nils-herrmann/clean_scirepeval huggingface dataset:
Digital Interactions and Brand Experience Design : a future perspective

This paper introduces an overview and positioning of the contemporary brand experience in the digital context. With technological advances in games, gamification and emerging technologies, such as Virtual Reality (VR) and Artificial Intelligence (AI), it is possible that brand experiences are getting more pervasive and seamless. In this paper, we review the current theories around multi-sensory brand experience and the role of new technologies in the whole consumer journey, including pre-purchase, purchase and post-purchase stages. After this analysis, we introduce a conceptual framework that promotes a continuous loop of consumer experience and engagement from different and new touch points, which could be augmented by games, gamification and emerging technologies. Based on the framework, we conclude this paper with propositions, examples and recommendations for future research in contemporary brand management, which could help brand managers and designers to deal with technological challenges posed by the contemporary society.
SpinLight: A High Accuracy and Robust Light Positioning System for Indoor Applications

This paper presents SpinLight, an indoor positioning system that uses infrared LED lamps as signal transmitters, and light sensors as receivers. The main idea is to divide the space into spatial beams originating from the light source, and identify each beam with a unique timed sequence of light signals. This sequence is created by a coded shade that covers and rotates around the LED, blocking the light or allowing it to pass through according to pre-defined patterns. The receiver, equipped with a light sensor, is able to determine its spatial beam by detecting the light signals, followed by optimization schemes to refine its location within that beam. We present both 2D and 3D localization designs, demonstrated by a prototype implementation. Experiments show that SpinLight produces a median location error of 3.8 cm, with a 95th percentile of 6.8 cm. The receiver design is very low power and thus can operate for months to years from a button coin battery.
Introducing Thing Descriptions and Interactions: An Ontology for the Web of Things

The Internet of Things (IoT) and the Web are closely related to each other. On the one hand, the Semantic Web has been including vocabularies and semantic models for the Internet of Things. On the other hand, the so-called Web of Things (WoT) advocates architectures relying on established Web technologies and RESTful interfaces for the IoT. In this paper, we present a vocabulary for WoT that aims at defining IoT concepts using terms from the Web. Notably, it includes two concepts identified as the core WoT resources: Thing Description (TD) and Interaction, that have been first elaborated by the W3C interest group for WoT. Our proposal is built upon the ontological pattern Identifier, Resource, Entity (IRE) that was originally designed for the Semantic Web. To better analyze the alignments our proposal allows, we reviewed existing IoT models as a vocabulary graph, complying with the approach of Linked Open Vocabularies (LOV).
Argos: A Novel 3-DoF Parallel Wrist Mechanism

This article presents a novel parallel spherical mechanism called Argos with three rotational degrees of freedom. Design aspects of the first prototype built of the Argos mechanism are discussed. The direct kinematic problem is solved, leading always to four nonsingular configurations of the end effector for a given set of joint angles. The inverse-kinematic problem yields two possible configurations for each of the three pantographs for a given orientation of the end effector. Potential applications of the Argos mechanism are robot wrists, orientable machine tool beds, joy sticks, surgical manipulators, and orientable units for optical components. Another pantograph based new structure named PantoScope having two rotational DoF is also briefly introduced. parallel robot, machine tool, 3 degree of freedom (DoF) wrist, pure orientation, direct kinematics, inverse kinematics, Pantograph based, Argos, PantoScope
Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.
The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It.

