Reviewerly/exHarmony huggingface dataset:
Naïve Bayes Classifier Models for Predicting the Colon Cancer

Abstract Cancer has been known as a disease consisting of several different types. Cancer is a life threatening disease in the world today. There are so many types of cancer in the world, one of which is colon cancer. Colon cancer is one of the number one killers in the world. However, because there isn’t any obvious symptom of colon cancer at an early stage, people do not realize that they suffer from it. Even though cancer formation is different for each type of cancer, it is still a big challenge to make cancer classification with good accuracy. Many machine learning has been applied to the data of human’s genes in order to get the most relevant genes in the classification of cancer. The author proposes the Naïve Bayes Classifier model as a classification method to show that the model has good accuracy, good precision, good recall, good f 1 — score in classifying the data of patients suffering from colon cancer or not. In this proposed model, Naïve Bayes Classifier is a technique prediction based on simple probabilistic and on the application of the Bayes theorem (or Bayes rule) with a strong independence assumption. Therefore, this model is able to make higher classification accuracy with less complexity. In particular, it achieves up to 95.24% classification accuracy, thus this model can be an efficient analysis tool.
Two-Stage Convolutional Neural Network for Medical Noise Removal via Image Decomposition

Most of the existing medical image denoising methods focus on estimating either the image or the residual noise. Moreover, they are usually designed for one specific noise with a strong assumption of the noise distribution. However, not only the random independent Gaussian or speckle noise but also the structurally correlated ring or stripe noise is ubiquitous in various medical imaging instruments. Explicitly modeling the distributions of these complex noises in the medical image is extremely hard. They cannot be accurately held by the Gaussian or mixture of Gaussian model. To overcome the two drawbacks, in this paper, we propose to treat the image and noise components equally and convert the image denoising task into an image decomposition problem naturally. More precisely, we present a two-stage deep convolutional neural network (CNN) to model both the noise and the medical image simultaneously. On the one hand, we utilize both the image and noise to separate them better. On the other hand, the noise subnetwork serves as a noise estimator which guides the image subnetwork with sufficient information about the noise, thus we could easily handle different noise distributions and noise levels. To better cope with the gradient vanishing problem in this very deep network, we introduce both the short-term and long-term connections in the network which could promote the information propagation between different layers efficiently. Extensive experiments have been performed on several kinds of medical noise images, such as the computed tomography and ultrasound images, and the proposed method has consistently outperformed state-of-the-art denoising methods.
Correlation between the Practical Aspect of the Course and the E-Learning Progress

The aim of the study was to analyze the correlation between the students’ involvement and their progress within the online component of the blended learning model during the theoretical and practical courses. The authors have applied data mining Moodle from learning portfolios of 1500 students (N = 1500). Comparative analysis of the courses under review showed significant differences in the behavior of the same students. For the theoretical course students spent less time than for a practice-oriented one. Students’ progress in the form of points and demand for non-binding elements of the course differed significantly in favor of the practical course. Based on the data obtained, the authors concluded that an important parameter that influences the behavior of students and their educational progress in general is precisely the focus on practice. Thus, the predominance of the online component in blended learning is appropriate for practical courses. Within the theoretical and general education courses, the predominance of face-to-face learning can positively affect the educational process and results of learning.
Automated API-usage update for Android apps

Mobile apps rely heavily on the application programming interface (API) provided by their underlying operating system (OS). Because OS and API can change frequently, developers must quickly update their apps to ensure that the apps behave as intended with new API and OS versions. To help developers with this tedious, error prone, and time consuming task, we developed a technique that can automatically perform app updates for API changes based on examples of how other developers evolved their apps for the same changes. Given a target app to be updated and information about the changes in the API, our technique performs four main steps. First, it analyzes the target app to identify code affected by API changes. Second, it searches existing code bases for examples of updates to the new version of the API. Third, it analyzes, ranks, and transforms into generic patches the update examples found in the previous step. Finally, it applies the generated patches to the target app in order of ranking, while performing differential testing to validate the update. We implemented our technique and performed an empirical evaluation on 15 real-world apps with promising results. Overall, our technique was able to update 85% of the API changes considered and automatically validate 68% of the updates performed.
Does Learning Specific Features for Related Parts Help Human Pose Estimation?

