nils-herrmann/clean_scirepeval huggingface dataset:
Contracting, equal, and expanding learning schedules: the optimal distribution of learning sessions depends on retention interval.

In laboratory and applied learning experiments, researchers have extensively investigated the optimal distribution of two learning sessions (i.e., initial learning and one relearning session) for the learning of verbatim materials. However, research has not yet provided a satisfying and conclusive answer to the optimal scheduling of three learning sessions (i.e., initial learning and two relearning sessions) across educationally relevant time intervals. Should the to-be-learned material be repeated at decreasing intervals (contracting schedule), constant intervals (equal schedule), or increasing intervals (expanding schedule) between learning sessions? Different theories and memory models (e.g., study-phase retrieval theory, contextual variability theory, ACT-R, and the Multiscale Context Model) make distinct predictions about the optimal learning schedule. We discuss the extant theories and derive clear predictions from each of them. To test these predictions empirically, we conducted an experiment in which participants studied and restudied paired associates with a contracting, equal, or expanding learning schedule. Memory performance was assessed immediately, 1 day, 7 days, or 35 days later with free- and cued-recall tests. Our results revealed that the optimal learning schedule is conditional on the length of the retention interval: A contracting learning schedule was beneficial for retention intervals up to 7 days, but both equal and expanding learning schedules were better for a long retention interval of 35 days. Our findings can be accommodated best by the contextual variability theory and indicate that revisions are needed to existing memory models. Our results are practically relevant, and their implications for real-world learning are discussed.
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection

Nearest neighbor graphs are widely used in data mining and machine learning. The brute-force method to compute the exact kNN graph takes Θ(dn) time for n data points in the d dimensional Euclidean space. We propose two divide and conquer methods for computing an approximate kNN graph in Θ(dn) time for high dimensional data (large d). The exponent t depends on an internal parameter and is larger than one. Experiments show that a high quality graph usually requires a small t which is close to one. A few of the practical details of the algorithms are as follows. First, the divide step uses an inexpensive Lanczos procedure to perform recursive spectral bisection. After each conquer step, an additional refinement step is performed to improve the accuracy of the graph. Finally, a hash table is used to avoid repeating distance calculations during the divide and conquer process. The combination of these techniques is shown to yield quite effective algorithms for building kNN graphs.
Efficient service handoff across edge servers via docker container migration

Supporting smooth movement of mobile clients is important when offloading services on an edge computing platform. Interruption-free client mobility demands seamless migration of the offloading service to nearby edge servers. However, fast migration of offloading services across edge servers in a WAN environment poses significant challenges to the handoff service design. In this paper, we present a novel service handoff system which seamlessly migrates offloading services to the nearest edge server, while the mobile client is moving. Service handoff is achieved via container migration. We identify an important performance problem during Docker container migration. Based on our systematic study of container layer management and image stacking, we propose a migration method which leverages the layered storage system to reduce file system synchronization overhead, without dependence on the distributed file system. We implement a prototype system and conduct experiments using real world product applications. Evaluation results reveal that compared to state-of-the-art service handoff systems designed for edge computing platforms, our system reduces the total duration of service handoff time by 80%(56%) with network bandwidth 5Mbps(20Mbps).
Learning to live with a permanent intestinal ostomy: impact on everyday life and educational needs.

The aim of the study was to explore the impact of a permanent stoma on patients' everyday lives and to gain further insight into their need for ostomy-related education.



The sample population comprised 15 persons with permanent ostomies. Stomas were created to manage colorectal cancer or inflammatory bowel disease. The research setting was the surgical department at a hospital in the Capitol Region of Denmark associated with the University of Copenhagen.



Focus group interviews were conducted using a phenomenological hermeneutic approach. Data were collected and analyzed using qualitative content analysis.



Stoma creation led to feelings of stigma, worries about disclosure, a need for control and self-imposed limits. Furthermore, patients experienced difficulties identifying their new lives with their lives before surgery. Participants stated they need to be seen as a whole person, to have close contact with health care professionals, and receive trustworthy information about life with an ostomy. Respondents proposed group sessions conducted after hospital discharge. They further recommended that sessions be delivered by lay teachers who had a stoma themselves.



