Reviewerly/exHarmony huggingface dataset:
Kullback–Leibler divergence for Bayesian nonparametric model checking

Bayesian nonparametric statistics is an area of considerable research interest. While recently there has been an extensive concentration in developing Bayesian nonparametric procedures for model checking, the use of the Dirichlet process, in its simplest form, along with the Kullback–Leibler divergence is still an open problem. This is mainly attributed to the discreteness property of the Dirichlet process and that the Kullback–Leibler divergence between any discrete distribution and any continuous distribution is infinity. The approach proposed in this paper, which is based on incorporating the Dirichlet process, the Kullback–Leibler divergence and the relative belief ratio, is considered the first concrete solution to this issue. Applying the approach is simple and does not require obtaining a closed form of the relative belief ratio. A Monte Carlo study and real data examples show that the developed approach exhibits excellent performance.
Privacy‐aware cloud ecosystems: Architecture and performance

Summary With an increasing number of cloud providers offering services made use of by both individual users and other providers, there is a realization that service provision now involves an “ecosystem” of providers. Some providers may be directly visible to a user, while others may be contributors to composite services and not directly known to the user—as only the provider offering the composite service is visible. Such services may include: domain specific services (eg, simulation), advertising services, or profiling/analytics services. Understanding the impact on data privacy of a user for such a composite service remains a challenge, and providing transparency (and obtaining user consent for data use) remains a key requirement of the European General Data Protection Regulation (GDPR). An architecture that makes use of blockchains and smart contracts is proposed that addresses this requirement. An implementation of the architecture is used to demonstrate how access control can be managed and audited. The scalability and cost of undertaking access control, as the number of actors (both service providers and “voters”) increases, is also described. The proposed approach can be used to support service aggregation across both private and public clouds.
Automatic blockchain whitepapers analysis via heterogeneous graph neural network

The blockchain whitepaper contains detailed technical and business information, so its analysis is important for blockchain text mining. Previous works focus on analyze homogeneous objects and relations. The main problem, however, is these works do not take into account the heterogeneity of information. This paper presents a new methodology for whitepapers analysis by designing heterogeneous graph neural network, named S-HGNN. In detail, this paper first builds a Heterogeneous Information Network (HIN) using heterogeneous objects and relationships extracted from the whitepaper to obtain similarity measures, then uses Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to integrate both structural information and internal semantic into the whitepaper embedding. Compared with the previous models, this model improves 0.96%∼33.34% in terms of F1-score for classification task, and 4.94%∼14.14% in terms of purity for clustering task, and gets stable results on different tasks. The results show the effectiveness and robustness of this model for whitepapers analysis.
On Visualizations in the Role of Universal Data Representation

The deep learning revolution changed the world of machine learning and boosted the AI industry as such. In particular, the most effective models for image retrieval are based on deep convolutional neural networks (DCNN), outperforming the traditional "hand-engineered" models by far. However, this tremendous success was redeemed by a high cost in the form of an exhaustive gathering of labeled data, followed by designing and training the DCNN models. In this paper, we outline a vision of a framework for instant transfer learning, where a generic pre-trained DCNN model is used as a universal feature extraction method for visualized unstructured data in many (non-visual) domains. The deep feature descriptors are then usable in similarity search tasks (database queries, joins) and in other parts of the data processing pipeline. The envisioned framework should enable practitioners to instantly use DCNN-based data representations in their new domains without the need for the costly training step. Moreover, by use of the framework the information visualization community could acquire a versatile metric for measuring the quality of data visualizations, which is generally a difficult task.
New Mode of Distance Learning in Primary Schools in the Environment of Multimedia Computer Assisted Instruction

Abstract with the Rapid Development of Science and Technology in China, Modern Information Technology Has Been Deepened and Popularized in China. At the Same Time, It Has Also Developed to a Certain Extent in the Classroom Teaching of Primary School Language. At the Same Time, the Education Reform Has Made Great Changes in the Teaching Methods of Education, and the Teaching Requirements for Each Stage Have Gradually Improved, Especially the Pupils Who Are Extremely Malleable. At the Same Time, Because of the Development of Science and Technology, Teachers Gradually Found Ways to Improve the Efficiency of Primary School Teaching: Traditional Education Combined with Modern Technology. the Development of Technology Has an Ever-Increasing Impact on Society. Therefore, in Primary School Teaching, Multimedia Technology and Traditional Media Teaching Are Combined.
Innovative Surgical Planning in Resecting Soft Tissue Sarcoma of the Foot Using Augmented Reality With a Smartphone

