nils-herrmann/clean_scirepeval huggingface dataset:
Follow-up study of the treatment outcomes at a psychiatric trauma clinic for refugees.

To describe change in mental health after treatment with antidepressants and trauma-focused cognitive behavioral therapy.



Patients receiving treatment at the Psychiatric Trauma Clinic for Refugees in Copenhagen completed self-ratings of level of functioning, quality of life, and symptoms of PTSD, depression and anxiety before and after treatment. Changes in mental state and predictors of change were evaluated in a sample that all received well-described and comparable treatment.



85 patients with PTSD or depression were included in the analysis. Significant improvement and effect size were observed on all rating scales (p-value <0.01 and Cohen's d 45-0.68). Correlation analysis showed no association between severity of symptoms at baseline and the observed change.



Despite methodological limitations, the finding of a significant improvement on all rating scales is important considering that previous follow-up studies of comparable patient populations have not found significant change in the patients'condition after treatment.
AVOIDIT : A Cyber Attack Taxonomy

Cyber-attacks have greatly increased over the years, and the attackers have progressively improved in devising attacks towards specific targets. To aid in identifying and defending against cyber-attacks we propose a cyber attack taxonomy called AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). We use five major classifiers to characterize the nature of an attack: classification by attack vector, classification by operational impact, classification by defense, classification by informational impact, and classification by attack target. Classification by defense is oriented towards providing information to the network administrator regarding attack mitigation or remediation strategies. Contrary to the existing taxonomies, AVOIDIT efficiently classifies blended attacks. We further propose an efficient cause, action, defense, analysis, and target (CADAT) process used to facilitate attack classification. AVOIDIT and CADAT are used by an issue resolution system (IRS) to educate the defender on possible cyber-attacks and the development of potential security policies. We validate the proposed AVOIDIT taxonomy using cyber-attacks scenarios and highlight future work intended to simulate AVOIDIT’s use within the IRS. Taxonomy; Cyber Attack Taxonomy; Vulnerability; Computer Security; Cyberspace; Issue Resolution System
The Application of Petri Nets to Workflow Management

Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.
Continuous Backbone “ Continuum ” Robot Manipulators : A Review

This paper describes and discusses the history and state of the art of continuous backbone robot manipulators. Also known as continuum manipulators, these robots, which resemble biological trunks and tentacles, offer capabilities beyond the scope of traditional rigid link manipulators. They are able to adapt their shape to navigate through complex environments and grasp a wide variety of payloads using their compliant backbones. In the paper, we review the current state of knowledge in the field, focusing particularly on kinematic and dynamic models for continuum robots. We discuss the relationships of these robots and their models to their counterparts in conventional rigid-link robots. Ongoing research and future developments in the field are discussed.
Connectivism and Dimensions of Individual Experience

Connectivism has been offered as a new learning theory for a digital age, with four key principles for learning: autonomy, connectedness, diversity, and openness. The testing ground for this theory has been massive open online courses (MOOCs). As the number of MOOC offerings increases, interest in how people interact and develop as individual learners in these complex, diverse, and distributed environments is growing. In their work in these environments the authors have observed a growing tension between the elements of connectivity believed to be necessary for effective learning and the variety of individual perspectives both revealed and concealed during interactions with these elements. In this paper we draw on personality and self-determination theories to gain insight into the dimensions of individual experience in connective environments and to further explore the meaning of autonomy, connectedness, diversity, and openness. The authors suggest that definitions of all four principles can be expanded to recognize individual and psychological diversity within connective environments. They also suggest that such expanded definitions have implications for learners’ experiences of MOOCs, recognizing that learners may vary greatly in their desire for and interpretation of connectivity, autonomy, openness, and diversity.
Data is More Than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory

In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. In this paper, I will explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal.
Grading Method of Leaf Spot Disease Based on Image Processing

Since current grading of plant diseases is mainly based on eyeballing, a new method is developed based on computer image processing. All influencing factors existed in the process of image segmentation was analyzed and leaf region was segmented by using Otsu method. In the HSI color system, H component was chosen to segment disease spot to reduce the disturbance of illumination changes and the vein. Then, disease spot regions were segmented by using Sobel operator to examine disease spot edges. Finally, plant diseases are graded by calculating the quotient of disease spot and leaf areas. Researches indicate that this method to grade plant leaf spot diseases is fast and accurate.
Hardware improvement of Cybernetic Human HRP-4C for entertainment use

Hardware improvement of cybernetic human HRP-4C for entertainment is presented in this paper. We coined the word “Cybernetic Human” to explain a humanoid robot with a realistic head and a realistic figure of a human being. HRP-4C stands for Humanoid Robotics Platform-4 (Cybernetic human). Its joints and dimensions conform to average values of young Japanese females and HRP-4C looks very human-like. We have made HRP-4C present in several events to search for a possibility of use in the entertainment industry. Based on feedback from our experience, we improved its hardware. The new hand, the new foot with active toe joint, and the new eye with camera are introduced.
The weathering hypothesis and the health of African-American women and infants: evidence and speculations.

