Reviewerly/exHarmony huggingface dataset:
Implementation and Validation of the Optimized Deduplication Strategy in Federated Cloud Environment

Cloud computing technology is the culmination of technical advancements in computer networks, hardware and software capabilities that collectively gave rise to computing as a utility. It offers a plethora of utilities to its clients worldwide in a very cost-effective way and this feature is enticing users/companies to migrate their infrastructure to cloud platform. Swayed by its gigantic capacity and easy access clients are uploading replicated data on cloud resulting in an unnecessary crunch of storage in datacenters. Many data compression techniques came to rescue but none could serve the purpose for the capacity as large as a cloud, hence, researches were made to de-duplicate the data and harvest the space from exiting storage capacity which was going in vain due to duplicacy of data. For providing better cloud services through scalable provisioning of resources, interoperability has brought many Cloud Service Providers (CSPs) under one umbrella and termed it as Cloud Federation. Many policies have been devised for private and public cloud deployment models for searching/eradicating replicated copies using hashing techniques. Whereas the exploration for duplicate copies is not restricted to any one type of CSP but to a set of public or private CSPs contributing to the federation. It was found that even in advanced deduplication techniques for federated clouds, due to the different nature of CSPs, a single file is stored at private as well as public group in the same cloud federation which can be handled if an optimized deduplication strategy be rendered for addressing this issue. Therefore, this study has been aimed to further optimize a deduplication strategy for federated cloud environment and suggested a central management agent for the federation. It was perceived that work relevant to this is not existing, hence, in this paper, the concept of federation agent has been implemented and deduplication technique following file level has been used for the accomplishment of this approach.
Segmentation and classification of renal tumors based on convolutional neural network

Kidney tumors are the second most frequent urology tumors. They are of many types, mostly existing as malignant tumors. In order to improve the accuracy of segmentation and classification of kidney tumors, this paper proposes to build a model of simultaneous segmentation and classification of kidney tumors based on convolutional neural networks to assist medical experts in diagnosis. A two-task neural network 2D SCNet is proposed by combining kidney tumor segmentation and classification. Based on our proposed framework, classification can feed back the global contextual information of the network, and segmentation can make the network focus on local features and regions of interest (ROI). Both tasks jointly promote network feature learning and both increase each other’s prior information. The combination of segmentation and classification of 2D SCNet can achieve an accuracy rate of 99.5% in both benign and malignant classification. The results of the ‘2D SCNet + three-label’ segmentation reached Dice coefficients of 0.946 and 0.846, respectively. Compared with PSPNet, our network kidney and tumor segmentation results are improved by 4.9% and 5.0%, respectively, which shows that the addition of classification module is beneficial to the learning of segmentation network. From the cross-validation results, we can see that 2D SCNet and the two-step segmentation strategy can obtain better results in the segmentation and classification tasks of kidney tumors. The base network of 2D SCNet can extract networks for any feature. This paper compares Res Net50+ PPM and Dense Net as the results of segmentation and classification of the base network. Res Net50+ PPM obtains better results. 2D SCNet can help to segment and examine kidney tumors more efficiently and accurately.
A dataset for quantum circuit mapping

Quantum computing is rapidly establishing itself as a new computing paradigm capable of obtaining advantages over its classical counterpart. However, a major limitation in the design of a quantum algorithm is related to the proper mapping of the corresponding circuit to a specific quantum processor so that the underlying physical constraints are satisfied. Moreover, current deterministic mapping algorithms suffer from high run times as the number of qubits to map increases. To bridge the gap in view of the next generation of quantum computers composed of thousands of qubits, this data paper proposes the first datasets that help address the quantum circuit mapping problem as a classification task. Each dataset is composed of random quantum circuits mapped onto a specific IBM quantum processor. In detail, each dataset instance contains some features related to the calibration data of the physical device and others related to the generated quantum circuit. Finally, the instance is labeled with a vector encoding the best mapping among those provided by deterministic mapping algorithms. Considering this, the proposed datasets allow the development of machine learning models capable of achieving mapping similar to those achieved with deterministic algorithms, but in a significantly shorter time.
Matching in decentralized two-sided markets via Blockchain-based deferred acceptance

