Reviewerly/exHarmony huggingface dataset:
An effective distributed predictive model with Matrix factorization and random forest for Big Data recommendation systems

Recommendation systems have been widely deployed to address the challenge of overwhelming information. They are used to enable users to find interesting information from a large volume of data. However, in the era of Big Data, as data become larger and more complicated, a recommendation algorithm that runs in a traditional environment cannot be fast and effective. It requires a high computational cost for performing the training task, which may limit its applicability in real-world Big Data applications. In this paper, we propose a novel distributed recommendation solution for Big Data. It is designed based on Apache Spark to handle large-scale data, improve the prediction quality, and address the data sparsity problem. In particular, thanks to a novel learning process, the model is able to significantly speed up the distributed training, as well as improve the performance in the context of Big Data. Experimental results on three real-world data sets demonstrate that our proposal outperforms existing recommendation methods in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and computational time.
2D, 3D or speech? A case study on which user interface is preferable for what kind of object interaction in immersive virtual reality

Recent developments in human machine interaction offer three principal different approaches to interact with 3D environments, namely: 2D overlays using icons, 3D interfaces resembling interactions of the real world and speech interfaces which matured in the last years and are becoming more and more popular in other context such as smartphones or smart homes. Faced with the task to select the best interaction strategy to interact with immersive environments one is left with best practice and literature. But neither offers a clear strategy and all the methods are used widely. In particular a consistent comparison providing insights on when to use what interface in immersive virtual environments is missing. In order to evaluate the relative strengths and weaknesses of each interface in relation to different tasks in immersive environments a quantitative user study has been conducted. Results showed significant differences on the interface performances according to different parameters: ease of learning (Speech and 2D are favored), uncomplicated handling (Speech), speed (Speech and 2D), overview (2D), fun (3D), comprehension (2D and 3D) and on how simple and efficient it is to handle text input (Speech).
Recommendation with diversity: An adaptive trust-aware model

Recommender systems have become an integral and critical part of various online businesses to achieve better user experience and drive customer and revenue growth. Recommendation accuracy and diversity are important criteria to evaluate recommender system performance. Many different strategies have been developed in existing literature to balance the trade-offs between accuracy and diversity. However, those methods often focus on a one-size-fit-all trade-off strategy without considering each individual user' specific recommendation situation, which leads to improvements only in individual diversity or aggregate diversity. In addition, the trust relationships among users have not been studied to improve the trade-off strategy aforementioned. In this paper, we propose an adaptive trust-aware recommendation model based on a new trust measurement developed using a user-item bipartite network. We show via experiments on three different datasets that our model can not only balance and adapt accuracy with both individual and aggregate diversities, but also achieve significant improvements on accuracy for cold-start users and long-tailed items.
Error analysis and auto correction of hybrid solar tracking system using photo sensors and orientation algorithm

In order to further improve the efficiency of daylight harvesting system, a high accuracy solar tracking system is required. The tracking error is the combined result of various sources, such as the azimuth rotational axis tilt, geo-positioning deviation, installation deviation, true north meridian deviation, calculation error and mismatch of photo sensors, etc. In this paper, we designed a dual-axis hybrid tracking system which uses GPS/BeiDou for geological location, photodiodes for closed loop tracking, and orientation algorithm for open loop tracking. The difference of our calculated the solar hour angle, altitude angle and azimuth angle to that of SOLPOS results are less than 1°. An initializing calibration is proposed to correct the errors caused by the mismatch of photodiodes. The installation deviation, geo-positioning error and true north meridian deviation for orientation algorithm are also analyzed and corrected. This field test shows that the overall tracking accuracy of the system is improved after the second dynamic compensation. The tracking system can be used in a low cost and small form factor solar tracker with easy setup by providing geological location, orientation sensing and auto corrections for weight and wind load if the photo sensors are positioned at the deflection location.
An Invoice Reading System Using a Graph Convolutional Network

