Reviewerly/exHarmony huggingface dataset:
Robotic Art in Creation and Development of Innovative Shapes and Programs for Automated Driven Cars with Artificial Intelligence

Nature is evolving continuously.Humanity seems like is always searching for something new and in a cultural (ex)change.Technology is the preferred instrument for (self-)development processes in human-nature relationship and interactions.In the present era many people (children and grown-ups) are interested in playing and working with smart phones and other technological products which are performing day by day more complex tasks and diverse interactions.As the level of intelligence does increase when it comes to technological products, higher the interest of the customers will be.Today, even computer browsers are learning the searching behaviours of their users.Programming and adjusting the robots and automatic systems to interact in quite complex fashion with the surrounding environment in other to perform different tasks and to interact with intelligent beings is still quite a difficult challenge due to the great amount of data that needs to be considered and processed.In the present paper the authors show a small part of the designing process for a robotic vehicle in virtual reality environment by using Unity 5 application and other engineering tools.Vehicle considered for digitisation is Chevrolet Camaro and the performed tasks should be automated driving in testing scenario.Self-driven cars are a challenging project for all automotive and robotics engineers nowadays.There is a strong competition between the automotive and robotic technicians to develop and test the first autonomous or self-driving vehicle.In the present case the authors bring to the public attention their interdisciplinary effort to research and development of an innovative vehicle, with the purpose of improving road safety and comfort.It is expected that automated vehicles to provide superior and complex experiences during road travel.Art creation and scientific research are furthermore supported by this field of human activity.
Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings

The unique characteristics of traditional buildings can provide fresh insights for sustainable building development. In this study, a deep learning model and methodology were developed for classifying traditional buildings by using artificial intelligence (AI)-based image analysis technology. The model was constructed based on expert knowledge of East Asian buildings. Videos and images from Korea, Japan, and China were used to determine building types and classify and locate structural members. Two deep learning algorithms were applied to object recognition: a region-based convolutional neural network (R-CNN) to distinguish traditional buildings by country and you only look once (YOLO) to recognise structural members. A cloud environment was used to develop a practical model that can handle various environments in real time.
Designing Effective E-Learning System in Kanisius School with Zachman Framework

In facing the current technological developments, Kanisius School must continue to develop and dare to take steps to innovate in the process of education services.Therefore the school needs to create a framework, methods and management practices in managing IS/IT in the school.The design of the E-Learning system at Kanisius School uses the Enterprise Architecture approach with Zachman Framework which is a reference in developing IS/IT in an organization or company.Besides, Enterprise Architecture utilizes information and communication technology to support the organization's operational activities and enhance the ability of data integration between parts of the organization.For modeling using UML such as Use Case Diagram and Activity Diagram described using PowerDesigner software by SAP.The results of this study are a blueprint of E-learning systems in Kanisius School.This Blueprint can be used as a reference in the future for developing the E-Learning system at Kanisius School.
A Mastery Approach to Flashcard-Based Adaptive Training

Students often use flashcards to study but they do not always use them effectively. In this experiment, we explored different methods of dropping flashcards to inform the development of an adaptive flashcard-based trainer. Forty-seven U.S. Marine Corps students were randomly assigned to one of three groups in an armored vehicle training task. In the Mastery Drop condition, cards were dropped from training based on objective criteria (i.e., accuracy and reaction time). In the Learner Drop condition, cards were dropped based on the learner’s choice. In the No Drop condition, cards were not dropped during training, which served as a control group. Using a pre-test post-test design, results showed that the Learner Drop condition had the lowest learning gains on the immediate post-test and the delayed post-test (two days after training), perhaps because participants were unsuccessful at self-regulating their learning and completed training too quickly. Although the No Drop condition had the highest learning gains on the immediate post-test, the gains significantly decreased on the delayed post-test. In contrast, the Mastery Drop condition maintained consistent learning gains from immediate to delayed post-test. Although the No Drop condition completed more training trials than the Mastery Drop condition, this additional practice did not significantly aid long-term retention. Finally, the No Drop condition had the highest immediate transfer test scores, which involved identifying images of real-world vehicles, but there were no group differences on the delayed transfer test. These results suggest that adaptive flashcard training should incorporate mastery criteria, rather than learner-driven decisions about when to drop flashcards from the deck.
Learning in Virtual Reality: Investigating the Effects of Immersive Tendencies and Sense of Presence

The goal of this study is to examine the effects of the sense of presence and immersive tendencies on learning outcomes while comparing different media formats (Interactive VR, Non-interactive VR and Video). An experiment was conducted with 36 students that watched a Biology lesson about the human cells. Contrary to expected, the results demonstrate that the Non-interactive VR was the most successful format. Sense of presence and immersive tendencies did not have an effect on learning gain, and the latter was not a critical factor to experience the sense of presence. The findings provide empirical evidence to help understand the influence of these variables on learning in VR.
Developing an AR Remote Collaboration System with Semantic Virtual Labels and a 3D Pointer

