Reviewerly/exHarmony huggingface dataset:
VR Disability Simulation Reduces Implicit Bias Towards Persons With Disabilities

This article investigates how experiencing Virtual Reality (VR) Disability Simulation (DS) affects information recall and participants' implicit association towards people with disabilities (PwD). Implicit attitudes are our actions or judgments towards various concepts or stereotypes (e.g., race) which we may or may not be aware of. Previous research has shown that experiencing ownership over a dark-skinned body reduces implicit racial bias. We hypothesized that a DS with a tracked Head Mounted Display (HMD) and a wheelchair interface would have a significantly larger effect on participants' information recall and their implicit association towards PwD than a desktop monitor and gamepad. We conducted a 2 x 2 between-subjects experiment in which participants experienced a VR DS that teaches them facts about Multiple Sclerosis (MS) with factors of display (HMD, a desktop monitor) and interface (gamepad, wheelchair). Participants took two Implicit Association Tests before and after experiencing the DS. Our study results show that the participants in an immersive HMD condition performed better than the participants in the non-immersive Desktop condition in their information recall task. Moreover, a tracked HMD and a wheelchair interface had significantly larger effects on participants' implicit association towards PwD than a desktop monitor and a gamepad.
Hierarchical Prototype Learning for Zero-Shot Recognition

Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. Key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary semantic prototypes (e.g., word or attribute vectors). However, the popularly learned projection functions in previous works cannot generalize well due to non-visual components included in semantic prototypes. Besides, the incompleteness of provided prototypes and captured images has less been considered by the state-of-the-art approaches in ZSL. In this paper, we propose a hierarchical prototype learning formulation to provide a systematical solution (named HPL) for zero-shot recognition. Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting. To narrow the gap of two domains, we further learn the interpretable super-prototypes in both visual and semantic spaces. Meanwhile, the two spaces are further bridged by maximizing their structural consistency. This not only facilitates the representativeness of visual prototypes, but also alleviates the loss of information of semantic prototypes. An extensive group of experiments are then carefully designed and presented, demonstrating that HPL obtains remarkably more favorable efficiency and effectiveness, over currently available alternatives under various settings.
Multi-criteria decision analysis in Bayesian networks - Diagnosing ecosystem service trade-offs in a hydropower regulated river

The paper demonstrates the use of Bayesian networks in multicriteria decision analysis (MCDA) of environmental design alternatives for environmental flows (eflows) and physical habitat remediation measures in the Mandalselva River in Norway. We demonstrate how MCDA using multi-attribute value functions can be implemented in a Bayesian network with decision and utility nodes. An object-oriented Bayesian network is used to integrate impacts computed in quantitative sub-models of hydropower revenues and Atlantic salmon smolt production and qualitative judgement models of mesohabitat fishability and riverscape aesthetics. We show how conditional probability tables are useful for modelling uncertainty in value scaling functions, and variance in criteria weights due to different stakeholder preferences. While the paper demonstrates the technical feasibility of MCDA in a BN, we also discuss the challenges of providing decision-support to a real-world habitat remediation process.
Multiscale receptive field based on residual network for pancreas segmentation in CT images

Medical image segmentation has made great achievements. Yet pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. The UNet often suffers from pancreas over-segmentation, under-segmentation and shape inconsistency between the predicted result and ground truth. We consider the UNet can not extract more deepen features and rich semantic information which can not distinguish the regions between pancreas and background. From this point, we proposed three cross-domain information fusion strategies to solve above three problems. The first strategy named skip network can efficiently restrain the over-segmentation through cross-domain connection. The second strategy named residual network mainly seeks to solve the under- and over- segmentation problem by cross-domain connecting on a small scale. The third multiscale cross-domain information fusion strategy named multiscale residual network added multiscale convolution operation on second strategy which can learn more accurate pancreas shape and restrain over- and under- segmentation. We performed experiments on a dataset of 82 abdominal contrast-enhanced three dimension computed tomography (3D CT) scans from the National Institutes of Health Clinical Center using 4-fold cross-validation. We report 87.57 ± 3.26 % of the mean Dice score, which outperforms the state-of-the-art method, producing 7.87 % improvement from the predicted result of original UNet. Our method is not only superior to the other established methods in terms of accuracy and robustness but can also effectively restrain pancreas over-segmentation, under-segmentation and shape inconsistency between the predicted result and ground truth. Our strategies prone to apply to clinical medicine.
Spatial Structure Preserving Feature Pyramid Network for Semantic Image Segmentation