Observed variation between populations in fertility-timing distributions has been thought to contribute to infant mortality differentials. This hypothesis is based, in part, on the belief that the 20s through early 30s constitute "prime" childbearing ages that are low-risk relative to younger or older ages. However, when stratified by racial identification over the predominant first child-bearing ages, maternal age patterns of neonatal mortality vary between groups. Unlike non-Hispanic white infants, African-American infants with teen mothers experience a survival advantage relative to infants whose mothers are older. The black-white infant mortality differential is larger at older maternal ages than at younger ages. While African Americans and non-Hispanic whites differ on which maternal ages are associated with the lowest risk of neonatal mortality, within each population, first births are most frequent at its lowest-risk maternal ages. As a possible explanation for racial variation in maternal age patterns of births and birth outcomes, the "weathering hypothesis" is proposed: namely, that the health of African-American women may begin to deteriorate in early adulthood as a physical consequence of cumulative socioeconomic disadvantage.
Facial Attributes Classification Using Multi-task Representation Learning

This paper presents a new approach for facial attribute classification using a multi-task learning approach. Unlike other approaches that uses hand engineered features, our model learns a shared feature representation that is wellsuited for multiple attribute classification. Learning a joint feature representation enables interaction between different tasks. For learning this shared feature representation we use a Restricted Boltzmann Machine (RBM) based model, enhanced with a factored multi-task component to become Multi-Task Restricted Boltzmann Machine (MT-RBM). Our approach operates directly on faces and facial landmark points to learn a joint feature representation over all the available attributes. We use an iterative learning approach consisting of a bottom-up/top-down pass to learn the shared representation of our multi-task model and at inference we use a bottom-up pass to predict the different tasks. Our approach is not restricted to any type of attributes, however, for this paper we focus only on facial attributes. We evaluate our approach on three publicly available datasets, the Celebrity Faces (CelebA), the Multi-task Facial Landmarks (MTFL), and the ChaLearn challenge dataset. We show superior classification performance improvement over the state-of-the-art.
Metaphor Annotation : A Systematic Study ( Technical Report CSRP-03-04 )

This document describes a study of metaphor annotation that was carried out as part of the ATT-Meta project. The study lead to a number of results about metaphor and how it is signalled that are reported elsewhere (e.g. Wallington et al 2003a, Wallington et al 2003b). It also resulted in a public database of metaphorical views (http://www.cs.bham.ac.uk/research/attmeta/DatabankDCA/index.html). The annotated files are also viewable on line at: http://www.cs.bham.ac.uk/~amw/dcaProject. The primary aim of this document is to describe how the project was set up and run, and to discuss the measures we took to identify and quantify inter-annotator (dis)agreement.
Root Cause AC Motor Failure Analysis with a Focus on Shaft Failures

The squirrel-cage induction motor remains the workhorse of the petrochemical industry because of its versatility and ruggedness. However, it has its limitations, which if exceeded will cause premature failure of the stator, rotor, bearings or shaft. This paper is the final abridgement and update of six previous papers for the Petroleum and Chemical Industry Committee of the IEEE Industry Applications Society presented over the last 24 years and includes the final piece dealing with shaft failures. A methodology is provided that will lead operations personnel to the most likely root causes of failure. Check-off sheets are provided to assist in the orderly collection of data to assist in the analysis. As the petrochemical industry evolves from reactive to time based, to preventive, to trending, to diagnostics, and to a predictive maintenance attitude, more and more attention to root cause analysis will be required. This paper will help provide a platform for the establishment of such an evolution. The product scope includes lowand medium-voltage squirrel-cage induction motors in the 1–3000–hp range with anti friction bearings. However, much of this material is applicable to other types and sizes.
How to grow a mind: statistics, structure, and abstraction.

