Reviewerly/exHarmony huggingface dataset:
Medical Image Enhancement For Lesion Detection Based On Class-Aware Attention And Deep Colorization

The lesion detection on medical images has been a challenging problem in computer vision due to the lack of high-quality annotations at the bounding box level and the difficulty to identify the lesion on gray-scale medical images which are commonly used in radiology tests like X-ray, CT and MRI. In this paper, we propose a novel framework of medical image enhancement based on the class-aware attention weight extraction and the deep colorization of medical images with generative adversarial networks motivated by human visual characteristics and cell staining. The evaluation conducted on the real public medical image datasets proves that, the performance of lesion detection based on the existing detectors is improved after enhancing the medical images with the proposed enhancement framework.
Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.
Design and food robots: Changing processes in the restaurant industry

The robotics industry is evolving and many restaurants and cafes are employing robots for preparing food and serving their customers. Due to this, new job profiles are emerging and the way humans and robots interact is changing. With the recent pandemic events, robots present an opportunity for both financial benefits and expansion. This chapter provides some cases of restaurants and cafes that employ robots within their organizations. Starting with stories of a few entrepreneurs who decided to invest in robotics, we outline how some companies have built a competitive advantage by investing in visionary projects. The chapter also discusses the sustainability of a technology-based competitive advantage and the effects on labor and workforce.
Inferring Power System Dynamics From Synchrophasor Data Using Gaussian Processes

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances.
Stacking Complexities: Reframing Uncertainty through Hybrid Literacies

In a context increasingly defined by post-normal science (Funtowicz and Ravetz 1993 Funtowicz, Silvio O. and Jerome R. Ravetz. 1993. “Science for the Post-Normal Age.” Futures 25: 735–755. doi:10.1016/0016-3287(93)90022-L.[Crossref], [Web of Science ®] , [Google Scholar]), it is acknowledged that complex world problems cannot be addressed by one discipline in isolation. To face increasingly uncertain futures, it is therefore crucial to develop approaches that work with uncertainty. Because of its future-facing nature and current drive to tackle complex world challenges, design has a leading role to play in this endeavor. The article proposes a research framework informed by the development of hybrid literacies – transversal toolkits across design, technologies, and futures studies – that can furnish learners with transdisciplinary skills. These are deemed necessary to address uncertainty and complexity by deploying speculative-pragmatic, imaginative practices that foster modes of working, learning, and unlearning together. To illustrate this approach, the article draws on the ongoing collaboration between two academic institutions renowned, respectively, in the field of Art and Design (Central Saint Martins, University of the Arts London) and Science and Technology (Tokyo Institute of Technology) to offer a personal reflection and insights around transdisciplinarity and hybrid literacies in action.
Schrödinger's Code

Undefined behavior ranks among the most baffling and perilous aspects of popular programming languages. This installment of Drill Bits clears up widespread misconceptions and presents practical techniques to banish undefined behavior from your own code and pinpoint meaningless operations in any software—techniques that reveal alarming faults in software supporting business-critical applications at Fortune 500 companies.
55 Rides: attention annotated head and gaze data during naturalistic driving

Trained eye patterns are essential for safe driving. Whether for exploration of the surrounding traffic or to make sure that a lane is clear through a shoulder check - quick and effective perception is the key to driving safety. Surprisingly though, free and open access data on gaze behavior during driving are yet extremely sparse. The environment inside a vehicle is challenging for eye-tracking technology due to rapidly changing illumination conditions, such as exiting a tunnel to brightest sunlight, proper calibration and safety. So far, available data exhibits environments that likely influence the viewing behavior, sometimes dramatically (e.g., driving simulators without mirrors, limited field of view).
RBAC protection-impacting changes identification: A case study of the security evolution of two PHP applications