A standard mode of inference in social and behavioral science is to establish stylized facts using statistical significance in quantitative studies. However, in a world in which measurements are noisy and effects are small, this will not work: selection on statistical significance leads to effect sizes which are overestimated and often in the wrong direction. After a brief discussion of two examples, one in economics and one in social psychology, we consider the procedural solution of open postpublication review, the design solution of devoting more effort to accurate measurements and within-person comparisons, and the statistical analysis solution of multilevel modeling and reporting all results rather than selection on significance. We argue that the current replication crisis in science arises in part from the ill effects of null hypothesis significance testing being used to study small effects with noisy data. In such settings, apparent success comes easy but truly replicable results require a more serious connection between theory, measurement, and data.
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations

The success of deep convolutional architectures is often attributed in part to their ability to learn multiscale and invariant representations of natural signals. However, a precise study of these properties and how they affect learning guarantees is still missing. In this paper, we consider deep convolutional representations of signals; we study their invariance to translations and to more general groups of transformations, their stability to the action of diffeomorphisms, and their ability to preserve signal information. This analysis is carried by introducing a multilayer kernel based on convolutional kernel networks and by studying the geometry induced by the kernel mapping. We then characterize the corresponding reproducing kernel Hilbert space (RKHS), showing that it contains a large class of convolutional neural networks with homogeneous activation functions. This analysis allows us to separate data representation from learning, and to provide a canonical measure of model complexity, the RKHS norm, which controls both stability and generalization of any learned model. In addition to models in the constructed RKHS, our stability analysis also applies to convolutional networks with generic activations such as rectified linear units, and we discuss its relationship with recent generalization bounds based on spectral norms.
Deep Learning Logo Detection with Data Expansion by Synthesising Context

Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs. In this work, we describe a model training image synthesising method capable of improving significantly logo detection performance when only a handful of (e.g., 10) labelled training images captured in realistic context are available, avoiding extensive manual labelling costs. Specifically, we design a novel algorithm for generating Synthetic Context Logo (SCL) training images to increase model robustness against unknown background clutters, resulting in superior logo detection performance. For benchmarking model performance, we introduce a new logo detection dataset TopLogo-10 collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context. Extensive comparisons show the advantages of our proposed SCL model over the state-of-the-art alternatives for logo detection using two real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-101.
Automatic Extraction of Event Information from Newspaper Articles and Web Pages

In this paper, we propose a method for extracting travelrelated event information, such as an event name or a schedule from automatically identified newspaper articles, in which particular events are mentioned. We analyze news corpora using our method, extracting venue names from them. We then find web pages that refer to event schedules for these venues. To confirm the effectiveness of our method, we conducted several experiments. From the experimental results, we obtained a precision of 91.5% and a recall of 75.9% for the automatic extraction of event information from news articles, and a precision of 90.8% and a recall of 52.8% for the automatic identification of eventrelated web pages.
The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments

This paper experimentally investigates the role of visual complexity (VC) and prototypicality (PT) as design factors of websites, shaping users’ first impressions by means of two studies. In the first study, 119 screenshots of real websites varying in VC (low vs. medium vs. high) and PT (low vs. high) were rated on perceived aesthetics. Screenshot presentation time was varied as a between-subject factor (50 ms vs. 500 ms vs. 1000 ms). Results reveal that VC and PT affect participants’ aesthetics ratings within the first 50 ms of exposure. In the second study presentation times were shortened to 17, 33 and 50 ms. Results suggest that VC and PT affect aesthetic perception even within 17 ms, though the effect of PT is less pronounced than the one of VC. With increasing presentation time the effect of PT becomes as influential as the VC effect. This supports the reasoning of the information-processing stage model of aesthetic processing (Leder et al., 2004), where VC is processed at an earlier stage than PT. Overall, websites with low VC and high PT were perceived as highly appealing. & 2012 Elsevier Ltd. All rights reserved.
Balanced Neighborhoods for Multi-sided Fairness in Recommendation

Fairness has emerged as an important category of analysis for machine learning systems in some application areas. In extending the concept of fairness to recommender systems, there is an essential tension between the goals of fairness and those of personalization. However, there are contexts in which equity across recommendation outcomes is a desirable goal. It is also the case that in some applications fairness may be a multisided concept, in which the impacts on multiple groups of individuals must be considered. In this paper, we examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. We explore the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. We show that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance.
Protected area needs in a changing climate

