Reviewerly/exHarmony huggingface dataset:
Recommendation with Heterogeneous Information Networks Based on Meta-Graph

In order to alleviate data sparsity and cold-start problems of traditional collaborative filtering recommendation algorithm, a meta-based fusion heterogeneous information network recommendation algorithm is adopted in this paper. The algorithm integrates the characteristics of multi-relationship social network and user’s preference degree and adopts a universal representation for different types of data. A meta-graph-based similarity measurement method makes it possible to better capture the semantic relationships between different types of data and a score matrix decomposition method based on multiple meta-graphs is used. Each project and user generates a variety of potential feature matrices based on different meta-graphs. Effectively integrates multiple feature matrices into a unified, final implicit feature matrix. We use each factor of each line of the implicit feature matrix as a neural network. The input node predicts user ratings by optimizing the scoring neural network. Finally, we used the data set provided by the Yelp website to do user rating prediction experiments, which proved the accuracy of this algorithm is 5% higher than the traditional collaborative filtering recommendation algorithm.
Considerations for Human-Machine Teaming in Cybersecurity

Understanding cybersecurity in an environment is uniquely challenging due to highly dynamic and potentially-adversarial activity. At the same time, the stakes are high for performance during these tasks: failures to reason about the environment and make decisions can let attacks go unnoticed or worsen the effects of attacks. Opportunities exist to address these challenges by more tightly integrating computer agents with human operators. In this paper, we consider implications for this integration during three stages that contribute to cyber analysts developing insights and conclusions about their environment: data organization and interaction, toolsmithing and analytic interaction, and human-centered assessment that leads to insights and conclusions. In each area, we discuss current challenges and opportunities for improved human-machine teaming. Finally, we present a roadmap of research goals for advanced human-machine teaming in cybersecurity operations.
Overcoming Organizational Barriers to the Integration of UX Methods in Software Development: A Case Study

Despite User Experience (UX)’ increased popularity, the willingness of organizations to integrate it into their processes and UX practitioners still encounter challenges in integrating UX methods. Research in HCI has presented theoretical and pragmatic models. Most of the time, these models lack presenting validation in practice, documentation or support for their use when difficulties appear. Even if difficulties could be expected. In this paper, we present a categorization of organizational barriers extracted from the relevant literature: lack of UX resources, lack of UX literacy, poor use of UX artifacts and communication breakdowns. Then, we propose a 4-step procedure to identify and overcome organizational barriers with a case study describing how this procedure helped us to anticipate and overcome organizational barriers encountered in a project. With a UX Capability/Maturity (UX CM) assessment conducted at the beginning of the project, we were expecting organizational barriers and constant readjustment of our UX strategy. We Communicated about findings and readjustment to stakeholders in order to increase their awareness about problems along the way of the project and we had prepared remediation strategies for the emergence of barriers.
A Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region Method

We present a scheme based on differential evolution and local search to solve continuous optimization problems. Improvements have been made to the basic differential evolution algorithm. We have opted for an approach with three types of population regeneration and parameter value adaptation. These improvements offer a better exploration of the search space and avoid blocking on local optima. In order to intensify the search around the neighbourhood of the solutions obtained by the differential evolution algorithm, a local search is carried out from time to time using a trust-region method. The proposed scheme was tested on the COmparing Continuous Optimizers (COCO) platform, and the obtained results showed the superiority of the proposed approach over state-of-the-art algorithms.
User Profiling Based on Application-Level Using Network Metadata

There is an increasing interest to identify users and behaviour profiling from network traffic metadata for traffic engineering and security monitoring. Network security administrators and internet service providers need to create the user behaviour traffic profile to make an informed decision about policing, traffic management, and investigate the different network security perspectives. Additionally, the analysis of network traffic metadata and extraction of feature sets to understand trends in application usage can be significant in terms of identifying and profiling the user by representing the user's activity. However, user identification and behaviour profiling in real-time network management remains a challenge, as the behaviour and underline interaction of network applications are permanently changing. In parallel, user behaviour is also changing and adapting, as the online interaction environment changes. Also, the challenge is how to adequately describe the user activity among generic network traffic in terms of identifying the user and his changing behaviour over time. In this paper, we propose a novel mechanism for user identification and behaviour profiling and analysing individual usage per application. The research considered the application-level flow sessions identified based on Domain Name System filtering criteria and timing resolution bins (24-hour timing bins) leading to an extended set of features. Validation of the module was conducted by collecting Net Flow records for a 60 days from 23 users. A gradient boosting supervised machine learning algorithm was leveraged for modelling user identification based upon the selected features. The proposed method yields an accuracy for identifying a user based on the proposed features up to 74%.
Edge-Guided Semi-Coupled Dictionary Learning Super Resolution for Retina Image

