nils-herrmann/clean_scirepeval huggingface dataset:
Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research.



Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries.



Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK.



The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis.



Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database.



We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis.



This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups.
"TweetGenie: automatic age prediction from tweets" by D. Nguyen, R. Gravel, D. Trieschnigg, and T. Meder; with Ching-man Au Yeung as coordinator

A person's language use reveals much about the person's social identity, which is based on the social categories a person belongs to including age and gender. We discuss the development of TweetGenie, a computer program that predicts the age of Twitter users based on their language use. We explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. An automatic system achieves better performance than humans on these tasks. Both humans and the automatic systems tend to underpredict the age of older people. We find that most linguistic changes occur when people are young, and that after around 30 years the studied variables show little change, making it difficult to predict the ages of older Twitter users.
Assessing facial beauty through proportion analysis by image processing and supervised learning

Perception of universal facial beauty has long been debated amongst psychologists and anthropologists. In this paper, we perform experiments to evaluate the extent of universal beauty by surveying a number of diverse human referees to grade a collection of female facial images. Results obtained show that there exists a strong central tendency in the human grades, thus exhibiting agreement on beauty assessment. We then trained an automated classifier using the average human grades as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry, cosmetic industry, virtual media, and plastic surgery.
Recommending twitter users to follow using content and collaborative filtering approaches

Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.
So close yet so far: Motor anomalies impacting on social functioning in autism spectrum disorder

Difficulties in the social domain and motor anomalies have been widely investigated in Autism Spectrum Disorder (ASD). However, they have been generally considered as independent, and therefore tackled separately. Recent advances in neuroscience have hypothesized that the cortical motor system can play a role not only as a controller of elementary physical features of movement, but also in a complex domain as social cognition. Here, going beyond previous studies on ASD that described difficulties in the motor and in the social domain separately, we focus on the impact of motor mechanisms anomalies on social functioning. We consider behavioral, electrophysiological and neuroimaging findings supporting the idea that motor cognition is a critical "intermediate phenotype" for ASD. Motor cognition anomalies in ASD affect the processes of extraction, codification and subsequent translation of "external" social information into the motor system. Intriguingly, this alternative "motor" approach to the social domain difficulties in ASD may be promising to bridge the gap between recent experimental findings and clinical practice, potentially leading to refined preventive approaches and successful treatments.
Simulation of attitude control of a wheeled inverted pendulum

In this research a computer simulation for implementing attitude controller of wheeled inverted pendulum is carried out. The wheeled inverted pendulum is a kind of an inverted pendulum that has two equivalent points. In order to keep the naturally unstable equivalent point, it should be controlling the wheels persistently. Dynamic equations of the wheeled inverted pendulum are derived with considering tilted road as one of various road conditions. A linear quadratic regulator is adopted for the attitude controller since it is easy to obtain full state variables from the sensors for that control scheme and based on controllable condition of the destination as well. Various computer simulation shows that the LQR controller is doing well not only flat road but also tilted road.
Closed loop task space control of an interleaved continuum-rigid manipulator

A new manipulation approach, referred to as interleaved continuum-rigid manipulation, which combines inherently safe, flexible actuated segments with more precise embedded rigid-link joints has recently been introduced , . The redundantly actuated manipulator possesses the safety characteristics inherent in flexible segment devices while gaining some of the performance attributes of rigid-link joint systems. In this paper, we describe a general controller developed for an interleaved manipulator. The controller is implemented on a clinically-relevant prototype, the results of which demonstrate the advantages of an interleaved manipulator. We also consider kinematic drivers of the interleaved manipulator workspace, showing that careful kinematic considerations can substantially improve manipulator workspace and task accuracy.
Artificial bee colony programming for symbolic regression

Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as Artificial Bee Colony Programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.
Smartbin: Smart waste management system

In this paper, we present the Smartbin system that identifies fullness of litter bin. The system is designed to collect data and to deliver the data through wireless mesh network. The system also employs duty cycle technique to reduce power consumption and to maximize operational time. The Smartbin system was tested in an outdoor environment. Through the testbed, we collected data and applied sense-making methods to obtain litter bin utilization and litter bin daily seasonality information. With such information, litter bin providers and cleaning contractors are able to make better decision to increase productivity.
From Argumentation Mining to Stance Classification

Argumentation mining and stance classification were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other machine learning approaches for argument tagging which often require large set of labeled data, the proposed model is minimally supervised and merely a one-to-one mapping between the pre-defined argument set and the extracted topics is required. These extracted arguments are subsequently exploited for stance classification. Additionally, a manuallyannotated corpus for stance classification and argument tagging of online news comments is introduced and made available. Experiments on our collected corpus demonstrate the benefits of using topic-modeling for argument tagging. We show that using Non-Negative Matrix Factorization instead of Latent Dirichlet Allocation achieves better results for argument classification, close to the results of a supervised classifier. Furthermore, the statistical model that leverages automatically-extracted arguments as features for stance classification shows promising results.
Fatigue Life and Frequency Response of Braided Pneumatic Actuators

