nils-herrmann/clean_scirepeval huggingface dataset:
TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.
Microtask programming: building software with a crowd

Microtask crowdsourcing organizes complex work into workflows, decomposing large tasks into small, relatively independent microtasks. Applied to software development, this model might increase participation in open source software development by lowering the barriers to contribu-tion and dramatically decrease time to market by increasing the parallelism in development work. To explore this idea, we have developed an approach to decomposing programming work into microtasks. Work is coordinated through tracking changes to a graph of artifacts, generating appropriate microtasks and propagating change notifications to artifacts with dependencies. We have implemented our approach in CrowdCode, a cloud IDE for crowd development. To evaluate the feasibility of microtask programming, we performed a small study and found that a small crowd of 12 workers was able to successfully write 480 lines of code and 61 unit tests in 14.25 person-hours of time.
A clustering approach for radar warning receivers

In this study a mission data base based clustering approach that can be used in radar warning receivers for the purpose of deinterleaving is suggested. Cell based deinterleaving technique, which is widely used at the present time, utilizes the information of direction of arrival, frequency and pulse width. In this study, different from this approach used in the literature, frequency, direction of arrival and pulse amplitude parameters are utilized for deinterleaving. With this technique it is shown that accurate results can be obtained by simulation.
Control Issues in Single-Stage Photovoltaic Systems: MPPT, Current and Voltage Control

Photovoltaic Systems (PVS) can be easily integrated in residential buildings hence they will be the main responsible of making low-voltage grid power flow bidirectional. Control issues on both the PV side and on the grid side have received much attention from manufacturers, competing for efficiency and low distortion and academia proposing new ideas soon become state-of-the-art. This paper aims at reviewing part of these topics (MPPT, current and voltage control) leaving to a future paper to complete the scenario. Implementation issues on Digital Signal Processor (DSP), the mandatory choice in this market segment, are discussed.
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.
Emotional Labor and Burnout : Comparing Two Perspectives of “ People Work ”

Although it has often been presumed that jobs involving “people work” (e.g., nurses, service workers) are emotionally taxing (Maslach & Jackson, 1982), seldom is the emotional component of these jobs explicitly studied. The current study compared two perspectives of emotional labor as predictors of burnout beyond the effects of negative affectivity: job-focused emotional labor (work demands regarding emotion expression) and employeefocused emotional labor (regulation of feelings and emotional expression). Significant differences existed in the emotional demands reported by five occupational groupings. The use of surface-level emotional labor, or faking, predicted depersonalization beyond the work demands. Perceiving the demand to display positive emotions and using deep-level regulation were associated with a heightened sense of personal accomplishment, suggesting positive benefits to this aspect of work. These findings suggest new antecedents of employee burnout and clarify the emotional labor literature by comparing different conceptualizations of this concept.
Deep Supervised and Contractive Neural Network for SAR Image Classification

The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches.
A distributed frequent itemset mining algorithm based on Spark

Frequent itemset mining is an important step of association rules mining. Traditional frequent itemset mining algorithms have certain limitations. For example Apriori algorithm has to scan the input data repeatedly, which leads to high I/O load and low performance, and the FP-Growth algorithm is limited by the capacity of computer's inner stores because it needs to build a FP-tree and mine frequent itemset on the basis of the FP-tree in memory. With the coming of the Big Data era, these limitations are becoming more prominent when confronted with mining large-scale data. In this paper, DPBM, a distributed matrix-based pruning algorithm based on Spark, is proposed to deal with frequent itemset mining. DPBM can greatly reduce the amount of candidate itemset by introducing a novel pruning technique for matrix-based frequent itemset mining algorithm, an improved Apriori algorithm which only needs to scan the input data once. In addition, each computer node reduces greatly the memory usage by implementing DPBM under a latest distributed environment-Spark, which is a lightning-fast distributed computing. The experimental results show that DPBM have better performance than MapReduce-based algorithms for frequent itemset mining in terms of speed and scalability.
Consumer video understanding: a benchmark database and an evaluation of human and machine performance

Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existing corpora for video analysis lack scale and/or content diversity, and thus limited the needed progress in this critical area. In this paper, we describe and release a new database called CCV, containing 9,317 web videos over 20 semantic categories, including events like "baseball" and "parade", scenes like "beach", and objects like "cat". The database was collected with extra care to ensure relevance to consumer interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques.
 We used Amazon MTurk platform to perform manual annotation, and studied the behaviors and performance of human annotators on MTurk. We also compared the abilities in understanding consumer video content by humans and machines. For the latter, we implemented automatic classifiers using state-of-the-art multi-modal approach that achieved top performance in recent TRECVID multimedia event detection task. Results confirmed classifiers fusing audio and video features significantly outperform single-modality solutions. We also found that humans are much better at understanding categories of nonrigid objects such as "cat", while current automatic techniques are relatively close to humans in recognizing categories that have distinctive background scenes or audio patterns.
Foster b-trees

