Reviewerly/exHarmony huggingface dataset:
Using an Eye Gaze New Combined Approach to Control a Wheelchair Movement

Persons with mobility impairments have a lower quality of life, being affected at physical, psychological, social and financial level. More than a half of wheelchair users have difficulty going outside their homes, and this is reflected on their employment rate - only seventeen percent are employed. More than half of power wheelchair users reported difficulty in learning how to maneuver a traditional power wheelchair. The progress registered in mobile robotics allowed the creation of smart wheelchairs, which opens the opportunity for new ways to develop user interfaces and even to add navigation aids - thus approaching the difficulties in learning how to maneuver the wheelchair. An intuitive way to give commands is using the eye gaze. This paper presents a smart user interface, based on eye gaze tracking, called Display/Eye Tracker device Set (DETS). DETS is a highly customizable user interface in relation to the specific, particular needs for each wheelchair user. DETS is also a command input for a semi-autonomous navigation system, based on VHF+ algorithm, in which the wheelchair user is able to select a destination in a local mapped space; the wheelchair control system will then move towards the selected destination in autonomous mode.
Sentiment analysis of customer data

This paper presents an application of sentiment analysis on customer feedback data in the area of heavy equipment repair services. Sentiment analysis is used as a part of a framework for text mining-based Customer Loyalty Improvement Recommender System (CLIRS). In order to provide business users of the system with accurate predictions for customer satisfaction metrics, the original algorithm for the opinion mining needed to be improved. The paper presents the background of the proposed approach, the current techniques used to mine text data and existing applications of sentiment analysis. We propose an aspect-based, taxonomy-driven approach for customized sentiment analysis. The contribution of this paper is the implementation and evaluation of the proposed methods that improve the accuracy and coverage of the opinion mining algorithm. The improvements are illustrated with examples covered by the algorithm in the customer dataset. The application of the proposed methods resulted in increasing the algorithm’s accuracy from 92% to 96%, and coverage from 36% to 48%. This research is an attempt to handle well-known issues in natural language processing that are currently not handled by text mining algorithms, such as ambiguity and context, opinionated verbs/nouns, subject recognition from pronouns. This is significant because the proposed techniques are generalizable to any application that uses sentiment analysis algorithm.
What are Effective Strategies of Handling Harassment on Twitch?

Harassment is an issue in online communities with the live streaming platform Twitch being no exception. In this study, we surveyed 375 Twitch users in person at TwitchCon, asking them about who should be responsible for deciding what should be allowed and what strategies they perceived to be effective in handling harassment. We found that users thought that streamers should be most responsible for enforcing rules and that either blocking bad actors, ignoring them, or trying to educate them were the most effective strategies.
Emoji Use in Twitter White Nationalism Communication

Hate crimes have risen in recent years in the United States and white nationalism appears related to the phenomenon [6]. A portion of the communication regarding white nationalism is taking place online through social media. We report the preliminary results of emoji use by actors involved with the white nationalist conversation on Twitter, both those who support and who oppose the ideology. We investigated the frames and meanings of emoji characters expressed by actors in defining their own identities. We found strikingly different emoji use between the two groups of actors. We present on notable emoji use by those who support and those who oppose the white nationalist ideology.
Correction Tower: A General Embedding Method of the Error Recognition for the Knowledge Graph Correction

Today, knowledge graphs (KGs) are growing by enrichment and refinement methods. The enrichment and refinement can be gained using the correction and completion of the KG. The studies of the KG completion are rich, but less attention has been paid to the methods of the KG error correction. The correction methods are divided into embedding and nonembedding methods. Embedding correction methods have been recently introduced in which a KG is embedded into a vector space. Also, existing correction approaches focused on the recognition of the three types of errors, the outliers, inconsistencies and erroneous relations. One of the challenges is that most outlier correction methods can recognize only numeric outlier entities by nonembedding methods. On the other hand, inconsistency errors are recognized during the knowledge extraction step and existing methods of this field do not pay attention to the recognition of these errors as post-correction by embedding methods. Also, to correct erroneous relations, new embedding techniques have not been used. Since the errors of a KG are variant and there is no method to cover all of them, a new general correction method is proposed in this paper. This method is called correction tower in which these three error types are corrected in three trays. In this correction tower, a new configuration will be suggested to solve the above challenges. For this aim, a new embedding method is proposed for each tray. Finally, the evaluation results show that the proposed correction tower can improve the KG error correction methods and proposed configuration can outperform previous results.
Millimeter Wave meets Edge Computing for Mobile VR with High-Fidelity 8K Scalable 360° Video

