Reviewerly/exHarmony huggingface dataset:
Image Inpainting for Digital Dunhuang Murals Using Partial Convolutions and Sliding Window Method

Abstract It is a difficult and challenging task to restore the digital murals to a visually pleasant result, and even the result is similar to the original murals without corruption. In this paper, to address the above problem, we propose an image inpainting strategy called PCSW for digital Dunhuang murals using partial convolutions and sliding window method. Specially, a deep neural network based on partial convolutions is used as the underlying model for image inpainting. Because the murals are somewhat damaged or even large areas are missing, in addition, digital murals are large and high resolution, it is unreasonable and impractical to use the original digital murals for training and then restoring the missing areas. Therefore, a data augmentation method based on sliding window technique is applied to increase samples and then improve the model accuracy. Experimental results have shown that the proposed strategy has a certain effect on the restoration of digital Dunhuang murals.
VEDI: Vision Exploitation for Data Interpretation

We present VEDI (Vision Exploitation for Data Interpretation), an integrated system to jointly assist the visitors of cultural sites and provide meaningful statistics about the visits to the managers of the sites. To address both goals, VEDI includes a wearable assistant (implemented through a wearable device such as HoloLens) which leverages Computer Vision algorithms to understand where the user is and what they are paying attention to. At the visitor's end, such information is leveraged to augment the visit by displaying additional information on the observed points of interest, helping the visitors to navigate the site and suggesting what to see next. Concurrently, a back-end extracts high-level behavioral information from the captured video content which is used to provide the site manager with meaningful statistics and performance indexes on the cultural site. Experiments show that VEDI achieves good results on both the indoor and outdoor cultural sites considered for the experimentation.
Finding next of kin: Cross-lingual embedding spaces for related languages

Abstract Some languages have very few NLP resources, while many of them are closely related to better-resourced languages. This paper explores how the similarity between the languages can be utilised by porting resources from better- to lesser-resourced languages. The paper introduces a way of building a representation shared across related languages by combining cross-lingual embedding methods with a lexical similarity measure which is based on the weighted Levenshtein distance. One of the outcomes of the experiments is a Panslavonic embedding space for nine Balto-Slavonic languages. The paper demonstrates that the resulting embedding space helps in such applications as morphological prediction, named-entity recognition and genre classification.
R-HOG Feature-Based Off-Line Odia Handwritten Character Recognition

Offline handwritten recognition system for Odia characters has received attention in the last few years. Although the recent research showed that there has been lots of work reported in different language, there is limited research carried out in Odia character recognition. Most of Odia characters are round in nature, similar in orientation and size also, which increases the ambiguity among characters. This chapter has harnessed the rectangle histogram-oriented gradient (R-HOG) for feature extraction method along with the principal component analysis. This gradient-based approach has been able to produce relevant features of individual ones in to the proposed model and helps to achieve high recognition rate. After certain simulations, the respective analysis of classifier shows that SVM performed better than quadratic. Among them, SVM produces with 98.8% and QC produces 96.8%, respectively, as recognition rate. In addition to it, the authors have also performed the 10-fold cross-validation to make the system more robust.
Multilingual News–An Investigation of Consumption, Querying, and Search Result Selection Behaviors

Chenjun Linga* , Ben Steichenb & Silvia Figueiraaa Department of Computer Engineering, Santa Clara University, Santa Clara, CA, USAb Department of Computer Science, California State Polytechnic University, Pomona, CA, USAChenjun Ling Ph.D. candidate in the Computer Science and Engineering at Santa Clara University. Her research interests include Information Retrieval, HCI, and Data Science.Ben Steichen is an Assistant Professor in the Computer Science Department at California State Polytechnic University, Pomona. Dr. Steichen's research focuses on Human-Centered Computing and Personalized Information Access, which involves techniques and concepts from the fields of HCI, Information Retrieval & Visualization, and Web & Data Science.Silvia Figueira is a Professor of Computer Science and Engineering at Santa Clara University and the director of the SCU Frugal Innovation Hub. Her research is in the area of performance evaluation and prediction, recently with a focus on energy efficiency.
Improving the Performance of Online Bitrate Adaptation with Multi-Step Prediction Over Cellular Networks