Augmented or hybrid reality is a display technology that combines the real world with the virtual world; it permits digital images of preoperative planning information to be combined with the surgeon's view of the real world. Augmented reality (AR) can increase the surgeon's intraoperative vision by providing virtual transparency of the real patient and has been applied to a wide spectrum of orthopedic procedures, such as tumor resection, fracture fixation, arthroscopy, and component's alignment in total joint arthroplasty. We present a case of a male patient who presented with pain in the medial aspect of his left foot after he underwent an incomplete mass excision elsewhere where it turned out to be synovial sarcoma. Because the mass was small, impalpable, and deeply positioned beneath both the plantar and the medial plantar aponeuroses, it was impossible to preoperatively decide a plan for resection. We opted to use the aid of AR in the form of an application using the camera of a smartphone. We were able to excise the tumor with negative surgical margins. On 12-month follow-up, the patient is in complete remission and has optimal mobility and functionality of his foot. In conclusion, AR holds great potential for use in the future of orthopedic surgical oncology. We emphasize using it via a handheld device that we found to be optimal for planning resection of the small and relatively fixed tumor. Based on our literature review, this is the first case describing the surgical planning in resecting an impalpable synovial sarcoma of the foot using AR technology.
Comparative Analysis of Rhino-Cytological Specimens with Image Analysis and Deep Learning Techniques

Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of cells benefit from the capacity of deep learning techniques in processing digital images of the cytological preparation. Even though the results of such automatic systems need to be validated by a specialized rhino-cytologist, this technology represents a valid support that aims at increasing the accuracy of the analysis while reducing the required time and effort. The quality of the rhino-cytological preparation, which is clearly important for the microscope observation phase, is also fundamental for the automatic classification process. In fact, the slide-preparing technique turns out to be a crucial factor among the multiple ones that may modify the morphological and chromatic characteristics of the cells. This paper aims to investigate the possible differences between direct smear (SM) and cytological centrifugation (CYT) slide-preparation techniques, in order to preserve image quality during the observation and cell classification phases in rhino-cytology. Firstly, a comparative study based on image analysis techniques has been put forward. The extraction of densitometric and morphometric features has made it possible to quantify and describe the spatial distribution of the cells in the field images observed under the microscope. Statistical analysis of the distribution of these features has been used to evaluate the degree of similarity between images acquired from SM and CYT slides. The results prove an important difference in the observation process of the cells prepared with the above-mentioned techniques, with reference to cell density and spatial distribution: the analysis of CYT slides has been more difficult than of the SM ones due to the spatial distribution of the cells, which results in a lower cell density than the SM slides. As a marginal part of this study, a performance assessment of the computer-aided diagnosis (CAD) system called Rhino-cyt has also been carried out on both groups of image slide types.
Fully Homomorphic based Privacy-Preserving Distributed Expectation Maximization on Cloud

Expectation maximization (EM) is a clustering-based machine learning algorithm that is widely used in many areas of science (e.g., bioinformatics and computer vision) to find maximum likelihood and maximum a posteriori estimates for models with latent variables. To deploy such an algorithm in cloud environments, security and privacy issues need be considered to avoid data breaches or abuses by external malicious parties or even by cloud service providers. However, the processing performance of the EM algorithm poses a challenge in terms of building a secure environment. This article describes an innovative and practical privacy-preserving EM algorithm for cloud systems that addresses this challenge, and estimates the EM parameters in an accurate and secure manner. Fully homomorphic encryption (FHE) is used to ensure the privacy of both the EM algorithm computations and the users' sensitive data in the cloud. A distributed-based approach is also proposed to overcome the overheads of FHE computations and ensure a fast convergence of the EM algorithm. The conducted experiments demonstrate a significant improvement in the convergence time of the distributed EM algorithm, while achieving a high level of accuracy and reducing the associated computational FHE overheads.
Domain Generalization via Universal Non-volume Preserving Approach

