Reviewerly/exHarmony huggingface dataset:
Self-Supervised Pre-Training with Bridge Neural Network for SAR-Optical Matching

Due to the vast geometric and radiometric differences between SAR and optical images, SAR-optical image matching remains an intractable challenge. Despite the fact that the deep learning-based matching model has achieved great success, SAR feature embedding ability is not fully explored yet because of the lack of well-designed pre-training techniques. In this paper, we propose to employ the self-supervised learning method in the SAR-optical matching framework, in order to serve as a pre-training strategy for improving the representation learning ability of SAR images as well as optical images. We first use a state-of-the-art self-supervised learning method, Momentum Contrast (MoCo), to pre-train an optical feature encoder and an SAR feature encoder separately. Then, the pre-trained encoders are transferred to an advanced common representation learning model, Bridge Neural Network (BNN), to project the SAR and optical images into a more distinguishable common feature representation subspace, which leads to a high multi-modal image matching result. Experimental results on three SAR-optical matching benchmark datasets show that our proposed MoCo pre-training method achieves a high matching accuracy up to 0.873 even for the complex QXS-SAROPT SAR-optical matching dataset. BNN pre-trained with MoCo outperforms BNN with the most commonly used ImageNet pre-training, and achieves at most 4.4% gains in matching accuracy.
You Shall Not Pass? The Design of Age Gates in an Emerging Cannabis Market

Age gates are becoming common on the websites of cannabis dispensaries, following design practices in other controversial industries. Yet, age gates can typically be bypassed through trial and error or basic arithmetic, raising questions over best practices. This study therefore characterized the age gates of dispensaries licensed in the Arizona adult use cannabis program, using a “digital mystery shopper” approach to examine the age gates’ location, appearance, and performance. After presenting the results, I outline possible best practices and next steps for technical and professional communicators.
Optimized Web Searching Using Inverted Indexing Technique

As the information on world wide web (WWW) is growing exponentially, it is very difficult to retrieve the relevant search results from datacenter repositories quickly. Prior research shows that the performance of an index deeply affects the retrieval results. However, the current practice of indexing in search engine is to recreate whenever any update of the dataset occurs in the repository. This practice can lead to performance efficiencies. To address this problem, we propose an approach based on an inverted index in this paper. We investigate various indexing approaches on the performance of the search engine. Experimentally analyze search engine performance improvement and efficiency by the creating Index. Compared with the traditional search engine architecture, the proposed scheme is containing various indexing modules for fitting the frequent update problem of index. The proposed approach will be compared with the conventional indexing approaches used in search engines and show that the proposed approach performs better than the traditional indexing schemes.
HCB Machine Learning Approach For Movie Recommendation System

The Recommendation System (RS) is a very powerful technique which supports the user to derive decision quickly. The RS is essential in any business model like B2B, B2C and C2C. The user wants to get the quality and essential information suitable to perception in an entertainment which falls on C2C category. The recommendation system can achieve this by filtering the essential choices from the large amount of rating of given by other users. The Hybrid Collaborative-Based (HCB) recommendation system in proposed work is experimented. The proposed experimental results on movie rating and review rating with movie lens data set shows high result than SVD, KNN and Co-Clustering Machine Learning (ML) algorithms.
GDESA: Greedy Diversity Encoder with Self-attention for Search Results Diversification

Search result diversification aims to generate diversified search results so as to meet the various information needs of users. Most of those existing diversification methods greedily select the optimal documents one-by-one comparing with the selected document sequences. Due to the fact that the information utilities of the candidate documents are not independent, a model based on greedy document selection may not produce the global optimal ranking results. To address this issue, some work proposes to model global document interactions regardless of whether a document is selected, which is inconsistent with actual user behavior. In this article, we propose a new supervised diversification framework as an ensemble of global interaction and document selection. Based on a self-attention encoder-decoder structure and an RNN-based document selection component, the model can simultaneously leverage both the global interactions among all the documents and the interactions between the selected sequence and each unselected document. This framework is called Greedy Diversity Encoder with Self-Attention (GDESA). Experimental results show that GDESA outperforms previous methods that rely just on global interactions, and our further analysis demonstrates that using both global interactions and document selection is necessary and beneficial.
Modified DMGC Algorithm using ReLU-6 with Improved Learning Rate for Complex Cluster Associations