Video streaming over mobile is flourishing, and most commercial players use adaptive bitrate (ABR) streaming to deliver video in varying network conditions. Using network capacity and buffer occupancy as system states, ABR algorithms adjust bitrate based on the instantaneous system states, which is able to adapt to network changes in real-time and ensure high quality of experience (QoE). However, they are incapable of providing good QoE over mobile. Due to the high dynamic characteristics of cellular network, the system states change rapidly over time. The instantaneous state-based adaptation can induce significant video quality fluctuation which greatly degrades QoE. In this paper, we propose an online ABR algorithm called MSPC to provide good QoE in cellular network. To balance the conflict between rapid adaptation and smooth bitrate, MSPC utilizes the multi-step prediction of future system states to select bitrates instead of the instantaneous current states. At the same time, it controls the buffer occupancy to eliminate the impact of prediction error on performance. We implement MSPC on a reference video player with performance evaluated based on realistic cellular traces. Experimental results show that MSPC reduces the bitrate change of existing online algorithms by 62.4 percent on average while maintaining high bitrates and achieving zero rebuffering over 97.83 percent of all tested sessions.
Towards Enhancing User Experience through a Web-Based Augmented Reality Museum

Museums act as a vehicle to collect, preserve and demonstrate historical, cultural and scientific heritage to a larger community of end users. However, there is neither increase in the number of physical visitors nor visitors to online museum, despite their availability on the web. On the other hand, Augmented Reality has emerged as a potential technology to support and enhance experience of end users in different communities especially in digital heritage. In this paper, we propose Augmented Reality Museum (ARM) as an application that can enhance online museum visitor experience. Augmented Reality can be integrated as a mobile application that provides 3D view of an artifact, along with information about its historic, artistic and/or scientific importance. We present the design and development of ARM and demonstrate it for the case study of Online British Museum. Our user experience study conducted with 21 volunteers shows that ARM is potentially a good way to enhance user experience.
An Enhanced Model for Increasing Awareness of Vocational Students Against Phishing Attacks

Information security for connected devices with network system is under threat due to fraudulent activities, denial-of-service (DoS), and malware. Phishing attacks are a major fraud activity which can be controlled by human factors. This research is aimed to increase the information security awareness of the vocational students in Malaysia. An enhanced knowledge-attitude-behavior model has been developed to increase the awareness level on phishing attacks. In this model, the existing knowledge of the sample students is measured and repeated after educational treatment. The model hypothesis is validated by the collected data on the students and the model output is thoroughly analyzed. Thus, it demonstrates that the proposed model is an enhanced model in strengthening the information security knowledge and awareness of the students.
FSM Modeling of Testing Security Policies for MapReduce Frameworks

Nowadays, MapReduce becomes one of the most common computational paradigms that provide an efficient parallel processing of large-scale data especially in public clouds in order to enable users to process data without considering physical infrastructures and software installations. However, the deployment of MapReduce in such public environment needs to deal with many security threats attacks during networks communication. In this paper, we present a novel approach to test Access Control List (ACL) policies in MapReduce framework. The proposed system uses these ACLs to control which users can submit to certain queues as well as which users can administer a queue for example. To this end, we used the FSM formalism to write our system specification that takes into account these policies expressed in XACML language.
A Coarse-to-Fine Adaptive Kalman Filter for Weak GNSS Signals Carrier Tracking

The tracking performance of the standard Kalman filter (KF)-based carrier tracking depends heavily on the potentially insufficient priori noise covariance. The inadequate prior knowledge will degrade the tracking sensitivity when the receiver experiences weak signal conditions. To improve the tracking performance, a coarse-to-fine adaptive Kalman filter (AKF) carrier tracking approach is proposed. In this method, the process and measurement noise covariance are updated in real-time according to the measurement information. The AKF-based fine tracking is initialized by the coarse tracking results, which can improve the convergence speed of the AKF tracking. The proposed method has been conducted in the software receiver and compared with standard KF-based tracking and conventional tracking algorithms. The test results show that the proposed AKF-based tracking can improve tracking sensitivity under weak signal conditions. Moreover, the proposed method can reduce the convergence time compared with the AKF-based tracking without the coarse tracking stage.
Does interferometry work? A critical look at the foundations of interferometric surface topography measurement