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted, or fine-tuned. Therefore, recent deep learning techniques, such as domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel approach to the problem of domain generalization in the context of deep learning. The proposed method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is evaluated on different datasets in various problems, i.e. (i) digit recognition on MNIST, SVHN, and MNIST-M, (ii) face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE, and (iii) pedestrian recognition on RGB and Thermal image datasets. The experimental results show that our proposed method consistently improves performance accuracy. It can also be easily incorporated with any other CNN frameworks within an end-to-end deep network design for object detection and recognition problems to improve their performance.
Achieving Complete UAV Delivery in the Presence of Motor Failures

This paper addresses a redundant flight recovery system (RFRS) in unmanned aerial vehicles (UAV) specifically applied for octocopter delivery or courier applications. RFRS implements a state diagram to allow octocopters to be fault-tolerant during motor breakdown or switches to less-energy mode consumption after package delivery. Conventional linear and nonlinear controllers are employed in achieving complete flight and landing stability of the UAV. Empirical experiments have confirmed the feasibility of RFRS being implemented in an octocopter platform.
Explaining with Counter Visual Attributes and Examples

In this paper, we aim to explain the decisions of neural networks by utilizing multimodal information. That is counter-intuitive attributes and counter visual examples which appear when perturbed samples are introduced. Different from previous work on interpreting decisions using saliency maps, text, or visual patches we propose to use attributes and counter-attributes, and examples and counter-examples as part of the visual explanations. When humans explain visual decisions they tend to do so by providing attributes and examples. Hence, inspired by the way of human explanations in this paper we provide attribute-based and example-based explanations. Moreover, humans also tend to explain their visual decisions by adding counter-attributes and counter-examples to explain what isnot seen. We introduce directed perturbations in the examples to observe which attribute values change when classifying the examples into the counter classes. This delivers intuitive counter-attributes and counter-examples. Our experiments with both coarse and fine-grained datasets show that attributes provide discriminating and human-understandable intuitive and counter-intuitive explanations.
Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

Recommender systems study the characteristics of its users and, applying different kinds of processing to the available data, find a subset of items that may be of interest to a given user in a specific situation.Argumentation-based tools offer the possibility of analyzing complex and dynamic domains by generating and analyzing arguments for and against recommending a specific item based on the users' preferences.This approach allows to analyze the qualitative and quantitative characteristics of the recommended items, and to provide explanations to increase transparency.In this work, we develop a set of software engineering guidelines for the analysis and design of recommender systems leveraging this approach.
Behind the Scenes of the Underworld: Hierarchical Clustering of Two Leaked Carding Forum Databases

Cybercriminals operate in obscurity to avoid detection of their illegal deeds. This fact makes studying them more difficult. Many cybercriminals meet in illicit online market places such as carding forums. The forums are often visible, but the actual transactions are carried out in private messages beyond view. However, there is no honor among thieves, and sometimes a carding forum server database will be hacked and leaked to the public. Existing research has been conducted on such leaked databases, but much of it is quantitative, rather than offering any qualitative interpretation into the nature of the forum user base. This research sought to analyze two such leaked carding forum databases by applying hierarchical clustering of 10,714 registered user accounts, grouping users based on 19 variables consisting of comment history style, site engagement activity, and explicit status markers. The results yielded 16 categories of users from four different domains composed of general consumers, location-based consumers, producers, and an “other” category. Following categorization, qualitative analyses were conducted to further shed insight into the nature of the two forums.
Performance Analysis of Cambricon MLU100

In recent years, domain-specific hardware has brought significant performance improvements in deep learning (DL). Many frequently-used optimization techniques, such as data parallelism, model parallelism, data pipeline, weights pruning and quantization have been proposed to accelerate the inference phase of DL workloads. However, there is still lack of a comparison of these optimization techniques to show their performance difference on dedicated accelerators. This paper evaluates these frequently-used optimization techniques on a commercial accelerator, namely Cambricon MLU100. Considering the requirement of accuracy of DL nature, our metric not only measures the inference throughput but also has an accuracy constraint. Based on our analysis methodology and performance numbers, we have some key observations and implications that are valuable for the future DL hardware and software co-design. Furthermore, we explore the upper bound of MLU100 inference performance under the standard ResNet-50 model and CIFAR-10 dataset.
Multiple-Image Super-Resolution for Networked Extremely Low-Resolution Thermal Sensor Array

