nils-herrmann/clean_scirepeval huggingface dataset:
Intent-based diversification of web search results: metrics and algorithms

We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks

Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowledge are difficult to incorporate when training new models, requiring increasingly longer training as problems become more complex. This is especially problematic for problems with sparse rewards. We provide a solution to these problems by introducing Concept Network Reinforcement Learning (CNRL), a framework which allows us to decompose problems using a multi-level hierarchy. Concepts in a concept network are reusable, and flexible enough to encapsulate feature extractors, skills, or other concept networks. With this hierarchical learning approach, deep reinforcement learning can be used to solve complex tasks in a modular way, through problem decomposition. We demonstrate the strength of CNRL by training a model to grasp a rectangular prism and precisely stack it on top of a cube using a gripper on a Kinova JACO arm, simulated in MuJoCo. Our experiments show that our use of hierarchy results in a 45x reduction in environment interactions compared to the state-of-the-art on this task.
Group Equivariant Capsule Networks

We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea. Our work can be divided into two contributions. First, we present a generic routing by agreement algorithm defined on elements of a group and prove that equivariance of output pose vectors, as well as invariance of output activations, hold under certain conditions. Second, we connect the resulting equivariant capsule networks with work from the field of group convolutional networks. Through this connection, we provide intuitions of how both methods relate and are able to combine the strengths of both approaches in one deep neural network architecture. The resulting framework allows sparse evaluation of the group convolution operator, provides control over specific equivariance and invariance properties, and can use routing by agreement instead of pooling operations. In addition, it is able to provide interpretable and equivariant representation vectors as output capsules, which disentangle evidence of object existence from its pose.
Characteristics of track cycling.

Track cycling events range from a 200 m flying sprint (lasting 10 to 11 seconds) to the 50 km points race (lasting approximately 1 hour). Unlike road cycling competitions where most racing is undertaken at submaximal power outputs, the shorter track events require the cyclist to tax maximally both the aerobic and anaerobic (oxygen independent) metabolic pathways. Elite track cyclists possess key physical and physiological attributes which are matched to the specific requirements of their events: these cyclists must have the appropriate genetic predisposition which is then maximised through effective training interventions. With advances in technology it is now possible to accurately measure both power supply and demand variables under competitive conditions. This information provides better resolution of factors that are important for training programme design and skill development.
When social networking is not working: individuals with low self-esteem recognize but do not reap the benefits of self-disclosure on Facebook.

The popular media have publicized the idea that social networking Web sites (e.g., Facebook) may enrich the interpersonal lives of people who struggle to make social connections. The opportunity that such sites provide for self-disclosure-a necessary component in the development of intimacy--could be especially beneficial for people with low self-esteem, who are normally hesitant to self-disclose and who have difficulty maintaining satisfying relationships. We suspected that posting on Facebook would reduce the perceived riskiness of self-disclosure, thus encouraging people with low self-esteem to express themselves more openly. In three studies, we examined whether such individuals see Facebook as a safe and appealing medium for self-disclosure, and whether their actual Facebook posts enabled them to reap social rewards. We found that although people with low self-esteem considered Facebook an appealing venue for self-disclosure, the low positivity and high negativity of their disclosures elicited undesirable responses from other people.
Comparing the Performance of Different NLP Toolkits in Formal and Social Media Text

Nowadays, there are many toolkits available for performing common natural language processing tasks, which enable the development of more powerful applications without having to start from scratch. In fact, for English, there is no need to develop tools such as tokenizers, partof-speech (POS) taggers, chunkers or named entity recognizers (NER). The current challenge is to select which one to use, out of the range of available tools. This choice may depend on several aspects, including the kind and source of text, where the level, formal or informal, may influence the performance of such tools. In this paper, we assess a range of natural language processing toolkits with their default configuration, while performing a set of standard tasks (e.g. tokenization, POS tagging, chunking and NER), in popular datasets that cover newspaper and social network text. The obtained results are analyzed and, while we could not decide on a single toolkit, this exercise was very helpful to narrow our choice.
Unsupervised Learning of Stereo Matching

Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo matching costs without human supervision. Our method updates network parameters in an iterative manner. It starts with a randomly initialized network. Left-right check is adopted to guide the training. Suitable matching is then picked and used as training data in following iterations. Our system finally converges to a stable state and performs even comparably with other supervised methods.
Patent Mining: A Survey

Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. Different from general web documents, patent documents have a well-defined format including frontpage, description, nclaims, and figures. However, they are lengthy and rich in technical terms, which requires enormous human efforts for analysis. Hence, a new research area, called patent mining, emerges in recent years, aiming to assist patent analysts in investigating, processing, and analyzing patent documents. Despite the recent advances in patent mining, it is still far from being well explored in research communities. To help patent analysts and interested readers obtain a big picture of patent mining, we thus provide a systematic summary of existing research efforts along this direction. In this survey, we first present an overview of the technical trend in patent mining. We then investigate multiple research questions related to patent documents, including patent retrieval, patent classification, and patent visualization, and provide summaries and highlights for each question by delving into the corresponding research efforts.
OSS/BSS Framework Based on NGOSS

As the key supporting system of telecommunication enterprises, OSS/BSS needs to support the service steadily in the long-term running and maintenance process. The system architecture must remain steady and consistent in order to accomplish its goal, which is quite difficult when both the technique and business requirements are changing so rapidly. The framework method raised in this article can guarantee the system architecture’s steadiness and business processing’s consistence by means of describing business requirements, application and information abstractly, becoming more specific and formalized in the planning, developing and maintaining process, and getting the results needed. This article introduces firstly the concepts of framework method, then recommends its applications and superiority in OSS/BSS systems, and lastly gives the prospect of its application.
Single cell systems biology by super-resolution imaging and combinatorial labeling

Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral overlap between fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using fluorescence in situ hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured mRNA levels of 32 genes simultaneously in single Saccharomyces cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells is a natural approach to bring systems biology into single cells.
Representing and querying disease networks using graph databases

Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data. We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes. Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.
Code obfuscation techniques for metamorphic viruses

This paper deals with metamorphic viruses. More precisely, it examines the use of advanced code obfuscation techniques with respect to metamorphic viruses. Our objective is to evaluate the difficulty of a reliable static detection of viruses that use such obfuscation techniques. Here we extend Spinellis’ result ( 280–284, 2003) on the detection complexity of bounded-length polymorphic viruses to metamorphic viruses. In particular, we prove that reliable static detection of a particular category of metamorphic viruses is an $${\mathcal{NP}}$$ -complete problem. Then we empirically illustrate our result by constructing a practical obfuscator which could be used by metamorphic viruses in the future to evade detection.
High voltage IGBTs in medium frequency traction power supply

A converter system comprising series-connected converter submodules based on medium-frequency (MF)-DC/DC converters can replace the conventional traction transformer. This reduces mass and losses. The use of multilevel topology permits the connection to the HV catenary. A suitably chosen DC link voltage avoids excessively oversizing the power semiconductors, while providing sufficient redundancy. The medium-frequency switching performance of typical and dedicated 6.5kV IGBTs has been characterized and is discussed here (→ZCS, ZVS).
The harsh rule of the goals: Data-driven performance indicators for football teams

Sports analytics in general, and football (soccer in USA) analytics in particular, have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In this paper we propose a data-driven approach and show that there is a large potential to boost the understanding of football team performance. From observational data of football games we extract a set of pass-based performance indicators and summarize them in the H indicator. We observe a strong correlation among the proposed indicator and the success of a team, and therefore perform a simulation on the four major European championships (78 teams, almost 1500 games). The outcome of each game in the championship was replaced by a synthetic outcome (win, loss or draw) based on the performance indicators computed for each team. We found that the final rankings in the simulated championships are very close to the actual rankings in the real championships, and show that teams with high ranking error show extreme values of a defense/attack efficiency measure, the Pezzali score. Our results are surprising given the simplicity of the proposed indicators, suggesting that a complex systems' view on football data has the potential of revealing hidden patterns and behavior of superior quality.
Unsuccessful retrieval attempts enhance subsequent learning.

Taking tests enhances learning. But what happens when one cannot answer a test question-does an unsuccessful retrieval attempt impede future learning or enhance it? The authors examined this question using materials that ensured that retrieval attempts would be unsuccessful. In Experiments 1 and 2, participants were asked fictional general-knowledge questions (e.g., "What peace treaty ended the Calumet War?"). In Experiments 3-6, participants were shown a cue word (e.g., whale) and were asked to guess a weak associate (e.g., mammal); the rare trials on which participants guessed the correct response were excluded from the analyses. In the test condition, participants attempted to answer the question before being shown the answer; in the read-only condition, the question and answer were presented together. Unsuccessful retrieval attempts enhanced learning with both types of materials. These results demonstrate that retrieval attempts enhance future learning; they also suggest that taking challenging tests-instead of avoiding errors-may be one key to effective learning.
Deep Learning vs . traditional Machine Learning algorithms used in Credit Card Fraud Detection