Foster B-trees are a new variant of B-trees that combines advantages of prior B-tree variants optimized for many-core processors and modern memory hierarchies with flash storage and nonvolatile memory. Specific goals include: (i) minimal concurrency control requirements for the data structure, (ii) efficient migration of nodes to new storage locations, and (iii) support for continuous and comprehensive self-testing. Like Blink-trees, Foster B-trees optimize latching without imposing restrictions or specific designs on transactional locking, for example, key range locking. Like write-optimized B-trees, and unlike Blink-trees, Foster B-trees enable large writes on RAID and flash devices as well as wear leveling and efficient defragmentation. Finally, they support continuous and inexpensive yet comprehensive verification of all invariants, including all cross-node invariants of the B-tree structure. An implementation and a performance evaluation show that the Foster B-tree supports high concurrency and high update rates without compromising consistency, correctness, or read performance.
Positive effects of entertainment technology on human behaviour

Worldwide the pros and cons of games and social behaviour are discussed. In Western countries the discussion is focussing on violent game and media content; in Japan on intensive game usage and the impact on the intellectual development of children. A lot is already discussed on the harmful and negative effects of entertainment technology on human behaviour, therefore we decided to focus primarily on the positive effects. Based on an online document search we could find and select 393 online available publications according the following categories: meta review (N=34), meta analysis (N=13), literature review (N=38), literature survey (N=36), empirical study (N=91), survey study (N=44), design study (N=91), any other document (N=46). In this paper a first preliminary overview over positive effects of entertainment technology on human behaviour is presented and discussed. The drawn recommendations can support developers and designers in entertainment industry.
Multi-Sided Platforms 1

The economics of two-sided markets or multi-sided platforms has emerged over the past decade as one of the most active areas of research in economics and strategy. The literature has constantly struggled, however, with a lack of agreement on a proper definition: for instance, some existing definitions imply that retail firms such as grocers, supermarkets and department stores are multi-sided platforms (MSPs). We propose a definition which provides a more precise notion of MSPs by requiring that they enable direct interactions between the multiple customer types which are affiliated to them. Several important implications of this new definition are derived. First, cross-group network effects are neither necessary nor sufficient for an organization to be a MSP. Second, our definition emphasizes the difference between MSPs and alternative forms of intermediation such as “re-sellers” which take control over the interactions between the various sides, or input suppliers which have only one customer group affiliated as opposed to multiple. We discuss a number of examples that illustrate the insights that can be derived by applying our definition. Third, we point to the economic considerations that determine where firms choose to position themselves on the continuum between MSPs and resellers, or MSPs and input suppliers. 1 Britta Kelley provided excellent research assistance. We are grateful to Elizabeth Altman, Tom Eisenmann and Marc Rysman for comments on an earlier draft. 2 Harvard University,
An Unsupervised Framework of Exploring Events on Twitter: Filtering, Extraction and Categorization

Twitter, as a popular microblogging service, has become a new information channel for users to receive and exchange the most up-to-date information on current events. However, since there is no control on how users can publish messages on Twitter, finding newsworthy events from Twitter becomes a difficult task like “finding a needle in a haystack”. In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization. To filter out noisy tweets, the filtering step exploits a lexicon-based approach to separate tweets that are event-related from those that are not. Then, based on these event-related tweets, the structured representations of events are extracted and categorized automatically using an unsupervised Bayesian model without the use of any labelled data. Moreover, the categorized events are assigned with the event type labels without human intervention. The proposed framework has been evaluated on over 60 millions tweets which were collected for one month in December 2010. A precision of 70.49% is achieved in event extraction, outperforming a competitive baseline by nearly 6%. Events are also clustered into coherence groups with the automatically assigned event type label.
Mining reference tables for automatic text segmentation

Automatically segmenting unstructured text strings into structured records is necessary for importing the information contained in legacy sources and text collections into a data warehouse for subsequent querying, analysis, mining and integration. In this paper, we mine tables present in data warehouses and relational databases to develop an automatic segmentation system. Thus, we overcome limitations of existing supervised text segmentation approaches, which require comprehensive manually labeled training data. Our segmentation system is robust, accurate, and efficient, and requires no additional manual effort. Thorough evaluation on real datasets demonstrates the robustness and accuracy of our system, with segmentation accuracy exceeding state of the art supervised approaches.
OVERCONFIDENCE IN CASE-STUDY JUDGMENTS.

