Reviewerly/exHarmony huggingface dataset:
TOSCA Lightning: An Integrated Toolchain for Transforming TOSCA Light into Production-Ready Deployment Technologies

The OASIS standard TOSCA provides a portable means for specifying multi-service applications and automating their deployment. Despite TOSCA is widely used in research, it is currently not supported by the production-ready deployment technologies daily used by practitioners, hence resulting in a gap between the state-of-the-art in research and the state-of-practice in industry. To help bridging this gap, we identified TOSCA Light, a subset of TOSCA enabling the transformation of compliant deployment models to the vast majority of deployment technology-specific models used by practitioners nowadays. In this paper, we demonstrate TOSCA Lightning by two contributions. We (i) present an integrated toolchain for specifying multi-service applications with TOSCA Light and transforming them into different production-ready deployment technologies. Additionally, we (ii) demonstrate the toolchain’s effectiveness based on a third-party application and Kubernetes.
Exploring Multiple and Coordinated Views for Multilayered Geospatial Data in Virtual Reality

Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, as compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make use of these advantages to implement a novel multiple and coordinated view (MCV) system in the form of a vertical stack, showing tilted layers of geospatial data. In a formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data, we compared it against more conventional systems similarly implemented in VR: a simpler grid of layers, and one map that allows for switching between layers. Performance and oculometric analyses showed a slight advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Subgrouping the participants based on their preferences, characteristics, and behavior allowed a more nuanced analysis, allowing us to establish links between e.g., saccadic information, experience with video games, and preferred system. In conclusion, we found that none of the three systems are optimal and a choice of different MCV systems should be provided in order to optimally engage users.
The Feasibility of Augmented Reality as a Support Tool for Motor Rehabilitation

The aim of this work is to facilitate the application of Augmented Reality (AR) in rehabilitative clinics to support the therapist by providing real-time AR visualizations. The feasibility of AR as a support tool in rehabilitation is explored by developing a prototype that would meet the demand of the therapists during their evaluations. In this work, a prototype which consists of two parts has been developed; one for chair training and another for walking training. Different combinations of AR technologies and concepts that would best serve the therapist’s needs in the rehabilitation and training setting are discussed. After the development phase, a qualitative user research was done through demonstrations and interviews to validate the effectiveness and feasibility. The overall feedback from the participating therapists was positive, confirming the potential of this work to be extended to everyday clinical use.
MT for subtitling: User evaluation of post-editing productivity

This paper presents a user evaluation of machine translation and post-editing for TV subtitles. Based on a process study where 12 professional subtitlers translated and post-edited subtitles, we compare effort in terms of task time and number of keystrokes. We also discuss examples of specific subtitling features like condensation, and how these features may have affected the post-editing results. In addition to overall MT quality, segmentation and timing of the subtitles are found to be important issues to be addressed in future work.
Computed tomography image retrieval via combination of two local bit plane-based dissimilarities using an adder

A new feature descriptor called local bit plane-based dissimilarities and adder pattern (LBPDAP) is proposed in this paper for content-based computed tomography (CT) image retrieval. To compute the descriptor, the bit planes of the input image are first extracted. For each pixel of an image, these bit planes are then locally encoded using an adder which combines the center-neighbor dissimilarity information and the neighbor–neighbor mutual dissimilarity information in each bit plane. The encoded bit plane values corresponding to each center pixel are finally compared with the intensity of the center pixel to compute the proposed LBPDAP. In order to limit the feature dimensions, we have considered only four most significant bit planes for LBPDAP computations as the higher bit planes contain more significant visual texture information. The proposed descriptor is low dimensional and experimental results on widely accepted NEMA and TCIA-CT image databases demonstrate better retrieval efficiency of LBPDAP over many recent local pattern-based approaches.
Enabling Team Autonomy in a Large Public Organization

Abstract This paper describes how autonomy emerged in a team in a large public organization and which factors were important in this process. The organization has back sourced software development and abandoned a stage-based software development process with many handovers between business, IT and vendors. We collected data in four semi-structured interviews and analyzed information on changes in the structure and responsibilities of the team. The team has refined its portfolio for better cohesion, stepwise taken over the responsibility for software development from the vendor and in parallel recruited software developers, UX designers and testers. Product owners have joined the team as well. Supported by changes to the financing model, the team has transformed from mediating between business and vendors to a cross-functional product team with autonomy over its budget, backlog and software development process. As a result, the team can better balance between delivering new features and quality improvements, continuously deliver software with less overhead and focus on its mission to deliver user-friendly services with increased involvement of domain experts. Defining a clear product boundary and reducing dependencies on other teams, developing necessary skills and changing the financing model are recognized as the main success factors, as well as the main challenges in the transition process.
Inference Acceleration Model of Branched Neural Network Based on Distributed Deployment in Fog Computing