Self-imposed isolation was often selected as a strategy for avoiding disclosing the presence of a stoma. Patient education, using health promotional methods, should take the settings into account and patients' possibility of effective knowledge transfer. Respondents recommend involvement of lay teachers, who have a stoma, and group-based learning processes are proposed, when planning and conducting patient education.
A transition from substrate integrated waveguide (SIW) to rectangular waveguide

In this work, a simple and compact transition from substrate integrated waveguide (SIW) to traditional rectangular waveguide is proposed and demonstrated. The substrate of SIW can be easily surface-mounted to the standard flange of the waveguide by creating a flange on the substrate. A longitudinal slot window etched on the broad wall of SIW couples energy between SIW and rectangular waveguide. An example of the transition structure is realized at 35 GHz with substrate of RT/Duroid 5880. HFSS simulated result of the transition shows a return loss less than −15 dB over a frequency range of 800 MHz. A back to back connected transition has been fabricated, and the measured results confirm well with the anticipated ones.
Query Based Arabic Text Summarization

With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, in this case domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper we introduce a query based, Arabic text, single document summarization using an existing Arabic language thesaurus and an extracted knowledge base. We use an Arabic corpus to extract domain knowledge represented by topic related concepts/ keywords and the lexical relations among them. The user’s query is expanded once by using the Arabic WordNet thesaurus and then by adding the domain specific knowledge base to the expansion. For the summarization dataset, Essex Arabic Summaries Corpus was used. It has many topic based articles with multiple human summaries. The performance appeared to be enhanced when using our extracted knowledge base than to just use the WordNet.
Convolutional Neural Network Architectures for Signals Supported on Graphs

Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large-scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best-performing GNN architecture.
Multisystemic treatment of serious juvenile offenders: long-term prevention of criminality and violence.

This article examined the long-term effects of multisystemic therapy (MST) vs. individual therapy (IT) on the prevention of criminal behavior and violent offending among 176 juvenile offenders at high risk for committing additional serious crimes. Results from multiagent, multimethod assessment batteries conducted before and after treatment showed that MST was more effective than IT in improving key family correlates of antisocial behavior and in ameliorating adjustment problems in individual family members. Moreover, results from a 4-year follow-up of rearrest data showed that MST was more effective than IT in preventing future criminal behavior, including violent offending. The implications of such findings for the design of violence prevention programs are discussed.
Deep Learning

Jährlich veröffentlichen Forscher neue Zahlen und Bestwerte ihrer Lernverfahren in verschiedensten Bereichen, mit welchen sie sich den menschlichen Fähigkeiten nähern oder diese sogar bereits übertreffen. Hierbei ist Deep Learning als Schlagwort prominent vertreten. Das bekannteste Beispiel darunter ist sicherlich der von Googles DeepMind-Gruppe entwickelte AlphaGoComputer, der erstmals professionelle menschliche Spieler im Go-Spiel, das weitaus komplexer als Schach ist, bezwang (vgl. ). Insbesondere die Mainstreammedien griffen das Thema auf und postulierten eine neue Ära von Künstlicher Intelligenz. Trotz wachsender Popularität des Deep Learning löst dieser Ansatz nicht pauschal jedes ungelöste oder nur unzufrieden gelöste Problem des maschinellen Lernens. Vielmehr ist es als eines von vielen Werkzeugen zu verstehen, das zum überwachten oder unüberwachten Lernen von insbesondere sehr großen Datensätzen verwendet werden kann. Deep Learning kann hierbei den geschichteten Aufbau hierarchischer Features automatisch sehr gut abbilden, weshalb es vor allem in der Bildund Sprachverarbeitung eine wichtige Rolle spielt. Weitere Bereiche des maschinellen Lernens wie iML [5] und OCR [9], in denen Deep Learning eingesetzt wird, wurden bereits in Artikeln des aktuellen Schlagworts tituliert.
Resistive Random Access Memory (RRAM)