In order to reconstruct more boundary information for the weak-boundary retina image via sparse representation, the paper proposes a super-resolution (SR) algorithm by combining edge difference with semi-coupled dictionary learning (ED-SCDL). Firstly, we consider the edge difference between low-resolution (LR) image and degraded version of reconstructed image as constraint term of the proposed ED-SCDL model to preserve the edge information. Then, in order to improve the contribution of edge difference constraint on preserving boundary information, the adaptive regular parameter is explored via the approximate Laplacian distribution of edge difference. Experiments on retina images demonstrate that our model is superior to other methods, especially retina images with weak edges.
Designing Dynamic Logotypes to Represent Data

This work explores how data can influence the design of logotypes and how they can convey information. The authors use the University of Coimbra, in Portugal, as a case study to develop data-driven logotypes for its faculties and, subsequently, for its students. The proposed logotypes are influenced by the current number of students in each faculty, the number of male and female students, and the nationality of the students. The resulting logotypes are able to portray the diversity of students in each faculty. The authors also test this design approach in the creation of logotypes for the students according to their academic information, namely the course and number of credits done. The resulting logotypes are able to adapt to the current students, evolving over time with the departure of students and admission of new ones.
Indoor Navigation Through Storytelling in Virtual Reality

Storytelling is a narrative method that passes on explicit but mostly implicit knowledge. Viewers are involved in the narrated story, so that the content of the story is easier to understand and accept because you become part of it. To help guest students learn to find their way to key locations in the new environment, we’ve built a virtual environment so they can take a virtual tour of our university from their home. Nevertheless, it is difficult to remember a route, if all hallways look identical. With our experiment, we’ve examined whether users can better remember directions by watching a VR video using storytelling instead of watching a VR video of the pathway without storytelling elements. While subjects that watched the VR video without storytelling need to think about which way to go at different junctions, the other subjects could use story elements to remember elements of storytelling at certain points, which path they need to take and find their way in less time and fewer steps as the subjects without storytelling. Through storytelling, parts of the video are emotionally linked, so that the track and its elements are anchored faster in the memory. The involvement in the search process generates a higher immersion, which increases the motivation during the learning process. Increased motivation makes things more responsive and builds sustainable knowledge.
Human Brain Computer/Machine Interface System Feasibility Study for Independent Core Observer Model Based Artificial General Intelligence Collective Intelligence Systems

This paper is primarily designed to help address the feasibility of building optimized mediation clients for the Independent Core Observer Model (ICOM) cognitive architecture for Artificial General Intelligence (AGI) mediated Artificial Super Intelligence (mASI) research program where this client is focused on collecting contextual information and the feasibility of various hardware methods for building that client on, including Brain Computer Interface (BCI), Augmented Reality (AR), Mobile and related technologies. The key criteria looked at is designing for the most optimized process for mediation services in the client as a key factor in overall mASI system performance with human mediation services is the flow of contextual information via various interfaces.
Evaluation of the Multifactor Authentication Technique for Mobile Applications

Nowadays, mobile smartphones are popular devices among general population. Compared to the traditional mobile phone, smartphone is closer to a personal computer with a good number of mobile applications installed. However, when a user browses certain mobile application, his or her account is at risks if static password is the only element required for authentication. To overcome this problem, multi-factor authentication is widely required for mobile applications. In this paper, we conduct a survey for a group of mobile application users and analyze the pros and cons of each authentication method by case studies. We focus on six main factors to evaluate each authentication method. Our study found that traditional password authentication, biometrics authentication methods such as fingerprint, face, and voice scored relatively high on Convenience, Time, Security and Accuracy. More important, these four aspects are exactly the most essential factors to a mobile application’s quality that relate to safety issue and user experience as well. Our evaluation results show that biometric authentication methods are currently used most often by mobile applications and well accepted by its users. Overall, we evaluated five cases with advantages and disadvantages. We found fingerprint, voice authentication played outstanding as biometric authentication methods. We also come up with a new authentication proposal for mobile application design and for the future research that in view of mobile devices are more and more functional nowadays, we recommend mobile applications to use biometric authentication in two steps authentication methods.
Auto-Retoucher(ART)—A Framework for Background Replacement and Foreground Adjustment