Web applications often use Role-Based Access Control (RBAC) to restrict operations and protect security sensitive information and resources. Web applications' RBAC security may be affected by source code changes between releases. Developers should re-validate their application prior to release, but this may be labor and resource-intensive. Among all changes between two versions during software evolution, we define Protection-Impacting Changes (PICs) as changed statements that potentially alter privilege protection of other statement(s). PICs may focus the attention of developers towards root cause candidates for security protection changes, especially when these protection changes are unexpected. The proposed automated static analysis identifies PICs between two versions of an application. It is based on the interprocedural flow graph reachability analysis of security checks and statements. We examined the software evolution of two PHP web applications. We examined 210 versions of WordPress, and 192 versions of MediaWiki. Additional experiments have been performed on 19 fix commits corresponding to Common Vulnerabilities and Exposures CVEs from WordPress. They are presented and discussed in this paper and show that PICs contain 98.2% of the CVE oracle root causes. PICs represent overall only 8% and 2% of total code changes, respectively for WordPress and MediaWiki. PICs may help developers to focus onto a smaller number of candidate security-related problems, during software evolution. Consequently, developers may re-validate application security and perform repairs more efficiently.
An Accountability of Various Clustering Techniques for Improvement of Page Searching Process and Analysis.

In internet era we have coming across tons of data and information as the usage of internet has been growing at a great speed. These data had been stored somewhere either with our concern or by policies of the providers of the website, internet etc. along with this these are used for good work like suggesting the pages, surfing made easy by tracking the surfing pattern, giving customer personalization facility for product buying and alternative product suggestions. It is also helping us to understand what one need today without being told to anyone. Web takes care of the same. These things are created analyzing interaction of users with the web structure in any form through social medias, shopping sites, blogs, e learning etc. In this paper we discuss in details about the web log mining process along with the mining methods and then clustering the data based on the requirement to study the logs etc. web log has also been growing tremendously due to increase data on WWW. But we need to keep in mind that these logs not only contain useful data but some incomplete data, noisy data, duplicate and ambiguous data. So we here focus on techniques to filter these data, convert it into required format, select specific fields needed and then classify them to understand the pattern which may be of our interest. Here the main focus is on discussing various techniques used by other authors in past and try to compare them.
Text-Guided Human Image Manipulation via Image-Text Shared Space

Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To overcome inaccuracy, we use structured information (e.g., poses) to help identify correct location to manipulate, by disentangling the control of appearance and spatial structure. Moreover, we learn the image-text shared space with derived disentanglement to improve accuracy and quality of manipulation, by separating relevant and irrelevant editing directions for the textual instructions in this space. Our model generates a series of manipulation results by moving source images in this space with different degrees of editing strength. Thus, to reduce the ambiguity in text, our model generates sequential output for manual selection. In addition, we propose an efficient pseudo-label loss to enhance editing performance when the text is incomplete. We evaluate our method on various datasets and show its precision and interactiveness to manipulate human images.
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article

In this paper, we proposed a multi-document summarization system using semantic role labeling (SRL) and semantic graph for Indonesian news articles. In order to improve existing summarizer, our system modified summarizer that employed subject, predicate, object, and adverbial (SVOA) extraction for predicate argument structure (PAS) extraction. SVOA extraction is replaced with SRL model for Indonesian. We also replace the genetic algorithm to identify important PAS with the decision tree classifier since the summarizer without genetic algorithm gave better performance. The decision tree model is employed to identify important PAS. The decision tree model with 10 features achieved better performance than decision tree with 4 sentence features. Experiments and evaluations are conducted to generate 100 words summary and 200 words summary. The evaluation of the proposed model shows 0.313 average ROUGE-2 recall in 100 words summary and 0.394 average ROUGE-2 recall in 200 words summary.
Uncertainty-Based Adaptive Data Augmentation For Ultrasound Imaging Anatomical Variations