RRAM technology has made significant progress in the past decade as a competitive candidate for the next generation non-volatile memory (NVM). This lecture is a comprehensive tutorial of metal oxide-based RRAM technology from device fabrication to array architecture design. State-of-theart RRAM device performances, characterization, and modeling techniques are summarized, and the design considerations of the RRAM integration to large-scale array with peripheral circuits are discussed. Chapter 2 introduces the RRAM device fabrication techniques and methods to eliminate the forming process, and will show its scalability down to sub-10 nm regime. Then the device performances such as programming speed, variability control, and multi-level operation are presented, and finally the reliability issues such as cycling endurance and data retention are discussed. Chapter 3 discusses the RRAM physical mechanism, and the materials characterization techniques to observe the conductive filaments and the electrical characterization techniques to study the electronic conduction processes. It also presents the numerical device modeling techniques for simulating the evolution of the conductive filaments as well as the compact device modeling techniques for circuit-level design. Chapter 4 discusses the two common RRAM array architectures for large-scale integration: one-transistor-one-resistor (1T1R) and cross-point architecture with selector. The write/read schemes are presented and the peripheral circuitry design considerations are discussed. Finally, a 3D integration approach is introduced for building ultra-high density RRAM array. Chapter 5 is a brief summary and will give an outlook for RRAM’s potential novel applications beyond the NVM applications.
A longitudinal study of emotional intelligence in graduate nurse anesthesia students

Emotional intelligence (EI) is an important component not only for success in the nurse anesthesia (NA) profession, but as a NA student as well. Using the ability-based EI model, the purpose of this was to examine the difference in EI between the first semester and last semester of NA training programs.



First semester NA students completed the online Mayer-Salovey-Caruso Emotional Intelligence Test V2.0 EI instrument, and then the same students repeated the instrument in their last (7th) semester.



There was a statistically significant correlation between overall EI and long-term overall EI (P = 0.000), reasoning area and long-term reasoning area (P = 0.035), experiencing area (P = 0.000) and long-term experiencing area, perceiving branch and long-term perceiving branch (P = 0.000), using and long-term using branch (P = 0.000), and the managing branch and long-term managing branch (P = 0.026). The correlation between the understanding branch and the long-term understanding branch was not statistically significant (P < 0.157). The paired sample t-test demonstrated no statistically significant change (n = 34) in overall EI, the two areas scores, or the four-branch scores between the first semester and the last semester of a NA training program.



This longitudinal study shows the lack of EI change in NA students over time. Thus, no change in EI occurs as a result of transitioning through a NA program based on the accrediting body's standardized curriculum, but the results helped the researcher provide useful data to inform future research on the use of EI measures as predictors of NA program success.
2 Conceptual Foundations of the Balanced Scorecard Abstract

David Norton and I introduced the Balanced Scorecard in a 1992 Harvard Business Review article (Kaplan & Norton, 1992). The article was based on a multi-company research project to study performance measurement in companies whose intangible assets played a central role in value creation (Nolan Norton Institute, 1991). Norton and I believed that if companies were to improve the management of their intangible assets, they had to integrate the measurement of intangible assets into their management systems. After publication of the 1992 HBR article, several companies quickly adopted the Balanced Scorecard giving us deeper and broader insights into its power and potential. During the next 15 years, as it was adopted by thousands of private, public, and nonprofit enterprises around the world, we extended and broadened the concept into a management tool for describing, communicating and implementing strategy. This paper describes the roots and motivation for the original Balanced Scorecard article as well as the subsequent innovations that connected it to a larger management literature.
Batteries for efficient energy extraction from a water salinity difference.

The salinity difference between seawater and river water is a renewable source of enormous entropic energy, but extracting it efficiently as a form of useful energy remains a challenge. Here we demonstrate a device called "mixing entropy battery", which can extract and store it as useful electrochemical energy. The battery, containing a Na(2-x)Mn(5)O(10) nanorod electrode, was shown to extract energy from real seawater and river water and can be applied to a variety of salt waters. We demonstrated energy extraction efficiencies of up to 74%. Considering the flow rate of river water into oceans as the limiting factor, the renewable energy production could potentially reach 2 TW, or ∼13% of the current world energy consumption. The mixing entropy battery is simple to fabricate and could contribute significantly to renewable energy in the future.
Propagation characteristics of leaky waves on a 2D periodic leaky-wave antenna

