nils-herrmann/clean_scirepeval huggingface dataset:
Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles

Advancing research into autonomous micro aerial vehicle navigation requires data structures capable of representing indoor and outdoor 3D environments. The vehicle must be able to update the map structure in real time using readings from range-finding sensors when mapping unknown areas; it must also be able to look up occupancy information from the map for the purposes of localization and path-planning. Mapping models that have been used for these tasks include voxel grids, multi-level surface maps, and octrees. In this paper, we suggest a new approach to 3D mapping using a multi-volume occupancy grid, or MVOG. MVOGs explicitly store information about both obstacles and free space. This allows us to correct previous potentially erroneous sensor readings by incrementally fusing in new positive or negative sensor information. In turn, this enables extracting more reliable probabilistic information about the occupancy of 3D space. MVOGs outperform existing probabilistic 3D mapping methods in terms of memory usage, due to the fact that observations are grouped together into continuous vertical volumes to save space. We describe the techniques required for mapping using MVOGs, and analyze their performance using indoor and outdoor experimental data.
Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs

Subgraph Isomorphism is a fundamental problem in graph data processing. Most existing subgraph isomorphism algorithms are based on a backtracking framework which computes the solutions by incrementally matching all query vertices to candidate data vertices. However, we observe that extensive duplicate computation exists in these algorithms, and such duplicate computation can be avoided by exploiting relationships between data vertices. Motivated by this, we propose a novel approach, BoostIso, to reduce duplicate computation. Our extensive experiments with real datasets show that, after integrating our approach, most existing subgraph isomorphism algorithms can be speeded up significantly, especially for some graphs with intensive vertex relationships, where the improvement can be up to several orders of magnitude.
Miniaturized loaded crossed dipole antenna with omnidirectional radiation pattern in the horizontal plane

In this paper, a new design of a loaded cross dipole antennas (LCDA) with an omni-directional radiation pattern in the horizontal plane and broad-band characteristics is investigated. An efficient optimization procedure based on a genetic algorithm is employed to design the LCDA and to determine the parameters working over a 25:1 bandwidth. The simulation results are compared with measurements.
CryPLH: Protecting Smart Energy Systems from Targeted Attacks with a PLC Honeypot

Smart grids consist of suppliers, consumers, and other parts. The main suppliers are normally supervised by industrial control systems. These systems rely on programmable logic controllers (PLCs) to control industrial processes and communicate with the supervisory system. Until recently, industrial operators relied on the assumption that these PLCs are isolated from the online world and hence cannot be the target of attacks. Recent events, such as the infamous Stuxnet attack [15] directed the attention of the security and control system community to the vulnerabilities of control system elements, such as PLCs. In this paper, we design and implement the Crysys PLC honeypot (CryPLH) system to detect targeted attacks against industrial control systems. This PLC honeypot can be implemented as part of a larger security monitoring system. Our honeypot implementation improves upon existing solutions in several aspects: most importantly in level of interaction and ease of configuration. Results of an evaluation show that our honeypot is largely indistinguishable from a real device from the attacker’s perspective. As a collateral of our analysis, we were able to identify some security issues in the real PLC device we tested and implemented specific firewall rules to protect the device from targeted attacks.
A Unified Architecture for Semantic Role Labeling and Relation Classification

This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL.
Large-scale validation and analysis of interleaved search evaluation

Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture of its efficiency and effectiveness is still lacking. In this paper we extend and combine the body of empirical evidence regarding interleaving, and provide a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature. In particular, we analyze the agreement of interleaving with manual relevance judgments and observational implicit feedback measures, estimate the statistical efficiency of interleaving, and explore the relative performance of different interleaving variants. We also show how to learn improved credit-assignment functions for clicks that further increase the sensitivity of interleaving.
Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots

On March 11, 2011, a massive earthquake (Magnitude 9.0) and accompanying tsunami hit the Tohoku region of eastern Japan. Since then, the Fukushima Daiichi Nuclear Power Plants have been facing a crisis because of loss of all power resulting in meltdown accidents. Three buildings housing nuclear reactors were seriously damaged from hydrogen explosions and, in one building, the nuclear reactions were also out of control. The situation was too ∗Webpage: http://www.astro.mech.tohoku.ac.jp/ 1 dangerous for humans to enter the buildings to inspect the damage as radioactive materials were also being released. In response to this crisis, it was decided that mobile rescue robots would be used to carry out surveillance missions. The mobile rescue robots needed could not be delivered to Tokyo Electric Power Company (TEPCO) until after resolving various technical issues. These issues included hardware reliability, communication functions, and radiation hardness of its electronic components. Additional sensors and functionality that would enable the robots to respond effectively to the crisis were also needed. Available robots were therefore retrofitted for the disaster reponse missions. First, the radiation tolerance of the electronic componenets were checked by means of gamma ray irradiation tests, conducted using the facilities of the Japan Atomic Energy Agency (JAEA). The commercial electronic devices used in the original robot systems worked long enough (more than 100 h at a 10% safety margin) in the assumed environment (100 mGy/h). Next, the usability of wireless communication in the target environment was assessed. Such tests were not possible in the target environment itself, so they were performed in the Hamaoka Daiichi Nuclear Power Plants, which is similar to the target environment. As previously predicted, the test results indicated that robust wireless communication would not be possible in the reactor buildings. It was therefore determined that a wired communication device would need to be installed. After TEPCO’s official urgent mission proposal was received, the team mounted additional devices to facilitate the installation of a water gauge in the basement of the reactor buildings to determine flooding levels. While these preparations were taking place, prospective robot operators from TEPCO trained in a laboratory environment. Finally, one of the robots was delivered to the Fukushima Daiichi Nuclear Power Plants on June 20, 2011, where it performed a number of important missions inside the buildings. In this paper, the requirements used for the exploration mission in the Fukushima Daiichi Nuclear Power Plants are presented, the implementation is discussed, and the results of the mission are reported.
Gamification as a Disruptive Factor in Software Process Improvement Initiatives

For any Software Process Improvement (SPI) initiative to succeed human factors, in particular, motivation and commitment of the people involved should be kept in mind. In fact, Organizational Change Management (OCM) has been identified as an essential knowledge area for any SPI initiative. However, enough attention is still not given to the human factors and therefore, the high degree of failures in the SPI initiatives is directly linked to a lack of commitment and motivation. Gamification discipline allows us to define mechanisms that drive people’s motivation and commitment towards the development of tasks in order to encourage and accelerate the acceptance of an SPI initiative. In this paper, a gamification framework oriented to both organization needs and software practitioners groups involved in an SPI initiative is defined. This framework tries to take advantage of the transverse nature of gamification in order to apply its Critical Success Factors (CSF) to the organizational change management of an SPI. Gamification framework guidelines have been validated by some qualitative methods. Results show some limitations that threaten the reliability of this validation. These require further empirical validation of a software organization.
Social Media Engagement Theory: Exploring the Influence of User Engagement on Social Media Usage

Business models that rely on social media and user-generated content have shifted from the more traditional business model, where value for the organization is derived from the one-way delivery of products and/or services, to the provision of intangible value based on user engagement. This research builds a model that hypothesizes that the user experiences from social interactions among users, operationalized as personalization, transparency, access to social resources, critical mass of social acquaintances, and risk, as well as with the technical features of the social media platform, operationalized as the completeness, flexibility, integration, and evolvability, influence user engagement and subsequent usage behavior. Using survey responses from 408 social media users, findings suggest that both social and technical factors impact user engagement and ultimately usage with additional direct impacts on usage by perceptions of the critical mass of social acquaintances and risk.  Use, User Engagement,
Comparing the maintainability of selenium WebDriver test suites employing different locators: a case study