Research based on deep neural networks (DNN) is becoming more common. In order to solve the problem that DNN needs to consume a lot of performance during the use prediction process and generate unacceptable delays for users, a distributed neural network deployment model based on fog computing is proposed. The distributed deployment of deep neural networks in fog computing scenarios is analyzed. A deployment algorithm based on Solution Space Tree Pruning (SSTP) is designed, and a suitable fog computing node deployment model is selected to reduce the delay of prediction tasks. An algorithm for Maximizing Accuracy based on Guaranteed Latency (MAL) is designed and implemented, and suitable fog computing nodes are selected for different tasks to exit the prediction task. Simulation experiment results show that compared with the method of deploying neural network models in the cloud, the model prediction delay of the distributed neural network model based on fog computing is reduced by an average of 44.79%. Reduced the average computing acceleration framework of similar algorithms by 28.75%.
Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning

Cross-domain recommendation is critically important to construct a practical recommender system. The challenges of building a cross-domain recommender system lie in both the data sparsity issue and lacking of sufficient semantic information. Traditional approaches focus on using the user-item rating matrix or other feedback information, but the contents associated with the objects like reviews and the relationships among the objects are largely ignored. Although some works merge the content information and the user-item rating network structure, they only focus on using the attributes of the items but ignore user generated contents such as reviews. In this paper, we propose a novel cross-domain recommender framework called ECHCDR (Embedding content and heterogeneous network for cross-domain recommendation), which contains two major steps of content embedding and heterogeneous network embedding. By considering the contents of objects and their relationships, ECHCDR can effectively alleviate the data sparsity issue. To enrich the semantic information, we construct a weighted heterogeneous network whose nodes are users and items of different domains. The weight of link is defined by an adjacency matrix and represents the similarity between users, books and movies. We also propose to use adversarial training method to learn the embeddings of users and cross-domain items in the constructed heterogeneous graph. Experimental results on two real-world datasets collected from Amazon show the effectiveness of our approach compared with state-of-art recommender algorithms.
Logistic Map-Based Image Steganography Using Edge Detection

Transferring data via an insecure network has become a challenge among researchers in this fast-growing technological world. In this paper, a double-layer security framework based on cryptography and steganography has been developed and tested. Logistic maps have been used for encrypting data before embedding it into cover images. The encrypted data has been hidden into edge areas of the cover images to ensure better imperceptivity at a given payload. The scheme has been evaluated in terms of various objective parameters like peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index (SSIM). Besides, the strength of the cryptographic algorithm based on logistic map using number of changes per rate (NPCR), unified average changed intensity (UACI), and entropy has been computed. Our framework reports the average PSNR value of 44.61 dB for payload of around 1.72 bits per pixel (bpp). In addition, the NPCR value of about 100%, UACI value of 36.72, and entropy value of 7.96 depict that our scheme is capable of providing ample security to the data to be transmitted.
DAN : Breast Cancer Classification from High-Resolution Histology Images Using Deep Attention Network

Millions of women succumb to breast cancer every year. Till date, it is mainly diagnosed by core needle biopsy of the breast tissue, followed by analysis of the histopathological image to detect the presence of malignant tumor. In the past few years, deep learning pipelines have been proposed for carcinoma type classification from the breast histology images. They mostly entail in dividing the high-resolution images into patches, followed by classifying the patches using convolutional neural network and finally integrating the patch-wise results for predicting the class of the image. But these methods give the same importance to all the patches and do not focus on the most salient regions of the image. In this paper, we present a novel attention mechanism, which aids the network to specifically focus on the most relevant parts of the image, that is, the design of the network allows for learning a weighted representation of all the constituent patches of an image. Experimental results reveal that our model achieved a $$85.50\%$$ and $$96.25\%$$ for patch- and image-wise classification accuracies, respectively, on the ICIAR 2018 breast histopathological images dataset. Our proposed method outperforms some state-of-the-art methods to the best of our knowledge.
Test set verification is an essential step in model building

Abstract Recently, Christin et al. published an article that reviewed the field of deep learning and offered advice on how to train a deep learning model. We write here to emphasize the importance of model verification, which can help ensure that the model will generalize to new data. Specifically, we discuss the importance of using a test set for model verification, and of defining an explicit research hypothesis. We then present a revised workflow that will help ensure that the accuracy reported for your deep learning model is reliable.
Revealing injection vulnerabilities by leveraging existing tests

Code injection attacks, like the one used in the high-profile 2017 Equifax breach, have become increasingly common, now ranking #1 on OWASP's list of critical web application vulnerabilities. Static analyses for detecting these vulnerabilities can overwhelm developers with false positive reports. Meanwhile, most dynamic analyses rely on detecting vulnerabilities as they occur in the field, which can introduce a high performance overhead in production code. This paper describes a new approach for detecting injection vulnerabilities in applications by harnessing the combined power of human developers' test suites and automated dynamic analysis. Our new approach, Rivulet, monitors the execution of developer-written functional tests in order to detect information flows that may be vulnerable to attack. Then, Rivulet uses a white-box test generation technique to repurpose those functional tests to check if any vulnerable flow could be exploited. When applied to the version of Apache Struts exploited in the 2017 Equifax attack, Rivulet quickly identifies the vulnerability, leveraging only the tests that existed in Struts at that time. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks, finding that Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.
Optimizing API Documentation

