nils-herrmann/clean_scirepeval huggingface dataset:
The motivations and experiences of the on-demand mobile workforce

On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.
Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review

Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a model's choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.
Creating Brand Equity by Leveraging Value Creation and Consumer Commitment in Online Brand Communities : A Conceptual Framework

Advancement in information technologies has created change in our economics as well as in our societies because companies strive to adapt the conditions that prevail in marketplace. In this scenario, marketing professionals are exploring advanced and effective ways to build a strong brand. The research objectives explore the impact of online brand communities on brand equity dimensions through which consumer could achieve commitment and value-creation. While, marketing managers could explore attributes regarding brand perception, information and resource sharing and brand loyalty. A framework is proposed based on the drivers of online brand community and its influence on the dimensions of brand equity. From theoretical perspective, this study contributes to branding literature by exploring strategies for value-creation, brand image and integrated marketing communication. Additionally from marketing perspective, the drivers will guide managers to understand the want and desire of target audience. The building of such online brand communities will raise the return of investment, save marketing cost, enhance consumer loyalty, improving product development and providing circumstances for co-creation. Thus, it will provide ever-lasting consumer-brand relationship and substantial information about consumer behavior.
Image segmentation based on histogram of depth and an application in driver distraction detection

This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.
The PeerRank Method for Peer Assessment

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages.
World Knowledge as Indirect Supervision for Document Clustering

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.
 A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a].  has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).
Axial-flux PM synchronous machines with air-gap profiling and very high ratio of spoke rotor poles to stator concentrated coils

Permanent magnet machines including a magnetic gearing effect are attractive for low-speed high-torque applications. This paper proposes a dual-stator axial-flux machine topology with a high ratio for the number of rotor poles to concentrated stator coils. The stator consists of a relatively small number of teeth, with each tooth including multiple smaller/auxiliary teeth. The rotor employs spoke-type permanent magnets, which result in high flux concentration, thereby further improving the torque density. The paper discusses the principle of operation of the proposed machine topology. Finite element results on example designs with 6 stator teeth, each having 2 and 3 auxiliary teeth, and with 20, 32, and 34 rotor poles are presented, respectively. Other feasible slot-pole combinations are also identified.
Efficient Visualization of Lagrangian Coherent Structures by Filtered AMR Ridge Extraction

This paper presents a method for filtered ridge extraction based on adaptive mesh refinement. It is applicable in situations where the underlying scalar field can be refined during ridge extraction. This requirement is met by the concept of Lagrangian coherent structures which is based on trajectories started at arbitrary sampling grids that are independent of the underlying vector field. The Lagrangian coherent structures are extracted as ridges in finite Lyapunov exponent fields computed from these grids of trajectories. The method is applied to several variants of finite Lyapunov exponents, one of which is newly introduced. High computation time due to the high number of required trajectories is a main drawback when computing Lyapunov exponents of 3-dimensional vector fields. The presented method allows a substantial speed-up by avoiding the seeding of trajectories in regions where no ridges are present or do not satisfy the prescribed filter criteria such as a minimum finite Lyapunov exponent.
CBIR Evaluation using Different Distances and DWT

In this paper, evaluation is made on the result of CBIR system based on haar wavelet transform with different distances for similarity matching to calculate the deviation from query image. So, different distances used are Chessboard distance, Cityblock distance and Euclidean distance. In this paper discrete wavelet transform is used to decompose the image. The image is decomposed till sixth level and last level approximate component is saved as feature vector. Comparison is made between different distances to see the best suited distance for CBIR. The wavelet used is &quot;Haar&quot;. Haar has compact support and it is the simplest orthogonal wavelet. It is very fast also.
PyThinSearch: A Simple Web Search Engine

We describe a simple, functioning web search engine for indexing and searching online documents using python programming language. Python was chosen because it is an elegant language with simple syntax, is easy to learn and debug, and supports main operating systems almost evenly. The remarkable characteristics of this program are an adjustable search function that allows users to rank documents with several combinations of score functions and the focus on anchor text analysis as we provide four additional schemes to calculate scores based on anchor text. We also provide an additional ranking algorithm based on link addition process in network motivated by PageRank and HITS as an experimental tool. This algorithm is the original contribution of this paper.
Catching Metaphors

Metaphors are ubiquitous in language and developing methods to identify and deal with metaphors is an open problem in Natural Language Processing (NLP). In this paper we describe results from using a maximum entropy (ME) classifier to identify metaphors. Using the Wall Street Journal (WSJ) corpus, we annotated all the verbal targets associated with a set of frames which includes frames of spatial motion, manipulation, and health. One surprising finding was that over 90% of targets from these frames are used metaphorically, underscoring the importance of processing figurative language. We then used this labeled data and each verbal target’s PropBank annotation to train a maximum entropy classifier to make this literal vs. metaphoric distinction. Using the classifier, we reduce the final error in the test set by 5% over the verb-specific majority class baseline and 31% over the corpus-wide majority class baseline.
How Does Corporate Ethics Contribute to Firm Financial Performance ? The Mediating Role of Collective Organizational Commitment and Organizational Citizenship Behavior Jinseok

http://jom.sagepub.com/content/39/4/853 The online version of this article can be found at: DOI: 10.1177/0149206311419662 2013 39: 853 originally published online 1 September 2011  Shin, Jin Nam Choi and Min Soo Kim Citizenship Behavior Mediating Role of Collective Organizational Commitment and Organizational How Does Corporate Ethics Contribute to Firm Financial Performance? : The
Evolutionary algorithm in Forex trade strategy generation

