Reviewerly/exHarmony huggingface dataset:
Virtual Companions and 3D Virtual Worlds: Investigating the Sense of Presence in Distance Education

Distance Education (DE) still have some challenges to be considered similar to the face-to-face mode of instruction regarding the quality of learning, including the lack in promoting the sense of presence. This research investigates whether a differentiated media support, complementary to the traditional Virtual Learning Environment (VLE), composed by the integration of 3D Virtual Worlds (3DVW) and Conversational Agents, in the role of a Virtual Companions, can promote the student’s sense of presence in order to contribute with the learning process in DE. A quasi-experiment pilot study was conducted with 36 students enrolled in the Financial Management discipline from a DE formal course. A 3DVW was developed in the light of the pedagogical model of Experiential Learning, in the form of a role-play simulation. The results reveal that although the students positively evaluated the experience in the 3DVW, it did not stimulate the sense of presence as expected. However, better performance rates were diagnosed for students who had the help of the Virtual Companion.
A Deep Learning Approach for Slice to Volume Biomedical Image Integration

Biomedical atlas images obtained from multiple sources need to be aligned and transformed into a single coordinate system so as to be able to integrate and relate these different sets of data. Formally known as image registration, this process of image pre-processing has proven to be integral in a wide array of computer vision ap- plications, most notably in the area of medical imaging. During the last decade slice-to-volume registration, a particular case of image registration problem, has received further attention in the medical imaging community due to the emergence of several medi- cal applications of slice-to-volume mapping. This paper proposes a Convolutional Neural Network (CNN) based deep learning ap- proach for registering a 2D image slice to the 3D volume of images in a Biomedical atlas. The proposed CNN model is trained to de- termine the distance and pitch values that are used to describe the position of the 2D slice in the atlas coordinate system. High-level features are automatically extracted from the training dataset of images, which addresses the limitation of shallow machine learning techniques for handcrafted features followed by the classification task. Then on the basis of predicted values of distance and pitch, the target image is registered to the 3D volume of images. Experimental results showing the effect on the similarity of images with variation in distance and the impact of varying the distances among the classes on the regression are presented. It was observed that using the successive images at a distance of 10 lead to the maxi- mum accuracy. These results demonstrate the applicability of the proposed approach to slice-to-volume image registration.
Predicate signatures from pair encodings via dual system proof technique

Abstract Recently, Attrapadung (Eurocrypt 2014) proposed a generic framework for fully (adaptively) secure predicate encryption (PE) based on a new primitive, called pair encodings . The author shows that if the underlying pair encoding scheme is either perfectly secure or computationally (doubly-selectively) secure, then the PE scheme will be fully secure. Although the pair encodings were solely introduced for PE, we show that these can also be used to construct predicate signatures, a signature analogue of PE. More precisely, we propose a generic construction of predicate signature (PS) from pair encoding schemes. Our construction provides unconditional signer privacy, and unforgeability in the adaptive model. Thereafter, we instantiate many PS schemes with new results, e.g., the first practical PS schemes for regular languages, the first attribute-based signature (ABS) scheme with constant-size signatures in the adaptive model, unbounded ABS with large universes in key-policy flavor, etc.
Spectral imaging system for money counterfeit detection

A prototype with three different wavelength lasers (448nm, 532nm and 659nm) for money counterfeit illumination, analyzation and detection using RGB crosstalk correction and comparing spectral image ration for different banknote elements will be presented.
Modeling Human Eye Movement Using Adaptive Neuro-Fuzzy Inference Systems

The eye's muscles are difficult to model to build an eye prototype or an interface between the eye's movements and computers; they require complex mechanical equations for describing their movements and the generated voltage signals from the eye are not always adequate for classification. However, they are very important for developing human machine interfaces based on eye movements. Previously, these interfaces have been developed for people with disabilities or they have been used for teaching the anatomy and movements of the eye's muscles. However, the eye's electrical signals have low amplitude and sometimes high levels of noise. Hence, artificial neural networks and fuzzy logic systems are implemented using an ANFIS topology to perform this classification. This paper shows how the eye's muscles can be modeled and implemented in a concept prototype using an ANFIS topology that is trained using experimental signals from an end user of the eye prototype. The results show excellent performance for prototype when the ANFIS topology is deployed.
Investigating the Balance Status of Nutrients in Wheat Plant Using Isometric Log Ratio and Field Validation of Balances

