nils-herrmann/clean_scirepeval huggingface dataset:
Actions speak as loud as words: predicting relationships from social behavior data

In recent years, new studies concentrating on analyzing user personality and finding credible content in social media have become quite popular. Most such work augments features from textual content with features representing the user's social ties and the tie strength. Social ties are crucial in understanding the network the people are a part of. However, textual content is extremely useful in understanding topics discussed and the personality of the individual. We bring a new dimension to this type of analysis with methods to compute the type of ties individuals have and the strength of the ties in each dimension. We present a new genre of behavioral features that are able to capture the "function" of a specific relationship without the help of textual features. Our novel features are based on the statistical properties of communication patterns between individuals such as reciprocity, assortativity, attention and latency. We introduce a new methodology for determining how such features can be compared to textual features, and show, using Twitter data, that our features can be used to capture contextual information present in textual features very accurately. Conversely, we also demonstrate how textual features can be used to determine social attributes related to an individual.
A study of Poisson query generation model for information retrieval

Many variants of language models have been proposed for information retrieval. Most existing models are based on multinomial distribution and would score documents based on query likelihood computed based on a query generation probabilistic model. In this paper, we propose and study a new family of query generation models based on Poisson distribution. We show that while in their simplest forms, the new family of models and the existing multinomial models are equivalent. However, based on different smoothing methods, the two families of models behave differently. We show that the Poisson model has several advantages, including naturally accommodating per-term smoothing and modeling accurate background more efficiently. We present several variants of the new model corresponding to different smoothing methods, and evaluate them on four representative TREC test collections. The results show that while their basic models perform comparably, the Poisson model can out perform multinomial model with per-term smoothing. The performance can be further improved with two-stage smoothing.
HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

Transductive classification using labeled and unlabeled objects in a heterogeneous information network for knowledge extraction is an interesting and challenging problem. Most of the real-world networks are heterogeneous in their natural setting and traditional methods of classification for homogeneous networks are not suitable for heterogeneous networks. In a heterogeneous network, various meta-paths connecting objects of the target type, on which classification is to be performed, make the classification task more challenging. The semantic of each meta-path would lead to the different accuracy of classification. Therefore, weight learning of meta-paths is required to leverage their semantics simultaneously by a weighted combination. In this work, we propose a novel meta-path based framework, HeteClass, for transductive classification of target type objects. HeteClass explores the network schema of the given network and can also incorporate the knowledge of the domain expert to generate a set of meta-paths. The regularization based weight learning method proposed in HeteClass is effective to compute the weights of symmetric as well as asymmetric meta-paths in the network, and the weights generated are consistent with the real-world understanding. Using the learned weights, a homogeneous information network is formed on target type objects by the weighted combination, and transductive classification is performed. The proposed framework HeteClass is flexible to utilize any suitable classification algorithm for transductive classification and can be applied on heterogeneous information networks with arbitrary network schema. Experimental results show the effectiveness of the HeteClass for classification of unlabeled objects in heterogeneous information networks using real-world data sets.
Laparoscopic surgery versus open surgery for colon cancer: short-term outcomes of a randomised trial.

The safety and short-term benefits of laparoscopic colectomy for cancer remain debatable. The multicentre COLOR (COlon cancer Laparoscopic or Open Resection) trial was done to assess the safety and benefit of laparoscopic resection compared with open resection for curative treatment of patients with cancer of the right or left colon.



627 patients were randomly assigned to laparoscopic surgery and 621 patients to open surgery. The primary endpoint was cancer-free survival 3 years after surgery. Secondary outcomes were short-term morbidity and mortality, number of positive resection margins, local recurrence, port-site or wound-site recurrence, metastasis, overall survival, and blood loss during surgery. Analysis was by intention to treat. Here, clinical characteristics, operative findings, and postoperative outcome are reported.



Patients assigned laparoscopic resection had less blood loss compared with those assigned open resection (median 100 mL [range 0-2700] vs 175 mL [0-2000], p<0.0001), although laparoscopic surgery lasted 30 min longer than did open surgery (p<0.0001). Conversion to open surgery was needed for 91 (17%) patients undergoing the laparoscopic procedure. Radicality of resection as assessed by number of removed lymph nodes and length of resected oral and aboral bowel did not differ between groups. Laparoscopic colectomy was associated with earlier recovery of bowel function (p<0.0001), need for fewer analgesics, and with a shorter hospital stay (p<0.0001) compared with open colectomy. Morbidity and mortality 28 days after colectomy did not differ between groups.



