nils-herrmann/clean_scirepeval huggingface dataset:
Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron

Events and their coreference offer useful semantic and discourse resources. We show that the semantic and discourse aspects of events interact with each other. However, traditional approaches addressed event extraction and event coreference resolution either separately or sequentially, which limits their interactions. This paper proposes a document-level structured learning model that simultaneously identifies event triggers and resolves event coreference. We demonstrate that the joint model outperforms a pipelined model by 6.9 BLANC F1 and 1.8 CoNLL F1 points in event coreference resolution using a corpus in the biology domain.
A Quantitative Study of the Relative Difficulty for Novices of Writing Seven Different Types of SQL Queries

This paper presents a quantitative analysis of data collected by an online testing system for SQL "select" queries. The data was collected from almost one thousand students, over eight years. We examine which types of queries our students found harder to write. The seven types of SQL queries studied are: simple queries on one table; grouping, both with and without "having"; natural joins; simple and correlated sub-queries; and self-joins. The order of queries in the preceding sentence reflects the order of student difficulty we see in our data.
Limitations of Highly-Available Eventually-Consistent Data Stores

Modern replicated data stores aim to provide high availability, by immediately responding to client requests, often by implementing objects that expose concurrency. Such objects, for example, multi-valued registers (MVRs), do not have sequential specifications. This paper explores a recent model for replicated data stores that can be used to precisely specify causal consistency for such objects, and liveness properties like eventual consistency, without revealing details of the underlying implementation. The model is used to prove the following results: An eventually consistent data store implementing MVRs cannot satisfy a consistency model strictly stronger than observable causal consistency (OCC). OCC is a model somewhat stronger than causal consistency, which captures executions in which client observations can use causality to infer concurrency of operations. This result holds under certain assumptions about the data store. Under the same assumptions, an eventually consistent and causally consistent replicated data store must send messages of unbounded size: If s objects are supported by n replicas, then, for every k > 1, there is an execution in which an Ω({n,s} k)-bit message is sent.
Is That Car Smiling at Me ? Schema Congruity as a Basis for Evaluating Anthropomorphized Products

The present research proposes schema congruity as a theoretical basis for examining the effectiveness and consequences of product anthropomorphism. Results of two studies suggest that the ability of consumers to anthropomorphize a product and their consequent evaluation of that product depend on the extent to which that product is endowed with characteristics congruent with the proposed human schema. Furthermore, consumers’ perception of the product as human mediates the influence of feature type on product evaluation. Results of a third study, however, show that the affective tag attached to the specific human schema moderates the evaluation but not the successful anthropomorphizing of theproduct.
Designing Cooperative Gamification: Conceptualization and Prototypical Implementation

Organizations deploy gamification in CSCW systems to enhance motivation and behavioral outcomes of users. However, gamification approaches often cause competition between users, which might be inappropriate for working environments that seek cooperation. Drawing on the social interdependence theory, this paper provides a classification for gamification features and insights about the design of cooperative gamification. Using the example of an innova-tion community of a German engineering company, we present the design of a cooperative gamification approach and results from a first experimental evaluation. The findings indicate that the developed gamification approach has positive effects on perceived enjoyment and the intention towards knowledge sharing in the considered innovation community. Besides our conceptual contribu-tion, our findings suggest that cooperative gamification may be beneficial for cooperative working environments and represents a promising field for future research.
Innovative design of a claw-wheel transformable robot

This research develops a claw-wheel transformable robot which could move on flat ground, stairways, or uneven terrain environment efficiently with a fairly simple structure. The robot can switch between claw mode and wheel mode by folding and unfolding the front body and rear body of the robot. The transformation could be made by controlling the motors on a pitch joint through the front claw and rear one in 90° angle shift after folding. The claw mode is mainly used to cross various uneven terrain or climb stairways. Yet, the wheel mode is for moving on flat ground. This robot can be adaptive to various riser and tread of stairs by appropriately controlling the transformation mechanism. Testing and verification have been done both in numerical simulation and the field test on the developed prototype. Compared with the existing hybrid robots, it has been proved to be well adaptive to various environments, structurally simple, easy to control and light in weight.
Substrate Integrated Waveguide Filter With Improved Stopband Performance for Satellite Ground Terminal