Nutrient status of plants is determined by using critical nutrient range and nutrient ratios. The obtained data may not be generally error‐free due to the nature of compositional data is not taken into account Here, nutrient status of wheat ( Triticum aestivum L.) in Entisols and Aridisols soils is investigated by isometric log ratio (ILR) technique. Leaf samples collected from 96 fields at booting stage. To validate the obtained ILR balances, two separate filed experiments were conducted employing a factorial experiment based on CRBD design with three replications. Concentrations of N, P, K, Ca, Mg, Zn, Fe, Mn, Cu, and B in the flag leaf tissue were determined at both fields. Nutrients were arranged into ad hoc balances and computed ILR and classified into four groups as balanced, misbalanced, high and low yielders by receiver operating characteristic (ROC) curve. The ROC classification applied to the data from survey fields and validation test respectively showed 4.67 and 4.02 as the critical Mahalanobis distance and 5034 and 6559 kg ha −1 as yield cut‐off for separating balanced and imbalanced samples at area under curve (AUC = 0.83 and 0.88). Comparing nutrient balance between reference (TN) and nonreference (TP) groups in the survey fields by Tukey's test showed that the balances of [Fe,Mn,Zn,Cu|B,Mg,Ca,K,P,N], [B|Mg,Ca,K,P,N], [Mg,Ca,K|P,N], [Mg,Ca|K], and [Mn|Zn,Cu] were negative. The [Fe|Mn,Zn,Cu] and [Zn|Cu] balances were positive. In the low yielding of survey fields, the balances of [Fe|Mn,Zn,Cu], [Mn|Zn,Cu], [Zn|Cu], [Fe,Mn,Zn,Cu|B,Mg,Ca,K,P,N], and [Ca,Mg|K] were imbalanced, but they become balanced through fertilizer treatments in the validation test. Core Ideas Nutrient balances can be interpreted simultaneously using pan balance approach. The values of isomeric log ratio represent the balances between two or more nutrients. Mahalanobis distance was set as a criterion for nutrient misbalance.
Visual Effects of Turning Point and Travel Direction for Outdoor Navigation Using Head-Mounted Display

We investigate the visual effects of superimposing turning points and travel directions within the user’s field of view in a navigation system using a subjective assessment procedure. Existing methods were developed without conducting subjective assessments of the effects of superimposing the turning points and travel directions on the user’s display while walking outdoors. We therefore designed a questionnaire-based subjective assessment of the use of these navigation methods. We developed an outdoor navigation system using a recently launched optical see-through head-mounted display (HMD) product that was compact and lightweight. We demonstrated that the subjective scores in terms of understanding of the turning points and the travel directions were significantly increased by the visual effects of superimposing these cues on the display. We confirmed that the HMD helps to increase user likeability of use of the navigation system while walking outdoors.
An Ontology Enhanced User Profiling Algorithm Based on Application Feedback

Eliciting precise user preferences and establishing a comprehensive user profile significantly contribute to personalized recommendations from numerous applications. However, existing methods do not adequately establish critical relationships at the knowledge level. In this paper, we argue that precisely processing applications' user feedback is essential for understanding user requirements and making application recommendations. Therefore, we first establish an ontological model of user feedback, guiding the generation of a knowledge graph regarding user reviews and user ratings. In particular, we augment the graph with topics of each review and application in order to deal with the sparsity of user feedback. Moreover, we explore in-depth knowledge from the graph by identifying three meaningful meta-paths, which are essential for calculating user similarity and thus making recommendations. Specifically, we propose a feedback-based similarity calculation model FSCM, with the purpose of predicting applications that are of interests of certain users. We have evaluated our model over 1386 reviews from a mobile application store, the results of which show that our approach can improve the prediction accuracy, as well as to enhance the interpretability of analysis results.
Teaching Computer-assisted Translation: A Proposal for Course Content

With wider adoption of machine translation and its integration in the standard translation workflow, the job of translator developed into various forms - post-editors, revisers, translators, proof-readers, etc. In this paper we argue for the inclusion of the post-editing, pre-editing, and machine translation content into the course on computer-assisted translation and give proposal for the course content. We also argue for the inclusion of added value services such as subtitling and localization. The course should be mandatory for all the graduate students studying a language. The content is influenced by the experiences gained from teaching a similar elective graduate-level course.
Gamification-Based VR Rowing Simulation System

Rowing is an efficient sport that exercises all major muscle groups. Considering the physical restrictions (i.e. rowable bodies, water, weather conditions) of rowing, indoor rowing machines are used alternatively to simulate the rowing action. But despite their popularity, rowing machines fail to produce the immersive experience and may make the exercise tedious with repetitive movements. Based on the gamification theory, this study designed the game mechanics that involved the incentive and competitive mechanisms and developed a virtual reality (VR) simulation system for indoor rowing training. In the control experiment, an improved rowing performance (i.e. finishing time, heart rate, stroke rate) and higher motivation levels were observed in the immersive VR environment. Results of the study showed that the gamification-based VR rowing simulation system could produce an improvement in exercise intensity and quality, and that the application of gamification theory could improve exercisers’ enjoyment and motivation.
A Statistical Non-Linear Model and Analysis for Photon-Level Photomultiplier Receiver