Recently, progress on semantic image segmentation is substantial, benefiting from the rapid development of Convolutional Neural Networks. Semantic image segmentation approaches proposed lately have been mostly based on Fully convolutional Networks (FCNs). However, these FCN-based methods use large receptive fields and too many pooling layers to depict the discriminative semantic information of the images. Specifically, on one hand, convolutional kernel with large receptive field smooth the detailed edges, since too much contexture information is used to depict the “center pixel.” However, the pooling layer increases the receptive field through zooming out the latest feature maps, which loses many detailed information of the image, especially in the deeper layers of the network. These operations often cause low spatial resolution inside deep layers, which leads to spatially fragmented prediction. To address this problem, we exploit the inherent multi-scale and pyramidal hierarchy of deep convolutional networks to extract the feature maps with different resolutions and take full advantages of these feature maps via a gradually stacked fusing way. Specifically, for two adjacent convolutional layers, we upsample the features from deeper layer with stride of 2 and then stack them on the features from shallower layer. Then, a convolutional layer with kernels of 1× 1 is followed to fuse these stacked features. The fused feature preserves the spatial structure information of the image; meanwhile, it owns strong discriminative capability for pixel classification. Additionally, to further preserve the spatial structure information and regional connectivity of the predicted category label map, we propose a novel loss term for the network. In detail, two graph model-based spatial affinity matrixes are proposed, which are used to depict the pixel-level relationships in the input image and predicted category label map respectively, and then their cosine distance is backward propagated to the network. The proposed architecture, called spatial structure preserving feature pyramid network, significantly improves the spatial resolution of the predicted category label map for semantic image segmentation. The proposed method achieves state-of-the-art results on three public and challenging datasets for semantic image segmentation.
Machine Learning Models for Paraphrase Identification and its Applications on Plagiarism Detection

Paraphrase Identification or Natural Language Sentence Matching (NLSM) is one of the important and challenging tasks in Natural Language Processing where the task is to identify if a sentence is a paraphrase of another sentence in a given pair of sentences. Paraphrase of a sentence conveys the same meaning but its structure and the sequence of words varies. It is a challenging task as it is difficult to infer the proper context about a sentence given its short length. Also, coming up with similarity metrics for the inferred context of a pair of sentences is not straightforward as well. Whereas, its applications are numerous. This work explores various machine learning algorithms to model the task and also applies different input encoding scheme. Specifically, we created the models using Logistic Regression, Support Vector Machines, and different architectures of Neural Networks. Among the compared models, as expected, Recurrent Neural Network (RNN) is best suited for our paraphrase identification task. Also, we propose that Plagiarism detection is one of the areas where Paraphrase Identification can be effectively implemented.
Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness

We propose a new blind watermarking algorithm for 3D printed objects that has applications in metadata embedding, robotic grasping, counterfeit prevention, and crime investigation. Our method can be used on fused deposition modeling (FDM) 3D printers and works by modifying the printed layer thickness on small patches of the surface of an object. These patches can be applied to multiple regions of the object, thereby making it resistant to various attacks such as cropping, local deformation, local surface degradation, or printing errors. The novelties of our method are the use of the thickness of printed layers as a one-dimensional carrier signal to embed data, the minimization of distortion by only modifying the layers locally, and one-shot detection using a common paper scanner. To correct encoding or decoding errors, our method combines multiple patches and uses a 2D parity check to estimate the error probability of each bit to obtain a higher correction rate than a naive majority vote. The parity bits included in the patches have a double purpose because, in addition to error detection, they are also used to identify the orientation of the patches. In our experiments, we successfully embedded a watermark into flat surfaces of 3D objects with various filament colors using a standard FDM 3D printer, extracted it using a common 2D paper scanner and evaluated the sensitivity to surface degradation and signal amplitude.
Survey on Threats and Risks in the Cloud Computing Environment

