nils-herrmann/clean_scirepeval huggingface dataset:
WARP: Workload-aware replication and partitioning for RDF

With the increasing popularity of the Semantic Web, more and more data becomes available in RDF with SPARQL as a query language. Data sets, however, can become too big to be managed and queried on a single server in a scalable way. Existing distributed RDF stores approach this problem using data partitioning, aiming at limiting the communication between servers and exploiting parallelism. This paper proposes a distributed SPARQL engine that combines a graph partitioning technique with workload-aware replication of triples across partitions, enabling efficient query execution even for complex queries from the workload. Furthermore, it discusses query optimization techniques for producing efficient execution plans for ad-hoc queries not contained in the workload.
The Effect of Digitizing and Gamifying Quizzing in Classrooms

The use of game-based learning in the classroom has become more common in recent years. Many game-based learning tools and platforms are based on a quiz concept where the students can score points if they can choose the correct answer among multiple answers. The article describes an experiment where the game-based student response system Kahoot! was compared to a traditional non-gamified student response system, as well as the usage of paper forms for formative assessment. The goal of the experiment was to investigate whether gamified formative assessments improve the students’ engagement, motivation, enjoyment, concentration, and learning. In the experiment, the three different formative assessment tools/methods were used to review and summarize the same topic in three parallel lectures in an IT introductory course. The first method was to have the students complete a paper quiz, and then review the results afterwards using hand raising. The second method was to use the non-gamified student response system Clicker where the students gave their response to a quiz through polling. The third method was to use the game-based student response system Kahoot!. All three lectures were taught in the exact same way, teaching the same syllabus and using the same teacher. The only difference was the method use to summarize the lecture. A total of 384 students participated in the experiment, where 127 subjects did the paper quiz, 175 used the non-gamified student response system, and 82 students using the gamified approach. The gender distribution was 48% female students and 52% male students. Preand a post-test were used to assess the learning outcome of the lectures, and a questionnaire was used to get data on the students’ engagement and motivation. The results show significant improvement in motivation, engagement, enjoyment, and concentration for the gamified approach, but we did not find significant learning improvement.
CloudFridge: A Testbed for Smart Fridge Interactions

We present a testbed for exploring novel smart refrigerator interactions, and identify three key adoption-limiting interaction shortcomings of state-of-the-art smart fridges: lack of 1) user experience focus, 2) low-intrusion object recognition and 2) automatic item position detection. Our testbed system addresses these limitations by a combination of sensors, software filters, architectural components and a RESTful API to track interaction events in real-time, and retrieve current state and historical data to learn patterns and recommend user actions. We evaluate the accuracy and overhead of our system in a realistic interaction flow. The accuracy was measured to 8388% and the overhead compared to a representative state-ofthe-art barcode scanner improved by 27%. We also showcase two applications built on top of our testbed, one for finding expired items and ingredients of dishes; and one to monitor your health. The pattern that these applications have in common is that they cast the interaction as an item-recommendation problem triggered when the user takes something out. Our testbed could help reveal further user-experience centric interaction patterns and new classes of applications for smart fridges that inherently, by relying on our testbed primitives, mitigate the issues with existing approaches.
Reinforcement Learning for Predictive Analytics in Smart Cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller (QC) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework.
Adopting “ design thinking ” in novice multidisciplinary teams : The application and limits of design methods and reflexive practices

Scholarly and practitioner literature have both described the potential benefits of using methods associated with a “design thinking” approach to develop new innovations. Most studies of these design methods—such as needfinding, brainstorming, and prototyping—are based either on analyses of experienced designers or examine each method in isolation. If design thinking is to be widely adopted, less-experienced users will employ the methods together, but we know little about their effect when newly adopted. We designed an inductive study to understand how novice multidisciplinary teams made use of design methods, drawing on twelve cases of concept generation and ten cases of concept refinement. Our results indicate formal methods were beneficial not only in the divergent generation phase but also the convergent refinement phase. However, while focused brainstorming could be a source of insight, an increased number of sessions was actually associated with lower performance. This was true except in the setting where new members may join a team. We find informal practices related to team reflexivity were associated with high performance during concept generation but low performance in concept refinement. For the adoption of design thinking, it is important to consider not only potential benefits but also the contingent nature of both formal methods and reflexive practices.
A Deep Dive into Word Sense Disambiguation with LSTM

LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code was released. This paper presents the results of a reproduction study and analysis of this technique using only openly available datasets (GigaWord, SemCor, OMSTI) and software (TensorFlow). Our study showed that similar results can be obtained with much less data than hinted at by Yuan et al. (2016). Detailed analyses shed light on the strengths and weaknesses of this method. First, adding more unannotated training data is useful, but is subject to diminishing returns. Second, the model can correctly identify both popular and unpopular meanings. Finally, the limited sense coverage in the annotated datasets is a major limitation. All code and trained models are made freely available.
Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?

Answering for complex questions is often modelled as a graph construction or traversal task, where a solver must build or traverse a graph of facts that answer and explain a given question. This “multi-hop” inference has been shown to be extremely challenging, with few models able to aggregate more than two facts before being overwhelmed by “semantic drift”, or the tendency for long chains of facts to quickly drift off topic. This is a major barrier to current inference models, as even elementary science questions require an average of 4 to 6 facts to answer and explain. In this work we empirically characterize the difficulty of building or traversing a graph of sentences connected by lexical overlap, by evaluating chance sentence aggregation quality through 9,784 manually-annotated judgements across knowledge graphs built from three freetext corpora (including study guides and Simple Wikipedia). We demonstrate semantic drift tends to be high and aggregation quality low, at between 0.04% and 3%, and highlight scenarios that maximize the likelihood of meaningfully combining information.
Development of BIM Execution Plan for BIM Model Management during the Pre-Operation Phase : A Case Study

Building information modeling (BIM) technologies use precise geometry and relevant data to enhance and improve the maintenance performance of facilities integrated with 3D object-oriented computer aided design (CAD). Although most owners agree on the potential benefits of integrating BIM technologies with facility management (FM), they must overcome many problems to plan and develop effective BIM execution plans for FM implementation. This study proposes and develops a BIM execution plan for BIM model management for FM during the pre-operation phase. Through the application of the proposed BIM execution plan, BIM can be effectively implemented during the operation and maintenance phases. In order to verify the proposed methodology and demonstrate its effectiveness in practice, the BIM execution plan is then applied in a selected case study of a building project in Taiwan. The combined results demonstrate that the proposed BIM execution plan is an effective approach for operation and maintenance management. The advantage of the proposed BIM execution plan lies not only in improving the efficiency of maintenance management work when integrated with BIM technologies, but also in maximizing the value and benefits of BIM to support maintenance management. Finally, limitations, difficulties, and suggestions are summarized for further development of the BIM execution plan for BIM model management during the pre-operation phase.
Variation in verbal fluency: a latent variable analysis of clustering, switching, and overall performance.

Verbal fluency tasks have long been used to assess and estimate group and individual differences in executive functioning in both cognitive and neuropsychological research domains. Despite their ubiquity, however, the specific component processes important for success in these tasks have remained elusive. The current work sought to reveal these various components and their respective roles in determining performance in fluency tasks using latent variable analysis. Two types of verbal fluency (semantic and letter) were compared along with several cognitive constructs of interest (working memory capacity, inhibition, vocabulary size, and processing speed) in order to determine which constructs are necessary for performance in these tasks. The results are discussed within the context of a two-stage cyclical search process in which participants first search for higher order categories and then search for specific items within these categories.
PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees

