Reviewerly/exHarmony huggingface dataset:
Single machine graph analytics on massive datasets using Intel optane DC persistent memory

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on Optane PMM and highlight principles that can apply to graph analytics on all large-memory platforms. To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks and one out-of-core graph framework on large real-world graphs using a machine with 6TB of Optane PMM. Our results show that frameworks using the runtime and algorithmic principles advocated in this paper (i) perform significantly better than the others and (ii) are competitive with graph analytics frameworks running on production clusters.
“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design

Abstract While the research area of artificial intelligence benefited from increasingly sophisticated machine learning techniques in recent years, the resulting systems suffer from a loss of transparency and comprehensibility, especially for end-users. In this paper, we explore the effects of incorporating virtual agents into explainable artificial intelligence (XAI) designs on the perceived trust of end-users. For this purpose, we conducted a user study based on a simple speech recognition system for keyword classification. As a result of this experiment, we found that the integration of virtual agents leads to increased user trust in the XAI system. Furthermore, we found that the user’s trust significantly depends on the modalities that are used within the user-agent interface design. The results of our study show a linear trend where the visual presence of an agent combined with a voice output resulted in greater trust than the output of text or the voice output alone. Additionally, we analysed the participants’ feedback regarding the presented XAI visualisations. We found that increased human-likeness of and interaction with the virtual agent are the two most common mention points on how to improve the proposed XAI interaction design. Based on these results, we discuss current limitations and interesting topics for further research in the field of XAI. Moreover, we present design recommendations for virtual agents in XAI systems for future projects.
Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained high-resolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1_{score}$ </tex-math></inline-formula> of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN.
Immersive Virtual Reality for Foreign Language Education: A PRISMA Systematic Review

Immersion is a proven method of learning a foreign language and using Virtual Reality to achieve that immersion has high potential educational benefits.However, there are no recent systematic reviews that combine both foreign language education and immersive Virtual Reality.This systematic review aims to identify features, educational methods, technologies, and gaps of immersive virtual reality for foreign and second language education.PRISMA method was followed to carry out the systematic review.From the analysis of the results, two main conclusions were drawn.Firstly, the relation between immersive Virtual Reality and foreign language learning is quite positive, particularly when compared with conventional pedagogical practices.Not only that, the connection between immersive Virtual Reality and the user's motivation and satisfaction is also quite positive.Lastly, limitations such as the low sample of studies and gaps in the literature are addressed, and directions for future work and the area's progress are pointed out.
Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India

In recent times, organizations are increasingly adopting blockchain technology in their supply chains due to various advantages such as cost optimization, effective and verified record-keeping, transparency, and route tracking. This paper aims to examine the factors influencing the intention of small and medium enterprises (SMEs) in India to adopt blockchain technology in their supply chains. A questionnaire-based survey was used to collect data from 216 SMEs in the northern states of India. The study has considered an integrated technology adoption framework consisting of the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and Technology-Organization-Environment (TOE). Using this integrated TAM-TOE-DOI framework, the study has proposed eleven hypotheses related to factors of blockchain technology adoption. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) have been used to test the hypotheses. The results show that relative advantage, technology compatibility, technology readiness, top management support, perceived usefulness, and vendor support have a positive influence on the intention of Indian SMEs to adopt blockchain technology in their supply chains. The complexity of technology and cost concerns act as inhibitors to the technology adoption by SMEs. Furthermore, the three factors, namely, security concerns, perceived ease of use, and regulatory support, do not influence the intention to adopt the technology. The study contributes to filling a significant gap in the academic literature since only a few studies have endeavored to ascertain the technology adoption factors by supply chains of SMEs in a developing country like India. The study has also proposed a novel integrated technology adoption framework that can be employed by future studies. The findings are expected to enable SMEs to understand important factors to be considered for adopting blockchain technology in their supply chains. Furthermore, the study may benefit the blockchain technology developers and suppliers as they can offer customized solutions based on the findings.
Preserving transaction privacy in bitcoin