Ever since the technology was first introduced, Blockchains have been considered as a potential enabler for decentralized markets. Starting with the initial usage of cryptocurrencies on conventional platforms, to the development of fully decentralized, smart-contract-based platforms and highly specific applications running on consortium chains, this potential has already been realized. However, competitive, two-sided peer-to-peer matching markets in which financial aspects play a secondary role have been researched less. In this paper, we propose the integration of the classic deferred acceptance algorithm with Blockchain technology in order to leverage the benefits of its clearinghouse architecture for sets of peers without requiring an intermediary. We furthermore present a prototypical implementation including a sealing mechanism and discuss its functionality and scalability.
Comparing the performance between Virtual Machines and Containers using deep learning credit models

The cloud computing paradigm utilizes neutralization to isolate data, workloads and network bandwidth in an elastic mode. The providers hence have historically used Virtual Machines (VM) to perform this workload isolation. However these virtual machines require huge resources and are costly to set-up and deploy. Containerization has of late been used as a different approach for neutralization albeit as a Software as a service approach. Docker containerization has been one of the most popular approaches and its a solution where containers all share the same resources and operating system as opposed to virtual machines which use hyper-visor technology to abstract hardware and operating systems. The study uses different cloud vendors to compare docker containers for response time, download time, CPU processing time as well as memory usage against virtual machines. The study did a performance comparison for Virtual Machines and Docker Containers in various cloud providers namely AWS, Google Cloud as well as Microsoft Azure cloud platforms. The dataset used included deep learning big data downloaded from Kaggle website and classified for loan defaulting with Keras and tensorflow python implementation frameworks. The comparisons do prove that docker containers are faster than KVMs and Xen virtual machines. The study also proves that by using Kubernetes framework for scaling the containers, the performance of the docker containers improves compared to the docker and bare metal as well as cloud frameworks.
Analysis on the Use of Declarative and Pull-based Deployment Models on GitOps Using Argo CD

Currently, the problems faced by software developers in implementing the DevOps method include security issues of a person&#x0027;s ability to directly access and change clusters and the ineffective rollback process in the application deployment process to an application platform. The GitOps method was applied in this study to investigate and test how GitOps was able to solve these problems. This research used pull-based deployment and declarative deployment approaches rather than the push-based deployment models commonly used in today&#x0027;s CI/CD pipelines. The Argo CD tool serves as an operator, and Kubernetes serves as a platform for deploying container-based applications. This study is expected to be a recommendation for companies or institutions as well as individuals who want to start adopting DevOps or who want to take DevOps implementation to the next level using the GitOps method
Face Mask Detection on Facial Images Using Convolutional Neural Network

During this COVID-19 pandemic our social activity are severely restricted, caused of COVID-19 virus which spread rapidly and can perch on any surface including our skin, it will be very dangerous if the virus is penetrating to our body. The virus can enter through the nose, eyes, or mouth. To prevent the spread of this virus, the local government has urged the public to always adhere to health protocols including social distancing and the use of masks in public places. There have been many systems built to assist the community in dealing with this pandemic, Likewise with this study, in this study a system has been be built to detect the use of masks on facial images. A dataset of 11,740 face images is collected and learned to the Convolutional Neural Network. With the help of CNN the system will be assisted in the process of object classification and detection. And using the specific model in this system, we using VGG-16. For the preprocessing phase we use the Augmentation Method to increase the result with the hope that it will get good results and can help in this pandemic. Turns out It gives the highest training accuracy of 100% and the highest validation accuracy is 99% and a good result for the test accuracy.
Generalizing Statistical Ineffective Fault Attacks in the Spirit of Side-Channel Attacks

At CHES 2018, Statistical Ineffective Fault Attacks were introduced to apply Differential Fault Analysis techniques on AES implementations protected against faults by detective or infective countermeasures. Soon after, other works have adapted SIFA to a couple of authenticated encryptions and a lightweight cipher. In this paper, we introduce the idea that SIFA is actually closer to Side-Channel Attacks than it is to DFA. We show how SIFA can actually target all selection functions known to be sensitive to SCA for any kind of algorithm. In particular, we apply for the first time SIFA in the context of asymmetric cryptography, reviving the threat of fault attacks on RSA even when faulty ciphertexts are not released to the adversary. Besides, with the results obtained by SIFA against proven side-channel countermeasures, this work opens new questions regarding established masking schemes.
Detecting Source Contextual Barriers for Understanding Neural Machine Translation