In this paper, we present a model-free system for reading digitized invoice images, which highlights the most useful billing entities and does not require any particular parameterization. The power of the system lies in the fact that it generalizes to both seen and unseen layouts of invoice. The system first breaks down the invoice data into various set of entities to extract and then learns structural and semantic information for each entity to extract via a graph structure, which is later generalized to the whole invoice structure. This local neighborhood exploitation is accomplished via a Graph Convolutional Network (GCN). The system digs deep to extract table information and provide complete invoice reading upto 27 entities of interest without any template information or configuration with an excellent overall F-measure score of 0.93.
A new deep learning-based method to detection of copy-move forgery in digital images

Thanks to ease of using image manipulation programs, copy move forgery is the easiest image modification approach, which aims to duplicate or remove objects in the image. The methods to detect this type of forgeries are partitioned into; block-based, keypoint-based approach that use hand-crafted features. A new deep learning-based forgery detection scheme is presented. An existing trained model AlexNet is utilized to extract feature vectors of image overlapped subblocks. After obtaining features, the similarity between feature vectors has been investigated for the detection and localization of forgery. The method has higher accuracy rate than the considered traditional method in the literature as reported in the obtained test results.
Blockchain Applications in Shipping, Transportation, Logistics, and Supply Chain

Blockchain, as a new technology, has aroused wide attention from industry and academia in recent years. There are a huge number of articles and news reports about blockchain applications in shipping, transportation, logistics, and supply chain. This short tutorial aims to clarify (i) the key characteristics of blockchain that enable its use in shipping, transportation, logistics, and supply chain and (ii) how these key characteristics are applied. The key characteristics are decentralization, open data, and authenticity of data. The applications include smart contract, fast payment, information sharing, track and trace, and supply chain finance. We hope this tutorial can provide introductory knowledge to practitioners in shipping, transportation, logistics, and supply chain people who are not familiar with blockchain.
Image Neural Style Transfer With Global and Local Optimization Fusion

This paper presents a new image synthesis method for image style transfer. For some common methods, the textures and colors in the style image are sometimes applied inappropriately to the content image, which generates artifacts. In order to improve the results, we propose a novel method based on a new strategy that combines both local and global style losses. On the one hand, a style loss function based on a local approach is used to keep the style details. On the other hand, another style loss function based on global measures is used to capture more global structural information. The results on various images show that the proposed method reduces artifacts while faithfully transferring the style image's characteristics and preserving the structure and color of the content image.
A Joint Context-Aware Embedding for Trip Recommendations

Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they jointly impact on a user's choice of POI visits. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a context-aware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our context-aware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm finds a heuristically optimal trip based on adaptive large neighborhood search. We perform extensive experiments on real datasets. The results show that our proposed algorithms consistently outperform state-of-the-art algorithms in trip recommendation quality, with an advantage of up to 43% in F_1-score.
Exploring Noticing as Method in Design Research

The aim of this one-day workshop is to explore, practice, and develop methodological approaches for HCI researchers and practitioners to "notice differently" and envision more ethical and responsible ways of engaging in technological interventions. In this workshop, we will focus on what anthropologist Anna Tsing calls the "arts of noticing", methods of looking beyond progress narratives, cultivating awareness of diverse actors, and engaging in alternative ways of knowing (e.g. embodied knowledge and activist commitments) in design research and practice. The workshop will include discussion, a walkshop, and hands-on group exercises that develop "arts of noticing" appropriate to the DIS community.
A saliency dataset for 360-degree videos

Despite the increasing popularity, realizing 360-degree videos in everyday applications is still challenging. Considering the unique viewing behavior in head-mounted display (HMD), understanding the saliency of 360-degree videos becomes the key to various 360-degree video research. Unfortunately, existing saliency datasets are either irrelevant to 360-degree videos or too small to support saliency modeling. In this paper, we introduce a large saliency dataset for 360-degree videos with 50,654 saliency maps from 24 diverse videos. The dataset is created by a new methodology supported by psychology studies in HMD viewing. We describe an open-source software implementing this methodology that can generate saliency maps from any head tracking data. Evaluation of the dataset shows that the generated saliency is highly correlated with the actual user fixation and that the saliency data can provide useful insight on user attention in 360-degree video viewing. The dataset and the program used to extract saliency are both made publicly available to facilitate future research.
When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images

