Reviewerly/exHarmony huggingface dataset:
Interactive Visualization of Comic Character Correlation Diagrams for Understanding Character Relationships and Personalities

Comic character relationships are an essential aspect of understanding comic content. These relationships are often expressed in a character correlation diagram. However, existing diagrams with complex relationships can confuse users, and simple correlation diagrams do not show information on character personalities. Therefore, it is necessary to 1) make these diagrams more visually clear and 2) to display character personality information in addition to relationships without increasing the diagram complexity. This paper presents an interactive visualization system for character correlation diagrams that display complex character relationships and personalities. We propose a relationship labeling method based on FOAF and implement a user selection function to emphasize specified relationships visually. This paper also provides visualizations of character personalities chosen by the user. Consequently, users can understand both comic character personalities and relationships. This paper verified whether users could correctly find a specific relationship when asked.
Real-Time Class-Incremental Learning for Voice Command Recognition via Adaptive oiSGNG

Online learning approach allows an Artificial Neural Network (ANN) to solve dynamic real-world problems. In this context, the objective of this work is to implement ANN-based voice recognition models with focus on class-incremental learning in real time, for low-cost implementations, such as an electric wheelchair. In this paper, the online incremental Supervised Growing Neural Gas (oiSGNG) with a feature extractor is proposed as a voice command classifier. About this model, two contributions are presented: (i) nodes are inserted according to an exponential function, that results in a higher accuracy rate with fewer nodes, which implies less latency; (ii) adaptive oiSGNG, this model is a novel implementation that enables online learning. In offline experiments, the model proposed performs better than the Self-Organizing Map (SOM) in its topological and supervised version. After simulations and experiments, it is proposed to use a keyword to avoid false positives. In the results, the accuracy of the proposed model is better than the original oiSGNG.
The Developer’s Dilemma

Abstract This chapter pulls together the book’s chapters, each examining the developer’s dilemma or ‘Kuznetsian tension’ between structural transformation and income inequality. Developing countries seek inclusive economic growth that improves the living standards of all, especially the poor. What are the types or ‘varieties’ of structural transformation that have been experienced in developing countries? What inequality dynamics are associated with each variety of structural transformation? What policies have been utilized to manage trade-offs between structural transformation, income inequality, and inclusive growth? The different chapters discuss these questions, while employing a common analytical framework and a set of common datasets. The intellectual contribution of the book is to provide a comparative analysis of the relationship between structural transformation, income inequality, and inclusive growth; to do so empirically at regional and national levels; and to draw conclusions from the cases on the varieties of structural transformation, their inequality dynamics, and the policies have been employed to mediate the developer’s dilemma.
VSCode Migrate: Semi-Automatic Migrations for Low Coverage Projects

Modern software is subject to continuous change and so are its interfaces to other software. Introducing breaking changes to an interface requires its consumers to make adaptions to their own code base in order to compensate. Oftentimes, the number of changes requires a large effort when performed manually. Additionally, the places that require changes may be hard to find using simple pattern matching. Both have lead to the development of tools like ClangMR, which automatically find and adapt affected pieces of code. Such automatic tools, however, assume that the correctness of the applied changes will be verified by tests. This makes them risky to use for projects with a low test coverage.In this paper we present VSCode Migrate, an IDE extension to perform semi-automatic migrations, enabling large refactorings in low coverage projects. The locations that need to be refactored can be found using alternative matching strategies, including AST based ones, and the changes to perform can be generated automatically. However, instead of applying the changes immediately, VSCode Migrate lets the developer verify and modify each adaption. If a change is not sufficiently covered, additional tests can be added before the change is applied. These mechanisms provide the safeguarding necessary for projects with low test coverage.
Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution

Computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.
Data Deduplication Scheme with Multiple Key Servers in Public Clouds

