nils-herrmann/clean_scirepeval huggingface dataset:
Virtual Reality: A Definition History - A Personal Essay

This essay, written in 1998 by an active participant in both virtual reality development and the virtual reality definition debate, discusses the definition of the phrase “Virtual Reality” (VR). I start with history from a personal perspective, concentrating on the debate between the “Virtual Reality” and “Virtual Environment” labels in the late 1980’s and early 1990’s. Definitions of VR based on specific technologies are shown to be unsatisfactory. I propose the following definition of VR, based on the striking effects of a good VR system: “Virtual Reality is the use of computer technology to create the effect of an interactive three-dimensional world in which the objects have a sense of spatial presence.” The justification for this definition is discussed in detail, and is favorably compared with the dictionary definitions of “virtual” and “reality”. The implications of this definition for virtual reality technology are briefly examined.
Connected domination in fuzzy graphs using strong arcs

In this paper, connected domination in fuzzy graphs using strong arcs is introduced. The strong connected domination number of different classes of fuzzy graphs is obtained. An upper bound for the strong connected domination number of fuzzy graphs is obtained. Strong connected domination in fuzzy trees is studied. It is established that the set of fuzzy cut nodes of a fuzzy tree is a strong connected dominating set. It is proved that in a fuzzy tree each node of a strong connected dominating set is incident on a fuzzy bridge. Also the characteristic properties of the existence of strong connected dominating set for a fuzzy graph and its complement are established.
Feedback Control For Cassie With Deep Reinforcement Learning

Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions. In these model-based control strategies, the controller is often not fully aware of many details, including torque limits, joint limits, and other non-linearities that are necessarily excluded from the control computations for simplicity. Deep reinforcement learning (DRL) offers a promising model-free approach for controlling bipedal locomotion which can more fully exploit the dynamics. However, current results in the machine learning literature are often based on ad-hoc simulation models that are not based on corresponding hardware. Thus it remains unclear how well DRL will succeed on realizable bipedal robots. In this paper, we demonstrate the effectiveness of DRL using a realistic model of Cassie, a bipedal robot. By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL. Controllers for different walking speeds are learned by imitating simple time-scaled versions of the original reference motion. Controller robustness is demonstrated through several challenging tests, including sensory delay, walking blindly on irregular terrain and unexpected pushes at the pelvis. We also show we can interpolate between individual policies and that robustness can be improved with an interpolated policy.
Empowering academic librarians for effective e-services: An assessment of Web 2.0 competency levels

Empowering academic librarians for effective e-services: an assessment of Web 2.0 competency levels Lilian Ingutia Oyieke Archie L Dick Article information: To cite this document: Lilian Ingutia Oyieke Archie L Dick , (2017)," Empowering academic librarians for effective e-services: an assessment of Web 2.0 competency levels ", The Electronic Library , Vol. 35 Iss 2 pp. Permanent link to this document:
Random Positional Deviations Correction for Each LED via ePIE in Fourier Ptychographic Microscopy

Fourier ptychography microscopy (FPM) is a lately developed technique, which achieves wide field, high resolution, and phase imaging, simultaneously. FPM stitches together the captured low-resolution images corresponding to angular varying illuminations in Fourier domain utilizing the concept of synthetic aperture and phase retrieval algorithms, which can surpass the space-bandwidth product limit of the objective lens and reconstruct a high-resolution complex image. In general FPM system, the LED source is important for the reconstructed quality and it is sensitive to the positions of each LED element. We find that the random positional deviations of each LED element can bring errors in reconstructed results, which is relative to a feedback parameter. To improve the reconstruction rate and correct random deviations, we combine an initial phase guess and a feedback parameter based on differential phase contrast and extended ptychographical iterative engine to propose an optimized iteration process for FPM. The simulated and experimental results indicate that the proposed method shows the reliability and validity towards the random deviations yet accelerates the convergence. More importantly, it is verified that this method can accelerate the convergence, reduce the requirement of LED array accuracy, and improve the quality of the reconstructed results.
Comparison of Data Mining Techniques for Insurance Claim Prediction

