Reviewerly/exHarmony huggingface dataset:
EACS: Expressible Access Control Scheme for Secure Services and Data Delegation in Collaborative E-health System

Recent technological advancements in health care delivery have paved the way for collaborative monitoring, diagnosis, and treatment of patients, which involve real-time monitoring of patients and remote storage of their health information in the cloud. However, controlling access to the stored health information in the cloud is not only difficult but also susceptible to different privacy- and security-related attacks. Besides, existing access control schemes for e-health do not support delegation of responsibilities, a common phenomenon in health care delivery, making complete adoption of e-health impracticable. In this paper, we propose a security scheme that provides fine-grained expressive access control on patients’ health information for secure data and responsibilities delegation among health officers in collaborative e-health systems. The scheme takes care of the key distribution and attribute or user revocation problems of CP-ABE and supports secure delegation of responsibilities. With this scheme, a health officer can treat patients and securely delegate responsibilities to other health officers within the team without jeopardizing the security and privacy of the patients. Security and performance analysis show that our proposed scheme is efficient, secure, and expressive.
CRANet: Cascade Residual Attention Network for Crowd Counting

The existing approaches for crowd counting usually estimate a density map with deep convolutional neural network to obtain the crowd counts. Influenced by the background noises, some approaches may result in incorrect pedestrian heads recognition. Therefore, some approaches try to estimate an attention map to mask background noises. However, since the background noises are complex and stochastic, single attention is of incompetence to recognize them. Consequently, we proposed softer, and more reasonable Cascade Residual Attention Network (CRANet), which cascades several effective residual attention modules to mask background noises. Also, due to pixel-level isolation of Euclidean loss, we designed a novel Pyramid Structural Similarity Loss to train our CRANet. The proposed approach was evaluated on three crowd datasets. Experimental results demonstrated that our approach achieves the state-of-the-art.
Online Additive Quantization

Approximate nearest neighbor search (ANNs) plays an important role in many applications ranging from information retrieval, recommender systems to machine translation. Several ANN indexes, such as hashing and quantization, have been designed to update for the evolving database, but there exists a remarkable performance gap between them and retrained indexes on the entire database. To close the gap, we propose an online additive quantization algorithm (online AQ) to dynamically update quantization codebooks with the incoming streaming data. Then we derive the regret bound to theoretically guarantee the performance of the online AQ algorithm. Moreover, to improve the learning efficiency, we develop a randomized block beam search algorithm for assigning each data to the codewords of the codebook. Finally, we extensively evaluate the proposed online AQ algorithm on four real-world datasets, showing that it remarkably outperforms the state-of-the-art baselines.
Formally Investigating Traceability Metrics of Data Warehouse Requirements Model using Briand's Framework

Data warehouse maintains historical information, which is used for taking important decisions in an enterprise. Hence, it becomes necessary to maintain information quality of data warehouse. Many researchers suggested various metrics for the evaluation of data model quality for maintaining information quality of data warehouse. Data models are at different level of abstraction namely; requirements, conceptual, logical and physical model. Quite a few investigations were witnessed in the past for evaluating quality of requirements model in formal and empirical manner. In this study, formal investigation of requirements traceability metrics of requirements model for data warehouse is carried out by employing Briand's property-based framework to ensure whether they are valid and are defined in the approved manner which in-turn build the quality based data warehouse. Results showed that out of seven traceability metrics, five metrics are found as cohesion measure and rest two metrics are found as coupling measure; which concluded that these metrics are valid and defined correctly. Thus, these requirements traceability metrics can be used successfully to assess the quality of requirements model of data warehouse.
Computer Vision-based Web Scraping for Internet Forums