The growing importance of APIs creates a need to support developers with effective documentation. Prior research has generated important findings regarding information needs of developers and expectations they form towards API documentation. Several guidelines have been proposed on the basis of these findings, but evidence is lacking whether such guidelines actually lead to better documentation. This paper contributes the results of an empirical test that compared the performance of two groups of developers working on a set of pre-defined tasks with an API they were unfamiliar with. One group had access to documentation which was optimized following guidelines for API documentation design proposed in the literature whereas the other group used non-optimized documentation. Results show that developers working with optimized documentation made fewer errors on the test tasks and were faster in planning and executing the tasks. We conclude that the guidelines used in our study do have the intended effect and effectively support initial interactions with an API.
Precise/not precise (PNP): A Brunswikian model that uses judgment error distributions to identify cognitive processes

Abstract In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik’s ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik’s claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik’s distinction between perceptual and conceptual tasks. In Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed “quasi-rational” nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.
A Reputation Based Hybrid Consensus for E-Commerce Blockchain

Blockchain can achieve non-tampering, non-repudiation, consistency and integrity that other data management technologies do not have. Especially in peer-to-peer networks, the decentralized nature of blockchain has drawn tremendous attention from academic and industrial communities. Recently, the field of e-commerce has also begun to realize its important role. Although blockchain technology has many advantages in achieving trust establishment and data sharing among distributed nodes, in order to make it better to be applied in e-commerce, it is necessary to improve the security of transactions and the efficiency of consensus mechanisms. In this paper, we present a reputation based hybrid consensus to solve the problem of transaction security and efficiency. Our scheme integrates the reputation mechanism into transactions and consensus, and any improper behavior of nodes will be reflected in the reputation system and fed back to a new round of transactions and consensus. We implement distributed reputation management and enable users to append new reputation evaluations to the transaction that has previously evaluated. Meanwhile, we demonstrated that the scheme can defend against existing attacks such as selfish mining attacks, double spending attacks and flash attacks. We implement a prototype and the result shows that our scheme is promising.
A Framework for the Analysis of Deep Neural Networks in Aerospace applications using Bayesian Statistics

Deep Neural Networks (DNNs) have gained tremendous popularity in many application areas over the recent years. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for system safety.In this paper, we present a framework to support testing of DNNs. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNNs used in Aerospace systems and also supports a comparison between different DNNs. In this paper, we will describe our statistical framework, the algorithms for model construction and the metric guiding the test case generation process.We will present a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a Boeing 747-100 aircraft.
Lightweight Cryptographic Instruction Set Extension on Xtensa Processor

The emerging popularity of the Internet of Everything makes the security an urgent issue, as well as the need for speed to cipher and decipher any information, which is essential for the embedded devices.Unlike many works in this field, where propositions considering application specific integrated circuits (ASICs), coprocessors, field-programmable gate arrays (FPGAs) or software were presented as alternatives to raise the efficiency of execution, we addressed the enhancement of the instruction set architecture (ISA) taking advantage of a hybrid design methodology to boost the performance as well as the design itself.We validate our ISA by measuring the area overhead, memory parameters and the speedup for different optimized implementations of AES, DES, 3DES and SHA using the Cadence R LX7 Processor and Xtensa R platform.The proposed architectures provided an excellent tradeoff with the area, memory and cycle count performance figures.Experimental results show that the proposed ISA can reduce cycle count between 1.76 and 10.99 with a cost of 6% in average of area overhead in a lightweight processor architecture.
A Two-Layer Image-Steganography System for Covert Communication Over Enterprise Network

Computers find application in virtually all aspect of human existence, especially in today’s modern world. Although this comes with a lot of benefits, it also comes with a number of undesirable consequences. Chief among these consequences is the rising concerns for security of information and even loss of privacy. Steganography, an Information hiding technique could be a fitting solution to these security issues. This paper presents an improved Steganography technique for ensuring the security of the widely spread and used internet materials or information that are shared globally with risk of vulnerability of attack. In this current research work, Elliptic Curve Cryptography (ECC) and Discrete Cosine Transform (DCT) Steganography were combined together to achieve effective security in the proposed system. A given plain text or secret message is first converted to a cipher text using ECC. The resultant encrypted text is then hidden under a cover-image using DCT-based image steganography technique to produce a final output called stego-image. To the Human Visual System (HVS), the stego-image remains the same as the original cover image. This ensures that the hidden secret message gets delivered to the target recipient without any suspicion. Standard metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), as well as average computation time, were used to evaluate the performance of the proposed steganography system. The results indicate better performance compared to the conventional steganography systems. Thus the proposed system is a 2-level steganography system effective for storing sensitive data and even for securely transferring information over an open/enterprise network.
Arbitrary Style Transfer Using Graph Instance Normalization

Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization (AdaIN) whitens the source images and applies the style of target images through normalizing the mean and variance of features. However, computing feature statistics for each instance would neglect the inherent relationship between features, so it is hard to learn global styles while fitting to the individual training dataset. In this paper, we present a novel learnable normalization technique for style transfer using graph convolutional networks, termed Graph Instance Normalization (GrIN). This algorithm makes the style transfer approach more robust by taking into account similar information shared between instances. Besides, this simple module is also applicable to other tasks like image-to-image translation or domain adaptation.