Automated object detectors on Unmanned Aerial Vehicles (UAVs) are increasingly employed for a wide range of tasks.However, to be accurate in their specific task they need expensive ground truth in the form of bounding boxes or positional information.Weakly-Supervised Object Detection (WSOD) overcomes this hindrance by localizing objects with only image-level labels that are faster and cheaper to obtain, but is not on par with fully-supervised models in terms of performance.In this study we propose to combine both approaches in a model that is principally apt for WSOD, but receives full position ground truth for a small number of images.Experiments show that with just 1% of densely annotated images, but simple imagelevel counts as remaining ground truth, we effectively match the performance of fully-supervised models on a challenging dataset with scarcely occurring wildlife on UAV images from the African savanna.As a result, with a very limited amount of precise annotations our model can be trained with ground truth that is orders of magnitude cheaper and faster to obtain while still providing the same detection performance.
Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning

Deep Reinforcement Learning has been applied in a number of fields to directly optimize non-differentiable reward functions, including in sequence to sequence settings using Self Critical Sequence Training (SCST). Previously, SCST has primarily been applied to bring conditional language models closer to the distribution of their training set, as in traditional neural machine translation and abstractive summarization. We frame the generation of search engine text ads as a sequence to sequence problem, and consider two related goals: to generate ads similar to those a human would write, and to generate ads with high click-through rates. We jointly train a model to minimize cross-entropy on an existing corpus of Landing Page/Text Ad pairs using typical sequence to sequence training techniques while also optimizing the expected click-through rate (CTR) as predicted by an existing oracle model using SCST. Through joint training we achieve a 6.7% increase in expected CTR without a meaningful drop in ROUGE score. Human experiments demonstrate that SCST training produces significantly more attractive ads without reducing grammatical quality.
Training Deep Photonic Convolutional Neural Networks With Sinusoidal Activations

Deep learning (DL) has achieved state-of-the-art performance in many challenging problems. However, DL requires powerful hardware for both training and deployment, increasing the cost and energy requirements and rendering large-scale applications especially difficult. Recognizing these difficulties, several neuromorphic hardware solutions have been proposed, including photonic hardware that can process information close to the speed of light and can benefit from the enormous bandwidth available on photonic systems. However, the effect of using these photonic-based neuromorphic architectures, which impose additional constraints that are not usually considered when training DL models, is not yet fully understood and studied. The main contribution of this paper is an extensive study on the feasibility of training deep neural networks that can be deployed on photonic hardware that employ sinusoidal activation elements, along with the development of methods that allow for successfully training these networks, while taking into account the physical limitations of the employed hardware. Different DL architectures and four datasets of varying complexity were used for extensively evaluating the proposed method.
Implementation of an E-Voting Scheme Using Hyperledger Fabric Permissioned Blockchain

Since the issue of using e-voting in both corporate and government voting has not yet been fully resolved, there remains a wide scope for improving existing approaches and proposing new protocols enabling the voting system to be resistant to various kinds of attacks. Due to the rapid development of distributed ledger technologies and their potential for solving existing problems we propose a modified protocol of the published earlier voting scheme which is complemented by blockchain technology to increase trust between participants. This approach allows carrying out combined voting of both traditional paper voting and e-voting. In this paper we describe the architecture of our solution, discuss its implementation based on Hyperledger Fabric platform and demonstrate its functionality.
Prediction of global solar radiation using a single empirical model for diversified locations across India

The primary aim of the present work is to develop a single Global Solar Radiation model applicable to the diversified locations across India. For the development of the global solar radiation models, meteorological data of 15-years are analyzed which were obtained from the Indian Meteorological Department. The performance of the global solar radiation models is tested using different statistical tests and also compared against those available in the literature. Global Performance Indicator is used to rank the established models. For the prediction of monthly average daily global solar radiation for the larger area, the first order Angström-Prescott equation was not found suitable and it was recommended that it should be replaced by higher order relation. Also, the relative humidity, latitude, and altitude were found to be the dominating parameter. The developed model M-03 is found to be applicable for all part of India with a maximum error of −11.8935% whereas the coefficient of determination was found in the range of 0.8454 to 0.9853. Also, the confidence interval (95%) was found to lie between 0.7763 MJ/m2 to 2.7386 MJ/m2.
Effectiveness of and user preferences for security awareness training methodologies