With the amount of data available on websites the need to transform this data from a human-understandable format, the visual representation, to a computer-understandable format, e.g. as entries in a database, rises. The approaches to solving web scraping that were published in the last two decades have the drawback that they all to a certain degree rely on the structure and existence of the underlying Hypertext Markup Language (HTML) or Cascading Style Sheets (CSS). To reduce this dependency and move to understanding websites more human-like, this short paper presents a scientific project that proposes a web scraping approach based solely on the visual representation of a given website. For this purpose existing approaches from the domain of Document Layout Analysis and Optical Character Recognition (OCR) are taken into concern. This short paper provides relevant background knowledge to the involved fields of science and proposes a methodology along which the suggested approach can be implemented and tested in further work.
Labeled Data Generation with Inexact Supervision

The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural networks in supervised learning heavily relies on a large amount of labeled data. However, obtaining labeled data with target labels is often challenging due to various reasons such as cost of labeling and privacy issues, which challenges existing deep models. In spite of that, it is relatively easy to obtain data with inexact supervision, i.e., having labels/tags related to the target task. For example, social media platforms are overwhelmed with billions of posts and images with self-customized tags, which are not the exact labels for target classification tasks but are usually related to the target labels. It is promising to leverage these tags (inexact supervision) and their relations with target classes to generate labeled data to facilitate the downstream classification tasks. However, the work on this is rather limited. Therefore, we study a novel problem of labeled data generation with inexact supervision. We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes. Experimental results on image and text datasets demonstrate the effectiveness of the proposed ADDES for generating realistic labeled data from inexact supervision to facilitate the target classification task.
Application of C4.5 and Naïve Bayes Algorithm for Detection of Potential Increased Case Fatality Rate Diarrhea

Abstract Case Fatality Rate or mortality percentage due to some extraordinary events (outbreaks) diarrhea in Indonesia is still above target expected by the government. Several factors have been known to be the cause of diarrhea, but the most influential factor to increase Case Fatality Rate of diarrhea is not known. Therefore, the purpose of this research is to create classification from diarrhea outbreaks data to obtain the data patterns in the form of classification rule that can be applied to detect Case Fatality Rate of diarrhea. Classification used the C4.5 algorithm and Naïve Bayes algorithm. C4.5 algorithm is a popular algorithm with decision tree approach, while Naïve Bayes algorithm is a popular with probabilistic approach in classification. Research implementation uses the stages in Knowledge Discovery in Databases. After obtaining the classification rule, this rule evaluated by Confussion Matrix and Receiver Operating Characteristic Curve. The evaluation was done by using training data and testing data. The evaluation result in this case indicates that C4.5 algorithm has a higher accuracy level than Naïve Bayes. While the factors that most influence in the Case Fatality Rate increase in diarrhea diseases are shelter and sanitation.
Unconstrained end-to-end text reading with feature rectification

• We propose an end-to-end trainable text spotting framework. • We find and deal with the features incompatibility problem. • PSN is proposed to rectify the proposal features in the recognition branch. • Experiments have demonstrated the effectiveness of our method. We propose an end-to-end trainable network that can simultaneously localize and recognize irregular text from images. Specifically, we find the feature incompatibility problem, which arises from the contradiction between detection and recognition tasks for feature extraction of the convolutional neural network, and propose to introduce the larger-scale features for the recognition part to improve the accuracy of recognition instead of using the same feature with the detection. To extract effective text features for perspective and curved text recognition, we propose a position-sensitive network to rectify the text proposal features in the recognition branch. The position-sensitive network, which is trained in a weak supervision way, takes the proposal detection feature as input and outputs the feature rectification information. Experiments demonstrate that the proposed method can achieve state-of-the-art or highly competitive performance compared with baselines on a number of benchmarks.
High-Definition Map-based Local Path Planning for Dynamic and Static Obstacle Avoidance

Parametric curves are frequently used in computer animation and virtual environments to control the movements of synthetic objects.In many of these applications it is essential to efficiently relate parameter values to the arc length of the curve.Current approaches to compute arc length or to construct an arc-length parameterized curve are impractical to use in real-time applications.This paper presents a simple and efficient technique to generate approximately arc-length parameterized spline curves that closely match spline curves typically used to model roads in high-fidelity driving simulators.
Fisheye Image Calibration and Super-Resolution Method Based On Deep Learning

