nils-herrmann/clean_scirepeval huggingface dataset:
Classification and Characteristics of Electronic Payment Systems

Electronic commerce and electronic business greatly need new payment systems that will support their further development. To better understand problems and perspectives of the electronic payment systems this article describes a classification and different characteristic aspects of payment systems. It suggests distinctions between payment systems and mediating systems, and is trying to illustrate advantages and limitations of diverse categories of payment systems using the defined characteristics. It is highlighting importance of userrelated aspects in design and introduction of electronic payment systems for mass customers.
Gender Classification with Decision Trees

With the evolution of HCI (Human-Computer Interaction), the computer vision systems are playing an important role in our lives. Some of the prime areas of computer vision applications include gender detection, face recognition, body tracking and ethnicity identification etc. Automated data analyses techniques help discover regularities and hidden associations in larger volumes of datasets. Classification being a data mining technique is largely used to group categorical data as well as a blend of continuous numeric values and categorical data. A number of classification techniques like decision trees, support vector machine (SVM), nearest neighbors and neural networks etc. have gained popularity in numerous areas of data mining practices. Among these classification techniques, decision trees offer an added advantage of producing easily interpretable rules and logic statements along with generating the classification tree for the given dataset. This study offers a distinct method for gender classification of facial images. We have used a variant of the decision tree algorithm for gender classification of frontal images due to its distinctive features. Our technique demonstrates robustness and relative scale invariance for gender classification. Details of the experimental design and the results are reported herein.
Causal memory: definitions, implementation, and programming

The abstraction of a shared memory is of growing importance in distributed computing systems. Traditional memory consistency ensures that all processes agree on a common order of all operations on memory. Unfortunately, providing these guarantees entails access latencies that prevent scaling to large systems. This paper weakens such guarantees by definingcausal memory, an abstraction that ensures that processes in a system agree on the relative ordering of operations that arecausally related. Because causal memory isweakly consistent, it admits more executions, and hence more concurrency, than either atomic or sequentially consistent memories. This paper provides a formal definition of causal memory and gives an implementation for message-passing systems. In addition, it describes a practical class of programs that, if developed for a strongly consistent memory, run correctly with causal memory.
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping

We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers. Finally, the reprojected triangulations are used as new labeled training data to improve the detector. We repeat this process, generating more labeled data in each iteration. We derive a result analytically relating the minimum number of views to achieve target true and false positive rates for a given detector. The method is used to train a hand keypoint detector for single images. The resulting keypoint detector runs in realtime on RGB images and has accuracy comparable to methods that use depth sensors. The single view detector, triangulated over multiple views, enables 3D markerless hand motion capture with complex object interactions.
RFID in the Warehouse : A Literature Analysis ( 1995-2010 ) of its Applications , Benefits , Challenges and Future Trends

Radio frequency identification (RFID) has been identified as a crucial technology for the modern 21 st century knowledge-based economy. Many businesses started realising RFID to be able to improve their operational efficiency, achieve additional cost savings, and generate opportunities for higher revenues. To investigate how RFID technology has brought an impact to warehousing, a comprehensive analysis of research findings available through leading scientific article databases was conducted. Articles from years 1995 to 2010 were reviewed and analysed according to warehouse operations, RFID application domains, and benefits achieved. This paper presents four discussion topics covering RFID innovation, including its applications, perceived benefits, obstacles to its adoption and future trends. This is aimed at elucidating the current state of RFID in the warehouse and giving insights for the academics to establish new research scope and for the practitioners to evaluate their assessment of adopting RFID in the warehouse.
Using Topic Modeling and Similarity Thresholds to Detect Events

This paper presents a Retrospective Event Detection algorithm, called Eventy-Topic Detection (ETD), which automatically generates topics that describe events in a large, temporal text corpus. Our approach leverages the structure of the topic modeling framework, specifically the Latent Dirichlet Allocation (LDA), to generate topics which are then later labeled as Eventy-Topics or non-Eventy-Topics. The system first runs daily LDA topic models, then calculates the cosine similarity between the topics of the daily topic models, and then runs our novel Bump-Detection algorithm. Similar topics labeled as an Eventy-Topic are then grouped together. The algorithm is demonstrated on two Terabyte sized corpuses a Reuters News corpus and a Twitter corpus. Our method is evaluated on a human annotated test set. Our algorithm demonstrates its ability to accurately describe and label events in a temporal text corpus.
Genetic optimization of modular neural networks with fuzzy response integration for human recognition