Test suite maintenance tends to have the biggest impact on the overall cost of test automation. Frequently modifications applied on a web application lead to have one or more test cases broken and repairing the test suite is a time-consuming and expensive task. 
 This paper reports on an industrial case study conducted in a small Italian company investigating on the analysis of the effort to repair web test suites implemented using different UI locators (e.g., Identifiers and XPath). 
 The results of our case study indicate that ID locators used in conjunction with LinkText is the best solution among the considered ones in terms of time required (and LOCs to modify) to repair the test suite to the new release of the application.
MoleculeNet: A Benchmark for Molecular Machine Learning

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem 1 ar X iv :1 70 3. 00 56 4v 3 [ cs .L G ] 2 6 O ct 2 01 8 open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.
Dynamic Neural Networks for Text Classification

This research proposes an approach for text classification that uses a simple neural network called Dynamic Text Classifier Neural Network (DTCNN). The neural network uses as input vectors of words with variable dimension without information loss called Dynamic Token Vectors (DTV). The proposed neural network is designed for the classification of large and short text into categories. The learning process combines competitive and Hebbian learning. Due to the combination of these learning rules the neural network is able to work in a supervised or semi-supervised mode. In addition, it provides transparency in the classification. The network used in this paper is quite simple, and that is what makes enough for its task. The results of evaluation the proposed method shows an improvement in the text classification problem using the DTCNN compared to baseline approaches.
G4LTL-ST: Automatic Generation of PLC Programs

G4LTL-ST automatically synthesizes control code for industrial Programmable Logic Controls (PLC) from timed behavioral specifications of inputoutput signals. These specifications are expressed in a linear temporal logic (LTL) extended with non-linear arithmetic constraints and timing constraints on signals. G4LTL-ST generates code in IEC 61131-3-compatible Structured Text, which is compiled into executable code for a large number of industrial field-level devices. The synthesis algorithm of G4LTL-ST implements pseudo-Boolean abstraction of data constraints and the compilation of timing constraints into LTL, together with a counterstrategy-guided abstraction-refinement synthesis loop. Since temporal logic specifications are notoriously difficult to use in practice, G4LTL-ST supports engineers in specifying realizable control problems by suggesting suitable restrictions on the behavior of the control environment from failed synthesis attempts.
TeleHuman: effects of 3d perspective on gaze and pose estimation with a life-size cylindrical telepresence pod

In this paper, we present TeleHuman, a cylindrical 3D display portal for life-size human telepresence. The TeleHuman 3D videoconferencing system supports 360 degree motion parallax as the viewer moves around the cylinder and optionally, stereoscopic 3D display of the remote person. We evaluated the effect of perspective cues on the conveyance of nonverbal cues in two experiments using a one-way telecommunication version of the system. The first experiment focused on how well the system preserves gaze and hand pointing cues. The second experiment evaluated how well the system conveys 3D body postural information. We compared 3 perspective conditions: a conventional 2D view, a 2D view with 360 degree motion parallax, and a stereoscopic view with 360 degree motion parallax. Results suggest the combined presence of motion parallax and stereoscopic cues significantly improved the accuracy with which participants were able to assess gaze and hand pointing cues, and to instruct others on 3D body poses. The inclusion of motion parallax and stereoscopic cues also led to significant increases in the sense of social presence and telepresence reported by participants.
Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts

We propose a retrieval model for searching microblog posts for a given topic of interest. We develop a language modeling approach tailored to microblogging characteristics, where redundancy-based IR methods cannot be used in a straightforward manner. We enhance this model with two groups of quality indicators: textual and microblog specific. Additionally, we propose a dynamic query expansion model for microblog post retrieval. Experimental results on Twitter data reveal the usefulness of boolean search, and demonstrate the utility of quality indicators and query expansion in microblog search.
A Revocable Backup System