This thesis investigates how data mining algorithms can be used to predict Bodily Injury Liability Insurance claim payments based on the characteristics of the insured customer’s vehicle. The algorithms are tested on real data provided by the organizer of the competition. The data present a number of challenges such as high dimensionality, heterogeneity and missing variables. The problem is addressed using a combination of regression, dimensionality reduction, and classification techniques. Questa tesi si propone di esaminare come alcune tecniche di data mining possano essere usate per predirre l’ammontare dei danni che un’ assicurazione dovrebbe risarcire alle persone lesionate a partire dalle caratteristiche del veicolo del cliente assicurato. I dati utilizzati sono reali e la loro analisi presenta diversi ostacoli dati dalle loro grandi dimensioni, dalla loro eterogeneitá e da alcune variabili mancanti. ll problema é stato affrontato utilizzando una combinazione di tecniche di regressione, di riduzione di dimensionalitá e di classificazione.
Health Benefits from Nature Experiences Depend on Dose

Nature within cities will have a central role in helping address key global public health challenges associated with urbanization. However, there is almost no guidance on how much or how frequently people need to engage with nature, and what types or characteristics of nature need to be incorporated in cities for the best health outcomes. Here we use a nature dose framework to examine the associations between the duration, frequency and intensity of exposure to nature and health in an urban population. We show that people who made long visits to green spaces had lower rates of depression and high blood pressure, and those who visited more frequently had greater social cohesion. Higher levels of physical activity were linked to both duration and frequency of green space visits. A dose-response analysis for depression and high blood pressure suggest that visits to outdoor green spaces of 30 minutes or more during the course of a week could reduce the population prevalence of these illnesses by up to 7% and 9% respectively. Given that the societal costs of depression alone in Australia are estimated at AUD$12.6 billion per annum, savings to public health budgets across all health outcomes could be immense.
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.
Motivation and Education : The Self-Determination Perspective

Self-determination theory (Deci & Ryan, 1985, 1991), when applied to the realm of education, is concerned primarily with promoting in students an interest in learning, a valuing of education, and a confidence in their own capacities and attributes. These outcomes are manifestations of being intrinsically motivated and internalizing values and regulatory processes. Research suggests that these processes result in high-quality learning and conceptual understanding, as well as enhanced personal growth and adjustment. In this article we also describe social-contextual factors that nurture intrinsic motivation and pralmote internalization, leading to the desired educational outcomes.
Deep Neural Architecture for News Recommendation

Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for recommender systems has received a relatively little introspection. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. Specifically in news recommendation a major problem is that of varying user interests. In this work, we use deep neural networks with attention to tackle the problem of news recommendation. The key factor in user-item based collaborative filtering is to identify the interaction between user and item features. Matrix factorization is one of the most common approaches for identifying this interaction. It maps both the users and the items into a joint latent factor space such that user-item interactions in that space can be modeled as inner products in that space. Some recent work has used deep neural networks with the motive to learn an arbitrary function instead of the inner product that is used for capturing the user-item interaction. However, directly adapting it for the news domain does not seem to be very suitable. This is because of the dynamic nature of news readership where the interests of the users keep changing with time. Hence, it becomes challenging for recommendation systems to model both user preferences as well as account for the interests which keep changing over time. We present a deep neural model, where a non-linear mapping of users and item features are learnt first. For learning a non-linear mapping for the users we use an attention-based recurrent layer in combination with fully connected layers. For learning the mappings for the items we use only fully connected layers. We then use a ranking based objective function to learn the parameters of the network. We also use the content of the news articles as features for our model. Extensive experiments on a real-world dataset show a significant improvement of our proposed model over the state-of-the-art by 4.7% (Hit Ratio@10). Along with this, we also show the effectiveness of our model to handle the user cold-start and item cold-start problems. ? Vaibhav Kumar and Dhruv Khattar are the corresponding authors
DSL Implementation in MetaOCaml, Template Haskell, and C++

A wide range of domain-specific languages (DSLs) has been implemented successfully by embedding them in general purpose languages. This paper reviews embedding, and summarizes how two alternative techniques—staged interpreters and templates—can be used to overcome the limitations of embedding. Both techniques involve a form of generative programming. The paper reviews and compares three programming languages that have special support for generative programming. Two of these languages (MetaOCaml and Template Haskell) are research languages, while the third (C++) is already in wide industrial use. The paper identifies several dimensions that can serve as a basis for comparing generative languages.
Query-Oriented Summarization of RDF Graphs