Data augmentation remains to be a simple and inexpensive method for generalizing across unseen domains. Current data augmentation methods for ultrasound imaging involve simple image transformations - rotations, flips, skews, and blurs - but are not able to adapt to the current state of the deep learning model. We present the first online adaptive data augmentation method that is able to generate synthetic training data on-the-fly, enabling the model to adapt to countless spatial deformations. Our proposed method leverages prior work on uncertainty quantification to understand the model's weaknesses at any given stage. Our method is also able to then ”spot-augment” subsets of regions within the ultrasound image, all in a real-time manner. We also show that our proposed method is able to perform significantly better in out-of-true-training distributions, when compared against models trained on the same dataset.
Toward Both Privacy and Efficiency of Homomorphic MACs for Polynomial Functions and Its Applications

Abstract Homomorphic message authentication codes (MACs) allow a user to outsource data to an untrusted server and verify the correctness of returned computation results over the outsourced data. Many cloud applications need delegation computations over outsourced data with dual capabilities. On one hand, they need to keep the outsourced data secret such that the server cannot trace and infer any sensitive information from the computation results. On the other hand, the user should be able to efficiently verify the computation results. Unfortunately, the state-of-the-art homomorphic MAC schemes are not so desirable due to either poor privacy or low verification efficiency. In this paper, we first put forward a new cryptographic primitive called privacy-preserving homomorphic MACs (PHMAC) that simultaneously provides data privacy and efficient verification. Then, we present a PHMAC construction capable for the evaluation of polynomials of fixed degree $d\geq 1$, in which the tag does not reveal any information of underlying authenticated data while being verifiable in constant time (in an amortized sense). As an application, we give a generic construction of homomorphic authenticated encryption (HAE) from proposed PHMAC and homomorphic encryption. Benefited from the functionalities of underlying PHMAC scheme, the derived HAE enjoys stronger authenticity and supports larger classes of functions than that of Lai et al. (Verifiable Computation on Outsourced Encrypted Data. In Computer Security—ESORICS 2014—19th European Symposium on Research in Computer Security, Wroclaw, Poland, September 7–11, Part I, pp. 273–291. Springer, Berlin). Such HAE enables verifiable delegation computations over growing outsourced encrypted data in an efficient way.
Vision-based bicycle and motorcycle detection using a YOLO-based Network

Abstract This paper describes a system that can distinguish between visually-similar objects, specifically bicycles and motorcycles, successfully from the vantage point of traffic surveillance cameras. The You Only Look Once (YOLO) is used as the main framework in this research due to its speed performance among various machine learning models and methods evaluated. We built a dataset consisting of motorcycle and bicycle images from different CCTV footage for this project. CCTV footage may vary on the angle of view from the object, image resolution, and ambient environment settings. Using this dataset, we trained YOLOv3-based models, and their performances were compared to the vanilla version of YOLOv3 and other pre-trained models. Four (4) models were trained and compared; the best-performing model is shown to be associated with a dataset with properly labeled data (i.e., marking every instance of the object of interest) and having the most number of instances in the training and testing set.Introduction.
Salient Object Detection Via Attention-Aware Cascaded Bottom-up Feature Aggregation

Fully convolutional neural network-based salient object detection has recently achieved great success with its performance benefits from the effective use of multi-layer features. Based on this, most of the existing saliency detectors design complex network structures to fuse the multi-level features of the backbone feature network. However, information in different layer play different roles in saliency object detection, how to integrate them is still an open problem. In this paper, a cascaded bottom-up feature aggregation module is designed to retain and strengthen more spatial details in the low-level features, and embed attention mechanism in the process of feature aggregation to filter more effective features. Extensive experiments show that the proposed networks can consistently improve saliency detection performance. The experimental results on five public datasets prove that this network is competitive in saliency detection.
Experiential-Walk: Experiencing and Representing the City for Urban Design Purposes