A very important job in handling Cloud Computing services is handling threats as early as possible, both threats to users or threats to cloud service providers. Threats in the cloud context are all things that will bring loss to company assets that will cause IT risks to be stored in cloud computing. The purpose of this study is to survey threats in the scope of: Data, Applications, Infrastructure, and services in general in the cloud computing environment. The types of threats or attacks in the context of the cloud have been defined through surveys of relevant articles. The research methodology used is conducting a survey on some of the results of previous studies relating to threats or obstacles in cloud computing carried out in 5 steps, namely: writing the formulation, conducting a search, conducting selection and evaluation, analysis, and summarizing the results. From the survey results, there are many types of threats / obstacles in the cloud computing environment, which are divided into 4 (four) groups, namely: Threats to applications, Threats to data, Threats to infrastructure, Threats to cloud services in general. The results of this study are to provide a more detailed understanding of the types of threats for each service in the cloud.
Deep label refinement for age estimation

Age estimation of unknown persons is a challenging pattern analysis task due to the lack of training data and various ageing mechanisms for different individuals. Label distribution learning-based methods usually make distribution assumptions to simplify age estimation. However, since different genders, races and/or any other characteristics may influence facial ageing, age-label distributions are often complicated and difficult to model parametrically. In this paper, we propose a label refinery network (LRN) with two concurrent processes: label distribution refinement and slack regression refinement. The label refinery network aims to learn age-label distributions progressively in an iterative manner. In this way, we can adaptively obtain the specific age-label distributions for different facial images without making strong assumptions on the fixed distribution formulations. To further utilize the correlations among age labels, we propose a slack regression refinery to convert the age-label regression model into an age-interval regression model. Extensive experiments on three popular datasets, namely, MORPH Album2, ChaLearn15 and MegaAge-Asian, demonstrate the superiority of our method.
Karcı summarization: A simple and effective approach for automatic text summarization using Karcı entropy

Increases in the amount of text resources available via the Internet has amplified the need for automated document summarizing tools. However, further efforts are needed in order to improve the quality of the existing summarization tools currently available. The current study proposes Karcı Summarization, a novel methodology for extractive, generic summarization of text documents. Karcı Entropy was used for the first time in a document summarization method within a unique approach. An important feature of the proposed system is that it does not require any kind of information source or training data. At the stage of presenting the input text, a tool for text processing was introduced; known as KUSH (named after its authors; Karcı, Uçkan, Seyyarer, and Hark), and is used to protect semantic consistency between sentences. The Karcı Entropy-based solution chooses the most effective, generic and most informational sentences within a paragraph or unit of text. Experimentation with the Karcı Summarization approach was tested using open-access document text (Document Understanding Conference; DUC-2002, DUC-2004) datasets. Performance achievement of the Karcı Summarization approach was calculated using metrics known as Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The experimental results showed that the proposed summarizer outperformed all current state-of-the-art methods in terms of 200-word summaries in the metrics of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-W-1.2. In addition, the proposed summarizer outperformed the nearest competitive summarizers by a factor of 6.4% for ROUGE-1 Recall on the DUC-2002 dataset. These results demonstrate that Karcı Summarization is a promising technique and it is therefore expected to attract interest from researchers in the field. Our approach was shown to have a high potential for adoptability. Moreover, the method was assessed as quite insensitive to disorderly and missing texts due to its KUSH text processing module.
P-MOD: Secure Privilege-Based Multilevel Organizational Data-Sharing in Cloud Computing

Cloud computing has changed the way enterprises store, access and share data. Big data sets are constantly being uploaded to the cloud and shared within a hierarchy of many different individuals with different access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme (P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle the management and sharing of big data sets. Our proposed privilege-based access structure helps reduce the complexity of defining hierarchies as the number of users grows, which makes managing healthcare records using mobile healthcare devices feasible. It can also facilitate organizations in applying big data analytics to understand populations in a holistic way. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds. The comprehensive performance and simulation analyses using the real U.S. Census Income data set demonstrate that P-MOD is more efficient in computational complexity and storage space than the existing schemes.
Blockchain in Health Data Systems: A Survey

There has been increasing interest in connecting disjointed Electronic Medical Records, mobile health data, and related health data systems for the purpose of improving preventative and precision medicine, while also providing individuals with greater access and control to their data. Blockchains provide data transparency, immutability, and decentralized trust - making them a promising solution to the interoperability and security issues faced by such health data systems. Several papers have proposed the use of blockchain technology in healthcare to determine its viability as a solution and to identify potential applications and challenges. We build upon their work by 1) presenting implementation details related to blockchain applications in health data systems, 2) discussing the security, privacy, and performance trade-offs of each, and 3) identifying a set of research questions regarding the use of blockchain technology in health data systems. We find that blockchain-based healthcare research should place greater emphasis on real-world deployments and testing, smart-contract security, efficient and usable audit tools, blockchain governance, and adherence to healthcare data regulations and standards.
CNN based spatial classification features for clustering offline handwritten mathematical expressions

