Reviewerly/exHarmony huggingface dataset:
The black box problem of AI in oncology

Abstract The rapidly increasing amount and complexity of data in healthcare, the pace of published research, drug development, biomarker discovery, and clinical trial enrolment in oncology renders AI an approach of choice in the development of machine assisted methods for data analysis and machine assisted decision making. Machine learning algorithms, and artificial neural networks in particular, drive recent successes of AI in oncology. Performances of AI driven methods continue to improve with respect to both speed and precision thus leading to a great potential for AI to improve clinical practice. But the acceptance and a lasting breakthrough of AI in clinical practice is hampered by the black box problem. The black box problem refers to limits in the interpretability of results and to limits in explanatory functionality. Addressing the black box problem has become a major focus of research [1]. This talk describes recent attempts to addressing the black box problem in AI, offers a discussion on the suitability of those attempts for applications to oncology, and provides some future directions.
TextStyleBrush: Transfer of Text Aesthetics From a Single Example

We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To these ends, we make a number of technical contributions: (1) We disentangle the style and content of a textual image into a non-parametric, fixed-dimensional vector. (2) We propose a novel approach inspired by StyleGAN but conditioned over the example style at different resolution and content. (3) We present novel self-supervised training criteria which preserve both source style and target content using a pre-trained font classifier and text recognizer. Finally, (4) we also introduce Imgur5K, a new challenging dataset for handwritten word images. We offer numerous qualitative photo-realistic results of our method. We further show that our method surpasses previous work in quantitative tests on scene text and handwriting datasets, as well as in a user study.
Boosting simple learners

Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund '12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed in order to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that Ω(1/γ2) weak hypotheses with γ-margin are sometimes necessary, our new method requires only Õ(1/γ) weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we identify a combinatorial-geometric parameter which captures the expressivity of base-classes in boosting. As a corollary we provide an affirmative answer to the second question for many well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
Hierarchical Attention Signed Network

As an extension of convolutional neural networks from Euclidean data to irregular data, Graph Convolutional Networks (GCNs) have attracted much attention and applied in a variety of tasks. However, current methods mainly consider unsigned graphs and ignore the positive and negative links in graphs that are common in our daily lives. The inherent social theories (balance theory and status theory) and the different semantic information of positive and negative links of a signed graph bring great challenges for modeling signed graphs by GCNs. As the latest signed graph convolutional network, Signed Graph Attention Networks (SiGAT) generalizes Graph Attention Networks (GATs) to signed graph by adopting motifs to model the social theories and shows superior performance. However, there are information loss and redundancy in the designed motifs and the different effects of positive and negative links are also not considered. In this paper, we propose a Hierarchical Attention Signed Network (HASN) which includes a hierarchical motif framework and a hierarchical attention framework. Specifically, the hierarchical motif framework divides 68 motifs into two layers according to if they can reflect the balance theory. The hierarchical attention framework contains a node-level attention layer and a sign-level attention layer. The node-level attention layer learns the importance of motif-based neighbors and performs weighted aggregations to update node embeddings. The sign-level attention layer can distinguish the different importance of positive and negative links. Based on the hierarchical motif framework and hierarchical attention framework, the social theories information, the importance of different motif-based neighbors, and the importance of signs can be preserved comprehensively. Experimental results on three real-world signed graphs for the signed link prediction task show that HASN outperforms state-of-the-art graph embedding models and recently proposed signed graph convolutional network models.
Remote Attacks on Drones Vision Sensors: An Empirical Study

Vision systems applied to drones, automatic vehicles, and robots have become an increasingly popular sensing method. However, vision sensors that make up these systems are vulnerable to malicious input attacks, which can lead to serious consequences. Privious work on attacking cameras of automatic vehicles shows that lasers can cause failure of camera-based functionalities, but it lacks analysis of the results and does not conduct experiments in the actual scenarios. In this article, a laser-based attack on cameras and binocular vision sensors of drones is presented. First, we propose a threat model that describes how an adversary attacks the drone then perform feasibility analysis of the attack from theory and practice. Next, we design multi-variable experiments in the lab to systematically study the effectiveness of the attack, and further analyze how each variable affects the results. To get intuitive and fine-grained results, multidimensional image similarity is used to measure the effects. In particular, experiments in the actual scenarios are carried out, and results show that the attack can make obstacle avoidance, target recognition and tracking completely failed. Finally, lightweight countermeasures based on hardware and software are proposed to improve sensor resilience against the attack.
A Color-blind 3-Approximation for Chromatic Correlation Clustering and Improved Heuristics