In machine translation evaluation, the traditional wisdom measures model's generalization ability in an average sense, for example by using corpus BLEU. However, the statistics of corpus BLEU cannot provide comprehensive understanding and fine-grained analysis on model's generalization ability. As a remedy, this paper attempts to understand NMT at fine-grained level, by detecting contextual barriers within an unseen input sentence that \textit{cause} the degradation in model's translation quality. It proposes a principled definition of source contextual barriers as well as its modified version which is tractable in computation and operates at word-level. Based on the modified one, three simple methods are proposed for barrier detection by search-aware risk estimation through counterfactual generation. Extensive analyses are conducted on those detected contextual barrier words on both Zh <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\Leftrightarrow$</tex></formula> En NIST benchmarks. Potential usages motivated from barrier words are also discussed.
The Flexible Interledger Bridge Design

In recent years, multiple distributed ledger technologies with different strengths and weaknesses have been introduced. This makes them better suited for different use cases, but it also makes interactions between the ledgers more difficult. Interledger technologies try to bridge this gap, but often suffer from obvious limitations: a large portion of the solutions addresses only value exchanges or transfers, others have restrictive assumptions about the ledgers, and some require separate relaying blockchains. This paper introduces the Flexible Interledger Bridge (FIB) design that supports a wide range of use cases and ledger types without requiring any changes to the ledgers themselves. An evaluation of the reference implementation shows it to be a lightweight solution that enables atomic data transfers between two ledgers.
ST-Tracking: Spatial-temporal Graph Convolution Neural Network for Multi-object Tracking

Multi-Object Tracking (MOT) which aims to estimate trajectories for objects of interest in videos, is a crucial component in intelligent transportation systems and has gained considerable attention from both academia and industry. However, existing MOT approaches apply simple motion and appearance features, ignoring the spatial-temporal correlations of interactions with other objects. To fill this gap, this paper proposes a novel MOT framework ST-Tracking to extract spatial-temporal features for tracking objects, which is trained in an end-to-end manner. Specifically, a directed graph is first constructed to represent the spatial-temporal correlations, where the node attributes include both geometric and appearance features, and the edges are deliberately defined to embrace location and topology cues. Then, a spatial-temporal graph convolutional module is proposed to aggregate information from both spatial and temporal dimensions, thus extracting discriminative features. Extensive experiments on the challenging KITTI benchmark demonstrate the effectiveness and superior of ST-Tracking as compared with several state-of-the-art methods.
Collective Narrative: Scaffolding Community Storytelling through Context-Awareness

Sharing mutual experiences on the same topic is an effective way to increase members' sense of belonging to the community, but existing approaches are insufficient in building such a narrative when a community tries to connect remotely, due to barriers in sharing and organizing content. In this paper, we present Collective Narrative (CN), a context-aware system that facilitates users in identifying share-worthy moments through in-context prompting, and automates the organization process through informed section assignment to a narrative structure. A preliminary study testing the CN iPhone app demonstrates that CN can potentially scaffold in-the-moment sharings and form narratives that are comprehensive yet diverse, thereby presenting contributions in a way that users could easily identify mutual experiences and feelings by reading the narrative.
Synchronous Colored Petri Net Based Modeling and Video Analysis of Conversational Head-Gestures for Training Social Robots

Social robotics is the future of help for elderly care, health care, child education, and space exploration. Social robots will share a common space with humans and exhibit human-like interaction capabilities such as facial-expression analysis, speech understanding, gesture analysis and generation. Currently, gesture generation and understanding are limited to the domain of cognitive psychology because of the lack of computationally meaningful classification of gestures, limited work on integrated gesture generation, and limited integration of gesture analysis, speech understanding and gesture generation. There is limited work on training humanoids to learn and adapt new gestures by observing, analyzing, mimicking and learning. This paper focuses on training humanoids to learn human head-gestures by observation. We propose a new synchronous colored Petri net model and video analysis of head-gestures to identify combinations of synchronized head-motions, combining a sequence of head-motions to make Petri nets for each gesture, similarity-based graph matching to recognize the gestures, and generate archived gestures using a declarative model of gesture generation for human verification. This paper describes the proposed model of head-gestures, implementation methodology and video analysis algorithms to identify synchronized head-motions corresponding to nonemotional conversational head-gestures. The methodology is based upon analyzing time-stamped sequences of motion, stillness and silence on Facial feature-points based video analysis. The experimental result shows that the methodology provides a high-level of accuracy and robustness in detecting places, transitions and synchronization needed for constructing the proposed synchronous colored Petri net based model for recognizing conversational head-gestures.
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation

In order to handle the challenges of autonomous driving, deep learning has proven to be crucial in tackling increasingly complex tasks, such as 3D detection or instance segmentation. State-of-the-art approaches for image-based detection tasks tackle this complexity by operating in a cascaded fashion: they first extract a 2D bounding box based on which additional attributes, e.g. instance masks, are inferred. While these methods perform well, a key challenge remains the lack of accurate and cheap annotations for the growing variety of tasks. Synthetic data presents a promising solution but, despite the effort in domain adaptation research, the gap between synthetic and real data remains an open problem. In this work, we propose a weakly supervised domain adaptation setting which exploits the structure of cascaded detection tasks. In particular, we learn to infer the attributes solely from the source domain while leveraging 2D bounding boxes as weak labels in both domains to explain the domain shift. We further encourage domain-invariant features through class-wise feature alignment using ground-truth class information, which is not available in the unsupervised setting. As our experiments demonstrate, the approach is competitive with fully supervised settings while outperforming unsupervised adaptation approaches by a large margin.
An Overview of Runtime Data Protection Enforcement Approaches

A regulatory framework such as the GDPR succeeds in (i) providing clarity about the nature and the reach of fundamental rights to data privacy and the sovereign role of the data subject, (ii) raising broader awareness of the substantial impact of large-scale, contemporary software-intensive data processing operations on these rights and freedoms, and (iii) creating urgency and imposing gravity, by forcing organizations to take these rights and fundamental principles seriously in a proactive manner. However, regulatory frameworks lack clarity on how these concerns are to be enacted. For example, guidance is lacking on how software should be constructed to consider these data protection principles by design and by default. In this paper, we argue how the direct translation of the GDPR data protection principles into design or code falls short in the context of contemporary software systems, which are both more dynamic and nature and rely on an increasing number of complex inter-organizational collaborations. This means that in such a system, data protection decisions cannot be ‘hard-coded’ but will have to be decided at run time. In addition, we provide an overview of promising existing approaches that contribute to the accomplishment of these fundamental data protection principles at run time.
Automated Selection of High-Quality Synthetic Images for Data-Driven Machine Learning: A Study on Traffic Signs

The utilization of automatically generated image training data is a feasible way to enhance existing datasets, e.g., by strengthening underrepresented classes or by adding new lighting or weather conditions for more variety. Synthetic images can also be used to introduce entirely new classes to a given dataset. In order to maximize the positive effects of generated image data on classifier training and reduce the possible downsides of potentially problematic image samples, an automatic quality assessment of each generated image seems sensible for overall quality enhancement of the training set and, thus, of the resulting classifier. In this paper we extend our previous work on synthetic traffic sign images by assessing the quality of a fully generated dataset consisting of 215,000 traffic sign images using four different measures. According to each sample's quality, we successively reduce the size of our training set and evaluate the performance with SVM and CNN classifiers to verify the approach. The comparability of real-world and synthetic training data is investigated by contrasting several classifiers trained on generated data to our baseline w.r.t. actual misclassifications during testing.
Squeezer - A Mixed-Initiative Tool for Designing Juice Effects

This paper presents a Mixed-Initiative version of Squeezer, a tool for designing juice effects in the Unity game engine. Drawing upon sound synthesizers and game description languages, Squeezer can synthesize common types of juice effects by combining simple building blocks into sequences. Additionally, Squeezer offers effect generation based on predefined recipes as well as an interface for interactively evolving effect sequences. We conducted a user study with five experts to verify the functionality and interest among game designers. By applying generative and evolutionary strategies to juice effect design, Squeezer allows game designers and researchers using games in their work to explore adding juice effects to their games and frameworks.
Adopting Trusted Types in ProductionWeb Frameworks to Prevent DOM-Based Cross-Site Scripting: A Case Study

Cross-site scripting (XSS) is a common security vulnerability found in web applications. DOM-based XSS, one of the variants, is becoming particularly more prevalent with the boom of single-page applications where most of the UI changes are achieved by modifying the DOM through in-browser scripting. It is very easy for developers to introduce XSS vulnerabilities into web applications since there are many ways for user-controlled, unsanitized input to flow into a Web API and get interpreted as HTML markup and JavaScript code. An emerging Web API proposal called Trusted Types aims to prevent DOM XSS by making Web APIs secure by default. Different from other XSS mitigations that mostly focus on post-development protection, Trusted Types direct developers to write XSS-free code in the first place. A common concern when adopting a new security mechanism is how much effort is required to refactor existing code bases. In this paper, we report a case study on adopting Trusted Types in a well-established web framework. Our experience can help the web community better understand the benefits of making web applications compatible with Trusted Types, while also getting to know the related challenges and resolutions. We focused our work on Angular, which is one of the most popular web development frameworks available on the market.