To help human markers mark a large number of answers of handwritten mathematical expressions (HMEs), clustering them makes marking more efficient and reliable. Clustering HMEs, however, faces the problem of extracting both localization and classification representation of mathematical symbols for an HME image and defining the distance between two HME images. First, we propose a method based on Convolutional Neural Networks (CNN) to extract the representations for an HME. Symbols in various scales are located and classified by a combination of features from a multi-scale CNN. We use weakly supervised training combined with symbols attention to enhance localization and classification predictions. Second, we propose a multi-level spatial distance between two representations for clustering HMEs. Experiments on CROHME 2016 and CROHME 2019 dataset show the promising results of 0.99 and 0.96 in purity, respectively.
BlockV: A Blockchain Enabled Peer-Peer Ride Sharing Service

Ride sharing is a centralized trust based system where users trust the service providers for the ride set up, tracking, cancellation, fare calculation etc. Any malicious activity in the centralized server based system or driver or rider destroys the fairness involved in the ride and causes inconvenience to the parties. After the completion of the ride, the drivers are rated by the riders. There are possibilities that, a malicious rider can claim the refund with a fake complain and give the driver poor rating. Current system is not capable of deciding the correctness of the objections raised by either parties and provides a biased outcome of each objections as per the centralized company's marketing strategies. We present BlockV, a blockchain enabled solution to ensure the fairness of the ride. The creation, completion, dissatisfaction or abortion of any ride will be written in the blockchain ledger, hence will be available to all participants in the peer to peer network. This simultaneously ensures the fairness in maintenance of the inbuilt reputation system. We have implemented a prototype in Ethereum private network and KOVAN test network and the analysis is included.
A Performance Comparison of Cloud-Based Container Orchestration Tools

Compared to the traditional approach of using virtual machines as the basis for the development and deployment of applications running in Cloud-based infrastructures, container technology provides developers with a higher degree of portability and availability, allowing developers to build and deploy their applications in a much more efficient and flexible manner. A number of tools have been proposed to orchestrate complex applications comprising multiple containers requiring continuous monitoring and management actions to meet application-oriented and non-functional requirements. Different container orchestration tools provide different features that incur different overheads. As such, it is not always easy for developers to choose the orchestration tool that will best suit their needs. In this paper we compare the benefits and overheads incurred by the most popular open source container orchestration tools currently available, namely: Kubernetes and Docker in Swarm mode. We undertake a number of benchmarking exercises from well-known benchmarking tools to evaluate the performance overheads of container orchestration tools and identify their pros and cons more generally. The results show that the overall performance of Kubernetes is slightly worse than that of Docker in Swarm mode. However, Docker in Swarm mode is not as flexible or powerful as Kubernetes in more complex situations.
Delocalized information in quantum networks

We consider entanglement-based quantum networks where information is stored in a delocalized way within regions or the whole network. This offers a natural protection against failure of network nodes, loss and decoherence, and has built-in security features. Quantum information is transmitted within the network by performing local measurements on individual nodes only. Information can be localized within regions or at a specific node by collaborative actions using only entanglement within a region, or sometimes even without entanglement. We discuss several examples based on error correction stabilizer codes, Dicke states and correlation space encodings. We show how to design fully functional networks using encoded states or correlation space resources.
The Applicability of Cycle GANs for Pupil and Eyelid Segmentation, Data Generation and Image Refinement

Eye tracking is increasingly influencing scientific areas such as psychology, cognitive science, and human-computer interaction. Many eye trackers output the gaze location and the pupil center. However, other valuable information can also be extracted from the eyelids, such as the fatigue of a person. We evaluated Generative Adversarial Networks (GAN) for eyelid and pupil area segmentation, data generation, and image refinement. While the segmentation GAN performs the desired task, the others serve as supportive Networks. The trained data generation GAN does not require simulated data to increase the dataset, it simply uses existing data and creates subsets. The purpose of the refinement GAN, in contrast, is to simplify manual annotation by removing noise and occlusion in an image without changing the eye structure and pupil position. In addition 100,000 pupil and eyelid segmentations are made publicly available for images from the labeled pupils in the wild data set (DOWNLOAD). These will support further research in this area.