is trained on synthetic human facial depth datasets whereas for validation purposes real as well as synthetic facial images are used.The results prove that the trained network outperforms current state-of-the-art networks performances, thus setting up a new baseline method for facial depth estimations.
Fine-Grained Ship Recognition for Complex Background Based on Global to Local and Progressive Learning

The existing deep learning fine-grained recognition models have low recognition accuracy (Acc) in complex natural environments. We propose a fine-grained ship recognition model [global to local progressive learning module (GLPM)] based on global to local and progressive learning for complex backgrounds to address this problem. GLPM improves fine-grained ship targets’ recognition Acc in natural complex backgrounds by progressively guiding high-level global features to low-level local features for learning and enhancing local fine-grained features expression ability. First, the global features are obtained by adding the feature pyramid attention (FPA) module to the deepest level of the backbone network. Second, the decoding and reconstruction of low-level local feature information at different levels are guided, and the global features are used to weight the key pixel values of local features of the target. Finally, the output of the backbone network is combined with the output vector of the global to local progressive module for classification, and the whole model can be trained end-to-end. We experiment on naturally collected complex background images containing 59 ship classes, and the public maritime (MAR)-ships contain pictures of 23 types of ships. The results show that the proposed method outperforms the methods compared in this letter while providing valuable ideas for the research of fine-grained ship recognition models in complex natural environments.
CosmoVis: An Interactive Visual Analysis Tool for Exploring Hydrodynamic Cosmological Simulations

We introduce CosmoVis, an open source web-based visualization tool for the interactive analysis of massive hydrodynamic cosmological simulation data. CosmoVis was designed in close collaboration with astrophysicists to enable researchers and citizen scientists to share and explore these datasets, and to use them to investigate a range of scientific questions. CosmoVis visualizes many key gas, dark matter, and stellar attributes extracted from the source simulations, which typically consist of complex data structures multiple terabytes in size, often requiring extensive data wrangling. CosmoVis introduces a range of features to facilitate real-time analysis of these simulations, including the use of "virtual skewers," simulated analogues of absorption line spectroscopy that act as spectral probes piercing the volume of gaseous cosmic medium. We explain how such synthetic spectra can be used to gain insight into the source datasets and to make functional comparisons with observational data. Furthermore, we identify the main analysis tasks that CosmoVis enables and present implementation details of the software interface and the client-server architecture. We conclude by providing details of three contemporary scientific use cases that were conducted by domain experts using the software and by documenting expert feedback from astrophysicists at different career levels.
Deep Learning in Human Activity Recognition from Videos: A Survey

Human activity recognition is a trending area of research in current times. It has variety of applications especially from security point of view. Recognizing human actions from video is a tedious task in computer vision due to largely invariant nature of scenarios that occur and also the infinite amount of data that needs to be worked upon that too accurately and fast. Activities are classified into two major groups. The simple activities come under single-layer group wherein simple activities like sitting, walking, running are performed by one or two subjects. Another group is called hierarchical group that consists of much complex activities or activities in sequence being performed by one or many subjects at a time. This paper discusses some papers associated with human activity recognition from videos. We examined each paper to highlight the method used for feature extraction and classification. We also noted the method used and accuracies achieved by the authors on various public datasets.
An English Text Classification Method Based on TextCNN and SVM

Text classification is an important problem in natural language processing. The text representation is a matrix with high sparseness and high latitude. However, traditional text classification requires manual feature engineering with subjective interference factors. Therefore, an English text classification method combining a convolutional neural network and support vector machine is proposed in this paper. This method uses convolutional neural networks’ advantages to extract features, avoiding feature engineering in traditional classification methods. Meanwhile, the proposed way fully plays the generalization performance of support vector machines. The Internet Movie Database (IMDB) movie review data were applied to evaluate the performance of the proposed model, and the experimental results showed that the proposed approach achieves an accuracy rate of 0.9062, which exceeds the other three methods.
ConLAR: Learning to Allocate Resources to Docker Containers under Time-Varying Workloads

Cloud platforms are increasingly using containers for lightweight virtualization. However, the mainstream operating systems are currently limited in their capabilities in customizing containers’ resource management. There remains two main challenges in resource allocations. First, the application workloads can be time-varying, leading to a problem of resource over- or under-allocations. Second, it becomes difficult to minimize the resource provisioning cost while guaranteeing the SLO (service level objective). To address these challenges, we propose ConLAR, a learning-to-allocate approach that predicts and allocates resources to Docker containers under time-varying workloads. ConLAR efficiently reduces over-provisioning cost with the SLO guarantees by taking an Observing-Predicting-Allocating-Executing paradigm: given a container, it observes the running of the online application and its environment, leverages the LSTM (long short term memory) model to predict its future workload, adaptively learns to construct resource allocation strategies with two objectives through RL (reinforcement learning), and executes them to scale container resources dynamically. We have evaluated ConLAR on two real-world workloads of ClarkNet and GoogleClusterData. The results clearly show the effectiveness of ConLAR. In particular, ConLAR achieves a resource over-provisioning cost of less than 16.5% and an SLO violations rate of 8.9%; it also shows good flexibility to learn different adaption policies.
Applying Cognitive Complexity to Checklist-Based Human-Machine Pair Inspection