Chromatic Correlation Clustering (CCC) models clustering of objects with categorical pairwise relationships. The model can be viewed as clustering the vertices of a graph with edge-labels (colors). Bonchi et al. [KDD 2012] introduced it as a natural generalization of the well studied problem Correlation Clustering (CC), motivated by real-world applications from data-mining, social networks and bioinformatics. We give theoretical as well as practical contributions to the study of CCC. Our main theoretical contribution is an alternative analysis of the famous Pivot algorithm for CC. We show that, when simply run color-blind, Pivot is also a linear time 3-approximation for CCC. The previous best theoretical results for CCC were a 4-approximation with a high-degree polynomial runtime and a linear time 11-approximation, both by Anava et al. [WWW 2015]. While this theoretical result justifies Pivot as a baseline comparison for other heuristics, its blunt color-blindness performs poorly in practice. We develop a color-sensitive, practical heuristic we call Greedy Expansion that empirically outperforms all heuristics proposed for CCC so far, both on real-world and synthetic instances. Further, we propose a novel generalization of CCC allowing for multi-labelled edges. We argue that it is more suitable for many of the real-world applications and extend our results to this model.
Big-Data-Based Research on the Architecture Design of University Hydropower Intelligent Decision Service Platform

With the continuous development and wide application of big data and artificial intelligence technology, how to efficiently use and mine the whole process data of university hydropower models, perception, business and flows, and realize the transformation of informationization of hydropower management to intelligentialize and wisdom, it has become one of the main tasks in the construction of universitiy informatization under the strategy of advocating energy conservation, lowcarbon sustainable development. Combining with the actual demand of university hydropower management, managing and serving the whole process of hydropower data collection, storage, analysis, monitoring and decision-making assistance, this paper proposes the architecture of an intelligent decision-making service platform for university hydropower on big data, and sorts out the core and key technologies in the platform development process and the current mainstream development frameworks and tools to provide technical references for the realization of intelligent hydropower management and application services in universities, and promote the overall planning and step-by-step implementation of smart campuses.
Modeling of Intelligent Sensor Duty Cycling for Smart Home Automation

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users’ living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors’ energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users’ future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users’ unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor’s role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme’s work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors’ energy consumption and prolongs the battery lifetime to approximately 137 days. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article presented an energy-efficient duty-cycling scheme for automating sensors’ operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users’ next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users’ activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user’s activities with less energy, which ultimately prolongs the battery lifetime of the sensors.
Multimodal image inpainting for an autonomous robot navigation application

Automatic 3-D recovery from multimodal images can be extremely useful for information extraction for the robot navigation application. In most cases, such a scene contains missing holes on depth maps that appear during the synthesis from multi-views. This paper presents an automated pipeline for processing multimodal images to 3-D digital surface models. The proposed approach uses the modified exemplar-based technique in quaternion space. We also perform depth completion by fusing data from multiple recorded multimodal images affected by occlusions. We propose an algorithm using the concepts of a sparse representation of quaternions, which uses a new gradient to calculate the priority function by integrating the structure of quaternions with local polynomial approximation - the intersection of confidence intervals). Moreover, the color information incorporates into the optimization criteria to obtain sharp inpainting results. Compared with state-of-the-art techniques, the proposed algorithm provides plausible restoration of the depth map from multimodal images, making them a promising tool for an autonomous robot navigation application.
Effect of Various Coordinate Points on Social Media