The Resource Description Framework (RDF) is a graphbased data model promoted by the W3C as the standard for Semantic Web applications. Its associated query language is SPARQL. RDF graphs are often large and varied, produced in a variety of contexts, e.g., scientific applications, social or online media, government data etc. They are heterogeneous, i.e., resources described in an RDF graph may have very different sets of properties. An RDF resource may have: no types, one or several types (which may or may not be related to each other). RDF Schema (RDFS) information may optionally be attached to an RDF graph, to enhance the description of its resources. Such statements also entail that in an RDF graph, some data is implicit. According to the W3C RDF and SPARQL specification, the semantics of an RDF graph comprises both its explicit and implicit data; in particular, SPARQL query answers must be computed reflecting both the explicit and implicit data. These features make RDF graphs complex, both structurally and conceptually. It is intrinsically hard to get familiar with a new RDF dataset, especially if an RDF schema is sparse or not available at all. In this work, we study the problem of RDF summarization, that is: given an input RDF graph G, find an RDF graph SG which summarizes G as accurately as possible, while being possibly orders of magnitude smaller than the original graph. Such a summary can be used in a variety of contexts: to help an RDF application designer get acquainted with a new dataset, as a first-level user interface, or as a support for query optimization as typically used in semistructured graph data management  etc. Our approach is query-oriented, i.e., a summary should enable static analysis and help formulating and optimizing queries; for instance, querying a summary of a graph should reflect whether the query has some answers against this graph, or finding a simpler way to formulate the query etc. Ours is the first semi-structured data summarization approach focused on partially explicit, partially implicit RDF graphs. In the sequel, Section 2 recalls RDF basics, and sets the
Challenges Facing Auditors in Detecting Financial Statement Fraud : Insights from Fraud Investigations

Audit regulators and the auditing profession have responded to this expectation by issuing a number of standards outlining auditors’ responsibilities to detect fraud (e.g., PCAOB 2010;  2009, PCAOB 2002; AICPA 2002; AICPA 1997; AICPA 1988). These standards indicate that auditors are responsible for providing reasonable assurance that audited financial statements are free of material misstatements due to fraud. Nonetheless, prior research indicates that auditors detect relatively few significant frauds (Dyck et al. 2010, KPMG 2009). This finding raises the obvious question: Why do auditors rarely detect fraud?
The impact of Positive Reinforcement on Employees ’ Performance in Organizations

Each employee’s performance is important in an organization. A way to motivate it is through the application of reinforcement theory which is developed by B. F. Skinner. One of the most commonly used methods is positive reinforcement in which one’s behavior is strengthened or increased based on consequences. This paper aims to review the impact of positive reinforcement on the performances of employees in organizations. It can be applied by utilizing extrinsic reward or intrinsic reward. Extrinsic rewards include salary, bonus and fringe benefit while intrinsic rewards are praise, encouragement and empowerment. By applying positive reinforcement in these factors, desired positive behaviors are encouraged and negative behaviors are eliminated. Financial and non-financial incentives have a positive relationship with the efficiency and effectiveness of staffs.
Unsupervised cleansing of noisy text

In this paper we look at the problem of cleansing noisy text using a statistical machine translation model. Noisy text is produced in informal communications such as Short Message Service (SMS), Twitter and chat. A typical Statistical Machine Translation system is trained on parallel text comprising noisy and clean sentences. In this paper we propose an unsupervised method for the translation of noisy text to clean text. Our method has two steps. For a given noisy sentence, a weighted list of possible clean tokens for each noisy token are obtained. The clean sentence is then obtained by maximizing the product of the weighted lists and the language model scores.
Scalable logo recognition in real-world images