With the rapid development of computer technology, the scale of software systems is increasing, the quality of software is getting more attention. Software inspection is a widely known practice of software quality assurance. However, most of the existing inspection techniques focus on the support of the peer review approach, but they may not be effective for complex systems due to the limitation of the approach. In this paper, we put forward a novel approach called Human-Machine Pair Inspection to support program inspection by the programmer who is constructing the program. The approach is characterized by the style that machine will intelligently guide the programmer to carry out inspections of the program code during the program construction process. To fulfill this goal, we use the Cognitive Complexity theory to determine what and how the inspection can be done by the programmer. We present an example to show how the method can be used and show its accuracy and usefulness. The result shows that our method can be automatic and effective in assisting programmers to detect defects during programming.
DCTNets: Deep crowd transfer networks for an approximate crowd counting

Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance systems, they are not appropriate for use in small surveillance systems. They also function poorly in a variety of sizes and densities, as well. Transfer learning and deep convolutional neural network architecture are used to create a modest but efficient network, which we describe herein. We named the proposed crowd counting architecture deep crowd transfer network (DCTNets) since it incorporates both deep learning and transfer learning principles into a single system. Among DCTNets’ key components are a detection module that is based on mask R-CNNs and an estimate module that is based on deep convolutional neural networks. In the first step, we apply transfer learning to the Mask R-CNN model using the datasets ShanghaiTech, JHU-CROWD++, and UCF-QNRF. After that, we train and evaluate the complete architecture on these datasets using the transfer learning results. Input images are sent through a Mask R-CNN, which counts individuals and segments the counted region, then through an estimation network, which estimates the population size, and finally through a merge of the outputs from the two models. According to the findings of comparative tests, the proposed model outperforms existing state-of-the-art approaches on the ShanghaiTech, JHU-CROWD++, and UCF-QNRF datasets.
Automated recognition of geographical named entities in titles of Ukiyo-e prints

This paper investigates the application of Natural Language Processing as a means to study the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints. We introduce a new dataset with titles of landscape prints that have been annotated by an art historian for any included place-names. The prints are hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto, one of the hubs of Digital Humanities in Japan. By applying, calibrating and assessing a Named Entity Recognition (NER) tool, we argue that ‘distant viewing’ or macroanalysis of visual datasets can be facilitated, which is needed to assist art historical studies of this rich, complex and diverse research material. Experimental results indicated that the performance of NER can be improved by 30% and reach 50% precision, by using part of the introduced dataset.
Unity in Diversity: Multilabel Emoji Identification in Tweets

Emojis or emoticons are not just a modern trend but have become an essential part of our day-to-day interactions. Predicting a suitable emoji for a given tweet is a challenging task because a wrong emoji prediction for a tweet can change the meaning of the message or can amplify the emotion of the message. This task is particularly challenging since it requires selecting an appropriate emoji from a huge list of prospective emojis that may or may not be equivalent to one another. Humans use multiple emojis to convey their emotions, thereby making the task a multilabel classification problem. In this article, we propose a multilabel emoji prediction system that predicts the appropriate emoji for a given tweet by using different state-of-the-art baselines. Due to the unavailability of a multi-emoji dataset, we create a large-scale multilabel emoji dataset named Mu-Emoji that comprises of more than 0.6 million tweets having varied emojis belonging to both positive and negative sentiments. For our proposed task, we employ graph attention network along with bidirectional encoder representations from transformer encoder for the accurate prediction of emojis. Qualitative and quantitative analyses show that our multilabel emoji prediction baselines perform well compared with the single-emoji prediction baselines for our proposed Mu-Emoji dataset. Our proposed framework also outperforms all the baselines for both single and multilabel emoji prediction tasks.
Adequacy of Histopathology Reports Representing Oncologic Resection Specimens: An Experience of Reporting Practice in Rural India

The histopathology reporting practice in rural areas is largely variable. To ensure the adequacy of histopathology reports (HPRs), the College of American Pathologists (CAP) has developed cancer reporting checklists.To assess the adequacy of resection specimen HPRs received from outside centers for a second opinion. Further, the adequacy of breast resection HPRs from these centers was compared with that of our center. Additionally, the reports representing endometrial carcinoma were assessed for impact on the treatment decision.This was a retrospective study conducted from June 2015 to December 2019. HPRs from outside centers and our institute were analyzed for mandatory reporting elements as per CAP 2013 checklists.A total of 730 HPRs (558 outside HPRs and 172 in-house breast HPRs) were reviewed for completeness. The outside HPRs were complete in 42 of 558 cases (7.5%). Only 11 of 143 reports (7.7%) from the academic centers were complete. Seventeen of 249 outside breast HPRs (6.8%) were complete, whereas predominant (n = 123 of 172; 71.5%) in-house breast HPRs were adequate. Most outside endometrial carcinoma reports (60.8%; n = 28 of 46) were inadequate with potential impact on the adjuvant treatment, whereas 10 of 46 reports (21.7%) were inadequate and had an actual impact on the adjuvant treatment decision.Minimal data set reporting using checklists is not yet widely operational in most rural laboratories. We call for continuous education and sensitization of the practicing pathologists, oncopathology education of the trainees, and regulatory standards for signing out an oncopathology report.
Design of Reinforcement Learning Environment for Multiple UAV Area Coverage Search

Unmanned aerial vehicle (UAV) plays an increasingly important role both in civil and military fields. As a typical mission, area coverage search is a primary measure for reconnaissance and surveillance of unknown areas. Due to the limited information about the mission area, UAV needs adaptiveness to the dynamic changes of the area and makes decisions based on local observation. It puts higher demands on the response speed of UAV, and traditional planning methods are tough to be directly applied. The problem becomes more noticeable for multiple UAV because of the heterogeneous function and large scale, and new solutions are urgently required. In this paper, the feasibility of reinforcement learning (RL) for performing area coverage search is explored, and an environment is designed for RL training and evaluation. Simulation results show that multiple UAV can cooperate and perform the mission with pretty high coverage.