We investigate a novel multiple user scalable 8K 360° video mobile virtual reality arcade streaming system that enables high reliability and immersion fidelity, and low interactive latency, by the synergistic integration of scalable 360° content, expected VR user viewport modeling, millimeter wave (mmWave) communication and network edge computation capability. The high data rate mmWave link is used to transmit the video content of the expected user 360 viewport at enhanced quality. To compensate for the dynamic nature of mmWave links and prospective expected viewport characterization error, we integrate a fall back transmission based on Wi-Fi broadcast of a baseline representation of the 360 panorama to all users. In our proposed transmission strategy, the expected viewport content can be sent as raw or encoded at different qualities, which enhances the end-to-end performance, by exploiting effective trade-offs between communication and computation latency at the receiving user. With the aim of maximizing the minimum VR immersion fidelity across all users, we investigate the joint optimization of the mmWave access point (AP) to user association, the data rate for the encoded portion of the 360 viewport content that is to be transmitted, and computation resource allocation. Our experimental results demonstrate that the proposed system can achieve significant improvement in delivered VR user immersion fidelity and quality of experience relative to a state-of-the-art reference method that leverages Wi-Fi transmission only.
Academic Excellence, Website Quality, SEO Performance: Is there a Correlation?

The academic excellence of universities around the globe has always been a matter of extended study and so has the quality of an institution’s presence in the World Wide Web. The purpose of this research is to study the extent to which a university’s academic excellence is related to the quality of its web presence. In order to achieve this, a method was devised that quantified the website quality and search engine optimization (SEO) performance of the university websites of the top 100 universities in the Academic Ranking of World Universities (ARWU) Shanghai list. A variety of tools was employed to measure and test each website and produced a Web quality ranking, an SEO performance ranking, as well as a combined overall web ranking for each one. Comparing these rankings with the ARWU shows that academic excellence is moderately correlated with website quality, but SEO performance is not. Moreover, the overall web ranking also shows a moderate correlation with ARWU which seems to be positively influenced by website quality and negatively by SEO performance. Conclusively, the results of the research indicate that universities place particular emphasis on issues concerning website quality, while the utilization of SEO does not appear to be of equal importance, indicating possible room for improvement in this area.
Compressing Deep Neural Networks With Sparse Matrix Factorization

Modern deep neural networks (DNNs) are usually overparameterized and composed of a large number of learnable parameters. One of a few effective solutions attempts to compress DNN models via learning sparse weights and connections. In this article, we follow this line of research and present an alternative framework of learning sparse DNNs, with the assistance of matrix factorization. We provide an underlying principle for substituting the original parameter matrices with the multiplications of highly sparse ones, which constitutes the theoretical basis of our method. Experimental results demonstrate that our method substantially outperforms previous states of the arts for compressing various DNNs, giving rich empirical evidence in support of its effectiveness. It is also worth mentioning that, unlike many other works that focus on feedforward networks like multi-layer perceptrons and convolutional neural networks only, we also evaluate our method on a series of recurrent networks in practice.
Grasper