We present a system which enables a user to remove a le from both the le system and all the backup tapes on which the le is stored. The ability to remove les from all backup tapes is desirable in many cases. Our system erases information from the backup tape without actually writing on the tape. This is achieved by applying cryptography in a new way: a block cipher is used to enable the system to \forget" information rather than protect it. Our system is easy to install and is transparent to the end user. Further, it introduces no slowdown in system performance and little slowdown in the backup procedure.
SCANNING DYNAMIC COMPETITIVE LANDSCAPES : A MARKET-BASED AND RESOURCE-BASED FRAMEWORK

Heterogeneity among rivals implies that each firm faces a unique competitive set, despite overlapping market domains. This suggests the utility of a firm-level approach to competitor identification and analysis, particularly under dynamic environmental conditions. We take such an approach in developing a market-based and resource-based framework for scanning complex competitive fields. By facilitating a search for functional similarities among products and resources, the framework reveals relevant commonalities in an otherwise heterogeneous competitive set. Beyond its practical contribution, the paper also advances resource-based theory as a theory of competitive advantage. Most notably, we show that resource substitution conditions not only the sustainability of a competitive advantage, but the attainment of competitive advantage as well. With equifinality among resources of different types, the rareness condition for even temporary competitive advantage must include resource substitutes. It is not rareness in terms of resource type that matters, but rareness in terms of resource functionality.
Modeling dwell time to predict click-level satisfaction

Clicks on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown that clicks are affected by position bias, caption bias, and other factors. A popular heuristic for reducing this noise is to only consider clicks with long dwell time, usually equaling or exceeding 30 seconds. The rationale is that the more time a searcher spends on a page, the more likely they are to be satisfied with its contents. However, having a single threshold value assumes that users need a fixed amount of time to be satisfied with any result click, irrespective of the page chosen. In reality, clicked pages can differ significantly. Pages have different topics, readability levels, content lengths, etc. All of these factors may affect the amount of time spent by the user on the page. In this paper, we study the effect of different page characteristics on the time needed to achieve search satisfaction. We show that the topic of the page, its length and its readability level are critical in determining the amount of dwell time needed to predict whether any click is associated with satisfaction. We propose a method to model and provide a better understanding of click dwell time. We estimate click dwell time distributions for SAT (satisfied) or DSAT (dissatisfied) clicks for different click segments and use them to derive features to train a click-level satisfaction model. We compare the proposed model to baseline methods that use dwell time and other search performance predictors as features, and demonstrate that the proposed model achieves significant improvements.
A taxonomy and survey of grid resource management systems for distributed computing

The resource management system is the central component of network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and functionalities. In this paper, we develop a comprehensive taxonomy for describing resource management architectures and use this taxonomy to survey existing resource management implementations in very large-scale network computing systems known as Grids. We use the taxonomy and the survey results to identify architectural approaches that have not been fully explored in the research.
Evolutionary Psychology and Feminism

This article provides a historical context of evolutionary psychology and feminism, and evaluates the contributions to this special issue of Sex Roles within that context. We briefly outline the basic tenets of evolutionary psychology and articulate its meta-theory of the origins of gender similarities and differences. The article then evaluates the specific contributions: Sexual Strategies Theory and the desire for sexual variety; evolved standards of beauty; hypothesized adaptations to ovulation; the appeal of risk taking in human mating; understanding the causes of sexual victimization; and the role of studies of lesbian mate preferences in evaluating the framework of evolutionary psychology. Discussion focuses on the importance of social and cultural context, human behavioral flexibility, and the evidentiary status of specific evolutionary psychological hypotheses. We conclude by examining the potential role of evolutionary psychology in addressing social problems identified by feminist agendas.
State-dependent computations: spatiotemporal processing in cortical networks

A conspicuous ability of the brain is to seamlessly assimilate and process spatial and temporal features of sensory stimuli. This ability is indispensable for the recognition of natural stimuli. Yet, a general computational framework for processing spatiotemporal stimuli remains elusive. Recent theoretical and experimental work suggests that spatiotemporal processing emerges from the interaction between incoming stimuli and the internal dynamic state of neural networks, including not only their ongoing spiking activity but also their 'hidden' neuronal states, such as short-term synaptic plasticity.