Human pose estimation (HPE) is inherently a homogeneous multi-task learning problem, with the localization of each body part as a different task. Recent HPE approaches universally learn a shared representation for all parts, from which their locations are linearly regressed. However, our statistical analysis indicates not all parts are related to each other. As a result, such a sharing mechanism can lead to negative transfer and deteriorate the performance. This potential issue drives us to raise an interesting question. Can we identify related parts and learn specific features for them to improve pose estimation? Since unrelated tasks no longer share a high-level representation, we expect to avoid the adverse effect of negative transfer. In addition, more explicit structural knowledge, e.g., ankles and knees are highly related, is incorporated into the model, which helps resolve ambiguities in HPE. To answer this question, we first propose a data-driven approach to group related parts based on how much information they share. Then a part-based branching network (PBN) is introduced to learn representations specific to each part group. We further present a multi-stage version of this network to repeatedly refine intermediate features and pose estimates. Ablation experiments indicate learning specific features significantly improves the localization of occluded parts and thus benefits HPE. Our approach also outperforms all state-of-the-art methods on two benchmark datasets, with an outstanding advantage when occlusion occurs.
CIPPPA: Conditional Identity Privacy-Preserving Public Auditing for Cloud-Based WBANs Against Malicious Auditors

Wireless body area networks (WBANs) rely on powerful cloud storage services to manage massive medical data. As precise medical diagnosis analysis is heavily based on these medical data, any altered medical data may cause severe consequences, the integrity of outsourced medical data has become the most concerning security issue. Up to date, most existing public auditing mechanisms have been proposed to check the data integrity, but they could not achieve conditional identity privacy, any patient would not like others to know his/her real identity corresponding to certain serious disease, and some malicious patients should be revoked timely due to misbehaviors. Additionally, they are vulnerable to malicious auditors, by colluding with the cloud server to cheat patients. In this paper, we propose a conditional identity privacy-preserving public auditing (CIPPPA) mechanism for cloud-based WBANs. CIPPPA is the first public auditing mechanism achieving conditional identity privacy of patients in WBANs, the real identity of a patient is unknown to anyone in cloud-based WBANs other than the private key generator (PKG). We attempt to integrate Ethereum blockchain into CIPPPA, which gives assistance to patients for validating malicious auditing behaviors. Formal security analysis and performance evaluation demonstrate that CIPPPA is practical for cloud-based WBANs.
Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many such methods are supplemented by spatial or morphological processing. Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call shape priors (SPs) with CNNs (SPs-CNN). We further extend the network to introduce an SP layer and then allowing it to become trainable (i.e., optimizable). We call this network as tunable SP-CNN (TSP-CNN). In summary, we present new network structures that can incorporate "expected behavior" of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information. Analytically, we formulate two new regularization terms that are targeted at: 1) learning the shapes and 2) reducing false positives while simultaneously encouraging detection inside the cell nucleus boundary. Experimental results on two challenging datasets reveal that the proposed SP-CNN and TSP-CNN can outperform the state-of-the-art alternatives.
Incentivized Blockchain-based Social Media Platforms

Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains, where no single entity can take control of the information and users can receive cryptocurrency as rewards for creating or curating high-quality contents. This paper presents an empirical analysis of Steemit, a key representative of the emerging incentivized social media platforms over Blockchains, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit, users create contents as posts which get curated based on votes from other users. The platform periodically issues cryptocurrency as rewards to creators and curators of popular posts. Although such a reward system is originally driven by the desire to incentivize users to contribute to high-quality contents, our analysis of the underlying cryptocurrency transfer network on the blockchain reveals that more than 16% transfers of cryptocurrency in Steemit are sent to curators suspected to be bots and also finds the existence of an underlying supply network for the bots, both suggesting a significant misuse of the current reward system in Steemit. Our study is designed to provide insights on the current state of this emerging blockchain-based social media platform including the effectiveness of its design and the operation of the consensus protocols and the reward system.
Formal Barriers to Longest-Chain Proof-of-Stake Protocols