stores such as LevelDB and RocksDB offer excellent write throughput, but suffer high write amplification. The write amplification problem is due to the Log-Structured Merge Trees data structure that underlies these key-value stores. To remedy this problem, this paper presents a novel data structure that is inspired by Skip Lists, termed Fragmented Log-Structured Merge Trees (FLSM). FLSM introduces the notion of guards to organize logs, and avoids rewriting data in the same level. We build PebblesDB, a high-performance key-value store, by modifying HyperLevelDB to use the FLSM data structure. We evaluate PebblesDB using micro-benchmarks and show that for write-intensive workloads, PebblesDB reduces write amplification by 2.4-3x compared to RocksDB, while increasing write throughput by 6.7x. We modify two widely-used NoSQL stores, MongoDB and HyperDex, to use PebblesDB as their underlying storage engine. Evaluating these applications using the YCSB benchmark shows that throughput is increased by 18-105% when using PebblesDB (compared to their default storage engines) while write IO is decreased by 35-55%.
Domain Aware Neural Dialog System

We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains. The domain in this case is the topic or theme of the conversation. To achieve this, we present DOM-Seq2Seq, a domain aware neural network model based on the novel technique of using domain-targeted sequence-to-sequence models (Sutskever et al., 2014) and a domain classifier. The model captures features from current utterance and domains of the previous utterances to facilitate the formation of relevant responses. We evaluate our model on automatic metrics and compare our performance with the Seq2Seq model.
Extending satellite remote sensing to local scales : land and water resource monitoring using high-resolution imagery

The potential of high-resolution IKONOS and QuickBird satellite imagery for mapping and analysis of land and water resources at local scales in Minnesota is assessed in a series of three applications. The applications and accuracies evaluated include: (1) classification of lake water clarity (r = 0.89), (2) mapping of urban impervious surface area (r = 0.98), and (3) aquatic vegetation surveys of emergent and submergent plant groups (80% accuracy). There were several notable findings from these applications. For example, modeling and estimation approaches developed for Landsat TM data for continuous variables such as lake water clarity and impervious surface area can be applied to high-resolution satellite data. The rapid delivery of spatial data can be coupled with current GPS and field computer technologies to bring the imagery into the field for cover type validation. We also found several limitations in working with this data type. For example, shadows can influence feature classification and their effects need to be evaluated. Nevertheless, high-resolution satellite data has excellent potential to extend satellite remote sensing beyond what has been possible with aerial photography and Landsat data, and should be of interest to resource managers as a way to create timely and reliable assessments of land and water resources at a local scale. D 2003 Elsevier Inc. All rights reserved.
A New Control Method of Cascaded Brushless Doubly Fed Induction Generators Using Direct Power Control

Cascaded brushless doubly fed induction machines have attracted much attention because of the elimination of slip rings and brushes. However, only limited works have been reported on the control strategies due to the complex machine modeling. Further developments of these kinds of machines are still under investigation, especially for wind energy applications. This paper proposes a new control strategy for cascaded brushless doubly fed induction generators (CBDFIGs) based on direct power control. The effects of voltage vectors on the output active and reactive powers are first investigated. A vector selection strategy is then proposed to achieve flexible power regulation. In addition, the behaviors of the CBDFIG under unbalanced grid voltage conditions are studied and a power compensation scheme is developed to improve the power quality. The effectiveness of the proposed control strategy is validated experimentally on a laboratory prototype.
Virtual morality: emotion and action in a simulated three-dimensional "trolley problem".

Experimentally investigating the relationship between moral judgment and action is difficult when the action of interest entails harming others. We adopt a new approach to this problem by placing subjects in an immersive, virtual reality environment that simulates the classic "trolley problem." In this moral dilemma, the majority of research participants behaved as "moral utilitarians," either (a) acting to cause the death of one individual in order to save the lives of five others, or (b) abstaining from action, when that action would have caused five deaths versus one. Confirming the emotional distinction between moral actions and omissions, autonomic arousal was greater when the utilitarian outcome required action, and increased arousal was associated with a decreased likelihood of utilitarian-biased behavior. This pattern of results held across individuals of different gender, age, and race.
Learning a SAT Solver from Single-Bit Supervision

