nils-herrmann/clean_scirepeval huggingface dataset:
Query Segmentation and Resource Disambiguation Leveraging Background Knowledge

Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.
Why Creating Web Page Objects Manually If It Can Be Done Automatically?

Page Object is a design pattern aimed at making web test scripts more readable, robust and maintainable. The effort to manually create the page objects needed for a web application may be substantial and unfortunately existing tools do not help web developers in such task.
 In this paper we present Apogen, a tool for the automatic generation of page objects for web applications. Our tool automatically derives a testing model by reverse engineering the target web application and uses a combination of dynamic and static analysis to generate Java page objects for the popular Selenium WebDriver framework. Our preliminary evaluation shows that it is possible to use around 3/4 of the automatic page object methods as they are, while the remaining 1/4 need only minor modifications.
An Effective Framework for Question Answering over Freebase via Reconstructing Natural Sequences

Question answering over knowledge bases has rapidly developed with the continuous expansion of resources. However, how to match the natural language question to the structured answer entities in the knowledge bases remains a major challenge. In this paper, we propose an effective framework that bridges the gap between the given question and the answer entities, by reconstructing the intermediate natural sequences on the basis of the entities and relations in knowledge bases. The intuitive idea is that these intermediate sequences may encode rich semantic information that can identify the candidate answer entities. Experimental evaluation is conducted on a benchmark dataset WebQuestions. Results demonstrate the effectiveness of our proposed framework, i.e., it outperforms state-of-the-art models by up to 6.8% in terms of F1 score.
GIS METHODS IN TIME-GEOGRAPHIC RESEARCH: GEOCOMPUTATION AND GEOVISUALIZATION OF HUMAN ACTIVITY PATTERNS

Over the past 40 years or so, human activities and movements in space-time have attracted considerable research interest in geography. One of the earliest analytical perspectives for the analysis of human activity patterns and movements in spacetime is time geography. Despite the usefulness of time geography in many areas of geographical research, there are very few studies that actually implemented its constructs as analytical methods up to the mid-1990s. With increasing availability of geo-referenced individual-level data and improvement in the geo-computational capabilities of Geographical Information Systems (GIS), it is now more feasible than ever before to operationalize and implement time-geographic constructs. This paper discusses recent applications of GIS-based geo-computation and three-dimensional (3-D) geo-visualization methods in time-geographic research. The usefulness of these methods is illustrated through examples drawn from the author’s recent studies. The paper attempts to show that GIS provides an effective environment for implementing timegeographic constructs and for the future development of operational methods in time-geographic research.
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects

We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction.
Wireless underground sensor networks: Research challenges

This work introduces the concept of a Wireless Underground Sensor Network (WUSN). WUSNs can be used to monitor a variety of conditions, such as soil properties for agricultural applications and toxic substances for environmental monitoring. Unlike existing methods of monitoring underground conditions, which rely on buried sensors connected via wire to the surface, WUSN devices are deployed completely belowground and do not require any wired connections. Each device contains all necessary sensors, memory, a processor, a radio, an antenna, and a power source. This makes their deployment much simpler than existing underground sensing solutions. Wireless communication within a dense substance such as soil or rock is, however, significantly more challenging than through air. This factor, combined with the necessity to conserve energy due to the difficulty of unearthing and recharging WUSN devices, requires that communication protocols be redesigned to be as efficient as possible. This work provides an extensive overview of applications and design challenges for WUSNs, challenges for the underground communication channel including methods for predicting path losses in an underground link, and challenges at each layer of the communication protocol stack.
Interobserver agreement among sleep scorers from different centers in a large dataset.

To evaluate epoch by epoch agreement in sleep stage assignment between scorers from different laboratories.



N/A.



62 NPSGs were selected for analysis from 3 sleep centers (38 diagnostic studies for sleep disordered breathing [SDB], 10 studies during CPAP titration, and 14 studies in subjects with no sleep related complaints or sleep pathology). The sleep recording montage consisted of at least 2 EEG leads, left and right EOG and a submental EMG. Scoring was performed manually by 5 experienced sleep technologists. No scorer had knowledge of any other scorers' results. Agreement was tabulated both for sleep stage distribution and on an epoch by epoch basis for the entire data set and the normal and SDB subsets.