We characterize practical optical signal receiver in a wide range of signal intensity for optical wireless communication, from discrete pulse regime to continuous waveform regime. We first propose a statistical non-linear model based on photomultiplier tube (PMT) multi-stage amplification and Poisson channel, and then derive the optimal and tractable suboptimal duty cycle with peak-power constraint and average-power constraint for on-off key (OOK) modulation in the linear regime. Subsequently, a threshold-based classifier is proposed to distinguish three PMT working regimes based on the non-linear model. The performance of photon counting detection with optimal threshold for different sampling rates is evaluated from both theoretical and numerical perspectives. The proposed model can be adopted to select the optimal duty cycle and analyze the detection performance of PMT output signal in real applications.
Making Meaning: How Experience Design Supports Data Commercialization

The work aims to analyze the different stages of data commercialization and explore the research paradigm of making meaning through the experience process. Based on the cases of data-driven products, a classification study was conducted through induction and summary, and the characteristics of four stages of data commercialization was illustrated. In the era of big data, data is not just about extra output or subsidiary force or core competency, but raw materials in the process of commercialization, which is an inevitable trend. The goal of data commercialization is to make meaning by the way of experience design. At different stages of data commercialization, the methods of experience design have to adjust as consumers’ demands for meaning are various. With the development of internet technology, the correlation and connection between people’s daily life and data will be strengthened unconsciously, and the appeal to meaningful experience of data products will become more common. When data is treated as a kind of raw material in the fourth stage of data, the mode of experience design is: data infrastructure as theater, data-driven services or products as the stage, data as props, and engaging data consumers as actors in a way that creates some memorable and meaningful events.
Research on Colleges and Universities Assistant Teaching Application Design Based on Cloud Computing

Abstract Based on infrastructure and computing power provided by cloud computing service providers, more and more colleges and universities have developed information-based educational resources and implemented information-based educational applications in a virtualized environment. After accessing cloud computing services, universities do not need to spend a lot of money to purchase commercial software licenses, because cloud computing can provide a large number of commonly used application software. However, there are few cases about these educational information software resources applied to actual teaching and combined with specific courses, which makes the information software resources disconnected from the courses and reduces the teaching efficiency and effectiveness of the classroom. Based on aforementioned background, this study is aimed at the existing teaching mode in colleges and universities, to conduct research on the specific cloud computing application and practice in the teaching of colleges and universities. With rapid development of information technology, the education informatization will certainly transit from the computer-aided education to the education centered on calculation, data and service, of which possibility and technical support can be provided by the development of cloud computing. With the continuous development and popularization of cloud computing technology, its advantages of low cost, convenience and security will surely attract more colleges and universities to carry out daily education on cloud platform.
Quadratic-Time Algorithms for Normal Elements

For any finite Galois field extension K/F, with Galois group G = Gal(K/F), there exists an element K whose orbit G · forms an F-basis of K. Such an is called a normal element and G · is a normal basis. We introduce a probabilistic algorithm for finding a normal element when G is either a finite abelian or a metacyclic group. The algorithm is based on the fact that deciding whether a random element K is normal can be reduced to deciding whether () K[G] is invertible (where ranges over all of G). Our algorithm requires a quadratic number of operations in the size of G for metacyclic G, and a slightly subquadratic number of operations for abelian G.
Toward Real-World Light Field Depth Estimation: A Noise-Aware Paradigm Using Multi-Stereo Disparity Integration

Producing depth maps of a scene is essential in many light field applications. Numerous algorithms have been developed to obtain an accurate depth map on public datasets. However, real-world light field images obtained by cameras have noises produced by hardware limitations. Existing methods do not perform well on noisy scenes. In this paper, we propose a noise-aware light field depth estimation algorithm which is insensitive to noise. First, an optical flow approach is used to compute disparities between the central view and each of the remaining views, then a weighted integration method is used to reduce calculation errors. Second, a refinement framework, which is measured using the refocus image and angular patch, is proposed to handle occlusion and texture-less parts. Third, a K-means clustering-based strategy is used to improve noise capability. Finally, a depth map is regularized by a Markov random field (MRF). Extensive experiments are conducted on synthetic datasets and real-world datasets. Both quantitative and qualitative results have demonstrated the superiority and robustness of our method.
Privacy-Preserving Data Sharing Framework for High-Accurate Outsourced Computation