G lobal climate is now rapidly changing, with consequent geographic rearrangement of species and recent climate-related extinctions (Root et al. 2003; Pounds et al. 2006). Yet protected areas (including national parks, nature reserves, and multiple-use conservation areas) are still the mainstay of modern conservation efforts (Rodrigues et al. 2004). Protected areas are geographically fixed and increasingly isolated by habitat destruction, and are therefore poorly suited to accommodating species range shifts due to climate change (Peters and Myers 1991). Here, we ask the question: are protected areas a relevant conservation response in an era of rapid climate change? Evaluating the effectiveness of protected areas is a problem in conservation planning that is made more complicated by climate change. A major goal of systematic conservation planning is to ensure that all species are represented within the protected areas of a given geographic region . Completing an existing protected area system in a given region so that it represents all known species generally proceeds by assessing the species already protected and then systematically adding complementary areas until all species are represented. Multiple representations of populations or species occurrences are usually necessary to ensure the conservation of each species, so for large numbers of species the process can be quite complex. For this reason, computer-automated selection routines, known as " reserve selection algorithms " , have been developed (Pressey and Cowling 2001). The problem is more complex when species' ranges become dynamic as the result of climate change. One approach is to couple species distribution models and reserve selection algorithms (Araújo et al. 2004; Williams et al. 2005). Species distribution models use statistical or heuristic packages that simulate the present range of a species, based on relationships between known points of species' occurrence and climate at the time those points were recorded. A simulated present range is required because no species' distribution is perfectly known, while a simulation of future range is needed to account for the range shift likely to accompany changing climatic conditions. When such modeled ranges are available for large numbers of species (ideally hundreds or thousands), a reserve selection algorithm can be used to design a protected-areas system that represents all species, both in the present and in the future. This is most easily done by starting with existing protected areas and adding additional areas to complete species representation. One possible goal for such a process in …
Secure Mobile Payments Based on Cloud Services: Concepts and Experiments

This paper presents an innovative and open concept for secure mobile payments, based on payment applications hosted in the cloud. It details an experimental Android platform, and NFC payment experiments performed with this platform. All the platform components rely on open technologies, i.e. available in commercial devices or specified by IETF drafts. On the mobile side security is enforced by a dedicated protocol (based on TLS), running in a secure element.  This approach creates a new entity that collects meaningful and relevant data, dealing with user's purchases. It also increases the level of trust both for consumers and banks.
A PRAGMATIC AND SEMANTIC UNIFIED FRAMEWORK FOR AGENT COMMUNICATION

In this thesis, we propose a unified framework for the pragmatics and the semantics of agent communication. Pragmatics deals with the way agents use communicative acts when conversing. It is related to the dynamics of agent interactions and to the way of connecting individual acts while building complete conversations. Semantics is interested in the meaning of these acts. It lays down the foundation for a concise and unambiguous meaning of agent messages. This framework aims at solving three main problems of agent communication: 1The absence of a link between the pragmatics and the semantics. 2The inflexibility of current agent communication protocols. 3The verification of agent communication mechanisms. The main contributions of this thesis are: 1A formal pragmatic approach based on social commitments and arguments. 2A new agent communication formalism called Commitment and Argument Network. 3A logical model defining the semantics of the elements used in the pragmatic approach. 4A tableau-based model checking technique for the verification of a kind of flexible protocols called dialogue game protocols. 5A new persuasion dialogue game protocol. The main idea of our pragmatic approach is that agent communication is considered as actions that agents perform on social commitments and arguments. The dynamics of agent conversation is represented by this notion of actions and by the evolution of these commitments and arguments. Our Commitment and Argument Network formalism based on this approach provides an external representation of agent communication dynamics. We argue that this formalism helps agents to participate in conversations in a flexible way because they can reason about their communicative acts using their argumentation systems and the current state of the conversation. Our logical model is a model-theoretic semantics for the pragmatic approach. It defines the meaning of the different communicative acts that we use in our pragmatic approach. It also expresses the meaning of some important speech acts and it captures the semantics of defeasible arguments. This logical model allows us to establish the link between the semantics and the pragmatics of agent communication. We address the problem of verifying dialogue game protocols using a tableau-based model checking technique. These protocols are specified in terms of our logical model. We argue that our model checking algorithm provides a technique, not only to verify if the dialogue game protocol satisfies a given property, but also if this protocol respects the underlying semantics of the communicative acts.
Prevalence, cause and diagnosis of lactose intolerance in children aged 1-5 years: a systematic review of 1995-2015 literature.