The mean epoch by epoch agreement between scorers for all records was 73% (range 67-82%). Agreements were higher in the normal subset (mean 76%, range 65-85%) than in the SDB subset (mean 71%, range 65-78%). There was significant variability in agreement between records and between pairs of scorers. Overall, 75% of epochs had at least 4 of the 5 scorers in agreement on the sleep stage and 96% of epochs had agreement of at least 3 of the 5 scorers.



The level of agreement in sleep stage assignment varies between scorers, by diagnosis, and by record. The level of agreement between laboratories is lower than what can be maintained between scorers within the same laboratory. This warrants caution when comparing data scored in separate laboratories. The lower agreement in SDB patients supports the generally held view that sleep fragmentation makes application of the R&K rules less reliable.
Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage

Data sharing is an important functionality in cloud storage. In this paper, we show how to securely, efficiently, and flexibly share data with others in cloud storage. We describe new public-key cryptosystems that produce constant-size ciphertexts such that efficient delegation of decryption rights for any set of ciphertexts are possible. The novelty is that one can aggregate any set of secret keys and make them as compact as a single key, but encompassing the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for flexible choices of ciphertext set in cloud storage, but the other encrypted files outside the set remain confidential. This compact aggregate key can be conveniently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the first public-key patient-controlled encryption for flexible hierarchy, which was yet to be known.
Designing game-based learning environments for elementary science education: A narrative-centered learning perspective

Game-based learning environments hold significant promise for STEM education, yet they are enormously complex.  UNCHARTED DISCOVERY, is a gamebased learning environment designed for upper elementary science education that has been under development in our laboratory for the past four years. This article discusses curricular and narrative interaction design requirements, presents the design of the CRYSTAL ISLAND learning environment, and describes its evolution through a series of pilots and field tests. Additionally, a classroom integration study was conducted to initiate a shift towards ecological validity. Results indicated that CRYSTAL ISLAND produced significant learning gains on both science content and problem-solving measures. Importantly, gains were consistent for gender across studies. This finding is key in light of past studies that revealed disproportionate participation by boys within game-based learning environments.
State-trajectory control of LLC converter implemented by microcontroller

This paper investigates how to integrate state-trajectory control functions of LLC resonant converter within a commercial low-cost microcontroller, including Simplified Optimal Trajectory Control (SOTC) for load transient, burst mode and soft start up. Simplified calculation process is proposed for SOTC to minimize digital delay. Guideline to select a proper microcontroller is presented to limit calculation delay and ADC delay within 1 switching cycle to ensure good transient performance. Optimized transient processes are proposed to eliminate large oscillation between burst mode and normal operation. Soft start-up is implemented with sensing the output voltage only. The whole control system structure is proposed to integrate all these control functions and for the first time all these control functions are integrated in one low-cost MCU. The results are demonstrated on a 130kHz 300W 380V/12V halfbridge LLC resonant converter with MCU TMS320F2808.
Multi-lingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable crosslingual alignment by human labor is very costly and error-prone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart TransE.
MobiBee: a mobile treasure hunt game for location-dependent fingerprint collection

Constructing a location-dependent fingerprint map is one of the essential steps for fingerprint-based indoor localization. A comprehensive site survey is time-consuming, labor-intensive, and subjects to environmental changes. In this work, we develop a mobile participatory game, MobiBee, to collect fingerprints with the help of quick response (QR) codes (posted on walls or pillars as location tags). Various incentive strategies, including monetary, entertainment and competition, are utilized. The location marker is deliberately designed to ensure that fingerprints are collected at the targeted locations in a stationary manner. Interestingly, a few instances of QR code forgery have been identified during the game play, which exposes the vulnerability of using QR code in indoor localization. A fraud detection mechanism is hence proposed. Experiments show that the proposed approach can accurately detect the QR code forgery and improve indoor localization accuracy.
Evaluating the Visualization of What a Deep Neural Network Has Learned

Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the “importance” of individual pixels with respect to the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper, we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets. Our main result is that the recently proposed layer-wise relevance propagation algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of the neural network performance.
The immune system's role in sepsis progression, resolution, and long-term outcome.

