Reviewerly/exHarmony huggingface dataset:
Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation

Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product.
Publication of scientific data from EU‐coordinated monitoring programmes and surveys

Open government data are about wide and free availability of public information created or collected by public entities. The International Open Data Charter and Findable, Accessible, Interoperable And Reusable Data (FAIR) data principles were selected as the guiding principles for the development of this report. A review of open data maturity reports indicated that most of the EU28+ are making significant progress in open government data, however there are different levels of adoption in food and feed safety organisations. EFSA's Scientific Data Warehouse (SDWH) access rules were defined in 2015 (EFSA, 2015a); this report extends them to an open-by-default approach. Precautions are specified to ensure people, products or food businesses are not identified, and that information deemed confidential is protected. A proactive scientific data publication process that is open, timely, comprehensive, comparable, interoperable, accessible and usable is defined. Publication can be performed on behalf of data providers by EFSA (Supported publications) or by organisations according to their own open data policy (self-publishers). Structural metadata to support interoperability and comply with data protection requirements is defined as are the minimum descriptive and administrative metadata to support accessibility and usability. Datasets will be published in the open evidence community Knowledge Junction and assigned a Digital Object Identifier to ensure persistence and allow tracking of reuse. API technology and Data Catalogue Vocabulary Application Profile (DCAT-AP) will be used to ensure integration with the EU open data ecosystem. Attribution and citation to give credit to parties that have contributed to the creation of datasets are emphasised. The working group identified many benefits of open data and proposes mechanisms to overcome perceived barriers. Upon endorsement, EFSA should proceed with the scheduling of the publication of data from the SDWH. New data collections with a requirement to store data in the SDWH would be subject to this open data approach.
Leveraging Design Innovation for Ensuring Creation of Value: An Approach to Identify the Corresponding Design Concerns Towards Enabling the Design Practice

Design Innovation aims at harnessing human creativity for value creation. Towards this aim, Design Innovation attempts to address simultaneously the dimensions of human desirability, technological feasibility, and business viability. The practice of design lends a handle to address each of these intersecting dimensions, whereby it leads to design outcomes, which correspond to respective business elements that are aimed at overall value creation and entrepreneurship. Design combined with Innovation makes the notion of value explicit, as innovation is exclusively about finding new ways of value creation. Accordingly, with respect to the context of Design Innovation, the design practice ought to explicitly address and ensure creation of value. This is where identifying the design concerns, in support of Design Innovation, with its exclusive emphasis on value creation, becomes very important. To this extent, it would require certain study and investigation. This is what has been attempted in the proposed paper.
Detection Accuracy of Soccer Players in Aerial Images Captured from Several Viewpoints

In the fields of professional and amateur sports, players’ health, physical and physiological conditions during exercise should be properly monitored and managed. The authors of this paper previously proposed a real-time vital-sign monitoring system for players using a wireless multi-hop sensor network that transmits their vital data. However, existing routing schemes based on the received signal strength indicator or global positioning system do not work well, because of the high speeds and the density of sensor nodes attached to players. To solve this problem, we proposed a novel scheme, image-assisted routing (IAR), which estimates the locations of sensor nodes using images captured from cameras mounted on unmanned aerial vehicles. However, it is not clear where the best viewpoints are for aerial player detection. In this study, the authors investigated detection accuracy from several viewpoints using an aerial-image dataset generated with computer graphics. Experimental results show that the detection accuracy was best when the viewpoints were slightly distant from just above the center of the field. In the best case, the detection accuracy was very good: 0.005524 miss rate at 0.01 false positive-per-image. These results are informative for player detection using aerial images and can facilitate to realize IAR.
Interactive exploratory soccer data analytics

Spatiotemporal soccer data enables in-depth analysis of a soccer game. However, the amount and the nature of the data makes it challenging for analysts to easily uncover insights from the data. In this article, we introduce an interactive visualization tool that uses novel data mining and machine learning methods to enable coaches and analysts to work on large amounts of data by moving most of the complicated models to the backend and presenting interactive visualization that can be manipulated in real-time. A unique interactive replay and modification feature enables creation of what-if scenarios on existing game data to explore alternative situations, such as a defensive player taking a different position or an offensive player choosing another pass, while making the experience seamless to the users.• Information systems ➝Information system applications • Applied computing ➝ Computers in other domains.
Flexible Inference for Cyberbully Incident Detection

