nils-herrmann/clean_scirepeval huggingface dataset:
MARKETING AND THE INTERNET : A RESEARCH REVIEW

This paper reviews the research to-date on how the Internet is impacting marketing. It focuses mainly on academic research published in US journals, but includes selected material from other sources. It covers internet adoption and usage; online purchasing behavior; internet advertising; internet economics and pricing; channels and intermediaries and online marketing strategy. The final section gives overall conclusions and briefly lists some opportunities for future research. Contrary to earlier hype, the Internet does not change the fundamental principles of marketing. Nor has its impact to-date – e.g. on consumer behavior, advertising, pricing, channels, intermediaries, strategy and globalization – been anything like as dramatic as predicted. Nevertheless, it has already had some effect on all of these areas. For instance, it is emerging as a flexible, fast-growing advertising medium and as a significant direct channel in many markets (PCs, books, music, travel, and even cars). It has led to some reduction in average prices and in price dispersion. It is bringing into the mainstream previously exotic business models such as auctions, electronic markets, and brand communities. Most of its successes have come from established businesses with ‘bricks and clicks’ strategies which use the Internet to supplement existing activities but rarely to generate much direct revenue We expect its impact to increase steadily over the next ten years, as the technology improves, its availability and usage continue to grow, and businesses learn better how to use it within their wider marketing strategy. Earlier expectations about the Internet were so overblown that the reality was bound to disappoint. But by any normal standards, it is a fast-growing young medium with wide-ranging effects across the whole marketing spectrum.
A general graph-based model for recommendation in event-based social networks

Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including multiple types of entities, such as users, events, groups and tags, and their interaction relations. Three recommendation tasks, namely recommending groups to users, recommending tags to groups, and recommending events to users, have been explored in three separate studies. However, none of the proposed methods can handle all the three recommendation tasks. In this paper, we propose a general graph-based model, called HeteRS, to solve the three recommendation problems on EBSNs in one framework. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities. Experimental results on two real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art methods for all the three recommendation tasks, and the learned influence weights help understanding user behaviors.
Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females

Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n = 123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n = 56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.
A Maximum Entropy Model for Part-Of-Speech Tagging

This paper presents a statistical model which trains from a corpus annotated with Part Of Speech tags and assigns them to previously unseen text with state of the art accuracy The model can be classi ed as a Maximum Entropy model and simultaneously uses many contextual features to predict the POS tag Furthermore this paper demonstrates the use of specialized fea tures to model di cult tagging decisions discusses the corpus consistency problems discovered during the implementation of these features and proposes a training strategy that mitigates these problems
C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty

We introduce a new problem of continuous, coverage-aware trajectory optimization under localization and sensing uncertainty. In this problem, the goal is to plan a path from a start state to a goal state that maximizes the coverage of a user-specified region while minimizing the control costs of the robot and the probability of collision with the environment. We present a principled method for quantifying the coverage sensing uncertainty of the robot. We use this sensing uncertainty along with the uncertainty in robot localization to develop C-OPT, a coverage-optimization algorithm which optimizes trajectories over belief-space to find locally optimal coverage paths. We highlight the applicability of our approach in multiple simulated scenarios inspired by surveillance, UAV crop analysis, and search-and-rescue tasks. We also present a case study on a physical, differential-drive robot. We also provide quantitative and qualitative analysis of the paths generated by our approach.
Environment-Driven Lexicon Induction for High-Level Instructions

As described in the main paper, we collected a dataset D = (x(n), e(n), a(n), π(n))500 n=1. Environment Complexity. Our environments are 3D scenarios consisting of complex objects such as fridge, microwave and television with many states. These objects can be in different spatial relations with respect to other objects. For example, “bag of chips” can be found behind the television. Figure 1 shows some sample environments from our dataset. For example, an object of category television consists of 6 channels, volume level and power status. An object can have different values of states in different environment and different environment consists of different set of objects and their placement. For example, television might be powered on in one environment and closed in another, microwave might have an object inside it or not in different environment, etc. Moreover, there are often more than one object of the same category. For example, our environment typically have two books, five couches, four pillows etc. Objects of the same category can have different appearance. For example, a book can have the cover of a math book or a Guinness book of world record; resulting in complex object descriptions such as in “throw the sticky stuff in the bowl”. They can also have the same appearance making people use spatial relations or other means while describing them such as in “get me the cup next to the microwave”. This dataset is significantly more challenging compared to the 2D navigation dataset or GUI actions in windows dataset considered earlier.
Restoration of Degraded Historical Document Image

Restoration plays a very important role in enhancing the degraded noisy images. To enhance the degraded image, the numerous algorithms have been designed. Since image processing algorithms are subjective, not all algorithms that developed will address all type of degradedness. To address specific type of problem the suitable algorithms need to be selected. In this paper a combination of spatial domain method along with set theory operations are used to enhance the historical image. The proposed method eliminates noise, uneven background and enhances the contrast of the script image. The result of the proposed method is compared with Mean and Gaussian filter. Performance of the proposed method proved to be better than these methods. The restored images will have clear uniform background and foreground with enhanced character appearance.
Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.
Neighbourhood Components Analysis