Data storage in clouds and data access from clouds have become routine activities by individuals as well as corporate because of ease of accessibility, availability, shareability and low-cost storage facility provided by the cloud service providers. While seeing multiple advantages in cloud storage, data duplication has been seen a concern in terms of data ownership, data redundancy, and data quality. Data deduplication mechanism not only minimizes the concern, but also gains customer confidence in storing/providing authentic data in cloud storage, which also achieves better storage space efficiently in the clouds. Data deduplication mechanism becomes easy when data are stored in plaintext, but challenging when data are stored in an encrypted form. In this paper, a scheme with strong tag consistency is proposed that can address data deduplication in storage of encrypted data. The proposed scheme performs tag based deduplication at the file levels with a non-interactive self-generation deduplication decision tree based on randomized tagging files. The scheme provides strong tag consistency and identifies fake file upload attack.
Digital Technology Trends Among English Teachers in the Condition of Modernization of the Higher Education

The paper explores the use of digital technologies in organizations of higher professional education. Authors believe that the implementation of digital technologies in the educational process is an urgent direction in the professional training of specialists of the higher education system and allows to improve the quality of training, significantly increase the level of competitiveness of future specialists in the labor market, in many ways helps to create interactive conditions in the classroom. The study focused on measuring the “use of digital resources” and was developed according to a quantitative and descriptive methodology. We worked With a sample of 119 English teachers who voluntarily and anonymously responded to the questionnaire developed. Data collection was carried out using a virtual questionnaire on the Google Form platform.
Guiding peer-feedback in learning software design using UML

Students find learning to design software challenging. There are often multiple ways to solve a problem, and it is not easy to recognise how well one is doing. Feedback from the lecturer, teaching assistant, or peers may help students learn from their mistakes. In this paper, we study students giving and receiving peer feedback on software design to discover the type of feedback that students find helpful, to provide guidance in giving good feedback, and to learn how students use the feedback they receive to improve their design. We examine data from a software project course for third-year informatics bachelor students. We asked students to give peer feedback and respond to the feedback they received. We discovered that students value i) explicit positive feedback, ii) feedback with specific examples, and iii) separate feedback on syntax and semantics. We present guidelines for stimulating helpful peer feedback and found that students' motivation or seriousness in working with the assignment affects their willingness to incorporate the feedback they received into their design.
How does code reviewing feedback evolve?

Code review is an integral part of modern software development, where fellow developers critique the content, premise, and structure of code changes. Organizations like DellEMC have made considerable investment in code reviews, yet tracking the characteristics of feedback that code reviews provide (a primary product of the code reviewing process) is still a difficult process. To understand community and personal feedback trends, we perform a longitudinal study of 39,249 reviews that contain 248,695 review comments from a proprietary project that is developed by DellEMC. To investigate generalizability, we replicate our study on the OpenStackN ova project. Through an analysis guided by topic models, we observe that more context-specific, technical feedback is introduced as the studied projects and communities age and as the reviewers within those communities accrue experience. This suggests that communities are reaping a larger return on investment in code review as they grow accustomed to the practice and as reviewers hone their skills. The code review trends uncovered by our models present opportunities for enterprises to monitor reviewing tendencies and improve knowledge transfer and reviewer skills.
Parallel Collision Detection Algorithms in Complex Scenes

In current research, collision detection technology has become more and more important, and conflict information is also a problem worth exploring. In order to obtain the information in time and make effective judgment, it is necessary to design corresponding algorithms to deal with the conflicts generated in these complex environments. From the perspective of utilizing the parallel computing power of multiprocessors, a parallel fast collision detection algorithm is proposed. Based on the mixed bounding box hierarchy tree, the algorithm traverses the mixed bounding box hierarchy in parallel by adopting the OpenMP parallel model. The experimental results show that the parallelization of OpenMP significantly improves the speed of the collision detection algorithm.
Advances in Coyote Optimization Algorithm: Variants and Applications

Coyote Optimization Algorithm (COA) is a recent population-based technique inspired by the attitude of coyotes in nature. COA has been widely applied to tackle different optimization issues in several areas and has proved its successfulness compared to several methods found in the literature. In this paper, we describe a brief overview of COA and its variants including adjusted and hybridized versions. Additionally, we present COA applications in various fields such as image segmentation, wireless mesh networks, economic dispatch, electric power systems, distributed generation, and other engineering issues. Finally, we recommend some interesting future research areas directions for COA.
Path following optimization of unmanned ships based on adaptive line-of-sight guidance and Deep Q-Network