Replacing the background and simultaneously adjusting foreground objects is a challenging task in image editing. Current techniques for generating such images are heavily relied on user interactions with image editing softwares, which is a tedious job for professional retouchers. Some exciting progress on image editing has been made to ease their workload. However, few models focused on guarantee the semantic consistency between the foreground and background. To solve this problem, we propose a framework - ART (Auto-Retoucher) to generate images with sufficient semantic and spatial consistency from a given image. Inputs are first processed by semantic matting and scene parsing modules, then a multi-task verifier model will give two confidence scores for the current matching and foreground location. We demonstrate that our jointly optimized verifier model successfully guides the foreground adjustment and improves the global visual consistency.
Braille Monopoly Game as a Tool of Disaster Mitigation Education for Visual Disabilities

Abstract Disaster mitigation is a series of efforts to reduce disaster risk, through physical development and capacity building to face disaster threats. This effort is important, especially in Aceh Province, which is a disaster-prone area. People with visual disabilities are vulnerable to disaster threat. Special facilities are needed to educate them about disaster mitigation. This program designs “Monopoli Bencana (Monca)” as a disaster mitigation learning media for people with visual disabilities. Monca aims to increase their awareness, knowledge and understanding related to disaster mitigation. Program implementation uses the one shoot pretest-posttest method. This program covers three important things, starting with socialization, direct discussion, and the practice of playing Monca. The target of this program is the people with visual disabilities in the UPTD Rumoh Seujahtera Beujroh Meukarya (RSBM). This program has three final results, namely: 1) Monca as an educational game equipped with braille: 2) Disaster mitigation pocket book printed in braille; 3) There is an increase in knowledge and awareness of the importance of disaster mitigation for at least 11 children who participated in the program.
Reform and Research on Teaching Mode of Computer Basic Course in College

The paper proposes the reform of the multi-dimensional teaching mode based on the research and analysis about the current teaching mode of computer basic course and teaching practice in the university in China, which is the integration of information and the contents of the courses. Finally, this paper studies and discusses the reform of curriculum contents, teaching methods and evaluation methods.
An intuitive and traceable human-based evolutionary computation system for solving problems in human organizations

Human-based evolutionary computation (EC), for which people act as executors of all evolutionary operators, can be used to solve problems in human organizations. We previously developed a human-based EC system that represents solutions as tags (words) and allows people to evaluate solutions by clicking corresponding tags. Although the system was easy and intuitive to use, it could not handle problems for which solutions are represented as long sentences. In addition, the system could not trace the evolution of solutions. Traceability is a must for the system to be widely and reliably used. In this study, we thus develop a human-based EC system that allows solutions to be represented as both sentences and tags. A function for tracing the evolution of solutions is embedded into the system. The function asks a solution creator to specify which existing solutions influenced the solution creation. We conduct an experiment in which 18 human subjects use the system and then fill out a survey. The results show that the system creates better solutions than those created by each human subject independently. Furthermore, the evolution tree generated from the information given by solution creators is used to confirm that the system allows the evolution of solutions to be traced.
Natural Noise Management in Recommender Systems Using Fuzzy Tools

Recommender Systems (RSs) are tools focused on suggesting items that match the interests and preferences of a target user. They have been used in several domains such as e-commerce, e-learning, and social networks. These systems require the elicitation of user preferences, which are not always precise because there are external factors such as human errors, or the inherent vagueness associated to human beings; which are usually related to user behaviors. In RSs, such imperfect behaviors are identified as natural noise (NN), and can bias negatively the recommendation, which affects the RS performance. The current chapter presents two fuzzy models for NN management in a flexible way, which guarantees robust modeling of the uncertainty associated to the user profiles. These models are conceived for individual and group recommendation scenarios respectively, as a data preprocessing step before the recommendation generation. Two case studies are developed to show that the proposals lead to improvements in the accuracy of individual and group recommender systems.
Questioned document examination