The property graph (PG) model is one of the most general graph data model and has been widely adopted in many graph analytics and processing systems. However, existing systems suffer from poor performance in terms of both latency and throughput for processing online analytical workloads on PGs due to their design defects such as expensive interactions with external databases, low parallelism, and high network overheads. In this paper, we propose Grasper, a high performance distributed system for OLAP on property graphs. Grasper adopts RDMA-aware system designs to reduce the network communication cost. We propose a novel query execution model, called Expert Model, which supports adaptive parallelism control at the fine-grained query operation level and allows tailored optimizations for different categories of query operators, thus achieving high parallelism and good load balancing. Experimental results show that Grasper achieves low latency and high throughput on a broad range of online analytical workloads.
Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation given only 2D annotations in Maximum Intensity Projections (MIP) of the training volumes. This significantly reduces the annotation time: We conducted a user study that suggests that annotating 2D projections is on average twice as fast as annotating the original 3D volumes. Our technical contribution is a loss function that evaluates a 3D prediction against annotations of 2D projections. It is inspired by space carving, a classical approach to reconstructing complex 3D shapes from arbitrarily-positioned cameras. It can be used to train any deep network with volumetric output, without the need to change the network’s architecture. Substituting the loss is all it takes to enable 2D annotations in an existing training setup. In extensive experiments on 3D light microscopy images of neurons and retinal blood vessels, and on Magnetic Resonance Angiography (MRA) brain scans, we show that, when trained on projection annotations, deep delineation networks perform as well as when they are trained using costlier 3D annotations.
Exploring the Use of a Robust Depth-sensor-based Avatar Control System and its Effects on Communication Behaviors

To interact as fully-tracked avatars with rich hand gestures in Virtual Reality (VR), we often need to wear a tracking suit or attach extra sensors on our bodies. User experience and performance may be impacted by the cumbersome devices and low fidelity behavior representations, especially in social scenarios where good communication is required. In this paper, we use multiple depth sensors and focus on increasing the behavioral fidelity of a participant’s virtual body representation. To investigate the impact of the depth-sensor-based avatar system (full-body tracking with hand gestures), we compared it against a controller-based avatar system (partial-body tracking with limited hand gestures). We designed a VR interview simulation for a single user to measure the effects on presence, virtual body ownership, workload, usability, and perceived self-performance. Specifically, the interview process was recorded in VR, together with all the verbal and non-verbal cues. Subjects then took a third-person view to evaluate their previous performance. Our results show that the depth-sensor-based avatar control system increased virtual body ownership and also improved the user experience. In addition, users rated their non-verbal behavior performance higher in the full-body depth-sensor-based avatar system.
Is seeing believing? The effects of virtual reality on young children’s understanding of possibility and impossibility

research group.His main area of research is the use of digital technologies to support learning.For example, he has conducted research on the potential of tablets for supporting children's learning of physics and the role of digital games, such as "Racing Academy", for promoting students' learning.Paul Golds is a research engineer at BBC Research & Development.His current projects focus on methods of using real-time graphics technologies including virtual reality, light fields, and novel uses of 360° video.His previous work has included augmenting broadcast video with timed interactive graphic overlays to tailor content individually for users, and large-scale metadata search systems. Is Seeing Believing? The Effects of Virtual Reality on Young UK Children's Judgments of Possibility and ImpossibilityThis study explored the effects of virtual reality on young children's understanding of possibility and impossibility.It involved four-year-old children (30 boys and 30 girls) who were randomly allocated to a virtual reality group, a video group, or a picture book group.Each child was individually presented with three impossible and three matched possible events using their assigned medium.After each event, children were asked whether it was possible in real life and why/why not.Children in the VR group were more likely to correctly judge the possibility of possible events than children in the video group and they were more likely to incorrectly judge the possibility of impossible events than children in the video group.Furthermore, they were more likely to correctly judge the possibility of possible events than impossible events.The results suggest that virtual reality affects four-year-old children's understanding of possibility and impossibility.Practical implications of these findings are discussed.
Historical Document Synthesis with Generative Adversarial Networks

The following topics are dealt with: learning (artificial intelligence); document image processing; convolutional neural nets; text analysis; handwritten character recognition; feature extraction; neural nets; optical character recognition; image segmentation; image recognition.
A probabilistic model for assigning queries at the edge