Fisheye lens is a kind of ultra-wide-angle lens, which can get more field of view information than ordinary perspective lens. However, there is radial lens distortion in the photos taken by fisheye lens. In order to improve the visual quality of fisheye images and to perform subsequent computer vision tasks, fisheye images must be calibrated first. Traditional calibration methods are often based on a specific type of fisheye lens, which is not universal, and often need to take multiple images of a calibration pattern (typically a checkerboard) for calibration. And limited by hardware, many fisheye cameras take photos with low resolution. The method proposed in this paper not only overcomes the above limitations, but also jointly solves the problem of image super-resolution, which makes the final result more appreciable. This method uses a depth convolution neural network model to predict the fisheye image correction parameters, and then completes the correction process, but also completes the image super-resolution processing. Our quantitative experiments have proved the feasibility and superiority of our method.
Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance

Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR’s performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.
A Hybrid Approach for Process Scheduling in Cloud Environment Using Particle Swarm Optimization Technique

Resource Management is a central aspect in Cloud Computing. This is because the Cloud Services are offered to several users, in a way which ensures rapid deployment and on demand availability. The primary motivation behind cloud computing is to access desired resource over cloud/internet, using thin-clients capable of running only an internet browser program. A given physical resources is extended as multiple virtual resources which can be allocated to several users through virtualization paradigm. One a particular requirement is completed; the corresponding virtual resource can be allocated to another user or shall be stopped for execution to save energy/ enhance throughput. Resource management in a scenario, consisting of large population of users, over a number of resources, with various priorities, under different load conditions is a complex optimization problem. This issue has been discussed at length in research literature over different service models like SaaS, PaaS and IaaS. In this paper, a generic model of cloud service facility is considered which falls under the category of public cloud. Nature inspired algorithm, particularly, particle swarm optimization is presented to manage the request-response architecture, for minimum latency. The proposed approach outperforms the benchmark approaches presented so far. The simulation is performed on CloudSim and the results obtained are in excellent agreement to the analytical model.
AI-powered Trademark Dispute Resolution - Expert Opinion Commissioned by the European Union Intellectual Property Office (EUIPO)

This paper discusses potential applications for computer analytics in EU courts given the technological transformations triggered by COVID 19. We argue that zoom hearings are only a temporary solution to the access to justice crisis, and more effort will be needed to address the issue of access to justice and legal help.We explore accessible AI-powered legal help and how such technology operates in practice. We have chosen trademark disputes as a test case, given recent innovations in this field. Specifically, we review how a combination of online dispute resolution (ODR) and predictive analytics employing machine learning and deep learning can be useful in EU trademark dispute resolution. The system we discuss serves as a legal aid tool for small- to medium-sized enterprises (SMEs) to determine potential IPR violations. We further discuss empirical evidence concerning the feasibility and performance of the system, especially concerning image retrieval, services and goods classification, verbal comparison, and the predictability of trademark disputes, with a focus on the likelihood of confusion. Finally, we discuss how legal aid technology can be incorporated into court processes, with specific focus on how adjudicators may use Artificial Intelligence tools to make better and more consistent decisions.
Group Activity Recognition in Visual Data: A Retrospective Analysis of Recent Advancements

Human-activity recognition has gained significant attention recently within the computer vision and machine learning community, due to its applications in diverse fields such as health, entertainment, visual surveillance and sports analytics. An important sub-category of human-activity recognition is group activity recognition (GAR) where a group of individuals is involved in an activity. The main challenge in such recognition tasks is to learn the relationship between a group of individuals in a scene and its evolution over time. Recently, many techniques based on deep networks and graphical models have been proposed for group activity recognition. In this paper, we critically analyze the state-of-the-art (SOTA) techniques for group activity recognition. We propose a new taxonomy for categorizing the SOTA techniques conducted in the field of group activity recognition and divide the existing literature into different subcategories. We also identify the available datasets and the existing research challenges for GAR.
Towards Rich-Detail 3D Face Reconstruction and Dense Alignment via Multi-Scale Detail Augmentation