Sepsis occurs when an infection exceeds local tissue containment and induces a series of dysregulated physiologic responses that result in organ dysfunction. A subset of patients with sepsis progress to septic shock, defined by profound circulatory, cellular, and metabolic abnormalities, and associated with a greater mortality. Historically, sepsis-induced organ dysfunction and lethality were attributed to the complex interplay between the initial inflammatory and later anti-inflammatory responses. With advances in intensive care medicine and goal-directed interventions, early 30-day sepsis mortality has diminished, only to steadily escalate long after "recovery" from acute events. As so many sepsis survivors succumb later to persistent, recurrent, nosocomial, and secondary infections, many investigators have turned their attention to the long-term sepsis-induced alterations in cellular immune function. Sepsis clearly alters the innate and adaptive immune responses for sustained periods of time after clinical recovery, with immune suppression, chronic inflammation, and persistence of bacterial representing such alterations. Understanding that sepsis-associated immune cell defects correlate with long-term mortality, more investigations have centered on the potential for immune modulatory therapy to improve long-term patient outcomes. These efforts are focused on more clearly defining and effectively reversing the persistent immune cell dysfunction associated with long-term sepsis mortality.
Guillain-Barré Syndrome in India: population-based validation of the Brighton criteria.

Case definitions of GBS were recently developed in response to the 2009 H1N1 vaccination programme but have undergone limited field testing. We validate the sensitivity of the Brighton Working Group case definitions for Guillain-Barré Syndrome (GBS) using a population-based cohort in India.



The National Polio Surveillance Unit of India actively collects all cases of acute flaccid paralysis (AFP) in children <15 years old, including cases of GBS. Cases of GBS with available cerebrospinal fluid (CSF) and nerve conduction studies (NCS) results, neurological examination, clinical history, and exclusion of related diagnoses were selected (2002-2003). Relevant data were abstracted and entered into a central database. Sensitivity of the Brighton GBS criteria for level 3 of diagnostic certainty which requires no clinical laboratory testing, level 2 which employs CSF or NCS, and level 1 which employs both, were calculated.



79 cases of GBS (mean age 6.5 years, range 4.0-14.5; 39% female) met the case definition. GBS cases were ascending (79%), symmetrical (85%), and bilateral (100%); involving lower extremity hypotonia (86%) and weakness (100%), upper extremity hypotonia (62%) and weakness (80%), areflexia/hyporeflexia (88%), respiratory muscles (22%), bulbar muscles (22%), and cranial nerves (13%). Four limbs were involved in 80% of cases. Mean time to maximal weakness was 5.2 days (range 0.5-30 days) with nadir GBS disability scores of 3 (7%), 4 (67%), 5 (15%), 6 (10%), or unclear (1%). CSF (mean time to lumbar puncture 29 days) was normal in 29% with cytoalbuminologic dissociation in 65% (mean protein 105 mg/dL, range 10-1000; mean cell count 11/μL, range 0-220, n=4 with >50 cells/μL). Significant improvement occurred in 73% whereas death (9%) occurred 6-29 days after sensorimotor symptom onset. The majority of cases (86%) fulfilled Brighton level 3, level 2 (84%), and level 1 (62%) of diagnostic certainty.



The diagnosis of GBS can be made using Brighton Working Group criteria in India with moderate to high sensitivity. Brighton Working Group case definitions are a plausible standard for capturing a majority of cases of GBS in field operations in low income settings during AFP surveillance.
Nonverbal Behavior Generator for Embodied Conversational Agents

Believable nonverbal behaviors for embodied conversational agents (ECA) can create a more immersive experience for users and improve the effectiveness of communication. This paper describes a nonverbal behavior generator that analyzes the syntactic and semantic structure of the surface text as well as the affective state of the ECA and annotates the surface text with appropriate nonverbal behaviors. A number of video clips of people conversing were analyzed to extract the nonverbal behavior generation rules. The system works in real-time and is userextensible so that users can easily modify or extend the current behavior generation rules.
Model-Driven Artificial Intelligence for Online Network Optimization

Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question “what is the right ONO technique?” remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.
Representation Learning for Measuring Entity Relatedness with Rich Information

Incorporating multiple types of relational information from heterogeneous networks has been proved effective in data mining. Although Wikipedia is one of the most famous heterogeneous network, previous works of semantic analysis on Wikipedia are mostly limited on single type of relations. In this paper, we aim at incorporating multiple types of relations to measure the semantic relatedness between Wikipedia entities. We propose a framework of coordinate matrix factorization to construct lowdimensional continuous representation for entities, categories and words in the same semantic space. We formulate this task as the completion of a sparse entity-entity association matrix, in which each entry quantifies the strength of relatedness between corresponding entities. We evaluate our model on the task of judging pair-wise word similarity. Experiment result shows that our model outperforms both traditional entity relatedness algorithms and other representation learning models.