Observing activeness of patients and elderly for long time at private space is a desired metric for elderly care. Many state-of-the-art mechanisms require either using wearable sensors, assists by medical staffs, or observation at designated location. Consequently, it is difficult, if not impossible, to acquire long term trend. Thermal sensors are not sensitive to illumination and can detect heat sources under low lightness. Moreover, it is not trivial to identify an individual from thermal images. However, a low cost and effective solution remain open. Low-resolution thermal sensors cost less but suffer from noisy reading. In this work, we fuse multiple low-resolution thermal images to reconstruct higher resolution thermal images to identify human activities. The evaluation results show that the accuracy of activity recognition while using low resolution thermal images are 96% of that while using high resolution thermal images, which requires 80 times of data and ten times of hardware cost.
A Survey on Deep Learning based Various Methods Analysis of Text Summarization

Due to the extremely huge amount of text available on the internet today, there is a need for a method that helps us gather concise and quality information according to our query. People expressing their views on social media, product reviews by customers, news articles, blogs, etc. are some sources from where text arises. There are two ways of summarization (SUMZ): abstractive (ABSR) and extractive (EXTR). It can be achieved using various methods like deep learning; Neural Networks (NN), fuzzy C-means clustering etc. and these methods can be either supervised or unsupervised. Moreover, SUMZ can be achieved keeping in mind the user’s emotions and views. Researchers are performing experiments on various datasets like views from social media, product reviews, news articles, or any other source online and propose various solutions to produce the best of the "gold summaries (summ.)" that are fair to the original piece of text. In this paper, we have made an attempt to study the various methods that are used for text SUMZ and observe the trends, the developments, the accomplishments and we explore new dimensions for future work to be done in this expanding field.
LocMIA: Membership Inference Attacks Against Aggregated Location Data

An increasing amount of users' locations are aggregated, and the statistical results about the collected data are further released to support mobile applications, such as point-of-interest recommendation and smart transportation. However, such statistical results cause users' membership privacy leakage. Unfortunately, most studies concerning data aggregation focused on privacy preservation and various attacks rather than the membership inference attacks. Moreover, literature about membership inference attacks mainly aimed at machine learning models and gene sequences rather than the locations in data aggregation. More importantly, these work concerning membership inference attacks assumed that adversaries know the exact data of victims, which is always impossible in practical scenarios. To this end, we propose LocMIA, a more invasive attack system that allows adversaries to launch membership inference attacks against aggregated location data without reliance on any prior knowledge of the locations of victims. The main idea of LocMIA is to train a binary classifier to infer whether a specific victim's location data is involved in the aggregation group, based solely on the data aggregation's output (i.e., the statistical results). Finally, experimental results on a real-world check-in data set prove the devastating privacy leaks caused by the proposed LocMIA.
Efficient Fitness Action Analysis Based on Spatio-Temporal Feature Encoding

Human action analysis has been an active research area in computer vision. Most of existing approaches are data-driven and focus on general actions. In this paper, we aim to recognize fitness actions from image sequences and propose an action evaluation method, which can be applied in artificial intelligence (AI) fitness system. Firstly, we extract human skeleton information from the captured fitness video with a simplified skeleton model. Secondly, the extracted skeleton images of an action sequence are transformed to an uniform two-dimensional plane with the proposed spatial-temporal skeleton encoding method, which describes a global action feature. Finally, an action classifier and a geometrical registration metric are constructed respectively to analyze the fitness actions. In addition, we build a dataset for fitness actions recognition and evaluation. Experimental results demonstrate that our method has a good performance both on the fitness action dataset and small-scale dataset.
An Analysis of Automated Answer Evaluation Systems based on Machine Learning

Evaluation of the answers remain as one of the most important factors in the learning and teaching process. Automatic evaluation of the answers is very necessary thus, many system has been developed in this digital era. Usually, the subjective answers are in either short form or long answers. The existing system available for evaluation has shown mediocre result in evaluating and scoring the answers. In such frameworks, the data recovery technique to gauge likeness between understudies answer and references answer is utilized, yet such scoring framework doesn't give the best outcome yet. There are very few keywords available in short answers. The answers with such limited number of keywords needs special care, especially while calculating the weighting score of the answers. In the presented study, we try to summarize the existing mechanism and analyses the performance of the system used for automatic grading of the long and descriptive answers.