This contribution presents an experimental methodology for sensing and communicating the city for urban design purposes. More in detail, the goal of the experiential-walk method developed by the author is mainly directed at improving the designer's attitude and ability in considering the multisensory and dynamic environmental conditions from the very beginning of the design process thanks to a personal immersion in motion and in place. The methodology consists of three main phases: (i) direct experience and observation, (ii) self-reflection and interpretation, (iii) data collection, representation and communication. The aim of the first phase is to feel what the place is and what the place might be or what the place is in nuce. The second phase is about mulling over the experience and interpreting the nature of the place. The last phase is directed at representing and communicating the experience and the place criticalities and potentialities. The entire (recursive) process can be intended as a sequence of interconnected actions to increase sensory awareness. It eventually aims at discovering and sharing the said and the unsaid of places. Besides the methodology description, some case studies applications and results are presented together with the lessons learned.
RETRACTED: Reflections on College English Translation Teaching Mode Based on Network Technology

Abstract With the addition of Internet technology, a new teaching method has been provided for education. For the teaching of English translation courses in colleges and universities, the rapid development of the current network environment has provided great advantages. College English translation teaching has an important influence on college students’ English literacy and English application ability, but the traditional teaching mode still occupies a dominant position in the process of college English translation teaching. Under the network environment, the use of various information-based teaching methods has greatly enriched the teaching contents of English translation and optimized the teaching forms. Curriculum reform can avoid the disadvantages of traditional teaching mode in the process of students’ learning, break the shackles and shackles brought by traditional teaching mode and teaching content, and expand students’ learning vision and practice space. Based on the network environment, this paper analyzes the teaching mode of College English translation, focusing on optimizing the teaching system of College English translation course, so as to improve students’ English translation ability and level.
Transfer Learning on Inception ResNet V2 for Expiry Reminder: A Mobile Application Development

Expiry dates are general information that every product has. It represents the recommended period to use a product. However, it is hard to keep track of the expiry date when there are many products to keep. In this work, we aim to solve the problem using the transfer learning technique in deep learning. We trained a Convolutional Neural Network (CNN) - Inception ResNet V2 with a synthetic data set that contains images of near-reality expiry dates. The Inception ResNet V2 has achieved an accuracy of 0.9964 using synthetic images and an accuracy of 0.9612 using real noisy images. Training and deploying the Inception ResNet v2 into the mobile application we built help users record and track expiry dates fast and efficiently. The usability test we conducted gave a score of 85.7 based on the System Usability Scale (SUS). The score shows that users had a good experience using the mobile application.
Vision Aided URLL Communications: Proactive Service Identification and Coexistence

The support of coexisting Ultra-Reliable and Low-Latency Communication (URLLC) and enhanced Mobile Broad-Band (eMBB) services is a cornerstone challenge to wireless communication networks. Those two types of services introduce strict resource allocation requirements resulting in a power-struggle between reliability, latency, and resource utilization. The difficulty in addressing that challenge could be rooted in the predominant reactive approach to resource allocation in wireless networks, where the allocation operation is carried out based on received service requests and global network statistics. This paper proposes a novel framework termed service identification to develop proactive resource allocation algorithms. The framework is based on visual data and deep learning, and its objective is to equip future wireless networks with the ability to anticipate incoming services and perform proactive resource allocation. To demonstrate the potential of the framework, a wireless network scenario with two coexisting URLLC and eMBB services is considered, and a deep learning algorithm is designed to utilize RGB video frames and predict incoming service type and its request time. An evaluation dataset is developed and used to evaluate the performance of the algorithm. The results show that the algorithm enables a 78% utilization of idle network resources, which emphasizes the value of proaction.
Self-Sovereign Identity creation on Blockchain using Identity based Encryption

The blockchain technology evolution in recent times has a hopefulness regarding the impression of self-sovereign identity that has a significant effect on the method of interacting with each other with security over the network. The existing system is not complete and procedural. There arises a different idea of self-sovereign identity methodology. To develop to the possibility, it is necessary to guarantee a better understanding in a proper way. This paper has an in-depth analysis of the attributes of the self-sovereign identity and it affects over the laws of identity that are being explored. The Identity management system(IMS) with no centralized authority is proposed in maintaining the secrecy of records, where as traditional systems are replaced by blockchains and identities are generated cryptographically. This study enables sharing of user data on permissioned blockchain which uses identity-based encryption to maintain access control and data security.