We study detection of cyberbully incidents in online social networks, focusing on session level analysis. We propose several variants of a customized convolutional neural networks (CNN) approach, which processes users’ comments largely independently in the front-end layers, but while also accounting for possible conversational patterns. The front-end layer’s outputs are then combined by one of our designed output layers – namely by either a max layer or by a novel sorting layer, proposed here. Our CNN models outperform existing baselines and are able to achieve classification accuracy of up to 84.29% for cyberbullying and 83.08% for cyberaggression.
Building hierarchical structures for 3D scenes with repeated elements

We propose a novel hierarchy construction algorithm for 3D scenes with repeated elements, such as classrooms with multiple desk–chair pairs. Most existing algorithms focus on scenes such as bedrooms or living rooms, which rarely contain repeated patterns. Consequently, such methods may not recognize repeated patterns, which are vital for understanding the structure and context of scenes such as classrooms. Therefore, we propose a new global optimization algorithm for recognizing repeated patterns and building hierarchical structures based on repeated patterns. First, we find a repeated template by calculating the coverage ratios and frequencies of many substructures in a scene. Once the repeated template has been determined, a minimum cost maximum flow problem can be solved to find all instances (repetitions) of it in the scene and then group objects accordingly. Second, we group objects in the region outside the repeated elements according to their adjacency. Finally, based on these two sets of results, we build the hierarchy of the entire scene. We test this hierarchy construction algorithm on the Princeton and SceneNN databases and show that our algorithm can correctly find repeated patterns and construct a hierarchy that is more similar to the ground truth than the results of previous methods.
Self-learnable Cluster-based Prefetching Method for DRAM-Flash Hybrid Main Memory Architecture

This article presents a novel prefetching mechanism for memory-intensive workloads used in large-scale data centers. We design a negative-AND-flash/dynamic random-access memory (DRAM) hybrid memory architecture as a cost-effective memory architecture to resolve the scalability and power consumption problems of a DRAM-based model. A smart prefetching mechanism based on a cluster-management scheme to cope with dynamically varying and complex access patterns of any given application is designed for maximizing the performance of the DRAM. In this article, we propose a new concept for page management, called a cluster, which prefetches data in our hybrid memory architecture. The cluster management is based on a self-learning scheme on dynamically changeable access patterns by considering any correlation between missed pages. Experimental results show that the overall performance is significantly improved in relation to hit rate, execution time, and energy consumption. Namely, our proposed model can enhance the hit rate by 15% and reduce the execution time by 1.75 times. In addition, we can save energy consumption by around 48% by cutting the number of flushed pages to about an eighth of that in a conventional system.
Development through digital family stories in Bangladesh

Bangladesh has a rich history of storytelling for cultural, historical, social, and personal memories. Family stories in particular are powerful ways to reflect on relationships along with social values in a particular snapshot of time. Our goal is to derive insights for the design of culturally and value-sensitive digital storytelling tools for families in Bangladesh to preserve and share their stories. This paper describes ongoing work to understand the kinds of stories people tell, as well as the experience of listening to recorded stories.
Reducing Web Page Complexity to Facilitate Effective User Navigation

As a website evolves to align with users' changing information needs and interests, its structure can outgrow the original design, accumulating links and pages in unanticipated places. This increases complexity to both web pages and the navigation structure, which could cause difficulty in locating relevant links and information. Though the increasing complexity of website and its impact on users' psychological perception have been anecdotally well-recognized, the need to address it with a formal and rigorous method is understudied in the literature. This paper is one of the first studies to examine how to streamline website structures to enhance navigation. We use a widely used metric in the literature - a page's outdegree (the number of links in a page) - as the measurement complexity, because it not only serves as a good proximity for page complexity but also has a significant implication on website structure. We propose a method based on mathematical programming (MP) model that can significantly reduce users' cognitive load by effectively eliminating appropriate links from pages with high complexity. We have performed extensive experiments on both a real dataset and very large synthetic datasets with statistical similarities to the real dataset. The results indicate that our method not only significantly reduces web page and structure complexity with very small impact to user navigation, but also can be effectively solved and scales up remarkably well, suggesting it is useful for website maintenance on a progressive basis. In addition, we conduct a study to evaluate the performance of streamlined website structures using the real dataset and the results confirm the validity of our method.
List Decoding Random Euclidean Codes and Infinite Constellations