Phishing is a primary vector used in cyber-attacks, and current technical measures are not sufficient to reduce their success to an acceptable level. Empowering users to identify phishing emails is crucial; thus, anti-phishing training is essential. We investigate participant phishing susceptibility in a 2 × 2 mixed factorial design to determine if instructor-led classroom training, in addition to a multiple approach video-, game-, and text-based training package, offers a significant difference in susceptibility reduction compared with the absence of classroom training. The results suggest an insignificant improvement in reducing phishing susceptibility by incorporating classroom training. Furthermore, we observe a significant preference from the participants for one training method (i.e., classroom training) only if a decision for one particular method was required.
Tool Choice Matters: JavaScript Quality Assurance Tools and Usage Outcomes in GitHub Projects

Quality assurance automation is essential in modern software development. In practice, this automation is supported by a multitude of tools that fit different needs and require developers to make decisions about which tool to choose in a given context. Data and analytics of the pros and cons can inform these decisions. Yet, in most cases, there is a dearth of empirical evidence on the effectiveness of existing practices and tool choices. We propose a general methodology to model the time-dependent effect of automation tool choice on four outcomes of interest: prevalence of issues, code churn, number of pull requests, and number of contributors, all with a multitude of controls. On a large data set of npm JavaScript projects, we extract the adoption events for popular tools in three task classes: linters, dependency managers, and coverage reporters. Using mixed methods approaches, we study the reasons for the adoptions and compare the adoption effects within each class, and sequential tool adoptions across classes. We find that some tools within each group are associated with more beneficial outcomes than others, providing an empirical perspective for the benefits of each. We also find that the order in which some tools are implemented is associated with varying outcomes.
Blowing in the wind: Increasing social presence with a virtual human via environmental airflow interaction in mixed reality

In this paper, we describe two human-subject studies in which we explored and investigated the effects of subtle multimodal interaction on social presence with a virtual human (VH) in mixed reality (MR). In the studies, participants interacted with a VH, which was co-located with them across a table, with two different platforms: a projection based MR environment and an optical see-through head-mounted display (OST-HMD) based MR environment. While the two studies were not intended to be directly comparable, the second study with an OST-HMD was carefully designed based on the insights and lessons learned from the first projection-based study. For both studies, we compared two levels of gradually increased multimodal interaction: (i) virtual objects being affected by real airflow (e.g., as commonly experienced with fans during warm weather), and (ii) a VH showing awareness of this airflow. We hypothesized that our two levels of treatment would increase the sense of being together with the VH gradually, i.e., participants would report higher social presence with airflow influence than without it, and the social presence would be even higher when the VH showed awareness of the airflow. We observed an increased social presence in the second study when both physical–virtual interaction via airflow and VH awareness behaviors were present, but we observed no clear difference in participant-reported social presence with the VH in the first study. As the considered environmental factors are incidental to the direct interaction with the real human, i.e., they are not significant or necessary for the interaction task, they can provide a reasonably generalizable approach to increase social presence in HMD-based MR environments beyond the specific scenario and environment described here.
Gesture and speech elicitation for 3D CAD modeling in conceptual design

Multimodal interaction techniques using gesture and speech offer architects and engineers a natural way to create 3D CAD (computer-aided design) models for conceptual design. Gestures and speech for such interfaces must be based on empirically grounded knowledge instead of arbitrary vocabularies as employed in extant literature. We conducted an experiment with 41 participants from architecture and engineering backgrounds to elicit preferences of gestures and speech for 3D CAD modeling referents such as primitives, manipulations, and navigation. In this paper, we present results from the thematic analysis of the elicited gestures. We present a compilation of gestures, which were evaluated by experts to be suitable for the articulation of 3D CAD modeling referents. We also present a set of speech command terms elicited from participants. Finally, we provide recommendations for the design of a gesture and speech based CAD modeling system for conceptual design.