To assess the prevalence, etiology, diagnosis of primary and secondary lactose intolerance (LI), including age of onset, among children 1-5 years of age. Suspected/perceived lactose intolerance can lead to dietary restrictions which may increase risk of future health issues.



MEDLINE, CAB Abstract, and Embase were searched for articles published from January 1995-June 2015 related to lactose intolerance in young children. Authors independently screened titles/abstracts, full text articles, for eligibility against a priori inclusion/exclusion criteria. Two reviewers extracted data and assessed quality of the included studies.



The search identified 579 articles; 20 studies, the majority of which were crosssectional, were included in the qualitative synthesis. Few studies reported prevalence of primary LI in children aged 1-5 years; those that did reported a range between 0-17.9%. Prevalence of secondary LI was 0-19%. Hydrogen breath test was the most common method used to diagnose LI. None of the included studies reported age of onset of primary LI.



There is limited recent evidence on the prevalence of LI in this age group. The low number of studies and wide range of methodologies used to diagnose LI means that comparison and interpretation, particularly of geographical trends, is compromised. Current understanding appears to rely on data generated in the 1960/70s, with varied qualities of evidence. New, high quality studies are necessary to understand the true prevalence of LI.
Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words

Social media use continues to grow and is especially prevalent among young adults. It is surprising then that, in spite of this enhanced interconnectivity, young adults may be lonelier than other age groups, and that the current generation may be the loneliest ever. We propose that only image-based platforms (e.g., Instagram, Snapchat) have the potential to ameliorate loneliness due to the enhanced intimacy they offer. In contrast, text-based platforms (e.g., Twitter, Yik Yak) offer little intimacy and should have no effect on loneliness. This study (N 1⁄4 253) uses a mixed-design survey to test this possibility. Quantitative results suggest that loneliness may decrease, while happiness and satisfaction with life may increase, as a function of image-based social media use. In contrast, text-based media use appears ineffectual. Qualitative results suggest that the observed effects may be due to the enhanced intimacy offered by imagebased (versus text-based) social media use. The more advanced the technology, on the whole, the more possible it is for a considerable number of human beings to imagine being somebody else.” -sociologist David Riesman.
Empowering convolutional networks for malware classification and analysis

Performing large-scale malware classification is increasingly becoming a critical step in malware analytics as the number and variety of malware samples is rapidly growing. Statistical machine learning constitutes an appealing method to cope with this increase as it can use mathematical tools to extract information out of large-scale datasets and produce interpretable models. This has motivated a surge of scientific work in developing machine learning methods for detection and classification of malicious executables. However, an optimal method for extracting the most informative features for different malware families, with the final goal of malware classification, is yet to be found. Fortunately, neural networks have evolved to the state that they can surpass the limitations of other methods in terms of hierarchical feature extraction. Consequently, neural networks can now offer superior classification accuracy in many domains such as computer vision and natural language processing. In this paper, we transfer the performance improvements achieved in the area of neural networks to model the execution sequences of disassembled malicious binaries. We implement a neural network that consists of convolutional and feedforward neural constructs. This architecture embodies a hierarchical feature extraction approach that combines convolution of n-grams of instructions with plain vectorization of features derived from the headers of the Portable Executable (PE) files. Our evaluation results demonstrate that our approach outperforms baseline methods, such as simple Feedforward Neural Networks and Support Vector Machines, as we achieve 93% on precision and recall, even in case of obfuscations in the data.