Abstract Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.
Model of variability estimation: factors influencing human prediction and estimation of variability in continuous information

Understanding the variability of trends and other continuously distributed quantities is a vital ability underlying many safety critical decisions, such as how widely to search for a downed aircraft, or whether to prepare for evacuation in the face of an uncertain hurricane or hurricane track. We first review the sparse research on this topic which indicates a general systematic tendency to underestimate such variability, akin to overconfidence in the precision of prediction. However, the magnitude of such underestimation varies across experiments and research paradigms. Based on these existing findings, and other known biases and vulnerabilities of the perception and cognition of multiple instances, we define the core elements of a computational model that can itself predict three measures of performance in variability estimation: bias (to over or underestimate variability), sensitivity (to variability differences) and precision (of variability judgements). Factors and approximate weighting in influencing these measures are then identified regarding attention, the number of instances across whose variability is estimated, the time delay affecting the memory system employed, familiarity of material, the anchoring heuristic and the method of judgement. These are then incorporated into foundations for a linear additive model.
Towards Zero-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia.XEL is challenging for most languages because of limited availability of requisite resources.However, much previous work on XEL has been on simulated settings that actually use significant resources (e.g.source language Wikipedia, bilingual entity maps, multilingual embeddings) that are unavailable in truly low-resource languages.In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems.Next, we propose three improvements to both entity candidate generation and disambiguation that make better use of the limited data we do have in resource-scarce scenarios.With experiments on four extremely low-resource languages, we show that our model results in gains of 6-23% in end-to-end linking accuracy. 1
Virtual target point-based obstacle-avoidance method for manipulator systems in a cluttered environment

This article presents an obstacle-avoidance method for manipulator systems working in a cluttered environment. Based on the concept of the artificial potential field, a velocity potential field method is proposed, with the attractive velocity function designed in configuration space and the repulsive velocity function designed in Cartesian space. The attractive and repulsive velocities calculated by the local information together drive the manipulator to reach the target point without colliding with obstacles. To overcome the local minima problem, virtual target points are selected based on the rapidly exploring random trees method, which can consider the global information. The manipulator reaches the virtual target points one by one until the desired target is reached. The proposed method considers both global and local information and can significantly increase the probability of achieving a feasible and continuous trajectory for manipulator systems in cluttered environments. The simulation results verify the effectiveness of the proposed method.
Iterative user and expert feedback in the design of an educational virtual reality biology game

This study focuses on an educational game titled Cellverse, a two-player cross-platform VR project intended to teach high school biology students about cell structure and function. In Cellverse, players work in pairs to explore a human lung cell and diagnose and treat a dangerous genetic disorder. Cellverse is being designed by the Collaborative Learning Environments in Virtual Reality (CLEVR) team, an interdisciplinary team consisting of game designers, educational researchers, and graduate and undergraduate students. Using a design-based research approach, we have enlisted the help of both subject matter experts and user testers to iteratively design and improve Cellverse. The objective of this paper is to share how user and expert feedback can inform and enhance the development of learning games. We describe how we gather and synthesize information to review and revise our game from in-game observations, semi-structured interviews, and video data. We discuss the input of subject matter experts, present feedback from our user testers, and describe how input from both parties influenced the design of Cellverse. Our results suggest that including feedback from both experts and users has provided information that can clarify gameplay, instruction, subject portrayal, narrative, and in-game goals.
Identifying the Presence of Graphical Texts in Scene Images using CNN

Scene images contain graphical texts that usually carry dynamic information. Therefore, knowing the presence of graphical texts in images can help advance further processes, such as graphical text mining and recognition. The goal of the study is to distinguish scene images with graphical text from those without graphical text using the convolution neural network (CNN) based classification framework. For validation, four different publicly available image datasets: ICDAR2003, MSRA-TD500, SVT and 15-Scene were used and the results were encouraging.