Laparoscopic surgery can be used for safe and radical resection of cancer in the right, left, and sigmoid colon.
The virtual census: representations of gender, race and age in video games

A large-scale content analysis of characters in video games was employed to answer questions about their representations of gender, race and age in comparison to the US population. The sample included 150 games from a year across nine platforms, with the results weighted according to game sales. This innovation enabled the results to be analyzed in proportion to the games that were actually played by the public, and thus allowed the first statements able to be generalized about the content of popular video games. The results show a systematic over-representation of males, white and adults and a systematic under-representation of females, Hispanics, Native Americans, children and the elderly. Overall, the results are similar to those found in television research. The implications for identity, cognitive models, cultivation and game research are discussed. new media & society
Small-Size Triple-Wideband LTE/WWAN Tablet Device Antenna

A small-size tablet device antenna with three wide operating bands to cover the Long Term Evolution/Wireless Wide Area Network (LTE/WWAN) operation in the 698 ~ 960-, 1710 ~ 2690-, and 3400 ~ 3800-MHz bands is presented. The antenna has a planar structure and is easy to fabricate on one surface of a thin FR4 substrate of size 10×45×0.8 mm3. The antenna is formed by adding a first branch (branch 1, an inductively coupled strip) and a second branch (branch 2, a simple branch strip) to a coupled-fed shorted strip antenna (main portion), and the two branches are configured with the main portion to achieve a compact antenna structure. The three widebands are easy to adjust and can cover the LTE/WWAN operation, which includes the most commonly commercial LTE bands and WWAN bands (698 ~ 960 and 1710 ~ 2690 MHz) and the LTE 3.5-GHz band (3400 ~ 3800 MHz).
Humanoids and personal robots: Design and experiments

This paper addresses the field of humanoid and personal robotics—its objectives, motivations, and technical problems. The approach described in the paper is based on the analysis of humanoid and personal robots as an evolution from industrial to advanced and service robotics driven by the need for helpful machines, as well as a synthesis of the dream of replicating humans. The first part of the paper describes the development of anthropomorphic components for humanoid robots, with particular regard to anthropomorphic sensors for vision and touch, an eight-d.o.f. arm, a three-fingered hand with sensorized fingertips, and control schemes for grasping. Then, the authors propose a user-oriented designmethodology for personal robots, anddescribe their experience in the design, development, and validation of a real personal robot composed of a mobile unit integrating some of the anthropomorphic components introduced previously and aimed at operating in a distributedworking environment. Based on the analysis of experimental results, the authors conclude that humanoid robotics is a tremendous and attractive technical and scientific challenge for robotics research. The real utility of humanoids has still to be demonstrated, but personal assistance can be envisaged as a promising application domain. Personal robotics also poses difficult technical problems, especially related to the need for achieving adequate safety, proper human–robot interaction, useful performance, and affordable cost. When these problems are solved, personal robots will have an excellent chance for significant application opportunities, especially if integrated into future home automation systems, and if supported by the availability of humanoid robots.
Background Inpainting for Videos with Dynamic Objects and a Free-Moving Camera

We propose a method for removing marked dynamic objects from videos captured with a free-moving camera, so long as the objects occlude parts of the scene with a static background. Our approach takes as input a video, a mask marking the object to be removed, and a mask marking the dynamic objects to remain in the scene. To inpaint a frame, we align other candidate frames in which parts of the missing region are visible. Among these candidates, a single source is chosen to fill each pixel so that the final arrangement is color-consistent. Intensity differences between sources are smoothed using gradient domain fusion. Our frame alignment process assumes that the scene can be approximated using piecewise planar geometry: A set of homographies is estimated for each frame pair, and one each is selected for aligning pixels such that the color-discrepancy is minimized and the epipolar constraints are maintained. We provide experimental validation with several real-world video sequences to demonstrate that, unlike in previous work, inpainting videos shot with free-moving cameras does not necessarily require estimation of absolute camera positions and per-frame per-pixel depth maps.
End-to-End Training of Deep Visuomotor Policies

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-toend provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot’s motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.
Data Mining: An Overview from a Database Perspective

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many di erent elds have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classi cation of the available data mining techniques is provided and a comparative study of such techniques is presented. Index Terms | Data mining, knowledge discovery, association rules, classi cation, data clustering, pattern matching algorithms, data generalization and characterization, data cubes, multiple-dimensional databases.  Han was supported in part by the research grant NSERC-A3723 from the Natural Sciences and Engineering Research Council of Canada, the research grant NCE:IRIS/Precarn-HMI5 from the Networks of Centres of Excellence of Canada, and research grants from MPR Teltech Ltd. and Hughes Research Laboratories.
Deep contextualized word representations for detecting sarcasm and irony

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.
Progressive simplicial complexes

In this paper, we introduce the progressive simplicial complex (PSC) representation, a new format for storing and transmitting triangulated geometric models. Like the earlier progressive mesh (PM) representation, it captures a given model as a coarse base model together with a sequence of refinement transformations that progressively recover detail. The PSC representation makes use of a more general refinement transformation, allowing the given model to be an arbitrary triangulation (e.g. any dimension, non-orientable, non-manifold, non-regular), and the base model to always consist of a single vertex. Indeed, the sequence of refinement transformations encodes both the geometry and the topology of the model in a unified multiresolution framework. The PSC representation retains the advantages of PM’s. It defines a continuous sequence of approximating models for runtime level-of-detail control, allows smooth transitions between any pair of models in the sequence, supports progressive transmission, and offers a space-efficient representation. Moreover, by allowing changes to topology, the PSC sequence of approximations achieves better fidelity than the corresponding PM sequence. We develop an optimization algorithm for constructing PSC representations for graphics surface models, and demonstrate the framework on models that are both geometrically and topologically complex. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling surfaces and object representations.
Exploitation of Unlabeled Sequences in Hidden Markov Models