Interferometers for the measurement of surface form and texture have a reputation for high performance. However, the results for many types of surface features can deviate from the expectation of one cycle of phase shift per half wavelength of surface height. Here we review the fundamentals of imaging interferometry and describe ways of defining instrument response, including the linear instrument transfer function. These considerations define practical regimes of linear behavior that are usually satisfied for traditional uses of interferometers; but that are increasingly challenged by applications involving complex textures and high surface slopes. We conclude by proposing pathways for further improving performance on difficult surface structures using advanced modeling techniques.
Facilitating collaborative design: a toolkit for integrating persuasive technologies in design activities

Motivation and focus of the participants are important success factors in collaborative design processes. Persuasive technologies can be applied in such processes to increase the motivation of the participants. Combining three different perspectives discussed in current literature, this paper presents a toolkit to support the creation and integration of persuasive technologies in collaborative design. The toolkit contains a navigator that guides the user through the selection of suitable examples from a set of 25 application scenarios. These contain a general description of the scenario, at least one example from extant literature, and persuasive design principles that support the implementation in the user’s collaborative design process.
A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

After the successful (yet continuing) era of both evolutionary and swarm based optimization algorithm, a new class of optimizations such as nature inspired optimization algorithms came into limelight. Although swarm intelligence based algorithms are a subset of nature inspired methods, but some methods are purely based on nature and its phenomenon. However, one of a leading swarm based algorithm is firefly optimization and has been a keen interest for solving many real world complex problems. In this chapter, focus has been attended for various applications of integrated firefly algorithm with neural network. Also, it is true that the research area of neural network is quite diversified and too vast. Since its inception, firefly algorithm has been efficiently used in neural network research to solve diversified applications. This chapter provides the detailed study about the applications and further, it discusses some of the major future challenges.
Towards the Identification of Concept Prerequisites Via Knowledge Graphs

Learning basic concepts before complex ones is a natural form of learning. This paper addresses the specific problem of identifying concept prerequisites to inform about the basic knowledge required to understand a particular concept. Briefly, given a target concept c, the goal is to (a) find candidate concepts in a Knowledge Graph (KG) that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our approach explores the DBpedia Knowledge Graph and its semantic relations to find candidate concepts as well as a pruning step to reduce the candidate concept set. Finally, we employ supervised learning algorithms to evaluate and generate a list of prerequisites for the target concept. A ground truth created based on expert knowledge is used to validate our approach, exhibiting promising results with a precision varying between 83% and 92.9%.
MSTGAR: Multioperator-Based Stereoscopic Thumbnail Generation With Arbitrary Resolution

At present, thumbnail generation for 2D images has been extensively studied, but the research in thumbnail generation for stereoscopic images is still relatively lacking. This paper presents a novel thumbnail generation technology for stereoscopic images based on multioperator with the following innovations: 1) The warping technique is used to retarget a stereopair into six-scale resolutions with different contexts, and the disparity is uniformly adjusted to a certain value based on just noticeable depth difference (JNDD) model, which overcomes the issues that 3D perception in stereoscopic thumbnail is uncontrollable and the sense of depth disappears in low-resolution stereoscopic images. 2) The six-scale images are cropped via cropping network, and are optimized to a target resolution based on the designed image visual representation energy. As a result, our method has better visual effect than state-of-the-art methods in generating thumbnail for stereoscopic display.
Manhattan distance-based histogram of oriented gradients for content-based medical image retrieval

Content-based medical image retrieval (CBMIR) is the emerging research area in the field of medical image retrieval and analysis. The proposed CBMIR system includes two phases: feature extraction and feature matching. In the current decades, the feature matching approaches are developed well, but still the feature extraction algorithms are facing difficulties such as poor result in illuminance, lighting variations, etc. For addressing these concerns, Manhattan-distance based histogram of oriented gradients (M-HOG) was proposed in this research paper by assuming the different contributions of the training and testing samples from (Dicom medical image dataset for computed tomography (CT) and magnetic resonance imaging (MRI) images). At first, the medical images were enhanced by using Gaussian filter. The respective pre-processed medical image features were extracted by applying M-HOG. Using a Euclidean distance measure, the similarity between the testing and training medical images was calculated and matched. In the experimental section, the proposed system improved the retrieval accuracy in CBMIR system up to 5–15% related to the existing methodologies.
Passengers’ Gesture Recognition Model in Self-driving Vehicles : Gesture Recognition Model of the Passengers’ Obstruction of the Vision of the Driver