A two-dimensional (2D) periodic leaky-wave antenna consisting of a periodic distribution of rectangular patches on a grounded dielectric substrate, excited by a narrow slot in the ground plane, is studied here. The TM0 surface wave that is normally supported by a grounded dielectric substrate is perturbed by the presence of the periodic patches to produce radially-propagating leaky waves. In addition to making a novel microwave antenna structure, this design is motivated by the phenomena of directive beaming and enhanced transmission observed in plasmonic structures in the optical regime.
Deep Reinforcement Learning from Policy-Dependent Human Feedback

To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will need to learn these behaviors within a reasonable amount of time while efficiently leveraging the sparse feedback a human trainer is capable of providing. Recent work has shown that human feedback can be characterized as a critique of an agent’s current behavior rather than as an alternative reward signal to be maximized, culminating in the COnvergent Actor-Critic by Humans (COACH) algorithm for making direct policy updates based on human feedback. Our work builds on COACH, moving to a setting where the agent’s policy is represented by a deep neural network. We employ a series of modifications on top of the original COACH algorithm that are critical for successfully learning behaviors from high-dimensional observations, while also satisfying the constraint of obtaining reduced sample complexity. We demonstrate the effectiveness of our Deep COACH algorithm in the rich 3D world of Minecraft with an agent that learns to complete tasks by mapping from raw pixels to actions using only real-time human feedback in 10–15 minutes of interaction.
Event Detection and Evolution Based on Knowledge Base

Social media and News media have become an important platform where people obtain latest events. Much research has been done to detect topics and events in social or news streams, but lack of comprehensiveness. In this paper, we propose a Knowledge-based Event Mining (KEM) model to automatically detect events and generate evolution chain in multi-channel text streams. Specifically, we merge the same entities and similar phrases by knowledge base and incremental word2vec model. We adopt a 7-tuple event description model to display events comprehensively and analyze their relationships. Finally, we generate an evolution chain for each event incrementally. Our evaluation on a massive human-generated dataset containing real world events demonstrates that our new model KEM outperforms the baseline method both in efficiency and effectiveness.
A Deep Learning Solution to Named Entity Recognition

Identifying Named Entities is vital for many Natural Language Processing (NLP) applications. Much of the earlier work for identifying named entities focused on using handcrafted features and knowledge resources (feature engineering). This is a barrier for resource-scarce languages as many resources are not readily available. Recently, Deep Learning techniques have been proposed for various NLP tasks requiring little/no hand-crafted features and knowledge resources, instead the features are learned from the data. Many proposed deep learning solutions for Named Entity Recognition (NER) still rely on feature engineering as opposed to feature learning. However, it is not clear whether the deep learning system or the engineered features are responsible for the positive results reported. This is in contrast with the goal of deep learning systems i.e., to learn the features from the data itself. In this study, we show that a feature learned deep learning system is a viable solution to NER task. We test our deep learning systems on CoNLL English and Spanish NER datasets. Our system is able to give comparable results with the existing state-of-the-art feature engineered systems for English. We report the best performance of 89.27 F-Score for English when comparing with systems which do not use any handcrafted features or knowledge resources. Evaluation of our trained system on out-of-domain data indicate that the results are promising with the reported results. Our system when tested on Spanish NER achieves the best reported F-Score of 82.59 indicating its applicability to other languages.
Digital image processing techniques for detecting, quantifying and classifying plant diseases

This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.
Stacking with Auxiliary Features for Visual Question Answering

Visual Question Answering (VQA) is a wellknown and challenging task that requires systems to jointly reason about natural language and vision. Deep learning models in various forms have been the standard for solving VQA. However, some of these VQA models are better at certain types of image-question pairs than other models. Ensembling VQA models intelligently to leverage their diverse expertise is, therefore, advantageous. Stacking With Auxiliary Features (SWAF) is an intelligent ensembling technique which learns to combine the results of multiple models using features of the current problem as context. We propose four categories of auxiliary features for ensembling for VQA. Three out of the four categories of features can be inferred from an image-question pair and do not require querying the component models. The fourth category of auxiliary features uses model-specific explanations. In this paper, we describe how we use these various categories of auxiliary features to improve performance for VQA. Using SWAF to effectively ensemble three recent systems, we obtain a new state-of-the-art. Our work also highlights the advantages of explainable AI models.