In this paper, patent pending substrate integrated waveguide (SIW) bandpass filters with moderate fractional bandwidth and improved stopband performance are proposed and demonstrated for a Ka-band satellite ground terminal. Nonphysical cross-coupling provided by higher order modes in the oversized SIW cavities is used to generate the finite transmission zeros far away from the passband for improved stopband performance. Different input/output topologies of the filter are discussed for wide stopband applications. Design considerations including the design approach, filter configuration, and tolerance analysis are addressed. Two fourth-order filters with a passband of 19.2-21.2 GHz are fabricated on a single-layer Rogers RT/Duroid 6002 substrate using linear arrays of metallized via-holes by a standard printed circuit board process. Measured results of the two filters agree very well with simulated results, showing the in-band insertion loss is 0.9 dB or better, and the stopband attenuation in the frequency band of 29.5-30 GHz is better than 50 dB. Measurements over a temperature range of -20degC to +40degC show the passband remains almost unchanged.
Formulation of PID Control for DC–DC Converters Based on Capacitor Current: A Geometric Context

The output voltage derivative term associated with a PID controller injects significant noise in a dc-dc converter. This is mainly due to the parasitic resistance and inductance of the output capacitor. Particularly, during a large-signal transient, noise injection significantly degrades phase margin. Although noise characteristics can be improved by reducing the cutoff frequency of the low-pass filter associated with the voltage derivative, this degrades the closed-loop bandwidth. A formulation of a PID controller is introduced to replace the output voltage derivative with information about the capacitor current, thus reducing noise injection. It is shown that this formulation preserves the fundamental principle of a PID controller and incorporates a load current feedforward, as well as inductor current dynamics. This can be helpful to further improve bandwidth and phase margin. The proposed method is shown to be equivalent to a voltage-mode-controlled buck converter and a current-mode-controlled boost converter with a PID controller in the voltage feedback loop. A buck converter prototype is tested, and the proposed algorithm is implemented using a field-programmable gate array.
A FULL-HEIGHT WAVEGUIDE TO THIN-FILM MICROSTRIP TRANSITION WITH EXCEPTIONAL RF BANDWIDTH AND COUPLING EFFICIENCY

We describe a waveguide to thin-film microstrip transition for highperformance submillimetre wave and teraherz applications. The proposed constant-radius probe couples thin-film microstrip line, to fullheight rectangular waveguide with better than 99% efficiency (VSWR ≤ 1.20) and 45% fractional bandwidth. Extensive HFSS simulations, backed by scale-model measurements, are presented in the paper. By selecting the substrate material and probe radius, any real impedance between ≈ 15-60 Ω can be achieved. The radial probe gives significantly improved performance over other designs discussed in the literature. Although our primary application is submillimetre wave superconducting mixers, we show that membrane techniques should allow broad-band waveguide components to be constructed for the THz frequency range.
Applications of the Discrete Hodge Helmholtz Decomposition to Image and Video Processing

The Discrete Hodge Helmholtz Decomposition (DHHD) is able to locate critical points in a vector field. We explore two novel applications of this technique to image processing problems, viz., hurricane tracking and fingerprint analysis. The eye of the hurricane represents a rotational center, which is shown to be robustly detected using DHHD. This is followed by an automatic segmentation and tracking of the hurricane eye, which does not require manual initializations. DHHD is also used for identification of reference points in fingerprints. The new technique for reference point detection is relatively insensitive to noise in the orientation field. The DHHD based method is shown to detect reference points correctly for 96.25% of the images in the database used.
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance

This paper analyses alternative techniques for deploying low-cost human resources for data acquisition for classifier induction in domains exhibiting extreme class imbalance - where traditional labeling strategies, such as active learning, can be ineffective. Consider the problem of building classifiers to help brands control the content adjacent to their on-line advertisements. Although frequent enough to worry advertisers, objectionable categories are rare in the distribution of impressions encountered by most on-line advertisers - so rare that traditional sampling techniques do not find enough positive examples to train effective models. An alternative way to deploy human resources for training-data acquisition is to have them "guide" the learning by searching explicitly for training examples of each class. We show that under extreme skew, even basic techniques for guided learning completely dominate smart (active) strategies for applying human resources to select cases for labeling. Therefore, it is critical to consider the relative cost of search versus labeling, and we demonstrate the tradeoffs for different relative costs. We show that in cost/skew settings where the choice between search and active labeling is equivocal, a hybrid strategy can combine the benefits.
A prototype for assessing information security awareness

Due to the intensified need for improved information security, many organisations have established information security awareness programs to ensure that their employees are informed and aware of security risks, thereby protecting themselves and their profitability. In order for a security awareness program to add value to an organisation and at the same time make a contribution to the field of information security, it is necessary to have a set of methods to study and measure its effect. The objective of this paper is to report on the development of a prototype model for measuring information security awareness in an international mining company. Following a description of the model, a brief discussion of the application results is presented. a 2006
- 1The Theory Underlying Concept Maps and How to Construct Them 1

Concept maps are graphical tools for organizing and representing knowledge. They include concepts, usually enclosed in circles or boxes of some type, and relationships between concepts indicated by a connecting line linking two concepts. Words on the line, referred to as linking words or linking phrases, specify the relationship between the two concepts. We define concept as a perceived regularity in events or objects, or records of events or objects, designated by a label. The label for most concepts is a word, although sometimes we use symbols such as + or %, and sometimes more than one word is used. Propositions are statements about some object or event in the universe, either naturally occurring or constructed. Propositions contain two or more concepts connected using linking words or phrases to form a meaningful statement. Sometimes these are called semantic units, or units of meaning. Figure 1 shows an example of a concept map that describes the structure of concept maps and illustrates the above characteristics.
Use of eye movements for video game control

We present a study that explores the use of a commercially available eye tracker as a control device for video games. We examine its use across multiple gaming genres and present games that utilize the eye tracker in a variety of ways. First, we describe a first-person shooter that uses the eyes to control orientation. Second, we study the use of eye movements for more natural interaction with characters in a role playing game. And lastly, we examine the use of eye tracking as a means to control a modified version of the classic action/arcade game Missile Command. Our results indicate that the use of an eye tracker can increase the immersion of a video game and can significantly alter the gameplay experience.
Mining Personal Context-Aware Preferences for Mobile Users

In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his/her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context independent and context dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world data set show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.
Predicting User's Political Party Using Ideological Stances

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users’ party prediction task.
Design analysis of XOR (4T) based low voltage CMOS full adder circuit

This paper presents a comparative study of highspeed, low-power and low voltage full adder circuits. Our approach is based on XOR-XNOR (4T) design full adder circuits combined in a single unit. This technique helps in reducing the power consumption and the propagation delay while maintaining low complexity of logic design. Simulation results illustrate the superiority of the designed adder circuits against the conventional CMOS, TG and Hybrid adder circuits in terms of power, delay and power delay product (PDP) at low voltage. Noise analysis shows designed full adder circuit's work at high frequency and high temperature satisfactorily. Simulation results reveal that the designed circuits exhibit lower PDP, more power efficiency and faster when compared to the available full adder circuits at low voltage. The design is implemented on UMC 0.18µm process models in Cadence Virtuoso Schematic Composer at 1.8 V single ended supply voltage and simulations are carried out on Spectre S.
MLaaS: Machine Learning as a Service

The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.
Ant Colony System for a Dynamic Vehicle Routing Problem

An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started. The technological advances of the last few years give rise to a new class of problems, namely the dynamic vehicle routing problems, where new orders are received as time progresses and must be dynamically incorporated into an evolving schedule. In this paper a dynamic vehicle routing problem is examined and a solving strategy, based on the Ant Colony System paradigm, is proposed. Some new public domain benchmark problems are defined, and the algorithm we propose is tested on them. Finally, the method we present is applied to a realistic case study, set up in the city of Lugano (Switzerland).
Cauchy Graph Embedding