In this paper we propose a new approach to genetic optimization of modular neural networks with fuzzy response integration. The architecture of the modular neural network and the structure of the fuzzy system (for response integration) are designed using genetic algorithms. The proposed methodology is applied to the case of human recognition based on three biometric measures, namely iris, ear, and voice. Experimental results show that optimal modular neural networks can be designed with the use of genetic algorithms and as a consequence the recognition rates of such networks can be improved significantly. In the case of optimization of the fuzzy system for response integration, the genetic algorithm not only adjusts the number of membership functions and rules, but also allows the variation on the type of logic (type-1 or type-2) and the change in the inference model (switching to Mamdani model or Sugeno model). Another interesting finding of this work is that when human recognition is performed under noisy conditions, the response integrators of the modular networks constructed by the genetic algorithm are found to be optimal when using type-2 fuzzy logic. This could have been expected as there has been experimental evidence from previous works that type-2 fuzzy logic is better suited to model higher levels of uncertainty.
Hand gesture-based visual user interface for infotainment

We present a real-time vision-based system that discriminates hand gestures performed by in-vehicle front-row seat occupants for accessing the infotainment system. The hand gesture-based visual user interface may be more natural and intuitive to the user than the current tactile interaction interface. Consequently, it may encourage a gaze-free interaction, which can alleviate driver distraction without limiting the user's infotainment experience. The system uses visible and depth images of the dashboard and center-console area in the vehicle. The first step in the algorithm uses the representation of the image area given by a modified histogram-of-oriented-gradients descriptor and a support vector machine (SVM) to classify whether the driver, passenger, or no one is interacting with the region of interest. The second step extracts gesture characteristics from temporal dynamics of the features derived in the initial step, which are then inputted to a SVM in order to perform gesture classification from a set of six classes of hand gestures. The rate of correct user classification into one of the three classes is 97.9% on average. Average hand gesture classification rates for the driver and passenger using color and depth input are above 94%. These rates were achieved on in-vehicle collected data over varying illumination conditions and human subjects. This approach demonstrates the feasibility of the hand gesture-based in-vehicle visual user interface.
Cats Rule and Dogs Drool!: Classifying Stance in Online Debate

A growing body of work has highlighted the challenges of identifying the stance a speaker holds towards a particular topic, a task that involves identifying a holistic subjective disposition. We examine stance classification on a corpus of 4873 posts across 14 topics on ConvinceMe.net, ranging from the playful to the ideological. We show that ideological debates feature a greater share of rebuttal posts, and that rebuttal posts are significantly harder to classify for stance, for both humans and trained classifiers. We also demonstrate that the number of subjective expressions varies across debates, a fact correlated with the performance of systems sensitive to sentimentbearing terms. We present results for identifing rebuttals with 63% accuracy, and for identifying stance on a per topic basis that range from 54% to 69%, as compared to unigram baselines that vary between 49% and 60%. Our results suggest that methods that take into account the dialogic context of such posts might be fruitful.
SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks

Generating texts of different sentiment labels is getting more and more attention in the area of natural language generation. Recently, Generative Adversarial Net (GAN) has shown promising results in text generation. However, the texts generated by GAN usually suffer from the problems of poor quality, lack of diversity and mode collapse. In this paper, we propose a novel framework SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems. In our framework, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision. We propose a penalty based objective in the generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own examples of a specific sentiment label accurately. Experimental results on four datasets demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the sentiment accuracy and quality of generated texts.
Sequence assembly demystified

Advances in sequencing technologies and increased access to sequencing services have led to renewed interest in sequence and genome assembly. Concurrently, new applications for sequencing have emerged, including gene expression analysis, discovery of genomic variants and metagenomics, and each of these has different needs and challenges in terms of assembly. We survey the theoretical foundations that underlie modern assembly and highlight the options and practical trade-offs that need to be considered, focusing on how individual features address the needs of specific applications. We also review key software and the interplay between experimental design and efficacy of assembly.
Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