In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for encoding and indexing the relative spatial layout of local features detected in the logo images. Based on the analysis of the local features and the composition of basic spatial structures, such as edges and triangles, we can derive a quantized representation of the regions in the logos and minimize the false positive detections. Furthermore, we propose a cascaded index for scalable multi-class recognition of logos.
 For the evaluation of our system, we have constructed and released a logo recognition benchmark which consists of manually labeled logo images, complemented with non-logo images, all posted on Flickr. The dataset consists of a training, validation, and test set with 32 logo-classes. We thoroughly evaluate our system with this benchmark and show that our approach effectively recognizes different logo classes with high precision.
HUNT: Scavenger Hunt with Augmented Reality

This project shows a creative approach to the familiar scavenger hunt game. It involved the implementation of an iPhone application, HUNT, with Augmented Reality (AR) capability for the users to play the game as well as an administrative website that game organizers can use to create and make available games for users to play. Using the HUNT mobile app, users will first make a selection from a list of games, and they will then be shown a list of objects that they must seek. Once the user finds a correct object and scans it with the built-in camera on the smartphone, the application will attempt to verify if it is the correct object and then display associated multi-media AR content that may include images and videos overlaid on top of real world views. HUNT not only provides entertaining activities within an environment that players can explore, but the AR contents can serve as an educational tool. The project is designed to increase user involvement by using a familiar and enjoyable game as a basis and adding an educational dimension by incorporating AR technology and engaging and interactive multimedia to provide users with facts about the objects that they have located.
Guide and Position of the International Society of Nutrigenetics/Nutrigenomics on Personalised Nutrition: Part 1 - Fields of Precision Nutrition.

Diversity in the genetic profile between individuals and specific ethnic groups affects nutrient requirements, metabolism and response to nutritional and dietary interventions. Indeed, individuals respond differently to lifestyle interventions (diet, physical activity, smoking, etc.). The sequencing of the human genome and subsequent increased knowledge regarding human genetic variation is contributing to the emergence of personalized nutrition. These advances in genetic science are raising numerous questions regarding the mode that precision nutrition can contribute solutions to emerging problems in public health, by reducing the risk and prevalence of nutrition-related diseases. Current views on personalized nutrition encompass omics technologies (nutrigenomics, transcriptomics, epigenomics, foodomics, metabolomics, metagenomics, etc.), functional food development and challenges related to legal and ethical aspects, application in clinical practice, and population scope, in terms of guidelines and epidemiological factors. In this context, precision nutrition can be considered as occurring at three levels: (1) conventional nutrition based on general guidelines for population groups by age, gender and social determinants; (2) individualized nutrition that adds phenotypic information about the person's current nutritional status (e.g. anthropometry, biochemical and metabolic analysis, physical activity, among others), and (3) genotype-directed nutrition based on rare or common gene variation. Research and appropriate translation into medical practice and dietary recommendations must be based on a solid foundation of knowledge derived from studies on nutrigenetics and nutrigenomics. A scientific society, such as the International Society of Nutrigenetics/Nutrigenomics (ISNN), internationally devoted to the study of nutrigenetics/nutrigenomics, can indeed serve the commendable roles of (1) promoting science and favoring scientific communication and (2) permanently working as a 'clearing house' to prevent disqualifying logical jumps, correct or stop unwarranted claims, and prevent the creation of unwarranted expectations in patients and in the general public. In this statement, we are focusing on the scientific aspects of disciplines covering nutrigenetics and nutrigenomics issues. Genetic screening and the ethical, legal, social and economic aspects will be dealt with in subsequent statements of the Society.
Characterizing and classifying IoT traffic in smart cities and campuses

Campuses and cities of the near future will be equipped with vast numbers of IoT devices. Operators of such environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This paper proposes the use of network traffic analytics to characterize IoT devices, including their typical behaviour mode. We first collect and synthesize traffic traces from a smart-campus environment instrumented with a diversity of IoT devices including cameras, lights, appliances, and health-monitors; our traces, collected over a period of 3 weeks, are released as open data to the public. We then analyze the traffic traces to characterize statistical attributes such as data rates and burstiness, activity cycles, and signalling patterns, for over 20 IoT devices deployed in our environment. Finally, using these attributes, we develop a classification method that can not only distinguish IoT from non-IoT traffic, but also identify specific IoT devices with over 95% accuracy. Our study empowers operators of smart cities and campuses to discover and monitor their IoT assets based on their network behaviour.