With the continuing growth of E-commerce, credit card fraud has evolved exponentially, where people are using more on-line services to conduct their daily transactions. Fraudsters masquerade normal behaviour of customers to achieve unlawful gains. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years Deep Learning has gained lot of popularity in image recognition, speech recognition and natural language processing. This paper seeks to understand how Deep Learning can be helpful in finding fraud in credit card transactions and compare Deep Learning against several state of the art algorithms (RF, GBM, GLM) and sampling methods (Over, Under, Hybrid, SMOTE and ROSE) used in fraud detection. The results show that Deep Learning performed best with the highest Recall (accuracy of identifying fraudulent transactions), which means lowest financial losses to the company. However, Deep Learning achieved the lowest Precision rate (classified more legitimate transactions as fraudulent), which can cause customer dissatisfaction. Among other chosen classifiers, oversampling method performed best in terms of AUC, precision was highest for GLM and F-Score was highest for model trained using ROSE sampling method. Recall and Precision both have high cost, so there cannot be any trade of one against the other. Selecting the best classifier to identify fraud is based on the business goal. Imbalanced class, Data mining, Sampling methods, H2O, Gradient Boosting, Random Forest, Generalized Linear Models, Deep Learning, Grid search, Hyper parameter optimization, Ensemble methods.
Class-D Amplifier Power Stage With PWM Feedback Loop

This paper presents a second-order pulsewidth modulation (PWM) feedback loop to improve power supply rejection (PSR) of any open-loop PWM class-D amplifiers (CDAs). PSR of the audio amplifier has always been a key parameter in mobile phone applications. In contrast to class-AB amplifiers, the poor PSR performance has always been the major drawback for CDAs with a half-bridge connected power stage. The proposed PWM feedback loop is fabricated using GLOBALFOUNDRIES' 0.18-μm CMOS process technology. The measured PSR is more than 80 dB and the measured total harmonic distortion is less than 0.04% with a 1-kHz input sinusoidal test tone.
Deep and Confident Prediction for Time Series at Uber

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction with a probabilistic formulation for uncertainty estimation. However, such models are hard to tune, scale, and add exogenous variables to. Motivated by the recent resurgence of Long Short Term Memory networks, we propose a novel end-to-end Bayesian deep model that provides time series prediction along with uncertainty estimation. We provide detailed experiments of the proposed solution on completed trips data, and successfully apply it to large-scale time series anomaly detection at Uber.
Knock x knock: the design and evaluation of a unified authentication management system

We introduce UniAuth, a set of mechanisms for streamlining authentication to devices and web services. With UniAuth, a user first authenticates himself to his UniAuth client, typically his smartphone or wearable device. His client can then authenticate to other services on his behalf. In this paper, we focus on exploring the user experiences with an early iPhone prototype called Knock x Knock. To manage a variety of accounts securely in a usable way, Knock x Knock incorporates features not supported in existing password managers, such as tiered and location-aware lock control, authentication to laptops via knocking, and storing credentials locally while working with laptops seamlessly. In two field studies, 19 participants used Knock x Knock for one to three weeks with their own devices and accounts. Our participants were highly positive about Knock x Knock, demonstrating the desirability of our approach. We also discuss interesting edge cases and design implications.
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic
Group model building: problem structuring, policy simulation and decision support

The authors use the term, “Group Model Building” (Richardson and Andersen 1995; Vennix 1996; 1999) to refer to a bundle of techniques used to construct system dynamics models working directly with client groups on key strategic decisions. We use facilitated face-to-face meetings to elicit model structure and to engage client teams directly in the process of model conceptualization, formulation, analysis, and decision making.
Pervasive games: bringing computer entertainment back to the real world

This article gives an introduction and overview of the field of pervasive gaming, an emerging genre in which traditional, real-world games are augmented with computing functionality, or, depending on the perspective, purely virtual computer entertainment is brought back to the real world.The field of pervasive games is diverse in the approaches and technologies used to create new and exciting gaming experiences that profit by the blend of real and virtual game elements. We explicitly look at the pervasive gaming sub-genres of smart toys, affective games, tabletop games, location-aware games, and augmented reality games, and discuss them in terms of their benefits and critical issues, as well as the relevant technology base.