This study investigated whether psychologists' confidence in their clinical decisions is really justified. It was hypothesized that as psychologists study information about a case (a) their confidence about the case increases markedly and steadily but (b) the accuracy of their conclusions about the case quickly reaches a ceiling. 32 judges, including 8 clinical psychologists, read background information about a published case, divided into 4 sections. After reading each section of the case, judges answered a set of 25 questions involving personality judgments about the case. Results strongly supported the hypotheses. Accuracy did not increase significantly with increasing information, but confidence increased steadily and significantly. All judges except 2 became overconfident, most of them markedly so. Clearly, increasing feelings of confidence are not a sure sign of increasing predictive accuracy about a case.
Crowd Documentation : Exploring the Coverage and the Dynamics of API Discussions on Stack Overflow

Traditionally, many types of software documentation, such as API documentation, require a process where a few people write for many potential users. The resulting documentation, when it exists, is often of poor quality and lacks sufficient examples and explanations. In this paper, we report on an empirical study to investigate how Question and Answer (Q&A) websites, such as Stack Overflow, facilitate crowd documentation — knowledge that is written by many and read by many. We examine the crowd documentation for three popular APIs: Android, GWT, and the Java programming language. We collect usage data using Google Code Search, and analyze the coverage, quality, and dynamics of the Stack Overflow documentation for these APIs. We find that the crowd is capable of generating a rich source of content with code examples and discussion that is actively viewed and used by many more developers. For example, over 35,000 developers contributed questions and answers about the Android API, covering 87% of the classes. This content has been viewed over 70 million times to date. However, there are shortcomings with crowd documentation, which we identify. In addition to our empirical study, we present future directions and tools that can be leveraged by other researchers and software designers for performing API analytics and mining of crowd documentation.
Accurate Dependency Parsing with a Stacked Multilayer Perceptron

DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in both the main and pilot subtasks. The contribution to the result of various choices is analyzed, in particular for taking advantage of the peculiar features of the TUT Treebank.
Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner

Micro aerial vehicles (MAV) pose a challenge in designing sensory systems and algorithms due to their size and weight constraints and limited computing power. We present an efficient 3D multi-resolution map that we use to aggregate measurements from a lightweight continuously rotating laser scanner. We estimate the robot's motion by means of visual odometry and scan registration, aligning consecutive 3D scans with an incrementally built map. By using local multi-resolution, we gain computational efficiency by having a high resolution in the near vicinity of the robot and a lower resolution with increasing distance from the robot, which correlates with the sensor's characteristics in relative distance accuracy and measurement density. Compared to uniform grids, local multi-resolution leads to the use of fewer grid cells without loosing information and consequently results in lower computational costs. We efficiently and accurately register new 3D scans with the map in order to estimate the motion of the MAV and update the map in-flight. In experiments, we demonstrate superior accuracy and efficiency of our registration approach compared to state-of-the-art methods such as GICP. Our approach builds an accurate 3D obstacle map and estimates the vehicle's trajectory in real-time.
Behavioral experiments on biased voting in networks.

Many distributed collective decision-making processes must balance diverse individual preferences with a desire for collective unity. We report here on an extensive session of behavioral experiments on biased voting in networks of individuals. In each of 81 experiments, 36 human subjects arranged in a virtual network were financially motivated to reach global consensus to one of two opposing choices. No payments were made unless the entire population reached a unanimous decision within 1 min, but different subjects were paid more for consensus to one choice or the other, and subjects could view only the current choices of their network neighbors, thus creating tensions between private incentives and preferences, global unity, and network structure. Along with analyses of how collective and individual performance vary with network structure and incentives generally, we find that there are well-studied network topologies in which the minority preference consistently wins globally; that the presence of "extremist" individuals, or the awareness of opposing incentives, reliably improve collective performance; and that certain behavioral characteristics of individual subjects, such as "stubbornness," are strongly correlated with earnings.
Building a State-of-the-Art Grammatical Error Correction System

This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system. We do this by analyzing the Illinois system that placed first among seventeen teams in the recent CoNLL-2013 shared task on grammatical error correction. The system focuses on five different types of errors common among non-native English writers. We describe four design principles that are relevant for correcting all of these errors, analyze the system along these dimensions, and show how each of these dimensions contributes to the performance.
Understanding seeking from electronic knowledge repositories: An empirical study

Knowledge is a critical resource that can help organizations to sustain strategic advantage in competitive environments. Organizations in Asia and elsewhere are turning to knowledge management (KM) initiatives and technologies to leverage their knowledge resources. As a key component of KM initiatives, electronic knowledge repositories (EKR) are deployed by organizations to store codified knowledge for future reuse. Although EKR have been in use for some time, there is a lack of understanding about factors that motivate employees to seek knowledge from EKR. This study formulates and empirically tests a theoretical model relating potential antecedents to EKR usage for knowledge seeking. The model was operationalized and the survey instrument subjected to a conceptual validation process. The survey was administered to 160 knowledge professionals in public sector organizations in Singapore who had accessed the contents of EKR in the course of their work. Results reveal that perceived output quality directly affects EKR usage for knowledge seeking. Further, resource availability impacts EKR usage for knowledge seeking particularly when task tacitness is low and incentives affect EKR usage particularly when task interdependence is high. Implications of these results for further research and improving EKR implementation