The V2X environment has recently been developing. In this study, we model gestures to recognize the gestures of drivers and passengers occurring in vehicles in the V2X environment. The gesture modeled as physical, logical, and conceptual gesture that can occur in vehicles. Based on the 3 steps of modeled gesture, we review existing studies on gesture recognition in vehicles. After this, we identify physical gestures interfering with the driver's vision. Lastly, we propose a model for recognizing passengers' gestures interfering with drivers' forward gaze.
Progressive complex illumination image appearance transfer based on CNN

Image appearance transfer is the process of transferring the appearance features from the user-supplied reference image to the target image. Since traditional appearance transfer methods are based on low-level feature, it is difficult to obtain natural effect in the case of complex illumination between the target image and the reference image. This is mainly because that the appearance mapping relation with complex illumination is a highly nonlinear problem. Although traditional methods are good at dealing with linear transformations, they are less suitable for solving the highly nonlinear problems caused by such complex illumination. Moreover, convolutional neural network (CNN) can extract the hierarchical abstraction features at different levels of the network layer, so it can be conveniently used to realize progressive transfer. In this paper, we propose a novel method for progressive appearance transfer for images with complex illumination. Firstly, we convert the input images from the RGB color space to the HSV color space. The illumination transfer is carried out only in the illumination channel of the image, and for the other two channels, the color distribution of the reference image is transferred to the target image. Secondly, CNN is specially designed to extract the hierarchical feature maps. To achieve progressive transfer, the histogram reshaping method is carried out by using the hierarchical feature maps extracted from the CNN. The appearance transfer results are obtained after the illumination transfer and color transfer. To optimize the transfer results, we adopt the joint bilateral filter to smooth the noises. The experimental results show that our method can effectively solve the problem of progressive appearance transfer for images with complex illumination.
Enabling Secure Voice Input on Augmented Reality Headsets using Internal Body Voice

Voice-based input is usually used as the primary input method for augmented reality (AR) headsets due to immersive AR experience and good recognition performance. However, recent researches have shown that an attacker can inject inaudible voice commands to the devices that lack voice verification. Even if we secure voice input with voice verification techniques, an attacker can easily steal the victim’s voice using low-cast handy recorders and replay it to voice-based applications. To defend against voice-spoofing attacks, AR headsets should be able to determine whether the voice is from the person who is using the AR headsets. Existing voice-spoofing defense systems are designed for smartphone platforms. Due to the special locations of microphones and loudspeakers on AR headsets, existing solutions are hard to be implemented on AR headsets. To address this challenge, in this paper, we propose a voice-spoofing defense system for AR headsets by leveraging both the internal body propagation and the air propagation of human voices. Experimental results show that our system can successfully accept normal users with average accuracy of 97% and defend against two types of attacks with average accuracy of at least 98%.
Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Attribute Self-Selection

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings.
Modeling and Code Generation Framework for IoT

In the Internet of Things (IoT) every physical device has an embedded technology that interacts with internal and external states. The heterogeneity of devices and networks complicates the mission of implementing and integrating the objects in IoT systems. In this paper, we present our model driven code generation framework, called CAPSml. The framework enables IoT designers and architects who are using CAPS environment to transform CAPS software model into ThingML model. CAPS is an architecture-driven modeling framework for the development of IoT Systems. ThingML includes modeling language and framework designed for IoT systems to support code generation for multi-platform targets.
Facilitating Students’ Digital Competence: Did They Do It?

The ability to facilitate the development of students' digital competence has become an integral part of teachers' professional demands. The focus on this area is relatively recent and therefore research available is limited. This paper aims at presenting students' perceptions regarding teachers' use of tablets to facilitate the development of their digital competence, when performing information and communication related tasks. Data were collected using a questionnaire and four focus groups. Findings suggest the lack of didactical and pedagogical elements in guidelines provided to perform such tasks and a poor mediation role played in facilitating students' digital competence.