This paper presents a method for effectively using unlabeled sequential data in the learning of hidden Markov models (HMMs). With the conventional approach, class labels for unlabeled data are assigned deterministically by HMMs learned from labeled data. Such labeling often becomes unreliable when the number of labeled data is small. We propose an extended Baum-Welch (EBW) algorithm in which the labeling is undertaken probabilistically and iteratively so that the labeled and unlabeled data likelihoods are improved. Unlike the conventional approach, the EBW algorithm guarantees convergence to a local maximum of the likelihood. Experimental results on gesture data and speech data show that when labeled training data are scarce, by using unlabeled data, the EBW algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach. keywords Unlabeled data, sequential data, hidden Markov models, extended Baum-Welch algorithm.
Struggling and Success in Web Search

Web searchers sometimes struggle to find relevant information. Struggling leads to frustrating and dissatisfying search experiences, even if searchers ultimately meet their search objectives. Better understanding of search tasks where people struggle is important in improving search systems. We address this important issue using a mixed methods study using large-scale logs, crowd-sourced labeling, and predictive modeling. We analyze anonymized search logs from the Microsoft Bing Web search engine to characterize aspects of struggling searches and better explain the relationship between struggling and search success. To broaden our understanding of the struggling process beyond the behavioral signals in log data, we develop and utilize a crowd-sourced labeling methodology. We collect third-party judgments about why searchers appear to struggle and, if appropriate, where in the search task it became clear to the judges that searches would succeed (i.e., the pivotal query). We use our findings to propose ways in which systems can help searchers reduce struggling. Key components of such support are algorithms that accurately predict the nature of future actions and their anticipated impact on search outcomes. Our findings have implications for the design of search systems that help searchers struggle less and succeed more.
High-Performance 1.5–2.5-GHz RF-MEMS Tunable Filters for Wireless Applications

This paper presents high-performance RF microelectromechanical systems (RF MEMS) tunable filters with constant absolute bandwidth for the 1.5-2.5-GHz wireless band. The filter design is based on corrugated coupled lines and ceramic substrates (εr=9.9) for miniaturization, and the 3-bit tuning network is fabricated using a digital/analog RF-MEMS device so as to provide a large capacitance ratio and continuous frequency coverage. Narrowband (72 ± 3 MHz) and wideband (115 ± 10 MHz) 1-dB bandwidth two-pole filters result in a measured insertion loss of 1.9-2.2 dB at 1.5-2.5 GHz with a power handling of 25 dBm and an IIP3 >> 33 dBm. The filters also showed no distortion when tested under wideband CDMA waveforms up to 24.8 dBm. The designs can be scaled to higher dielectric-constant substrates to result in smaller filters. To our knowledge, these filters represent the state-of-the-art at this frequency range using any planar tuning technology.
A Linear Programming Approach to the Cutting-Stock Problem

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MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
Beyond energy monitors: interaction, energy, and emerging energy systems

Motivated by a recent surge of research related to energy and sustainability, this paper presents a review of energy-related work within HCI as well as from literature outside of HCI. Our review of energy-related HCI research identifies a central cluster of work focused on electricity consumption feedback (ECF). Our review of literature outside of HCI highlights a number of emerging energy systems trends of strong relevance to HCI and interaction design, including smart grid, demand response, and distributed generation technologies. We conclude by outlining a range of opportunities for HCI to engage with the experiential, behavioral, social, and cultural aspects of these emerging systems, including highlighting new areas for ECF research that move beyond our field's current focus on energy feedback displays to increase awareness and motivate individual conservation behavior.
Graph based Representation and Analysis of Text Document: A Survey of Techniques

A common and standard approach to model text document is bag-of-words. This model is suitable for capturing word frequency, however structural and semantic information is ignored. Graph representation is mathematical constructs and can model relationship and structural information effectively. A text can appropriately represented as Graph using vertex as feature term and edge relation can be significant relation between the feature terms. Text representation using Graph model provides computations related to various operations like term weight, ranking which is helpful in many applications in information retrieval. This paper presents a systematic survey of existing work on Graph based representation of text and also focused on Graph based analysis of text document for different operations in information retrieval. In this process taxonomy of Graph based representation and analysis of text document is derived and result of different methods of Graph based text representation and analysis are discussed. The survey results shows that Graph based representation is appropriate way of representing text document and improved result of analysis over traditional model for different text applications.