With the advances of outsourced computation, the threats of data leakage and loss of computational accuracy over rational domain are attracting increasing concerns. In this paper, we propose a framework for Privacy-preserving Data Sharing and high-accurate outsourced Computation system, referred as PDSC. PDSC system can perform secure data sharing with multiple data providers. Besides, the original data and computed results in the rational field can be securely processed and stored in the cloud without privacy leakage. Specifically, we design privacy-preserving computation protocols over rational numbers to guarantee computational accuracy and handle outsourced operations on-the-fly. Detailed security analysis and experimental results demonstrate that PDSC system is secure and feasible, respectively.
Construction of a Japanese Sign Language Database with Various Data Types

We have constructed a sign language database which shows 3D animations. We are aiming at constructing an interdisciplinary database which can be used by researchers in various academic fields. This database helps the researchers analyze Japanese sign language. We have recorded nearly 2,000 Japanese signs to now, and we are planning to record on the database approximately 5,000 signs. Firstly, we decided to pick up frequently used Japanese words on the database. Each sign language expression corresponds to the Japanese words is examined. Secondly, we recorded 3D motion data of the determined sign language expressions. We used optical motion capture to record 3D motion data. The data format obtained through motion capture is C3D data, BVH data and FBX data, and frame rate is 120 fps. In addition, we also recorded a full HD video data at 60 fps, super-slow HD data at 30 fps, and depth data at 30 fps, for use in analysis of sign language. These are recorded synchronously. In addition, we have developed a new annotation system which can reproduce different types of data synchronously to make the database the most effective. Because it is necessary for data analysis to reproduce synchronously all data, which have been recorded at different frame rates.
In Search of Scientific Agile

Over the last twenty years, authors of systematic literature reviews have repeatedly criticized the state of agile research, describing it as nascent, random, and lacking appropriate recruitment strategies. This paper highlights some historical misperceptions and assumptions that tend to misdirect researchers, especially those new to the field. It also calls attention to some challenges intrinsic to agile research and comments on how those challenges impact research design. The intent is to help new researchers more easily make sense of the existing literature, so that they can focus their energy and resources on meaningful contributions to the field.
RecEvent: Multiple Features Hybrid Event Recommendation in Social Networks

The large volume of event information makes it difficult for users to find interesting events in social networks. Therefore, we would like to develop an intelligent event recommendation to reduce information overload. Specifically, by exploring the behavior of users during the selection process, we are able to find particular rules associated with various event attributes which reflect the willingness of users. However, traditional event recommendations in social networks mainly concern the basic items like time and location. It is noted that few studies have yielded specific aspects such as the influence and spread capability of events and hosts. In this paper, we propose an event recommender approach fusing multiple features that can provide users with customized contents. To be specific, we consider hybridizing features including event influence, host impact, fee, social relationship and spatiotemporal characteristics. In order to achieve better performance, we concern the match degree between user and event properties especially in terms of their content and impact. Based on the improved idea of RankNet with neural networks, we build a Learning to Rank algorithm to reveal the importance of each feature. We rectify the problem of data sparse and cold start to grasp the balance of accuracy and novelty. Extensive experiments on datasets demonstrate that our method achieves promising results in comparison with other schemes.
Application of Electronic Speckle Pattern Shearing Interferometry with High-Speed Camera in Vibration Analysis of Piezoelectric Transducer

Electronic speckle pattern shearing interferometry, also known as shearography, is a highly sensitive technique that can measure the distribution of the displacement derivatives of the object surface. This method has also been used to describe the mode shapes of vibrating objects. In the current research, electronic speckle pattern shearing interferometry with high-speed camera was proposed for full-field measurement of the derivative of the vibration amplitude. One of the important limitations of time-average shearography is that this method is only able to provide the qualitative measurement of vibration amplitude. Stroboscopic shearography also has an inevitable limitation on the measurement of vibration amplitude at frequencies lower than 50[Formula: see text]Hz. To study the performance of shearography with high-speed camera, the research concerned the low-frequency vibration of a piezoelectric transducer during its operation. The proposed method overcomes the limitations of conventional shearography methods in quantitative measurement of vibration amplitude at low frequencies. Laser Doppler vibrometry (LDV) method was used to verify the measurement results of shearography with high-speed camera. The obtained results indicated good agreement between both shearography with high-speed camera and LDV methods. However, due to the rigid body motion of the piezoelectric transducer, LDV results generally represented more values for the maximum amplitude.
How Do Humans Identify Human-Likeness from Online Text-Based Q&amp;A Communication?

This study aims to clarify a person’s impressions during the course of a conversation. In conversations between a person and a chatbot, we evaluated the impressions formed on reading text created by a human and text created by a chatbot. In terms of question/answer relevance, it was found that chatbot-created answers could not relate to or meet the expectations of questions and that human-created answers, while not necessarily meeting the expectations of questions, had relevance.