As bitcoin has received considerable attentions, a large number of transactions are increasingly published through the Internet. The potential threat to online transactions is not only limited to the anonymity of identities such as address and pseudonyms, but also other sensitive information shown on scripts like transaction amounts. We propose a framework to hide the amounts by employing the Paillier cryptosystem for encryption and decryption. Due to its homomorphic properties, we create a dumbaccount which can receive the same amounts of bitcoins in each transaction but cannot spend them. This ensures that the hidden amounts are always positive, and keeps the equality between the Input-sum and the Output-sum via the commitment proofs. Our framework achieves delicate anonymity and prevents active and passive attacks, which effectively improves the transaction privacy. Analyses show that our proposal is secure and practical.
RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.
Weighted Graph Embedding-Based Metric Learning for Kinship Verification

Given a group photograph, it is interesting and useful to judge whether the characters in it share specific kinship relation, such as father-daughter, father-son, mother-daughter, or mother-son. Recently, facial image-based kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods.
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks

Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications, and in particular, safety-critical ones. In this work, we study the calibration of uncertainty prediction for regression tasks which often arise in real-world systems. We show that the existing definition for the calibration of regression uncertainty has severe limitations in distinguishing informative from non-informative uncertainty predictions. We propose a new definition that escapes this caveat and an evaluation method using a simple histogram-based approach. Our method clusters examples with similar uncertainty prediction and compares the prediction with the empirical uncertainty on these examples. We also propose a simple, scaling-based calibration method that preforms as well as much more complex ones. We show results on both a synthetic, controlled problem and on the object detection bounding-box regression task using the COCO and KITTI datasets.
iBTune

Tuning the buffer size appropriately is critical to the performance of a cloud database, since memory is usually the resource bottleneck. For large-scale databases supporting heterogeneous applications, configuring the individual buffer sizes for a significant number of database instances presents a scalability challenge. Manual optimization is neither efficient nor effective, and even not feasible for large cloud clusters, especially when the workload may dynamically change on each instance. The difficulty lies in the fact that each database instance requires a different buffer size that is highly individualized, subject to the constraint of the total buffer memory space. It is imperative to resort to algorithms that automatically orchestrate the buffer pool tuning for the entire database instances. To this end, we design iBTune that has been deployed for more than 10, 000 OLTP cloud database instances in our production system. Specifically, it leverages the information from similar workloads to find out the tolerable miss ratio of each instance. Then, it utilizes the relationship between miss ratios and allocated memory sizes to individually optimize the target buffer pool sizes. To provide a guaranteed level of service level agreement (SLA), we design a pairwise deep neural network that uses features from measurements on pairs of instances to predict the upper bounds of the request response times. A target buffer pool size can be adjusted only when the predicted response time upper bound is in a safe limit. The successful deployment on a production environment, which safely reduces the memory footprint by more than 17% compared to the original system that relies on manual configurations, demonstrates the effectiveness of our solution.
A Lightweight Feature Optimizing Network for Ship Detection in SAR Image

Deep learning-based methods have achieved great success in target detection tasks of computer vision, but when it comes to Synthetic Aperture Radar (SAR) image ship detection, some new challenges appear because of the wide swath of images, diverse appearances of ships and lack of detail information, which make the detection inefficient and less effective.Aiming to these issues, in this paper, a lightweight feature optimizing network (LFO-Net) based on popular single shot detector (SSD) model is proposed for single polarization SAR image ship detection.Firstly, a simpler structure called lightweight single shot detector (LSSD) is designed, which can be trained from scratch and can reduce the training and testing time without accuracy cost.Secondly, a new bi-directional feature fusion module including one semantic aggregation block and one feature reuse block is proposed to improve the performance of multi-scale targets detection by enhancing the features of both low feature layers and high feature layers.Then the features are further optimized by leveraging attention mechanism, which is beneficial to catch the silent information more efficiently.A set of experiments are implemented to verify the effectiveness of the proposed method using the public SAR ship detection dataset (SSDD).The results show that the proposed method has significant advantages in both speed and accuracy, and outperforms other state-of-art methods.Additionally, a test on GF-3 satellite SAR data with multiple modes verifies the generalization performance of this model.
Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches

