Reviewerly/exHarmony huggingface dataset:
Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

Lung cancer is the prevalent cause of death among people around the world. The detection of existence of lung cancer can be performed in a variety of ways, such as magnetic resonance imaging (MRI), radiography, and computed tomography (CT). Such techniques take up a lot of time and financial resources. Nevertheless, for the detection of lung cancer, CT provides a lower cost, fast imaging time and increased availability. Early diagnosis of lung cancer may help physicians treat patients in order to minimize the number of deaths. This paper revolves around the categorization of lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours. The central objective of this study is therefore to establish an image processing technique for extracting features of lung cancer from CT scan images. Extracting the features from the segmented image can help to detect the cancer inside the lung. The purposed method comprises the following steps by using image processing techniques: data collection, data pre-processing, features selection and lung cancer classification. The pre-processing was done using a median filter to remove noise contained in the images. There are three features that need to be extracted which are; area, perimeter and centroid. Finally, the set of data with these features were used as inputs for lung cancer classification. By analysis results, kNN method has a high accuracy of 98.15%.
The Impact of Negative Triple Generation Strategies and Anomalies on Knowledge Graph Completion

Even though knowledge graphs have proven very useful for several tasks, they are marked by incompleteness. Completion algorithms aim to extend knowledge graphs by predicting missing (subject, predicate, object) triples, usually by training a model to discern between correct (positive) and incorrect (negative) triples. However, under the open-world assumption in which a missing triple is not negative but unknown, negative triple generation is challenging. Although negative triples are known to drive the accuracy of completion models, its impact has not been thoroughly examined yet. To evaluate accuracy, test triples are considered positive and negative triples are derived from them. The evaluation protocol is thus impacted by the generation of negative triples, which remains to be analyzed. Another issue is that the knowledge graphs available for evaluation contain anomalies like severe redundancy, and it is unclear how anomalies affect the accuracy of completion models. In this paper, we analyze the impact of negative triple generation during both training and testing on translation-based completion models. We examine four negative triple generation strategies, which are also used to evaluate the models when anomalies in the test split are included and discarded. In addition to previously-studied anomalies like near-same predicates, we include another anomaly: knowledge present in the test that is missing from the training split. Our main conclusion is that the most common strategy for negative triple generation (local-closed world assumption) can be mimicked by a combination of a naive and a immediate neighborhood strategies. This result suggests that completion models can be learned independently for certain subgraphs, which would render completion models useful in the context of knowledge graph evolution. Although anomalies are considered harmful since they artificially increase the accuracy of completion models, our results show otherwise for certain knowledge graphs, which calls for further research efforts.
Differentially private data publishing for arbitrarily partitioned data

Many models have been proposed to preserve data privacy for different data publishing scenarios. Among these models, ∊-differential privacy is receiving increasing attention because it does not make assumptions about adversaries’ prior knowledge and can provide a rigorous privacy guarantee. Although there are numerous proposed approaches using ∊-differential privacy to publish centralized data of a single-party, differentially private data publishing for distributed data among multiple parties has not been studied extensively. The challenge in releasing distributed data is how to protect privacy and integrity during collaborative data integration and anonymization. In this paper, we present the first differentially private solution to anonymize data from two parties with arbitrarily partitioned data in a semi-honest model. We aim at satisfying two privacy requirements: (1) the collaborative anonymization should satisfy differential privacy; (2) one party cannot learn extra information about the other party’s data except for the final result and the information that can be inferred from the result. To meet these privacy requirements, we propose a distributed differentially private anonymization algorithm and guarantee that each step of the algorithm satisfies the definition of secure two-party computation. In addition to the security and cost analyses, we demonstrate the utility of our algorithm in classification analysis.
A Novel Centralization Method for Pipe Image Stitching