This research work has presented a modified deep multi-graph clustering (MDMGC) for the enhancement of clustering accuracy. Multi-graph clustering is an important method for shallow models. Shallow models are known to have highly complex data. The proposed MDMGC is based on a graph auto encoder with a deep neural network. ReLU-6 is used as an activation function in the proposed method with a learning rate of 0.0002. A loss function is used for performance improvement. In this proposed MDMGC. T-distributed stochastic neighbor embedding (TSNE) uses data set visualization as well as embedding. Use Google Co-Lab for the implementation of the proposed method. The proposed modified clustering method shows better results as compared to other methods in terms of different parameters, normalized mutual information (NMI), and loss function. There are different data sets available to perform the proposed work; they are 20 news and DBLP. The proposed method also shows better clustering visual outcomes when it is calculated with the help of TSNE.
Automatic Question Generation for Chatbot Development

It is a labor intensive task to prepare a list of questions for the intents in creating a chatbot system. It is not so easy to predict the variation of questions to be matched with the answers or what we want to provide the information to the chatbot users. In this research, we aim to solve the problem by applying a transformer framework to generate a question sentence from a list of keywords and explanatory text. In this paper, the question text is generated by using a trained Japanese T5 model by applying a list of keywords extracted from the questions and the explanatory text of the Amagasaki City FAQ database. To expand the coverage of the questions in matching the chatbot intent, we apply WordNet to expand the keywords in the questions. Finally, Semantic Textual Similarity (STS) Sentence-BERT is applied to measure the similarity of the user query and the question list in the chatbot.
Analysing user reviews of interactive educational apps: a sentiment analysis approach

The aim of this study is to investigate the public opinion (i.e., the learners themselves) of apps incorporating emerging technologies Augmented Reality (AR) and Virtual Reality (VR) and find out how satisfied mobile platform users are with these technologies for educational purposes. We performed sentiment analysis of more than one million reviews from 800 different Android apps collected by systematic scrapping of the Google Play Store. Identified apps were separated into five categories: AR, AR+Educational, VR, VR+Educational, and Educational. We identify reviews from each app as positive, negative, or neutral, and apply aspect labels depending on the content of the review. We adopted Bidirectional Encoder Representations from Transformers (BERT) and classify the labels into seven categories: technical issues, usability, content, user interaction, feature request, learning qualities, and advert-related. The results indicate that the positive sentiments are 54.6% for AR, 49.6% for AR+Educational, 47.7% for VR, 71.4% for VR+Educational, and 75.2% for Educational. The results suggest that education apps that do not incorporate AR or VR are receiving higher user satisfaction than apps that incorporate these emerging technologies. Analyzing and understanding user reviews will help instructional designers, software developers, and hardware designers to resolve the key inhibitors .of these apps.
Automated detection of vascular remodeling in tumor‐draining lymph nodes by the deep‐learning tool <scp>HEV</scp>‐finder

Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
To Actively Initialize Active Learning

Though much effort has been spent on designing new active learning algorithms, little attention has been paid to the initialization problem of active learning, i.e., how to find a set of labeled samples which contains at least one instance per category. This work identifies the initialization of active learning as a separate and novel research problem, reviews existing methods that can be adapted to be used for this task and, in addition, proposes a new active initialization criterion: the Nearest Neighbor Criterion. Experiments on 16 benchmark datasets verify that the novel method often finds an initialization set with fewer queried samples than other methods do.
Manipulation as digital invasion

This chapter discusses the creation of a more systematic interaction between the fields of philosophy of technology and political philosophy. Political actors in the public sphere often manipulate others: they provide incentives and other means to purposely influence and alter individuals’ behaviours and beliefs. In general, manipulation is deemed to be a kind of intentional disruption or imposition in the expected functioning of individuals’ decision-making processes. The neo-republican political theory may offer a promising account of the conception of manipulation in digital contexts for several reasons. However, something more is needed to characterize interferences as dominating interventions: the absence of control or arbitrariness. Manipulation, understood as a practice, can be defined as a direct, non-contingent and non-deceptive misrepresentation that affects the manipulated agents’ cognitive capabilities in understanding a set of options and leaves them unsure about the means and intentions of such misrepresentation.
Facial action unit detection via hybrid relational reasoning