In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?
Acquiring and processing verb argument structure: Distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.
Overview of IEC 61850 and Benefits

Legacy substation automation protocols and architectures typically provided basic functionality for power system automation and were designed to accommodate the technical limitations of the networking technology available for implementation. There has recently been a vast improvement in networking technology that has changed dramatically what is now feasible for power system automation in the substation. Technologies such as switched Ethernet, TCP/IP, high-speed wide area networks, and high-performance low-cost computers are providing capabilities that could barely be imagined when most legacy substation automation protocols were designed. In order to take advantage of modern technology to deliver additional new benefits to users of substation automation, the International Electrotechnical Commission (IEC) has developed and released a new global standard for substation automation: IEC 61850. The paper provides a basic technical overview of IEC 61850 and discusses the benefits of each major aspect of the standard. The concept of a virtual model comprising both physical and logical device models that includes a set of standardized communications services are described along with explanations of how these standardized models, object naming conventions, and communication services bring significant benefits to the substation automation user. New services to support self-describing devices and object-orient peer-to-peer data exchange are explained with an emphasis on how these services can be applied to reduce costs for substation automation. The substation configuration language (SCL) of IEC 61850 is presented with information on how the standardization of substation configuration will impact the future of substation automation. The paper concludes with a brief introduction to the UCA International Users Group as a forum where users and suppliers cooperate in improving substation automation with testing, education, and demonstrations of IEC 61850 and other IEC standards technology
An End-to-end Neural Natural Language Interface for Databases

The ability to extract insights from new data sets is critical for decision making. Visual interactive tools play an important role in data exploration since they provide non-technical users with an effective way to visually compose queries and comprehend the results. Natural language has recently gained traction as an alternative query interface to databases with the potential to enable non-expert users to formulate complex questions and information needs efficiently and effectively. However, understanding natural language questions and translating them accurately to SQL is a challenging task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet made their way into practical tools and commercial products. In this paper, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests partial query extensions to users during query formulation and thus helps to write complex queries.
Hierarchical Spatial Transformer Network

Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time. Emergence of spatial transformer network makes dream come true. Spatial transformer network and its variants can handle global displacement well, but lack the ability to deal with local spatial variance. Hence how to achieve a better manner of deformation in the neural network has become a pressing matter of the moment. To address this issue, we analyze the advantages and disadvantages of approximation theory and optical flow theory, then we combine them to propose a novel way to achieve image deformation and implement it with a hierarchical convolutional neural network. This new approach solves for a linear deformation along with an optical flow field to model image deformation. In the experiments of cluttered MNIST handwritten digits classification and image plane alignment, our method outperforms baseline methods by a large margin.
A Hybrid Variational Autoencoder for Collaborative Filtering

In today’s day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the work of [11] on using variational autoencoders (VAE) for collaborative filtering with implicit feedback by proposing a hybrid, multi-modal approach. Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation. We empirically show how the VAE network is empowered by incorporating movie embeddings. We also visualize movie and user embeddings by clustering their latent representations obtained from a VAE. CCS CONCEPTS •Information systems→Collaborative filtering; Personalization; Clustering;
Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

The 3G iPhone was the first consumer device to provide a seamless integration of three positioning technologies: Assisted GPS (A-GPS), WiFi positioning and cellular network positioning. This study presents an evaluation of the accuracy of locations obtained using these three positioning modes on the 3G iPhone. A-GPS locations were validated using surveyed benchmarks and compared to a traditional low-cost GPS receiver running simultaneously. WiFi and cellular positions for indoor locations were validated using high resolution orthophotography. Results indicate that A-GPS locations obtained using the 3G iPhone are much less accurate than those from regular autonomous GPS units (average median error of 8 m for ten 20-minute field tests) but appear sufficient for most Location Based Services (LBS). WiFi locations using the 3G iPhone are much less accurate (median error of 74 m for 58 observations) and fail to meet the published accuracy specifications. Positional errors in WiFi also reveal erratic spatial patterns resulting from the design of the calibration effort underlying the WiFi positioning system. Cellular positioning using the 3G iPhone is the least accurate positioning method (median error of 600 m for 64 observations), consistent with previous studies. Pros and cons of the three positioning technologies are presented in terms of coverage, accuracy and reliability, followed by a discussion of the implications for LBS using the 3G iPhone and similar mobile devices.
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering

Mention Detection Analysis In Table 1, we compare our model with previous state-of-the-art systems on the CoNLL-2012 Shared Task English data. We obtain the best results in all F1 metrics. Our single model achieves 67.8% F1 and our 5-model ensemble achieves 69.2% F1. In particular, compared with Lee et al. (2017), our improvement mainly results from the precision scores. This indicates that the mention detection loss does produce better mention scores and the biaffine attention more effectively determines if two spans are coreferent.
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding

Vector representations of word meaning have found many applications in the field of natural language processing. Word vectors intuitively represent the average context in which a given word tends to occur, but they cannot explicitly model the diversity of these contexts. Although region representations of word meaning offer a natural alternative to word vectors, only few methods have been proposed that can effectively learn word regions. In this paper, we propose a new word embedding model which is based on SVM regression. We show that the underlying ranking interpretation of word contexts is sufficient to match, and sometimes outperform, the performance of popular methods such as Skip-gram. Furthermore, we show that by using a quadratic kernel, we can effectively learn word regions, which outperform existing unsupervised models for the task of hypernym detection.