We study the list decodability of different ensembles of codes over the real alphabet under the assumption of an omniscient adversary. It is a well-known result that when the source and the adversary have power constraints <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N $ </tex-math></inline-formula> respectively, the list decoding capacity is equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {1}{2}\log \frac {P}{N}$ </tex-math></inline-formula> . Random spherical codes achieve constant list sizes, and the goal of the present paper is to obtain a better understanding of the smallest achievable list size as a function of the gap to capacity. We show a reduction from arbitrary codes to spherical codes, and derive a lower bound on the list size of typical random spherical codes. We also give an upper bound on the list size achievable using nested Construction-A lattices and infinite Construction-A lattices. We then define and study a class of infinite constellations that generalize Construction-A lattices and prove upper and lower bounds for the same. Other goodness properties such as packing goodness and AWGN goodness of infinite constellations are proved along the way. Finally, we consider random lattices sampled from the Haar distribution and show that if a certain conjecture that originates in analytic number theory is true, then the list size grows as a polynomial function of the gap-to-capacity.
Cognate Identification to improve Phylogenetic trees for Indian Languages

Cognates are present in multiple variants of the same text across different languages. Computational Phylogenetics uses algorithms and techniques to analyze these variants and infer phylogenetic trees for a hypothesized accurate representation based on the output of the computational algorithm used. In our work, we detect cognates among a few Indian languages namely Hindi, Marathi, Punjabi, and Sanskrit for helping build cognate sets for phylogenetic inference. Cognate detection helps phylogenetic inference by helping isolate diachronic sound changes and thus detect the words of a common origin. A cognate set manually annotated with the help of a lexicographer is generally used to automatically infer phylogenetic trees. Our work creates cognate sets of each language pair and infers phylogenetic trees based on a bayesian framework using the Maximum likelihood method.
On the support of inter-node P2P GPU memory copies in rCUDA

Although GPUs are being widely adopted in order to noticeably reduce the execution time of many applications, their use presents several side effects such as an increased acquisition cost of the cluster nodes or an increased overall energy consumption. To address these concerns, GPU virtualization frameworks could be used. These frameworks allow accelerated applications to transparently use GPUs located in cluster nodes other than the one executing the program. Furthermore, these frameworks aim to offer the same API as the NVIDIA CUDA Runtime API does, although different frameworks provide different degree of support. In general, and because of the complexity of implementing an efficient mechanism, none of the existing frameworks provides support for memory copies between remote GPUs located in different nodes. In this paper we introduce an efficient mechanism devised for addressing the support for this kind of memory copies among GPUs located in different cluster nodes. Several options are explored and analyzed, such as the use of the GPUDirect RDMA mechanism. We focus our discussion on the rCUDA remote GPU virtualization framework. Results show that is possible to implement this kind of memory copies in such an efficient way that performance is even improved with respect to the original performance attained by CUDA when GPUs located in the same cluster node are leveraged.
An investigation into cataloguers’ experiences with RDA

Cataloguing has been undergoing significant transformation for the last several years in order to keep up with the changing world of digital technologies, dramatically increasing the number of resources to be catalogued and data to be managed in such a way that would satisfy library users’ needs and expectations. Cataloguers had to face a shift in thinking about bibliographic data and consequently a shift in practice to the new cataloguing standard of Resource Description and Access. This research investigated cataloguers’ experiences with using RDA and the current issues they encounter and discuss while RDA is still being reviewed and shaped. It also documented and explored their opinions and concerns regarding understanding of theoretical concepts behind RDA and the new Library Reference Model. The investigation took place in a virtual environment of two of the most popular listservs dedicated to cataloguing and, in this way, allowed a direct and immediate access to opinions expressed by cataloguers around the world. In order to explore their experiences, a qualitative, based on elements of grounded theory, content analysis of archive and most recent posts was conducted and compared with the literature on the research done in the initial period after RDA implementation. The research results indicated some strong divisions among cataloguers and different levels of understanding of the changes being introduced. It also showed a potential paradigm shift in cataloguing, professional knowledge and mental flexibility required of cataloguers. Moreover, the significant amount of learning that cataloguers have had to and will have to undertake in the nearest future, suggests an inordinately important need for adequate training and dialogue between them and the RDA Steering Committee that would be led in a more comprehensible way to enable those who call themselves ‘average cataloguers’ understand the model better and become confident, rather than confused, practitioners.
Topology: A Theory of a Pseudometric-Based Clustering Model and Its Application in Content-Based Image Retrieval