Correlations in facial action units (AUs) convey significant information for AU detection yet have not been thoroughly exploited. Most existing methods learn the regional correlation distribution of each AU, or reason the dependencies among AUs. However, these methods typically either predefine the correlations based on prior knowledge, which often ignores useful information, or directly learn the correlations guided by AU detection, which often includes irrelevant information. To resolve these limitations, we propose a novel hybrid relational reasoning framework for AU detection. In particular, we propose to adaptively reason pixel-level correlations of each AU, under the constraint of predefined regional correlations by facial landmarks, as well as the supervision of AU detection. Moreover, we propose to adaptively reason AU-level correlations using a graph convolutional network, by considering both predefined AU relationships and learnable relationship weights. Our framework is beneficial for integrating the advantages of correlation predefinition and correlation learning. Extensive experiments demonstrate that our approach (i) soundly outperforms the state-of-the-art AU detection methods on the challenging BP4D, DISFA, and GFT benchmarks, and (ii) can precisely reason the regional correlation distribution of each AU.
A Masked Self-Supervised Pretraining Method for Face Parsing

Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. However, pixel-wise manually labeling is time-consuming and labor-intensive, which motivates us to explore the unlabeled data. In this paper, we present a self-supervised learning method attempting to make full use of the unlabeled facial images for face parsing. In particular, we randomly mask some patches in the central area of facial images, and the model is required to reconstruct the masked patches. This self-supervised pretraining is capable of making the model capture facial feature representations through these unlabeled data. After self-supervised pretraining, the model is fine-tuned on a few labeled data for the face parsing task. Experimental results show that the model achieves better performance for face parsing assisted by the self-supervised pretraining, which greatly decreases the labeling cost. Our approach achieves 74.41 mIoU on the LaPa test set fine-tuned on only 0.2% of the labeled data of the whole training data, surpassing the model that is directly trained by a large margin of +5.02 mIoU. In addition, our approach achieves a new state-of-the-art on the LaPa and CelebAMask-HQ test set.
Triboelectric biometric signature

Biometric signatures based on either the physiological or behavioural features of a person have been widely used for identification and authentication. However, few strategies have been developed that combine the two types of features in one signature. Here, we report a type of biometric signature based on the triboelectricity of the human body (TEHB) that combines these two types of features. This triboelectric biometric signature (TEBS) can be accomplished by anyone regardless of the physical condition, as it can be performed by many parts of the body. Different TEBS can be identified using a convolutional neural network (CNN) model with a test accuracy of up to 1.0. The TEBS has been further used for text encryption and decryption with a high sensitivity to changes. Moreover, a dual signed digital signature for enhanced security has been proposed. Our findings provide a new type of TEBS that can be generally used and demonstrated in applications.
Improved English to Hindi MT using Long Short-Term Memory

It is in the field of language translation that robots are unable to compete with humans. Statistical Machine Translation (SMT) is one of the traditional methods for solving the Machine Translation (MT) problem. This approach is best suited for grammatically organized language pairings with comparable syntax, and it necessitates a large number of training data sets. Recent years have seen the rise of Neural Machine Translation (NMT) as a potential alternate way of dealing with the same issue. Various NMT systems for the Indian language Hindi are examined in this study. Earlier NMT approaches have the problem with longer sequences and not being able to capture object importance. In this proposed solution, Long Short-Term Memory (LSTM) mechanism has been used which efficiently deals with such problems. When translating from English to Hindi, eight different architectures of NMT are experimented and the results are compared with those of more standard MT methods. NMT only needs a tiny parallel corpus for training, which means that it can handle tens of thousands of training data sets with ease.
Concept Mapping in Teaching Mathematics in Slovakia: Pedagogical Experiment Results