Abstract Currently information technology has developed rapidly, many have implemented information technology in various fields. It is undeniable that Indonesian people often use social media to interact indirectly, for example uploading vacation photos. But with the rapid advancement of technology itself to find someone’s personal data on the internet today is not something difficult. Conscious or not, many internet users are ignorant of their privacy. What they do in the real world are sometimes all shared on social media in the form of photos or status updates, without choosing which ones can be seen by the public and which will not have a negative impact and can be dangerous for users of social media itself. On the one hand, the location of the presence of the user’s coordinate point is one sensitive information that may not every day other people need to know because it can endanger the user Other people know where the user is when the user posts a photo or status update, the user indirectly notifies where the User is at that time.
Face mask recognition based on object detection

Earlier in 2019, a novel coronavirus pneumonia outbreak occurred in most countries and regions of the world. Thus, judging whether individuals are wearing masks or not has become an important part of entrance inspection in many places. In this paper, object detection in Google Cloud Platform's AutoML is used to implement mask detection. 1,000 from these 2,000 pictures are selected to refine the dataset for training, including 500 faces with masks and 500 faces without masks. After training, the accuracy achieves 94% and the map achieves 97.3%, which can meet the requirements of practical application. What’s more, tflite is used to deploy the model on the edge, to realize the application of the model in the real scenes.
Blockchain-Enabled Microfinance Model with Decentralized Autonomous Organizations

Microfinance which promises alleviation of poverty to the less privileged, oppressed community of people and to empower womenfolk, will get enrichment if it is implemented in a blockchain with a digital currency. Microfinance promises to end the vicious cycle of debt, and poverty can be deployed in a permissioned or permissionless blockchain, and the stakeholders can form a decentralized autonomous organization which enables a transparent successful functionality. Blockchain which is a decentralized, distributed, the tamper-proof ledger can be used for reading and writing of transactions of self-help groups in a much transparent way. The decentralized autonomous organization enabled with smart contracts to trigger our intended functionality in blockchain will give the microfinance, it's streamlined intended functionality with expert panel member's contingency measures to meet contingency situations, angel investors to invest in the new proposals of self-help group(SHG) microfinance and initial coin offerings for funding of new projects with potential investors. The hash of the transactions is used with crypto addresses, and the elliptic curve digital signature algorithm is used for authentication. The peer pressure model of SHG leads to a 90% loan repayment with more than 50% of the line of credit for the modern-day microfinance loan applicants. The self-help group working model, know your customer of SHG, initial coin offering model is given as proof of concept in solidity code in remix IDE.
Augmented Data Selector to Initiate Text-Based CAPTCHA Attack

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.
An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms

In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes. Our system consists of (1) a pseudo matching score module that provides reliable training labels; (2) a ranking model that scores every candidate teacher; (3) a novelty boosting module that gives additional opportunities to new teachers; and (4) a diversity metric that guardrails the recommended results to reduce the chance of collision. Offline experimental results show that our approach outperforms a wide range of baselines. Furthermore, we show that our approach is able to reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a five-month observation on a third-party online education platform.
Citation context-based topic models: discovering cited and citing topics from full text

Purpose Full text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms. Design/methodology/approach The authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated. Findings The results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references. Originality/value The automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.
A learning-based approach for solving shear stress vector distribution from shear-sensitive liquid crystal coating images

A learning-based approach for solving wall shear stresses from Shear-Sensitive Liquid Crystal Coating (SSLCC) color images is presented in this paper. The approach is able to learn and establish the mapping relationship between the SSLCC color-change responses in different observation directions and the shear stress vectors, and then uses the mapping relationship to solve wall shear stress vectors from SSLCC color images. Experimental results show that the proposed approach can solve wall shear stress vectors using two or more SSLCC images, and even using only one image for symmetrical flow field. The accuracy of the approach using four or more observations is found to be comparable to that of the traditional multi-view Gauss curve fitting approach; the accuracy is slightly reduced when using two or fewer observations. The computational efficiency is significantly improved when compared with the traditional Gauss curve fitting approach, and the wall shear stress vectors can be solved in nearly real time. The learning-based approach has no strict requirements on illumination direction and observation directions and is therefore more flexible to use in practical wind tunnel measurement when compared with traditional liquid crystal-based methods.