The security of most existing cryptocurrencies is based on a concept called Proof-of-Work, in which users must solve a computationally hard cryptopuzzle to authorize transactions ("one unit of computation, one vote''). This leads to enormous expenditure on hardware and electricity in order to collect the rewards associated with transaction authorization. Proof-of-Stake is an alternative concept that instead selects users to authorize transactions proportional to their wealth ("one coin, one vote"). Some aspects of the two paradigms are the same. For instance, obtaining voting power in Proof-of-Stake has a monetary cost just as in Proof-of-Work: a coin cannot be freely duplicated any more easily than a unit of computation. However some aspects are fundamentally different. In particular, exactly because Proof-of-Stake is wasteless, there is no inherent resource cost to deviating (commonly referred to as the "Nothing-at-Stake'' problem).
Tight upper bounds for the convergence of the randomized extended Kaczmarz and Gauss–Seidel algorithms

Summary The randomized extended Kaczmarz and Gauss–Seidel algorithms have attracted much attention because of their ability to treat all types of linear systems (consistent or inconsistent, full rank or rank deficient). In this paper, we present tight upper bounds for the convergence of the randomized extended Kaczmarz and Gauss–Seidel algorithms. Numerical experiments are given to illustrate the theoretical results.
Privacy-Preserving Average Consensus: Privacy Analysis and Algorithm Design

Privacy-preserving average consensus aims to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial values. In this paper, it is achieved by adding variance-decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of the data privacy protection. In this paper, we introduce the maximum disclosure probability that other nodes can infer one node's initial state within a given small interval to quantify the data privacy. We utilize a novel privacy definition, named (α, β)-data-privacy, to depict the relationship between the maximum disclosure probability and the estimation accuracy. Then, we prove that the general privacy-preserving average consensus provides (α, β)-data-privacy, and obtain the closed-form expression of the relationship between α and β given the noise distribution. We reveal that the added noise with a uniform distribution is optimal in terms of achieving the highest (α, β)-data-privacy. We also prove that under what condition, the data-privacy will be compromised. Finally, an optimal privacy-preserving average consensus algorithm is proposed to achieve the highest (α, β)-data-privacy. Simulations verify the analytical results.
Effective Application of Mixed Reality Device HoloLens: Simple Manual Alignment of Surgical Field and Holograms

Summary: The technology used to add information to a real visual field is defined as augmented reality technology. Augmented reality technology that can interactively manipulate displayed information is called mixed reality technology. HoloLens from Microsoft, which is a head-mounted mixed reality device released in 2016, can display a precise three-dimensional model stably on the real visual field as hologram. If it is possible to accurately superimpose the position/direction of the hologram in the surgical field, surgical navigation-like use can be expected. However, in HoloLens, there was no such function. The authors devised a method that can align the surgical field and holograms precisely within a short time using a simple manual operation. The mechanism is to match the three points on the hologram to the corresponding marking points of the body surface. By making it possible to arbitrarily select any of the three points as a pivot/axis of the rotational movement of the hologram, alignment by manual operation becomes very easy. The alignment between the surgical field and the hologram was good and thus contributed to intraoperative objective judgment. By using the method of this study, the clinical usefulness of the mixed reality device HoloLens will be expanded.
EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets

Developing robot perception systems for recognizing objects in the real world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms. This paper presents the EasyLabel tool for easily acquiring high-quality ground truth annotation of objects at pixel-level in densely cluttered scenes. In a semi-automatic process, complex scenes are incrementally built and EasyLabel exploits depth changes to extract precise object masks at each step. We use this tool to generate the Object Cluttered Indoor Dataset (OCID) that captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. OCID is used to perform a systematic comparison of existing object segmentation methods. The baseline comparison supports the need for pixel- and object-wise annotation to progress robot vision towards realistic applications. This insight reveals the usefulness of EasyLabel and OCID to better understand the challenges that robots face in the real world.
Conversation Analysis and Online Interaction

This article reviews research that uses conversation analysis to analyze online interaction. Research in this area has tended to focus on several key areas, and these are addressed in detail: turn taking, sequence organization, repair, openings, and embodied conduct. The focus is on how these features of interaction are organized online and across online platforms and how the interactional practices may orient to particular affordances of the platforms. The debates around the use of CA terminology when analyzing online interaction are also discussed. Overall, this review suggests that CA can offer in-depth evidence about the nature of online interaction and so can contribute to wider debates about the role of technology in society. At the same time, through analyzing online interaction there is the possibility for understanding how interactional practices arise and develop over time. Data are in American English, British English, and German.
Integrating aspect analysis and local outlier factor for intelligent review spam detection