Laplacian embedding provides a lowdimensional representation for the nodes of a graph where the edge weights denote pairwise similarity among the node objects. It is commonly assumed that the Laplacian embedding results preserve the local topology of the original data on the low-dimensional projected subspaces,i.e., for any pair of graph nodes with large similarity, they should be embedded closely in the embedded space. However, in this paper, we will show that the Laplacian embedding often cannot preserve local topology well as we expected. To enhance the local topology preserving property in graph embedding, we propose a novel Cauchy graph embedding which preserves the similarity relationships of the original data in the embedded space via a new objective. Consequentially the machine learning tasks (such as k Nearest Neighbor type classifications) can be easily conducted on the embedded data with better performance. The experimental results on both synthetic and real world benchmark data sets demonstrate the usefulness of this new type of embedding.
On cognitive small cells in two-tier heterogeneous networks

In a two-tier heterogeneous network (HetNet) where small base stations (SBSs) coexist with macro base stations (MBSs), the SBSs may suffer significant performance degradation due to the inter- and intra-tier interferences. Introducing cognition into the SBSs through the spectrum sensing (e.g., carrier sensing) capability helps them detecting the interference sources and avoiding them via opportunistic access to orthogonal channels. In this paper, we use stochastic geometry to model and analyze the performance of two cases of cognitive SBSs in a multichannel environment, namely, the semi-cognitive case and the full-cognitive case. In the semi-cognitive case, the SBSs are only aware of the interference from the MBSs, hence, only inter-tier interference is minimized. On the other hand, in the full-cognitive case, the SBSs access the spectrum via a contention resolution process, hence, both the intra- and intertier interferences are minimized, but at the expense of reduced spectrum access opportunities. We quantify the performance gain in outage probability obtained by introducing cognition into the small cell tier for both the cases. We will focus on a special type of SBSs called the femto access points (FAPs) and also capture the effect of different admission control policies, namely, the open-access and closed-access policies. We show that a semi-cognitive SBS always outperforms a full-cognitive SBS and that there exists an optimal spectrum sensing threshold for the cognitive SBSs which can be obtained via the analytical framework presented in this paper.
User participation in an academic social networking service: A survey of open group users on Mendeley

Although there are a number of social networking services that specifically target scholars, little has been published about the actual practices and the usage of these so-called academic social networking services (ASNSs). To fill this gap, we explore the populations of academics who engage in social activities using an ASNS; as an indicator of further engagement, we also determine their various motivations for joining a group in ASNSs. Using groups and their members in Mendeley as the platform for our case study, we obtained 146 participant responses from our online survey about users’ common activities, usage habits, and motivations for joining groups. Our results show that 1) participants did not engage with social-based features as frequently and actively as they engaged with research-based features, and 2) users who joined more groups seemed to have a stronger motivation to increase their professional visibility and to contribute the research articles they had read to the group reading list. Our results generate interesting insights into Mendeley’s user populations, their activities, and their motivations relative to the social features of Mendeley. We also argue that further design of ASNSs is needed to take greater account of disciplinary differences in scholarly communication and to establish incentive mechanisms for encouraging user participation.
NoPhish App Evaluation : Lab and Retention Study

Phishing is a prevalent issue of today’s Internet. Previous approaches to counter phishing do not draw on a crucial factor to combat the threat the users themselves. We believe user education about the dangers of the Internet is a further key strategy to combat phishing. For this reason, we developed an Android app, a game called –NoPhish–, which educates the user in the detection of phishing URLs. It is crucial to evaluate NoPhish with respect to its effectiveness and the users’ knowledge retention. Therefore, we conducted a lab study as well as a retention study (five months later). The outcomes of the studies show that NoPhish helps users make better decisions with regard to the legitimacy of URLs immediately after playing NoPhish as well as after some time has passed. The focus of this paper is on the description and the evaluation of both studies. This includes findings regarding those types of URLs that are most difficult to decide on as well as ideas to further improve NoPhish.