3D face reconstruction based on a single image is a longstanding challenging problem in computer vision. Existing end-to-end methods are difficult to reconstruct rich 3D face details. To solve this problem, we propose a two-stream convolutional neural network combined with a face super-resolution method, which can effectively restore the image’s 3D position information. Our method combines an attention fusion mechanism, which can learn the individual attention mapping of each feature subspace, and effectively learn cross-channel information while learning multi-scale and multi-frequency features. Meanwhile, our module obtains the most discriminative features in different local areas, and enhances the consistency and correlation between the attention areas. Experimental results show that our SRCNet has made significant improvements in the 3D face reconstruction and face alignment of the AFLW2000-3D and AFLW datasets.
Antonym Disambiguation for a German-language Conversational Intelligent Tutoring System

Conversational agent systems often rely on semantic similarity measures for capturing the meaning of the user's textual input. Among the popular similarity measures are those based on word embeddings. State-of-the-art solutions show good results at measuring the semantic relatedness, but can have problems with accurately estimating the semantic similarity. Antonyms are an example of such words that despite being semantically related have opposite meanings. In this work, related to the development of a conversational intelligent tutoring system for students in nursing subjects, we address the problem of antonyms disambiguation by performing an empirical evaluation of the performance of different scores for German antonyms and synonyms detection. The scores are calculated based on word embeddings-based similarity measure. The results show that the investigated scores are not significantly different, indicating that only the relatedness instead of the meaning of the words was captured. This poses a problem for the development of a conversational agent that needs to capture the meaning of the input text. To address this problem, we discuss strategies for improving the performance by integrating knowledge about the semantic structure of the words.
A collection of easily deployable adversarial traffic sign stickers

Abstract The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.
Study on control methods based on identification of unmanned vehicle model

This paper presents the results of nonlinear model identification of an unmanned vehicle in the Gazebo simulator based on a neural network autoregressive model. The dynamic characteristics of the control object can vary significantly that complicates the problem. The training sample of movement along the spatial path was obtained at the Robotics Center of the FRC CSC RAS. The model parameters were found by the particle swarm optimization method. Using the identified model, real-time control methods were experimentally compared. For a more accurate assessment of the methods, the model was subjected to random disturbances, and the tracking path of the control object was significantly complicated.
When Your Boss Says, “You Need to Sound More Professional”: Writing Style and Writer Attributions

One line of prior research has focused on the effect of style on readers’ ability to comprehend or willingness to engage with a message. A separate line has illuminated the effect of errors on the impressions readers form about writers, identifying potentially serious consequences (e.g., the willingness to accept the writer as a coworker or to fund the writer’s business pitch). To date, few studies have investigated the effect of style on the impressions readers form of business writers. In this paper, we report a study of the relationship between business writer attributions and word- or sentence-level style features often emphasized by advocates of plain style. Using data from 614 respondents, we found statistically significant evidence that business writers conveyed (a) confidence by avoiding non-requisite words, jargon, and nominals and by using standard connotations and grammar, and (b) professionalism by avoiding non-requisite words and hedges and by using standard homonyms.
Acquisition/Processing

In seismic data processing, static corrections for near-surface velocities are derived from first-break picking. The quality of the static corrections is paramount to developing an accurate shallow velocity model, a model that in turn greatly impacts the subsequent seismic processing steps. Because even small errors in first-break picking can greatly impact the seismic velocity model building, it is necessary to pick high-quality traveltimes. Whereas various artificial intelligence-based methods have been proposed to automate the process for data with medium to high signal-to-noise ratio (S/N), these methods are not applicable to low-S/N data, which still require intensive labor from skilled operators. We successfully replace 160 hours of skilled human work with 10 hours of processing by a single NVIDIA Quadro P6000 graphical processing unit by reducing the number of human picks from the usual 5%–10% to 0.19% of available gathers. High-quality inferred picks are generated by convolutional neural network-based machine learning trained from the human picks.