Questioned document examination is an important forensic activity for making connections between written communications of all types and their authors. It has been forced to slowly shift its emphasis from primarily handwriting to a much broader look at many other forms of communications, due to the rapid development of technology. The most well-known activities of questioned document examiners are examinations and comparisons of handwriting, typewriting, and copier output. A document examiner can look at the paper of a questioned document and quickly tell whether it is expensive paper, cheap paper, has a high content of cotton fibers in it, and something about the surface treatment. There are many types of cases where questioned document examiners can provide a useful service that do not involve handwriting or handprinting evidence. The questioned document examiner’s two most powerful aides, in such a determination, are context and technology.
Corneal-Reflection-Based Wide Range Gaze Tracking for a Car

When an eye tracking system is used in a car, user-calibration-free system is suitable because a driver can sometimes change. In addition, we usually look at the mirrors while driving a car, and so the system should track the driver’s gaze in a wide range. In this study, we proposed a new method that calculates the gaze directions in a wide range by improving a user-calibration-free gaze tracking method. Our new method changes the calculation method based on the detected number of feature points of the eye. We installed cameras and LEDs in a car simulator that used a real car in a laboratory based on a simulator for developing a gaze sensitive environment. The evaluation results of a developed system for one participant showed that the system could track gazes when the participant looked mirrors.
Textonboost based on differential evolution

Semantic segmentation on a pixel basis is necessary for the semantic understanding of an image. Although the use of CNN is mainstream in the case where there are sufficient test images, in this research we aim to develop a method that is robust in an environment with few test images. Specifically, we improve Textonboost by using a differential evolution method.
No-Reference Image Quality Assessment Based on Multi-scale Convolutional Neural Networks

Big data brings more challenges to image quality assessment (IQA). First of all, reference images are not always available, so full-reference IQA (FR-IQA) and reduced-reference IQA (RR-IQA) can be problematic for big data. Because of no requirement for any pristine knowledge, no-reference IQA (NR-IQA) has recently received a great deal of attention. In recent years, deep convolution neural network (CNN) has achieved many successes in visual recognition tasks. Researches in literature have shown that NR-IQA metrics can be learned from features extracted using deep CNN. But existing CNN-based IQA models focus on local spatial information but ignore global spatial structure. In this paper, we propose a NR-IQA model learns from multi-scale CNN (MSCNN). Multi-scale mechanism has boosted the performances of many singe-scale methods in a variety of applications. MSCNN is a multi-scale CNN model which uses fully connected layers on top of multiple single-scale CNN models which responsible for feature extraction at different scales. In this way, the features extracted at multiple scales work together to assess image quality. Extensive experiments are conducted on the LIVE database and the experimental results validate the effectiveness of our MSCNN index compared to typical FR-IQA and state-of-the-art NR-IQA metrics. Furthermore, the results of cross database experiments show that MSCNN has good generalization ability.
Visualization of Equivalence in 2D Bivariate Fields

Abstract In this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from ℝ n to ℝ n . We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.
User Experience Design Methodology for Optimizing Kids’ Toy Customization Platform Architecture: A Case Study

There is a study concerning the optimization of kids’ toy customization platform architecture with user experience design methodology. In this paper, we aimed at the kids’ toy internet customization platform, analyzed the basic characteristics of children who are the main target users of service and described dual user mode. This paper presented a case study on the Kids Creation Station toy customization platform. There are the characteristics of complex service process, many stakeholders and many users with special feature on kids’ toy internet customization platform, therefore, this paper synthesized and implemented some methods and tools in interaction design and service design, and used behavior logic to organize processes, contact points and interfaces to build a better user experience. Basing on the case analysis and following with the dual user mental model, we applied the methods of service blueprint analysis to evaluate, insight and optimize existing platforms, proposed the architecture of the kids’ toy internet customization platform with 3D printing technology. We explored the compared and comprehensive approach in service design and interaction design. This research conceived the future overall services format based on the principle of customization-design-manufacture-logistics-social, and provided reference for the development and promotion of personalized customization and intelligent manufacturing.