The creation of unwrapped stitched images of pipework internal surfaces is being increasingly used to augment routine visual inspection.A significant challenge to the creation of these stitched images is the need to estimate the pose and position of the camera for each frame, which is often alleviated through the use of a mechanical centralizer to ensure the camera is held in the center of the pipe.This article proposes a novel method for image centralization and pose estimation, which is particularly beneficial to circumstances where mechanical centralization is impractical.The approach involves post-inspection centralization of the captured video, by first estimating the probe's position relative to the pipe, using an integrated laser ring projector combined with the camera sensor, and then using this position to unwrap the image, so it produces an undistorted view of the pipe interior (equivalent to unwrapping a centralized view).These unwrapped images are then stacked to produce a stitched image of the pipe interior.In this paper pose estimation was successfully demonstrated to have a 90% confidence interval of ±0.5 mm and ±0.5° in translation and rotation changes.This pose estimation is then used to create stitched images for both a visual test card image mounted inside a pipe and an aluminum pipe sample with artificial defects, in both cases demonstrating near equivalent results to those obtained using traditional mechanical centralization.
Identifying Sensitive URLs at Web-Scale

Several data protection laws include special provisions for protecting personal data relating to religion, health, sexual orientation, and other sensitive categories. Having a well-defined list of sensitive categories is sufficient for filing complaints manually, conducting investigations, and prosecuting cases in courts of law. Data protection laws, however, do not define explicitly what type of content falls under each sensitive category. Therefore, it is unclear how to implement proactive measures such as informing users, blocking trackers, and filing complaints automatically when users visit sensitive domains. To empower such use cases we turn to the Curlie.org crowdsourced taxonomy project for drawing training data to build a text classifier for sensitive URLs. We demonstrate that our classifier can identify sensitive URLs with accuracy above 88%, and even recognize specific sensitive categories with accuracy above 90%. We then use our classifier to search for sensitive URLs in a corpus of 1 Billion URLs collected by the Common Crawl project. We identify more than 155 millions sensitive URLs in more than 4 million domains. Despite their sensitive nature, more than 30% of these URLs belong to domains that fail to use HTTPS. Also, in sensitive web pages with third-party cookies, 87% of the third-parties set at least one persistent cookie.
DMIB: Dual-Correlated Multivariate Information Bottleneck for Multiview Clustering

Multiview clustering (MVC) has recently been the focus of much attention due to its ability to partition data from multiple views via view correlations. However, most MVC methods only learn either interfeature correlations or intercluster correlations, which may lead to unsatisfactory clustering performance. To address this issue, we propose a novel dual-correlated multivariate information bottleneck (DMIB) method for MVC. DMIB is able to explore both interfeature correlations (the relationship among multiple distinct feature representations from different views) and intercluster correlations (the close agreement among clustering results obtained from individual views). For the former, we integrate both view-shared feature correlations discovered by learning a shared discriminative feature subspace and view-specific feature information to fully explore the interfeature correlation. This allows us to attain multiple reliable local clustering results of different views. Following this, we explore the intercluster correlations by learning the shared mutual information over different local clusterings for an improved global partition. By integrating both correlations, we formulate the problem as a unified information maximization function and further design a two-step method for optimization. Moreover, we theoretically prove the convergence of the proposed algorithm, and discuss the relationships between our method and several existing clustering paradigms. The experimental results on multiple datasets demonstrate the superiority of DMIB compared to several state-of-the-art clustering methods.
ReMap

People often seek help online while using complex software. Currently, information search takes users' attention away from the task at hand by creating a separate search task. This paper investigates how multimodal interaction can make in-task help-seeking easier and faster. We introduce ReMap, a multimodal search interface that helps users find video assistance while using desktop and web applications. Users can speak search queries, add application-specific terms deictically (e.g., "how to erase this"), and navigate search results via speech, all without taking their hands (or mouse) off their current task. Thirteen participants who used ReMap in the lab found that it helped them stay focused on their task while simultaneously searching for and using learning videos. Users' experiences with ReMap also raised a number of important challenges with implementing system-wide context-aware multimodal assistance.
Automatic Traffic Sign Recognition Artificial Inteligence - Deep Learning Algorithm