Product recommendation systems are important for major movie studios during the movie greenlight process and as part of machine learning personalization pipelines. Collaborative Filtering (CF) models have proved to be effective at powering recommender systems for online streaming services with explicit customer feedback data. CF models do not perform well in scenarios in which feedback data is not available, in ‘cold start’ situations like new product launches, and situations with markedly different customer tiers (e.g., high frequency customers vs. casual customers). Generative natural language models that create useful theme-based representations of an underlying corpus of documents can be used to represent new product descriptions, like new movie plots. When combined with CF, they have shown to increase the performance in ‘cold start’ situations. Outside of those cases though in which explicit customer feedback is available, recommender engines must rely on binary purchase data, which materially degrades performance. Fortunately, purchase data can be combined with product descriptions to generate meaningful representations of products and customer trajectories in a convenient product space in which proximity represents similarity (in the case of product-toproduct comparisons) and affinity (in the case of customer-toproduct comparisons). Learning to measure the distance between points in this space can be accomplished with a deep neural network that trains on customer histories and on dense vectorizations of product descriptions. We developed a system based on Collaborative (Deep) Metric Learning (CML) to predict the purchase probabilities of new theatrical releases. We trained and evaluated the model using a large dataset of customer histories spanning multiple years, and tested the model for a set of movies that were released outside of the training window. Initial experiments show gains relative to models that don’t train on collaborative preferences.
PRemiSE: personalized news recommendation via implicit social experts

A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing the implicit "social" factors (i.e., the potential influential experts in news reading community) among news readers to facilitate news personalization. In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques. We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks are treated as auxiliary resources for recommendation. Empirical results demonstrate the efficacy and effectiveness of our method, particularly, on handling the so-called cold-start problem.
Regular bipolar fuzzy graphs

We introduce the concepts of regular and totally regular bipolar fuzzy graphs. We prove necessary and sufficient condition under which regular bipolar fuzzy graph and totally bipolar fuzzy graph are equivalent. We introduce the notion of bipolar fuzzy line graphs and present some of their properties. We state a necessary and sufficient condition for a bipolar fuzzy graph to be isomorphic to its corresponding bipolar fuzzy line graph. We examine when an isomorphism between two bipolar fuzzy graphs follows from an isomorphism of their corresponding bipolar fuzzy line graphs.
The Portuguese plastic carrier bag tax: The effects on consumers' behavior.

Marine litter from lightweight plastic bags is a global problem that must be solved. A plastic bag tax was implemented in February 2015 to reduce the consumption of plastic grocery bags in Portugal and in turn reduce the potential contribution to marine litter. This study analyzes the effect of the plastic bag tax on consumer behavior to learn how it was received and determine the perceived effectiveness of the tax 4months after its implementation. In addition, the study assessed how proximity to coastal areas could influence behaviors and opinions. The results showed a 74% reduction of plastic bag consumption with a simultaneously 61% increase of reusable plastic bags after the tax was implemented. Because plastic bags were then reused for shopping instead of garbage bags, however, the consumption of garbage bags increased by 12%. Although reduction was achieved, the tax had no effect on the perception of marine litter or the impact of plastic bags on environment and health. The majority of respondents agree with the tax but view it as an extra revenue to the State. The distance to the coast had no meaningful influence on consumer behavior or on the perception of the tax. Although the tax was able to promote the reduction of plastics, the role of hypermarkets and supermarkets in providing alternatives through the distribution of reusable plastic bags was determinant to ensuring the reduction.
Scalable Attribute-Value Extraction from Semi-structured Text

This paper describes a general methodology for extracting attribute-value pairs from web pages. It consists of two phases: candidate generation, in which syntactically likely attribute-value pairs are annotated; and candidate filtering, in which semantically improbable annotations are removed. We describe three types of candidate generators and two types of candidate filters, all of which are designed to be massively parallelizable. Our methods can handle 1 billion web pages in less than 6 hours with 1,000 machines. The best generator and filter combination achieves 70% F-measure compared to a hand-annotated corpus.
Preventing Information Leaks in Email