The paper deals with the application of concept mapping in teaching mathematics at the primary level of education (ISCED 1), as well as the experimental verification of its qualitative contribution to the process of education. The authors present results obtained in a sample survey aimed at monitoring possibilities of influencing pupils’ relationship to teaching and mastery of mathematics using digital concept maps within the process of pedagogical intervention. The authors tested the degree to which digital concept maps influence changes in the pupils’ opinions and attitudes to mathematics (subject popularity, interest in the subject, easiness of understanding, fear and apprehension of mathematics) while explaining and fixating the curriculum of selected mathematics topics in the third grade of a primary school. The results of the experiment did not confirm that teaching supported by concept maps contributes to reducing pupils’ negative attitudes towards subjects, particularly in mathematics at the primary level (ISCED 1). Due to the pedagogical intervention of concept mapping, no statistically significant changes occurred in the pupils’ attitudes to the observed factors of mathematics. However, the authors have come up with research findings that may be notable in the field of branch didactics of mathematics and digital technologies.
Evaluating StackGenVis with a Comparative User Study

Stacked generalization (also called stacking) is an ensemble method in machine learning that deploys a metamodel to summarize the predictive results of heterogeneous base models organized into one or more layers. Despite being capable of producing high-performance results, building a stack of models can be a trial-and-error procedure. Thus, our previously developed visual analytics system, entitled StackGen Vis, was designed to monitor and control the entire stacking process visually. In this work, we present the results of a comparative user study we performed for evaluating the StackGen-Vis system. We divided the study participants into two groups to test the usability and effectiveness of StackGen Vis compared to Orange Visual Stacking (OVS) in an exploratory usage scenario using health-care data. The results indicate that StackGen Vis is significantly more powerful than OVS based on the qualitative feedback provided by the participants. However, the average completion time for all tasks was comparable between both tools.
Multi-lifespan Envisioning Cards:

Design theory often stands apart from design practice. Tools offer one means to bridge this divide. Yet how to move from abstract concepts of design theory and principles to specific toolkits that can structure and enable action remains elusive. This pictorial presents a way forward, illustrating the movement from theory to toolkit and concrete action for multi-lifespan design. Our key contributions are three-fold: First, we offer an expansion to an existing design toolkit, the Multi-lifespan Envisioning Cards. We demonstrate the toolkit's use through case studies. Second, we provide insights on the process of constructing a theoretically-informed design toolkit. We pay special attention to the interplay between text, visuals, and embodied actions. Third, we provide a proof-of-concept for how to extend an existing toolkit to account for new theories and perspectives. We reflect on the timeless qualities of the Envisioning Cards and discuss heuristics for designing a robust yet flexible toolkit.
Parallel Query Processing: To Separate Communication from Computation

In this paper, we study parallel query processing with a focus on reducing the communication cost, which is the dominating factor in parallel query processing. The communication cost becomes large if the intermediate results between operators are large in intra-operator parallelism. In the existing approaches, it optimizes an SQL query by arranging relational algebra operators to reduce the total cost, where, for each operator, it involves (i) distribution of data partitioned to computing nodes by communication, and (ii)computation on computing nodes locally. The communication and computation are dealt with inside an operator and are not separable. In other words, it is difficult to avoid large intermediate results and hence reduce the communication cost. To reduce communication cost, we separate communication from computation using several new operators proposed in this paper. One is a pair operator () to pair the partitions of a relation R with the partitions of a relation S, where a partition is specified by a hash function. With the pair operator defined, we can explicitly deal with communication to deliver pairs of partitions to computing nodes. Together with , we can also explicitly treat the local computation on a computing node as op for any RA (relational algebra) operator op. We give a merge operator (U), to collect all partial results from computing nodes as they are. In short, with , op, and U, we are able to explicitly specify communication and computation for RA operators. Furthermore, we propose new techniques, namely, partitioning push-down and computation push-up to separate communication from computation for RA expressions. We prove that we can push-down/up for a wide range of relational expressions. We have developed a distributed system named Secco (Separate Communication from Computation) by revamping SparkSQL on Spark, and confirmed the efficiency of our approach in our performance studies using real datasets.