Due to the large number of deaths and car accidents caused by the driver's lack of attention, car manufacturers are trying to integrate ADAS systems with artificial intelligence and CV.One function that helps the driver is traffic sign recognition (TSR). This is a technology with which a vehicle is able to recognize road signs placed on the road, e.g. "speed limit" or "give way" or "stop" all this being possible with the help of computer vision and Convolutional Neural Networks. In this article we propose an implementation based on LeNet architecture for traffic sign recognition using CNN.We preferred the deep learning approach for this challenge as methods like shape based or color based share a common weakness in factors as light changes, scale change, rotation.The paper presents implementation of the network architecture based on LeNet5 using Keras and TensorFlow library, how we trained the CNN using the GTSRB dataset, and what results we obtained training and using the network for real time applications using only CPU power, with Intel i7 7700K. The experimental results showed a 95% accuracy in recognizing traffic signs.
Mining Feature Revisions in Highly-Configurable Software Systems

Highly-Configurable Software Systems (HCSSs) support the systematic evolution of systems in space, i.e., the inclusion of new features, which then allow users to configure software products according to their needs. However, HCSSs also change over time, e.g., when adapting existing features to new hardware or platforms. In practice, HCSSs are thus developed using both version control systems (VCSs) and preprocessor directives (#ifdefs). However, the use of a preprocessor as variability mechanism has been criticized regarding the separation of concerns and code obfuscation, which complicates the analysis of HCSS evolution in VCSs. For instance, a single commit may contain changes of totally unrelated features, which may be scattered over many variation points (#ifdefs), thus making the evolution history hard to understand. This complexity often leads to error-prone changes and high costs for maintenance and evolution. In this paper, we propose an automated approach to mine HCSS features taking into account evolution in space and time. Our approach uses constraint satisfaction problem solving to mine newly introduced, removed and changed features. It finds a configuration containing the feature revisions which are needed to activate a specific program location. Furthermore, it increments the revision number of each changed feature. Thus, our approach enables to analyze when and which features often change over time, as well as their interactions, for every single commit of a HCSS. Our approach can contribute to future research on understanding the characteristics of HCSS and supporting developers during maintenance and evolution tasks.
Contact Tracing and COVID-19: The South Korean Context for Public Health Enforcement

Although South Korea's response to COVID-19 has received international praise, the nation's public health policy raises numerous privacy concerns, with a growing number of civil society groups joining the conversation. Following changes to public health law in 2015 in response to the MERS (Middle Eastern Respiratory Syndrome) crisis, South Korea's KCDC (Center for Disease Control) reconfigured its enforcement practice with measures tied to the movements of infected patients. New laws allow for the use of information communications technology and personal data (cell phone, CCTV, credit card transactions) to track patients, thereby identifying the possible routes of transmission for disease. Through mid-April 2020, this system received extensive praise, but more recently, with the "Itaewon Cluster," centered in a popular nightclub district, citizens are starting to raise concerns. Itaewon is associated with prostitution due to its legacy of proximity to an American military base, and by extension, the presence of foreigners in general, including LGBT clubs. While contact tracing promises to preserve the anonymity of data, the significant rise in case numbers since May 2020 has resulted in calls for targeting these groups—foreigners, LGBT, English teachers—suggesting that xenophobia and social stigma continue to represent powerful forces.
Recognition of Kannada Handwritten Words using SVM Classifier with Convolutional Neural Network

In an area of handwriting recognition, many algorithms for recognition are proposed for Indian document images of Latin and Devanagari scripts. These documents generally lack in their layout organizations and low print quality. In order to overcome these drawbacks, a character segmentation algorithm is proposed for kannada handwriting recognition. In this work, a primary segmentation paths are obtained using structural property of characters, whereas overlapped and joined characters are separated using graph distance theory. Finally, segmentation results are validated using Support Vector Machine (SVM) classifier. Comprehensive simulation is carried out on different databases containing printed as well as handwritten texts. Benchmarking results illustrate that the proposed algorithms have better performance in terms of accuracy and sensitivity compared to other conventional approaches.
Foregrounding Algorithms: Preparing Users for Co-design with Sensitizing Activities