We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw during training by simply running for more iterations. Moreover, NeuroSAT generalizes to novel distributions; after training only on random SAT problems, at test time it can solve SAT problems encoding graph coloring, clique detection, dominating set, and vertex cover problems, all on a range of distributions over small random graphs.
Agricultural technology and its societal implications

Although the refinement of laboresque technologies that save farm labor continues, its boom (in terms of sheer number of machines) passed during the quarter-century lifetime of Technology in Society. Instead, landesque technology, which spares land, holds the spotlight. Landesque is exemplified by high-yielding varieties, the Green Revolution, and genetically modified organisms. The contribution of landesque technologies to national performance can be charted on a plane with the dual dimensions of sustainability: 1) present need and 2) environmental impact. In the dimension of need, national crop production has increased. In the dimension of environmental impact, landesque technology plus consumption that increases more slowly than income has countered population and wealth to steer national journeys toward sustainability. On the sustainability plane, the genius to discover new landesque technology and the courage to apply it can steer nations toward still greater production without veering toward higher impact.
MultiLabel Classification on Tree- and DAG-Structured Hierarchies

Many real-world applications involve multilabel classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree-structured hierarchies. In this paper, we present a novel hierarchical multilabel classification algorithm which can be used on both treeand DAG-structured hierarchies. The key idea is to formulate the search for the optimal consistent multi-label as the finding of the best subgraph in a tree/DAG. Using a simple greedy strategy, the proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies. Theoretical results guarantee the optimality of the obtained solution. Experiments are performed on a large number of functional genomics data sets. The proposed method consistently outperforms the state-of-the-art method on both treeand DAG-structured hierarchies.
Design of Resistive Synaptic Array for Implementing On-Chip Sparse Learning

The resistive cross-point array architecture has been proposed for on-chip implementation of weighted sum and weight update operations in neuro-inspired learning algorithms. However, several limiting factors potentially hamper the learning accuracy, including the nonlinearity and device variations in weight update, and the read noise, limited ON/OFF weight ratio and array parasitics in weighted sum. With unsupervised sparse coding as a case study algorithm, this paper employs device-algorithm co-design methodologies to quantify and mitigate the impact of these non-ideal properties on the accuracy. Our analysis shows that the realistic properties in weight update are tolerable, while those in weighted sum are detrimental to the accuracy. With calibration of realistic synaptic behaviors from experimental data, our study shows that the recognition accuracy of MNIST handwriting digits degrades from ∼96 to ∼30 percent. The strategies to mitigate this accuracy loss include 1) redundant cells to alleviate the impact of device variations; 2) a dummy column to eliminate the off-state current; and 3) selector and larger wire width to reduce IR drop along interconnects. The selector also reduces the leakage power in weight update. With improved properties by these strategies, the accuracy increases back to ∼95 percent, enabling reliable integration of realistic synaptic devices in neuromorphic systems.
Evaluating EcoLexiCAT: a Terminology-Enhanced CAT Tool

EcoLexiCAT is a web-based tool for the terminology-enhanced translation of specialized environmental texts for the language combination English-Spanish-English. It uses the open source version of the web-based CAT tool MateCat and enriches a source text with information from: (1) EcoLexicon, a multimodal and multilingual terminological knowledge base on the environment (Faber et al., 2014; Faber et al., 2016); (2) BabelNet, an automatically constructed multilingual encyclopedic dictionary and semantic network (Navigli & Ponzetto, 2012); (3) Sketch Engine, the well-known corpus query system (Kilgarriff et al., 2004); (4) IATE, the multilingual glossary of the European Commission; and (4) other external resources (i.e. Wikipedia, Collins, Wordreference, Linguee, etc.) that can also be customized by the user. The tool was built with the aim of integrating terminology management – often considered complex and time-consuming – in the translation workflow of a CAT tool. In this paper, EcoLexiCAT is described along the procedure with which it was evaluated and the results of the evaluation.