The clustering problem has been extensively studied over the last 50 years; however, it still has the attention of researchers. This paper presents a topological basis of a pseudometric-based clustering model which takes into account the local and global topological properties of the data to be clustered, as per the definition of homogeneity measurement. The proposed approach takes into account the homogeneity effect produced when a new particle is added to a group. The additional element can be accumulated in the group if its local homogeneity is not altered and, therefore, it is not necessary to carry out tests in another group. A new group needs to be generated if the threshold of the local homogeneity of the group exceeds. Theoretical results, their implementation, and their application to the problem of Content Based Image Retrieval (CBIR) are presented. The tests were performed using three image databases widely used in the literature, which are “Vogel and Shiele,” “Oliva and Torralba,” and “L. Fei- Fei, R. Fergus and P. Perona.” The results are presented and compared with the most competitive methods available in the literature.
A Study and Comparison of Sentiment Analysis Techniques Using Demonetization

Sentiment analysis is an analytical approach that is used for text analysis. The aim of sentiment analysis is to determine the opinion and subjectivity of any opinion, review, or tweet. The aim of this chapter is to study and compare some of the techniques used to classify opinions using sentiment analysis. In this chapter, different techniques of sentiment analysis have been discussed with the case study of demonetization in India during 2016. Based on the sentiment analysis, people's opinion can be classified on different polarities such as positive, negative, or neutral. These techniques will be classified on different categories based on size of data, document type, and availability. In addition, this chapter also discusses various applications of sentiment analysis techniques in different domains.
Dynamic Cross-Layer Signaling Exchange for Real-Time and On-Demand Multimedia Streams

Multimedia streams consume a significant chunk of the consumer Internet traffic exchanged and will continue to do so due to the ever-increasing connection among people, businesses, and industries. To cope with the deviation of the Internet's intended use, unreliable underlying infrastructure, and best effort protocols while leveraging existing technologies, Hypertext Transfer Protocol Adaptive Streaming is utilized by numerous multimedia services. Performance of HAS-based streaming services is limited by the growing control overhead generated by the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as the stream length, multimedia fidelity, and network conditions vary. In this paper, a novel cross-layer steganographic-enabled signaling scheme is proposed to reduce service provider costs while improving multimedia session performance and maintaining expected Quality-of-Service (QoS). The proposed scheme is designed to encode control stream messages from any TCP/IP layer within payload messages to reduce the total amount of overhead exchanged, thereby decreasing resource utilization within source and intermediate nodes. Furthermore, the encoding scheme probes network conditions and session statistics for adaptive decision-making to enable real-time pliability of the proposed process. A utility function is developed to find the optimal cost savings where simulations are conducted to verify the designs. The proposed solution is then implemented using VideoLan Media Player transceivers residing in linux containers virtual machines, where a multimedia file is exchanged in the popular Advanced Video Coding (H.264) format. The results show a decrease in bandwidth and average queue waiting time costs of 4.71% and 29.61%, respectively, with a throughput increase of 5.77%.
New Directions in Approximate Nearest-Neighbor Searching

Approximate nearest-neighbor searching is an important retrieval problem with numerous applications in science and engineering. This problem has been the subject of many research papers spanning decades of work. Recently, a number of dramatic improvements and extensions have been discovered. In this paper, we will survey some of recent techniques that underlie these developments. In particular, we discuss local convexification, Macbeath regions, Delone sets, and how to apply these concepts to develop new data structures for approximate polytope membership queries and approximate vertical ray-shooting queries.