In this paper we propose a novel method for learning a Mahalan obis distance measure to be used in the KNN classification algorit hm. The algorithm directly maximizes a stochastic variant of the le ave-one-out KNN score on the training set. It can also learn a low-dimensi o al linear embedding of labeled data that can be used for data visual ization and fast classification. Unlike other methods, our classific at on model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performa nce of the method is demonstrated on several data sets, both for metric lea ning and linear dimensionality reduction.
Simulation of lightning impulse voltage stresses in underground cables

The goal of this work is to study the transient behavior of the cable against the application of standard and non-standard lightning impulse voltage waveforms. A 66 kV cable model has been developed in MATLAB Simulink and standard and non-standard impulse voltages are applied to it. A preliminary comparative study on the obtained voltages indicated that non-standard impulse voltage waveform developed higher voltage stress in the cable. Simulation results helped to investigate which impulses (standard or non-standard) represent the worst possible voltage stresses on the cables. This study provides the basis for further study of effects of non-standard impulse voltage waveforms and make necessary correction in the existing impulse testing standards.
Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assember. In the first stage, Assember filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assember generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assember is effective, efficient, and scalable.
Quantifying social group evolution

The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community’s lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.
The empathic brain: how, when and why?

Recent imaging results suggest that individuals automatically share the emotions of others when exposed to their emotions. We question the assumption of the automaticity and propose a contextual approach, suggesting several modulatory factors that might influence empathic brain responses. Contextual appraisal could occur early in emotional cue evaluation, which then might or might not lead to an empathic brain response, or not until after an empathic brain response is automatically elicited. We propose two major roles for empathy; its epistemological role is to provide information about the future actions of other people, and important environmental properties. Its social role is to serve as the origin of the motivation for cooperative and prosocial behavior, as well as help for effective social communication.
Building a Chatbot with Serverless Computing

Chatbots are emerging as the newest platform used by millions of consumers worldwide due in part to the commoditization of natural language services, which provide provide developers with many building blocks to create chatbots inexpensively. However, it is still difficult to build and deploy chatbots. Developers need to handle the coordination of the cognitive services to build the chatbot interface, integrate the chatbot with external services, and worry about extensibility, scalability, and maintenance. In this work, we present the architecture and prototype of a chatbot using a serverless platform, where developers compose stateless functions together to perform useful actions. We describe our serverless architecture based on function sequences, and how we used these functions to coordinate the cognitive microservices in the Watson Developer Cloud to allow the chatbot to interact with external services. The serverless model improves the extensibility of our chatbot, which currently supports 6 abilities: location based weather reports, jokes, date, reminders, and a simple music tutor.
Document dewarping via text-line based optimization

This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, linespacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg–Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. & 2015
Automatic Text Summarization based on Betweenness Centrality

Automatic text summary plays an important role in information retrieval. With a large volume of information, presenting the user only a summary greatly facilitates the search work of the most relevant. Therefore, this task can provide a solution to the problem of information overload. Automatic text summary is a process of automatically creating a compressed version of a certain text that provides useful information for users. This article presents an unsupervised extractive approach based on graphs. The method constructs an indirected weighted graph from the original text by adding a vertex for each sentence, and calculates a weighted edge between each pair of sentences that is based on a similarity/dissimilarity criterion. The main contribution of the work is that we do a study of the impact of a known algorithm for the social network analysis, which allows to analyze large graphs efficiently. As a measure to select the most relevant sentences, we use betweenness centrality. The method was evaluated in an open reference data set of DUC2002 with Rouge scores.
Brain imaging and psychotherapy: methodological considerations and practical implications

The development of psychotherapy has been based on psychological theories and clinical effects. However, an investigation of the neurobiological mechanisms of psychological interventions is also needed in order to improve indication and prognosis, inform the choice of parallel pharmacotherapy, provide outcome measures and potentially even aid the development of new treatment protocols. This neurobiological investigation can be informed by animal models, for example of learning and conditioning, but will essentially need the non-invasive techniques of functional neuroimaging in order to assess psychotherapy effects on patients’ brains, which will be reviewed here. Most research so far has been conducted in obsessive compulsive disorder (OCD), anxiety disorders and depression. Effects in OCD were particularly exciting in that both cognitive behavioural therapy and medication with a selective serotonin inhibitor led to a reduction in blood flow in the caudate nucleus. In phobia, brief courses of behavioural therapy produced marked reductions of paralimbic responses to offensive stimuli in line with the clinical improvement. Findings in depression are less consistent, with both increases and decreases in prefrontal metabolism being reported. However, they are important in pointing to different mechanisms for the clinical effects of pharmacotherapy (more “bottom up”) and psychotherapy (more “top down”). For the future it would be desirable if the findings of psychotherapy changes to brain activation patterns were confirmed in larger groups with homogenous imaging protocols. Functional imaging has already made great contributions to the understanding of the neural correlates of psychopathology. For example, evidence converges to suggest that the subgenual cingulate is crucial for mood regulation. One current clinical application of these findings is deep brain stimulation in areas highlighted by such imaging studies. I will discuss their initial application in depression and OCD, and suggest potential alternative options based on recent developments in neurofeedback technology.
BreakDancer: An algorithm for high resolution mapping of genomic structural variation

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.