The widespread use of email has raised serious privacy concerns. A critical issue is how to prevent email information leaks, i.e., when a message is accidentally addressed to non-desired recipients. This is an increasingly common problem that can severely harm individuals and corporations — for instance, a single email leak can potentially cause expensive law suits, brand reputation damage, negotiation setbacks and severe financial losses. In this paper we present the first attempt to solve this problem. We begin by redefining it as an outlier detection task, where the unintended recipients are the outliers. Then we combine real email examples (from the Enron Corpus) with carefully simulated leak-recipients to learn textual and network patterns associated with email leaks. This method was able to detect email leaks in almost 82% of the test cases, significantly outperforming all other baselines. More importantly, in a separate set of experiments we applied the proposed method to the task of finding real cases of email leaks. The result was encouraging: a variation of the proposed technique was consistently successful in finding two real cases of email leaks. Not only does this paper introduce the important problem of email leak detection, but also presents an effective solution that can be easily implemented in any email client — with no changes in the email server side.
Interplay of Hippocampus and Prefrontal Cortex in Memory

Recent studies on the hippocampus and the prefrontal cortex have considerably advanced our understanding of the distinct roles of these brain areas in the encoding and retrieval of memories, and of how they interact in the prolonged process by which new memories are consolidated into our permanent storehouse of knowledge. These studies have led to a new model of how the hippocampus forms and replays memories and how the prefrontal cortex engages representations of the meaningful contexts in which related memories occur, as well as how these areas interact during memory retrieval. Furthermore, they have provided new insights into how interactions between the hippocampus and prefrontal cortex support the assimilation of new memories into pre-existing networks of knowledge, called schemas, and how schemas are modified in this process as the foundation of memory consolidation.
Ground reaction forces during downhill and uphill running.

We investigated the normal and parallel ground reaction forces during downhill and uphill running. Our rationale was that these force data would aid in the understanding of hill running injuries and energetics. Based on a simple spring-mass model, we hypothesized that the normal force peaks, both impact and active, would increase during downhill running and decrease during uphill running. We anticipated that the parallel braking force peaks would increase during downhill running and the parallel propulsive force peaks would increase during uphill running. But, we could not predict the magnitude of these changes. Five male and five female subjects ran at 3m/s on a force treadmill mounted on the level and on 3 degrees, 6 degrees, and 9 degrees wedges. During downhill running, normal impact force peaks and parallel braking force peaks were larger compared to the level. At -9 degrees, the normal impact force peaks increased by 54%, and the parallel braking force peaks increased by 73%. During uphill running, normal impact force peaks were smaller and parallel propulsive force peaks were larger compared to the level. At +9 degrees, normal impact force peaks were absent, and parallel propulsive peaks increased by 75%. Neither downhill nor uphill running affected normal active force peaks. Combined with previous biomechanics studies, our normal impact force data suggest that downhill running substantially increases the probability of overuse running injury. Our parallel force data provide insight into past energetic studies, which show that the metabolic cost increases during downhill running at steep angles.
Micro Tells Macro: Predicting the Popularity of Micro-Videos via a Transductive Model

Micro-videos, a new form of user generated contents (UGCs), are gaining increasing enthusiasm. Popular micro-videos have enormous commercial potential in many ways, such as online marketing and brand tracking. In fact, the popularity prediction of traditional UGCs including tweets, web images, and long videos, has achieved good theoretical underpinnings and great practical success. However, little research has thus far been conducted to predict the popularity of the bite-sized videos. This task is non-trivial due to three reasons: 1) micro-videos are short in duration and of low quality; 2) they can be described by multiple heterogeneous channels, spanning from social, visual, acoustic to textual modalities; and 3) there are no available benchmark dataset and discriminant features that are suitable for this task. Towards this end, we present a transductive multi-modal learning model. The proposed model is designed to find the optimal latent common space, unifying and preserving information from different modalities, whereby micro-videos can be better represented. This latent space can be used to alleviate the information insufficiency problem caused by the brief nature of micro-videos. In addition, we built a benchmark dataset and extracted a rich set of popularity-oriented features to characterize the popular micro-videos. Extensive experiments have demonstrated the effectiveness of the proposed model. As a side contribution, we have released the dataset, codes and parameters to facilitate other researchers.