Referential communication is common in physical remote collaboration. To successfully transfer instructions, remote instructors have to refer objects in the local worker’s environment. However, it is known that nonverbal behaviors are hard to be transferred correctly through modern remote collaboration systems. We focused on enhancing verbal communication and developed an AR-based remote collaboration system. Past research has shown that annotation and labels can support communication in collaboration. Thus, we proposed combining the two functions to achieve a smooth remote collaboration. Concerning the labeling function, we improved Chang et al’s work [1] and introduced semantic virtual labels that were attached to the objects in local workers’ environment. Besides, concerning the annotation function, we introduced a 3D pointer and the instructor can easily draw workers’ attention by moving the 3D pointer. In addition, the 3D pointer can highlight the virtual labels to improve the mutual understanding between the instructors and the workers. Later, a simple experiment was conducted to evaluate the usability of the proposed system.
Agent EXPRI: Licence to Explain

Online social networks are known to lack adequate multi-user privacy support. In this paper we present EXPRI, an agent architecture that aims to assist users in managing multi-user privacy conflicts. By considering the personal utility of sharing content and the individually preferred moral values of each user involved in the conflict, EXPRI identifies the best collaborative solution by applying practical reasoning techniques. Such techniques provide the agent with the cognitive process that is necessary for explainability. Furthermore, the knowledge gathered during the practical reasoning process allows EXPRI to engage in contrastive explanations.
Gender Differences in Simulation Sickness in Static vs. Moving Platform VR Automated Driving Simulation

Simulation sickness is a condition of physiological discomfort felt during or after exposure to any virtual environment. An immersive virtual environment can be accessed through a head mounted display, which provides the user with an entrance to the virtual world. The onset of simulation sickness is one of the main disadvantages of virtual reality (VR) systems. The study presented in this paper aims to expand the knowledge on how gender affects simulation sickness in an innovative VR driving environment. A between-subjects design (n = 62) was conducted to investigate the effect of gender and motion on simulation sickness and physiological responses induced by a fully automated urban virtual driving simulation . The results showed that women significantly experienced more simulation sickness while using the driving simulation compared to men. Furthermore, there was no significant difference between the static and moving platform conditions regarding simulation sickness onset. These findings indicate that there is a real separation of how much simulation sickness has an effect on the users depending on their gender. Therefore, female users should be more cautious while using an automated VR driving simulations with a moving platform.
Usability Heuristics Evaluation in Search Engine

Network growth through recent years reveals our continuous search for information. With the urge of consuming information, search engine popularity rises, becoming our browser’s primary function when typing in the navigation bar. The increasing information volume amplifies the demands of search refining with emphasizes on the concern about presenting data correctly and delivering answers to the users successfully. This paper presents a usability evaluation in the Brazilian Federal Court of Accounts (Tribunal de Contas da União - TCU) search engine for official documents. The need to update and centralize the search engines arised with the increasing technology quality in recent years. Despite good functionalities already implemented, the outdated interfaces with no common standards would no longer fit in today’s use. The urge to create a pattern in TCU systems brought the idea of centralizing different types of documents in only one search engine. Applying the inspection method proposed by Nielsen’s [1], it was possible to gather issues that were neglected in the development process. The results were outstanding: 49 issues found in a product that had already been released. Expecting user’s feedback to correct a software product is far from ideal, making necessary to apply inspection methods in evaluating the system’s usability. Severity ratings were applied in the inspection method, in order to categorize the issues and sort their solutions priorities. By summarizing their frequency, impact and persistence to rates, it was possible to combine those rates in a single severity rate, assigned to each issue. The work reported in this paper made possible the correction of several usability errors occurring in the system. Being a wide project, the amount of issues found, though seemed a large number, are proportionally not so huge: approximately one issue per screen is an acceptable result. The limitations of the studies were related to the amount of specialists involved, although the results indicate that a small group of evaluators is sufficient, specially when the projects, despite having many screens, has a lot of replicated components, where screens are not the same, but reuses interfaces modules. The contribution is primarily internal, by assisting in the deliver of a better product. But the study and reporting used in this paper allowed the authors to summarize the benefits of the heuristic inspection method, presenting the results in a real context, with explicit numbers and categorizing.
Issues and Challenges in Cloud Storage Architecture: A Survey

Not Available for Download Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Deep Contextual Clinical Prediction with Reverse Distillation

Healthcare providers are increasingly using machine learning to predict patient outcomes to make meaningful interventions. However, despite innovations in this area, deep learning models often struggle to match performance of shallow linear models in predicting these outcomes, making it difficult to leverage such techniques in practice. In this work, motivated by the task of clinical prediction from insurance claims, we present a new technique called reverse distillation which pretrains deep models by using high-performing linear models for initialization. We make use of the longitudinal structure of insurance claims datasets to develop Self Attention with Reverse Distillation, or SARD, an architecture that utilizes a combination of contextual embedding, temporal embedding and self-attention mechanisms and most critically is trained via reverse distillation. SARD outperforms state-of-the-art methods on multiple clinical prediction outcomes, with ablation studies revealing that reverse distillation is a primary driver of these improvements. Code is available at https://github.com/clinicalml/omop-learn.
Extending and Matching a High Dynamic Range Image from a Single Image

Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas.
Crime Analysis Mapping, Intrusion Detection - Using Data Mining

Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K - Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
Generating dynamical S-boxes using 1D Chebyshev chaotic maps

This paper considers the construction of chaotic dynamic substitution boxes (S-boxes) using chaotic Chebyshev polynomials of the first kind. The proposed algorithm provides dynamic S-boxes with acceptable security performance compared to twenty-one recent schemes. This algorithm is used to generate 80 random 8 × 8 S-boxes and analyzed their security performance. Their average performance shows acceptable security. The security of the generated S-boxes is measured against several mandatory security requirements for S-box designs including bijective property, strict avalanche criterion (SAC), linear approximation probability (LAP), differential approximation probability, bit independence criterion, correlation immunity, algebraic immunity, auto-correlation, and propagation criterion. Moreover, the set of majority logic criterion measures is used to measure the quality and robustness of the generated S-boxes in image encryption. Because obtaining a chaotic sequence with one dimensional Chebyshev polynomials of the first kind is very simpler and efficient than the hyper-chaotic mappings, the proposed algorithm is of lower computational costs compared with recent chaotic S-box generation algorithms.
Artificial Intelligence (AI) in Journalism: Is Bangladesh Ready for it? A Study on Journalism Students in Bangladesh

While talking about technological development of the world, we always define the word "world" with developed nations or first world countries predicting that developing nations will catch up or follow the trend.This statement is also true for journalism as it is one of the world's fastest growing tech-based sectors.First world countries newsrooms are getting ready for the fourth major wave of journalism (after online, mobile and social media) to use large scale amount of data with the help of AI.In this circumstance, this study focuses on future journalists of Bangladesh; those who have enrolled themselves in this discipline in different universities to pursue a career in the field of journalism.A survey on undergraduate university students from different universities of Bangladesh helped us to predict the readiness of Bangladeshi future journalists for the new wave of digital journalism which will likely hit Bangladesh within the next three to four years.This study will help journalism practitioners and academicians to prepare for the new wave of digital journalism.
Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers

We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence. We introduce SAT++, as a novel quantum programming paradigm, that can compile classical algorithms (implemented in classical programming languages) and execute them on quantum annealers. Moreover, we introduce a post-quantum error correction method that can find samples with significantly lower energy values, compared to the state-of-the-art techniques in quantum annealing. We also demonstrate that performing post-quantum error correction methods can make results of quantum annealers reproducible—i.e., more robust behavior of quantum annealers, compared to recent software and hardware advancements in quantum annealing. In addition, we offer to conjugate both optimization and sampling aspects of the quantum annealers and introduce two AI hybrid approaches. In reinforcement quantum annealing (RQA) scheme, an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and searches in the space of Hamiltonians, rather than exploring the Hilbert space of a given Ising model. In the same manner, greedy quantum annealing (GQA) is a novel model that utilizes quantum annealers to better select candidates in greedy algorithms. We experimentally demonstrate that our proposed AI hybrid approaches outperform the best-known techniques in quantum annealing, specifically when problems require longer chains for forming virtual qubits with higher connectivity. Furthermore, we introduce the theory of ensemble quantum annealing (EQA) that generates multiple (distinct) Ising Hamiltonians whose ground states are all identical to a solution to the problem of interest. Our experimental results reveal that applying EQA can significantly boost the performance of the quantum annealers. After benchmarking the D-Wave 2000Q quantum processors, as a proof-of-concept, we apply our proposed models on two real-world problems. As our first study case, we use SAT++ for factoring pseudo-prime numbers on a D-Wave quantum processor that can jeopardize the security of modern public-key cryptography systems. As our second case of study, we address the NP-hard problem of compressive sensing (i.e., the L0-norm problem of sparse recovery) through reducing the original problem of compressive sensing to Weighted-MAX-SAT instances, casting the L0-norm problem of binary compressive sensing to quadratic unconstrained binary optimization (QUBO), and proposing to solve the original problems of binary compressive sensing and binary compressive sensing with matrix uncertainty on quantum annealers.
Virtual Reality and Its Applications in Interventional Radiology

Virtual reality (VR) is dependent on the creation of an immersive environment. Virtual reality-based simulation technology is better suited for interventional radiology because the image-guided procedures involve viewing video monitors and place the interventional radiologist's hands manipulating instruments at a distance from the actual anatomic site being treated. The main use of VR systems in IR is the training of user skills, improving hand-eye coordination, decision-making skills, and evaluating treatment approaches for patient-specific pretreatment planning ( 1 Dalgarno B Lee MJW What are the learning affordances of 3-D virtual environments?. Br J Educ Technol. 2010; 41: 10-32 Crossref Scopus (931) Google Scholar ).