Algorithms are present in many of our everyday activities. However, there is generally low awareness of their presence among users, and there are various conceptualizations to define them. Additionally, algorithms are often both complex and opaque. These characteristics raise challenges when applying co-design activities to the interaction design of algorithms. We argue that researchers can overcome these challenges by developing sensitizing activities: activities that foreground the presence of algorithms, thus raising algorithmic awareness and a shared understanding, without influencing their initial experiences and expectations. We share how we applied sensitizing activities in two case studies: sensitizing interviews, and diary studies together with two-phase workshops. We share our experiences applying these techniques to overcome the challenges of low algorithmic awareness and multiple algorithmic understandings of participants. Finally, we offer recommendations for researchers and practitioners when applying sensitizing activities in this design context and invite further methodological discussion on this challenging topic.
Research on Text Detection and Recognition Based on OCR Recognition Technology

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.
A dynamical mean-field theory for learning in restricted Boltzmann machines

Abstract We define a message-passing algorithm for computing magnetizations in restricted Boltzmann machines, which are Ising models on bipartite graphs introduced as neural network models for probability distributions over spin configurations. To model nontrivial statistical dependencies between the spins’ couplings, we assume that the rectangular coupling matrix is drawn from an arbitrary bi-rotation invariant random matrix ensemble. Using the dynamical functional method of statistical mechanics we exactly analyze the dynamics of the algorithm in the large system limit. We prove the global convergence of the algorithm under a stability criterion and compute asymptotic convergence rates showing excellent agreement with numerical simulations.
Learning in the Wild

Although deep learning has been widely applied in practical applications, many embedded, mobile, and IoT systems performing deep learning tasks in the wild suffer from the problem of dataset shift, where their train and real data distributions are different, which has an adversarial effect on the quality of deep models. In this paper, we introduce a holistic online dataset adaptation strategy for on-device deep models running in the wild by tackling three challenges of dataset adaption: 1) when to start learning for adaptation, 2) how to learn for adaptation, and 3) what (examples) to learn for adaptation. To enable computing resource- and learning example-efficient online model update, we perform a model adaptation based on peroutput feature distributions, allowing efficient representation and high-level learning of new datasets. All the decisions on adaptation, i.e., when, how, and what to learn, as well as adaptation itself, are performed with the outputs of feed-forward execution without an explicit back-propagation of the model, enabling lightweight adaptation on resource-constrained devices. We implement a deep IoT device with a microcontroller and evaluate it with three datasets under various dataset shift scenarios. Our evaluation shows that the deep models effectively adapt to new data on the device, which achieves up to 28% higher and 2.5x efficient learning performance.
Variational Inference over Nonstationary Data Streams for Exponential Family Models

In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires continuous model updating and the ability to handle a posterior distribution conditioned on an unbounded data set. Secondly, the underlying data distribution may drift from one time step to another, and the classic i.i.d. (independent and identically distributed), or data exchangeability assumption does not hold anymore. In this paper, we present an approximate Bayesian inference approach using variational methods that addresses these issues for conjugate exponential family models with latent variables. Our proposal makes use of a novel scheme based on hierarchical priors to explicitly model temporal changes of the model parameters. We show how this approach induces an exponential forgetting mechanism with adaptive forgetting rates. The method is able to capture the smoothness of the concept drift, ranging from no drift to abrupt drift. The proposed variational inference scheme maintains the computational efficiency of variational methods over conjugate models, which is critical in streaming settings. The approach is validated on four different domains (energy, finance, geolocation, and text) using four real-world data sets.
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models

The ubiquity of social media has transformed online interactions among individuals. Despite positive effects, it has also allowed anti-social elements to unite in alternative social media environments (e.g. Gab.com) like never before. Detecting such hateful speech using automated techniques can allow social media platforms to moderate their content and prevent nefarious activities like hate speech propagation. In this work, we propose a weak supervision deep learning model that - (i) quantitatively uncover hateful users and (ii) present a novel qualitative analysis to uncover indirect hateful conversations. This model scores content on the interaction level, rather than the post or user level, and allows for characterization of users who most frequently participate in hateful conversations. We evaluate our model on 19.2M posts and show that our weak supervision model outperforms the baseline models in identifying indirect hateful interactions. We also analyze a multilayer network, constructed from two types of user interactions in Gab (quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users. We utilize the multilayer network embedding methods to generate features for the prediction task and we show that considering user context from multiple networks help achieving better predictions of hateful users in Gab. We receive upto 7% performance gain compared to single layer or homogeneous network embedding models.