Recently, online reviews are used increasingly by individuals and organizations for making purchase and business decisions. Unfortunately, driven by profit and fame, spammers post spurious reviews to mislead the customers. Therefore, intelligent detection of the spam reviews from a large scale of texts is a great challenge. This paper describes an unsupervised method aiming for intelligently detecting online review spams. Review spam detection is transformed into a density-based outlier detection problem. The proposed method generates a sentiment lexicon to calculate the aspect rating of reviews,and proposes an aspect-rating local outlier factor model (AR-LOF) to identify the spam reviews. The experiments on TripAdvisor demonstrate the high effectiveness and intelligence of the proposed model, which has the potential to significantly help the online web business.
Deep Neural Networks for No-Reference Video Quality Assessment

Video quality assessment (VQA) is a challenging task due to the complexity of modeling perceived quality characteristics in both spatial and temporal domains. A novel no-reference (NR) video quality metric (VQM) is proposed in this paper based on two deep neural networks (NN), namely 3D convolution network (3D-CNN) and a recurrent NN composed of long short-term memory (LSTM) units. 3D-CNNs are utilized to extract local spatiotemporal features from small cubic clips in video, and the features are then fed into the LSTM networks to predict the perceived video quality. Such design can elaborately tackle the issue of insufficient training data whilst also efficiently capture perceptive quality features in both spatial and temporal domains. Experimental results with respect to two publicly available video quality datasets have demonstrate that the proposed quality metric outperforms the other compared NR quality metrics.
Understanding Programmatic Creative: The Role of AI

In this article, we expand the concept of programmatic advertising to include programmatic creative as a vital component. While artificial intelligence (AI) has already automated the media buying process, the advertising creative process still requires extensive human efforts. Such discrepancy calls for AI to transform the advertising creative process. We provide a framework for understanding and investigating programmatic creative by drawing evidence from the advertising industry in China. We specifically discuss how big data and machine learning algorithms underpin programmatic advertising. We argue that AI will integrate programmatic buying and programmatic creative in the future. We also discuss the technological, regulatory, and legal challenges faced by programmatic creative. We argue that new theories and methods are needed to conduct research in this area and provide guidance for the advertising industry.
See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

In recent years, intelligent driving navigation and security monitoring have made considerable progress with the help of deep Convolutional Neural Networks (CNNs). As one of the state-of-the-art perception approaches, semantic segmentation unifies distinct detection tasks widely desired by both autonomous driving and security monitoring. Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination conditions. However, in face of adverse conditions such as the nighttime, semantic segmentation loses its accuracy significantly. One of the main causes of the problem is the lack of sufficient annotated segmentation datasets of nighttime scenes. In this paper, we propose a framework to alleviate the accuracy decline when semantic segmentation is taken to adverse conditions by using Generative Adversarial Networks (GANs). To bridge the daytime and nighttime image domains, we made key observation that compared to datasets in adverse conditions, there are considerable amount of segmentation datasets in standard conditions such as BDD and our collected ZJU datasets. Our GAN-based nighttime semantic segmentation framework includes two methods. In the first method, GANs were used to translate nighttime images to the daytime, thus semantic segmentation can be performed using robust models already trained on daytime datasets. In another method, we use GANs to translate different ratio of daytime images in the dataset to the nighttime but still with their labels. In this sense, synthetic nighttime segmentation datasets can be generated to yield models prepared to operate at nighttime conditions robustly. In our experiment, the later method significantly boosts the performance at the nighttime evidenced by quantitative results using Intersection over Union (IoU) and Pixel Accuracy (Acc). We show that the performance varies with respect to the proportion of synthetic nighttime images in the dataset, where the sweet spot corresponds to most robust performance across the day and night. The proposed framework not only makes contribution to the optimization of visual perception in intelligent vehicles, but also can be applied to diverse navigational assistance systems.