Aiming at the path following control problem of underactuated unmanned ship, an intelligent control method of path following of unmanned ship based on adaptive line-of-sight(LOS) algorithm is proposed. In terms of guidance algorithm, considering the ship motion characteristics, an adaptive LOS(ALOS) algorithm that automatically adjusts the target point switching mechanism according to the angle between adjacent paths is proposed to improve the LOS guidance performance. On this basis, a path following controller of unmanned ship based on Deep Q-Network(DQN) algorithm is designed. The controller can learn the path following control strategy through interaction with the environment without any prior knowledge and experience. The simulation results show that ALOS method can obtain better path following performance, and the proposed control method has good self-learning ability and control performance.
Swaasthya Sampathee: Blockchain based EHR Framework

Advancement in the field of technology has trans-formed healthcare system with increased healthcare records. Electronic health records (EHR) helps improve wellness of patient and promotes quality of medical care. However, lack of ubiquitous access to fool proof EHR leads to poor health care facilities with higher expenses. Traditional approaches of storage and access of EHR compromises the ownership, authenticity of the records along with privacy and security issues. To mitigate these issues and promote widespread adoption of EHR especially in rural areas, an Ehereum's Ropsten blockchain based EHR framework in cloud environment is presented in this paper. The framework and its implementation is based upon the use of asymmetric cryptographic keys to securely store the EHR on Inter planetary file system (IPFS) and provide controlled access. The blockchain powered EHR facilitates security and privacy of patient's records and renders authorized ubiquitous access of patient's EHR.
Low power and area efficient AES implementation using ROM based key expansion and rotational shift

The tremendous increase in information transmission and data storage has lead to a high demand for secure information storage and transfer. Various security services for a wide range of applications use Advanced Encryption Standard (AES) as the default algorithm for encryption. With an ubiquitous access to information through mobile and handheld devices, the need for providing security with minimum area and power consumption is very essential. This paper deals with the design and implementation of a low power and area efficient AES architecture. This is achieved by avoiding the use of registers and using ROM during the sorting operation of AddRound keys and by merging of shift row operation and byte substitution. Further reduction in area and power is achieved by using a rotational shift instead of the standard cyclic shifting during the ShiftRow operation. The results from the study indicate that implementation of the proposed design on a Virtex-7 FPGA utilizes 62% less slice registers and 47% less slice LUTs as compared to the standard AES design. Moreover, calculation of power and area in 90 nm GPDK technology results in 30% reduced area and 75% less power.
Correlation and binding technology of passenger security check information based on video target detection and tracking

The analysis of passenger safety check information plays an important role in predicting safety risks in advance and assisting emergency disposal and decision- making. For a long time, all kinds of security inspection information of civil aviation passengers have been scattered and stored in different security inspection equipment or system. Due to the lack of reliable and efficient technology, this information has not been effectively correlated and utilized. This is the first time to use video target detection and tracking technology to correlate and bind passenger's security check information. A scheme of arranging multiple overhead cameras in the security check passage is proposed, which monitor the whole process of passport control, carry-on baggage inspection, personal detection and baggage search. Starting from the moment when the passenger completes the passport control, the passenger in the video is detected and tracked. When passengers and carry-on baggage are inspected by different security inspection equipment, these devices will output identity information, flight information, metal detection information, millimeter-wave human body inspection information, X-ray inspection information for carry-on baggage, trace explosive detection information, prohibited items information found in baggage search, video of the security check process, and more. The video object detection and tracking system correlates them with passengers one by one and stores them. Passengers do not need to perform any additional actions during the security check and will feel comfortable. All security check information of each passenger can be quickly retrieved in a few seconds in a variety of ways, which greatly improves the efficiency of emergency disposal.
A Novel Low-light Image Enhancement Algorithm Based On Information Assistance