Sentiment Analysis (SA) is focused on mining opinion (identification and classification) from unstructured text data such as product reviews or microblogs.It is widely used for brand reviews, political campaigns, marketing analysis or gaining feedback from customers.One of the prominent approaches for SA is using supervised machine learning (SML), an algorithm that uses datasets with defined class labels based on mathematical learning from a training dataset.While the results are promising especially with indomain sentiments, there is no guarantee the model provides the same performance against real time data due to the diversity of new data.In addition, previous studies suggest the result of SML decrease when applied to cross-domain datasets because new features are appeared in different domains.So far, studies in SA emphasise the improvement of the sentiment result whereas there is little discussion focusing on how to detect the degradation of performance for the proposed model.Therefore, we provide a method known as Contextual Analysis (CA), a mechanism that constructs a relationship between words and sources that is constructed in a tree structure identified as Hierarchical Knowledge Tree (HKT).Then, Tree Similarity Index (TSI) and Tree Differences Index (TDI), a formula generate from tree structure are proposed to find similarity as well as changes between train and actual dataset.The regression analysis of datasets reveals that there is a highly significant positive relationship between TSI and SML accuracies.As a result, the prediction model created indicated estimation error within 2.75 to 3.94 and 2.30 for 3.51 for average absolute differences.Moreover, this method also can cluster sentiment words into positive and negative without having any linguistics resources used and at the same time capturing changes of sentiment words when a new dataset is applied.
Classification of glomerular hypercellularity using convolutional features and support vector machine

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results on FIOCRUZ data set in a binary classification (lesion or normal). Additionally, classification of hypercellularity sub-lesions was also evaluated, considering mesangial, endocapilar and both lesions, reaching an average accuracy of 82%. Either in binary task or in the multi-classification one, our proposed method outperformed Xception, ResNet50 and InceptionV3 networks, as well as a traditional handcrafted-based method. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.
On The Evaluation of Machine Translation Systems Trained With Back-Translation

Back-translation is a widely used data augmentation technique which leverages target monolingual data.However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese.This is believed to be due to translationese inputs better matching the back-translated training data.In this work, we show that this conjecture is not empirically supported and that backtranslation improves translation quality of both naturally occurring text as well as translationese according to professional human translators.We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs.BLEU cannot capture human preferences because references are translationese when source sentences are natural text.We recommend complementing BLEU with a language model score to measure fluency.
Augmented Reality to Support Geometry Learning

Currently digital educational applications are employed in education, since technological tools are very interesting and engaging for pupils. Augmented Reality (AR) is one of the most explored and successfully used technology. Thanks to the AR, the situated learning can occur; it suggests that people easily acquire new knowledge if learning occurs in a specific context and is embedded in a physical environment. This paper presents an AR application, called Geo+, for supporting primary school students in the acquisition of knowledge on the solid geometry. Geo+ has been developed by following a Human-Centered Design approach, thus several formative evaluation studies were performed to enhance the quality of the resulting application. A user study involving 96 pupils of the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> grade of primary school was performed in order to investigate the learning effectiveness, the engagement and the perceived workload of Geo+ at school. The results showed that Geo+ is effective in terms of student learning gain. In addition, pupils really appreciated the ease of use of the game; they were satisfied and felt engaging and comfortable during the interaction, none of them ask for help during the execution of the learning activities.
From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
MichiGAN

Despite the recent success of face image generation with GANs, conditional hair editing remains challenging due to the under-explored complexity of its geometry and appearance. In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation. To provide user control over every major hair visual factor, we explicitly disentangle hair into four orthogonal attributes, including shape, structure, appearance, and background. For each of them, we design a corresponding condition module to represent, process, and convert user inputs, and modulate the image generation pipeline in ways that respect the natures of different visual attributes. All these condition modules are integrated with the backbone generator to form the final end-to-end network, which allows fully-conditioned hair generation from multiple user inputs. Upon it, we also build an interactive portrait hair editing system that enables straightforward manipulation of hair by projecting intuitive and high-level user inputs such as painted masks, guiding strokes, or reference photos to well-defined condition representations. Through extensive experiments and evaluations, we demonstrate the superiority of our method regarding both result quality and user controllability.