This paper shows an evolutionary algorithm application to generate profitable strategies to trade futures contracts on foreign exchange market (Forex). Strategy model in approach is based on two decision trees, responsible for taking the decisions of opening long or short positions on Euro/US Dollar currency pair. Trees take into consideration only technical analysis indicators, which are connected by logic operators to identify border values of these indicators for taking profitable decision(s). We have tested the efficiency of presented approach on learning and test time-frames of various characteristics.
Deeper Inside PageRank

This paper serves as a companion or extension to the “Inside PageRank” paper by Bianchini et al. . It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.
Improving automated source code summarization via an eye-tracking study of programmers

Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool and provide evidence to support the development of source code summarization systems.
Sentence-level Arabic sentiment analysis

Arabic sentiment analysis research existing currently is very limited. While sentiment analysis has many applications in English, the Arabic language is still recognizing its early steps in this field. In this paper, we show an application on Arabic sentiment analysis by implementing a sentiment classification for Arabic tweets. The retrieved tweets are analyzed to provide their sentiments polarity (positive, or negative). Since, this data is collected from the social network Twitter; it has its importance for the Middle East region, which mostly speaks Arabic.
MoveMeant: Anonymously Building Community Through Shared Location Histories

Awareness of and connections to a local community are important for building social capital, sharing resources, and providing physical support, but have been elusive to create in dense urban environments. We describe the design and implementation of MoveMeant, a system aimed to increase local community awareness through shared location traces. MoveMeant securely uses anonymized location data generated automatically by mobile devices to display aggregate, community-level location data. We report findings from interviews with residents in the Bronx, New York City who participated in a deployment of MoveMeant over a 6-week period. Our findings show that people use the anonymous information to make judgments about the people and places in their community, while opting to reveal their identity for third places where there is an opportunity to connect socially.
Efficiency optimization of half-bridge series resonant inverter with asymmetrical duty cycle control for domestic induction heating

In this paper, a method to improve efficiency in half-bridge series resonant inverter applied to domestic induction heating is presented. Low and medium output powers required for this application implies the use of higher switching frequencies, which leads to an efficiency decrease. Asymmetrical duty cycle (ADC) modulation scheme is proposed to improve efficiency due to its switching frequency reduction and absence of additional hardware requirements. Study methodology comprises, in a first step, a theoretical analysis of power balance as a function of control parameters: duty cycle and switching frequency. In addition, restrictions due to snubber and dead time, and variability of the induction loads have been considered. Afterwards, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine global importance of each one for different switching devices. ADC modulation efficiency improvement is achieved by means of a switching frequency reduction, mainly at low-medium power range and low quality factor (Q) loads. The analytical results obtained with this study have been validated through an induction heating test-bench. A discrete 3-kW RL load has been designed to emulate a typical induction heating load. Then, a commercial induction heating inverter is used to evaluate ADC modulation scheme.
Weakly Supervised Tweet Stance Classification by Relational Bootstrapping

Supervised stance classification, in such domains as Congressional debates and online forums, has been a topic of interest in the past decade. Approaches have evolved from text classification to structured output prediction, including collective classification and sequence labeling. In this work, we investigate collective classification of stances on Twitter, using hinge-loss Markov random fields (HLMRFs). Given the graph of all posts, users, and their relationships, we constrain the predicted post labels and latent user labels to correspond with the network structure. We focus on a weakly supervised setting, in which only a small set of hashtags or phrases is labeled. Using our relational approach, we are able to go beyond the stance-indicative patterns and harvest more stance-indicative tweets, which can also be used to train any linear text classifier when the network structure is not available or is costly.
Data-Driven De-Anonymization in Bitcoin

We analyse the performance of several clustering algorithms in the digital peerto-peer currency Bitcoin. Clustering in Bitcoin refers to the task of finding addresses that belongs to the same wallet as a given address. In order to assess the effectiveness of clustering strategies we exploit a vulnerability in the implementation of Connection Bloom Filtering to capture ground truth data about 37,585 Bitcoin wallets and the addresses they own. In addition to well-known clustering techniques, we introduce two new strategies, apply them on addresses of the collected wallets and evaluate precision and recall using the ground truth. Due to the nature of the Connection Bloom Filtering vulnerability the data we collect is not without errors. We present a method to correct the performance metrics in the presence of such inaccuracies. Our results demonstrate that even modern wallet software can not protect its users properly. Even with the most basic clustering technique known as multiinput heuristic, an adversary can guess on average 68.59% addresses of a victim. We show that this metric can be further improved by combining several more sophisticated heuristics.
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects – which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is benchmarked against a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness.
Multiresolution Tree Networks for 3D Point Cloud Processing

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed treestructured encoders can be used to classify shapes and outperform existing pointbased architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.