Low-light image enhancement plays a significant role in image processing and analysis. Nevertheless, most current algorithms are suffering from the problem of color distortion. With the assistance of an image captured in normal illumination as a reference, the matter of color distortion can be effectively settled by means of the color transfer method. The enhanced images in our proposed algorithm are much closer to the color distribution of the original objects under normal illumination. Our proposed algorithm takes a low-light image and a reference image with a different viewpoint or similar scene as input. Two images are both divided into regions by FCM (Fuzzy C-means). Accordingly, they are then transferred into HSV color space, and the optimal matched results would be obtained by minimizing the weighted distance of the H and S channels. Eventually, the original image would be significantly enhanced using our improved color transfer algorithm. Therefore, this novel low-light image enhancement algorithm based on information assistance expands the application of color transfer in the field of low-light image enhancement. In conclusion, the proposed algorithm has made superior achievements in image contrast retention, color consistency, and naturalness preservation, which would be promising in broad application scenarios.
Summarization of Medical Document using Pointwise mutual information (PMI)-based for web document summarization

Since the time, Internet has come into existence, the speed of generation of information and informative documents has grown exponentially and growing day by day. It is impossible for any human being to read all the documents, even from the domain of his interest. Summarized information will be a great help for any human to decide whether to read the document or not. In this paper, we are presenting PMI based summarization of web document from medical domain. This approach calculate the Total PMI of every sentence of the document and the arranges them in the sorted order. This paper also present a comparison of proposed approach and some well known online systems like tools4noob.com and brevity document summarized, which show a clear improvement.
Augmentation of Imbalanced Training Data for Image Classifier Learning by Image-to-Image Transformation from CG

Image classification techniques using machine learning have been applied to various applications, however, a large number of images are generally required for training. For example, in the specific fields such as medical, inspection and agricultural tasks, it is sometimes difficult to obtain enough images, and some classes are imbalanced in training dataset. Data augmentation is a solution to increase the amount of data in such classes. In general, data augmentation methods using computer graphics (CG) does not perform well due to their expressions are not enough to recognize objects in real world. In this paper, we propose a data augmentation method by a data transformation using CG. The method transforms CG images modeled as simplified object into real-like images. Furthermore, a classifier is trained with the dataset balanced by the augmented data. In experiments, the performance of the proposed method was verified in classification of real worlds objects.
Entropy Based Analysis of Gaze Fixation Duration

When viewing the surrounding environment, the human eyes perform different eye movements that reflect human's interest and attention. A large amount of research efforts have focused on Areas of Interest (AOIs)-based transition analysis of eye movement. However, to the best of our knowledge, there is little work on fixation duration from eye tracking data. In this paper, we investigate entropy based eye movement transition analysis of fixation duration. Specifically, we divide the fixation duration into three categories - express fixations (called short duration, SD), cognitive fixations (called medium duration, MD), and very long fixations (called long duration, LD), and model the eye tracking sequences of fixation duration as a Markov chain, which is further considered from the perspective of a discrete information channel. Subsequently, we exploit the information channel paradigm by computing the entropy measures from the fixation duration information channel to explore the visual behaviour. The proposed method, that allows a straightforward clustering of the results, is demonstrated on two eye tracking data sets, Van Gogh paintings and scientific posters, and shows that the differences between observers are more pronounced that the differences between the observed images. The findings from this work might also provide future insights to quantitative assess human visual cognitive process.
The Presence of a Remote Participant Seen from Multiple Directions

Our study aims to improve the presence of a remote participant in communications. We examined how display methods affect the presence of a remote participant with some communication tasks. As a result, a method using a hologram pyramid enhanced the presence more than the others regardless of the tasks.
Unsupervised Object Re-identification via Instances Correlation Loss

This paper studies the fully unsupervised object re-identification (re-ID) problem which can learn re-ID without any human-annotated labeled data. Recent works show that self-supervised momentum contrastive learning is an effective method for unsupervised object re-ID, but they neglect to optimize one important component - the similarity relationships among instances. Previous works focus on enforcing instance-to-centroid learning, which does not fully utilize the inter-instances information. Thus, we propose an Instances Correlation Loss (ICL) to enforce instance-to-instance learning in each training iteration. Experimental results show that the proposed ICL effectively boost the performance, which demonstrates that learning strategy is also a central importance to unsupervised re-ID task. Extensive experiments are performed on three mainstream person re-ID datasets and one vehicle re-ID dataset.