Although braided pneumatic actuators are capable of producing phenomenal forces compared to their weight, they have yet to see mainstream use due to their relatively short fatigue lives. By improving manufacturing techniques, actuator lifetime was extended by nearly an order of magnitude. Another concern is that their response times may be too long for control of legged robots. In addition, the frequency response of these actuators was found to be similar to that of human muscle.
Strategic Prototyping for Developing Big Data Systems

Conventional horizontal evolutionary prototyping for small-data system development is inadequate and too expensive for identifying, analyzing, and mitigating risks in big data system development. RASP (Risk-Based, Architecture-Centric Strategic Prototyping) is a model for cost-effective, systematic risk management in agile big data system development. It uses prototyping strategically and only in areas that architecture analysis can't sufficiently address. Developers use less costly vertical evolutionary prototypes instead of blindly building full-scale prototypes. An embedded multiple-case study of nine big data projects at a global outsourcing firm validated RASP. A decision flowchart and guidelines distilled from lessons learned can help architects decide whether, when, and how to do strategic prototyping. This article is part of a special issue on Software Engineering for Big Data Systems.
Violent video games and the Supreme Court: lessons for the scientific community in the wake of Brown v. Entertainment Merchants Association.

In June 2011 the U.S. Supreme Court ruled that video games enjoy full free speech protections and that the regulation of violent game sales to minors is unconstitutional. The Supreme Court also referred to psychological research on violent video games as "unpersuasive" and noted that such research contains many methodological flaws. Recent reviews in many scholarly journals have come to similar conclusions, although much debate continues. Given past statements by the American Psychological Association linking video game and media violence with aggression, the Supreme Court ruling, particularly its critique of the science, is likely to be shocking and disappointing to some psychologists. One possible outcome is that the psychological community may increase the conclusiveness of their statements linking violent games to harm as a form of defensive reaction. However, in this article the author argues that the psychological community would be better served by reflecting on this research and considering whether the scientific process failed by permitting and even encouraging statements about video game violence that exceeded the data or ignored conflicting data. Although it is likely that debates on this issue will continue, a move toward caution and conservatism as well as increased dialogue between scholars on opposing sides of this debate will be necessary to restore scientific credibility. The current article reviews the involvement of the psychological science community in the Brown v. Entertainment Merchants Association case and suggests that it might learn from some of the errors in this case for the future.
Run-time Detection of Heap-based Overflows

Buffer overflows belong to the most common class of attacks on today’s Internet. Although stack-based variants are still by far more frequent and well-understood, heap-based overflows have recently gained more attention. Several real-world exploits have been published that corrupt heap management information and allow arbitrary code execution with the privileges of the victim process. This paper presents a technique that protects the heap management information and allows for run-time detection of heap-based overflows. We discuss the structure of these attacks and our proposed detection scheme that has been implemented as a patch to the GNU Lib C. We report the results of our experiments, which demonstrate the detection effectiveness and performance impact of our approach. In addition, we discuss different mechanisms to deploy the memory protection.
What makes interruptions disruptive ? A study of length , similarity , and complexity

Classic work on interruptions by Zeigarnik showed that tasks that were interrupted were more likely to be recalled after a delay than tasks that were not interrupted. Much of the literature on interruptions has been devoted to examining this effect, although more recently interruptions have been used to choose between competing designs for interfaces to complex devices. However, none of this work looks at what makes some interruptions disruptive and some not. This series of experiments uses a novel computer-based adventure-game methodology to investigate the effects of the length of the interruption, the similarity of the interruption to the main task, and the complexity of processing demanded by the interruption. It is concluded that subjects make use of some form of nonarticulatory memory which is not affected by the length of the interruption. It is affected by processing similar material however, and by a complex mentalarithmetic task which makes large demands on working memory.
Brand Loyalty Programs : Are They Shams ?

B loyalty and the more modern topics of computing customer lifetime value and structuring loyalty programs remain the focal point for a remarkable number of research articles. At first, this research appears consistent with firm practices. However, close scrutiny reveals disaffirming evidence. Many current so-called loyalty programs appear unrelated to the cultivation of customer brand loyalty and the creation of customer assets. True investments are up-front expenditures that produce much greater future returns. In contrast, many socalled loyalty programs are shams because they produce liabilities (e.g., promises of future rewards or deferred rebates) rather than assets. These programs produce short-term revenue from customers while producing substantial future obligations to those customers. Rather than showing trust by committing to the customer, the firm asks the customer to trust the firm—that is, trust that future rewards are indeed forthcoming. The entire idea is antithetical to the concept of a customer asset. Many modern loyalty programs resemble old-fashioned trading stamps or deferred rebates that promise future benefits for current patronage. A true loyalty program invests in the customer (e.g., provides free up-front training, allows familiarization or customization) with the expectation of greater future revenue. Alternative motives for extant programs are discussed.
Ultralight and highly compressible graphene aerogels.

Chemically converted graphene aerogels with ultralight density and high compressibility are prepared by diamine-mediated functionalization and assembly, followed by microwave irradiation. The resulting graphene aerogels with density as low as 3 mg cm(-3) show excellent resilience and can completely recover after more than 90% compression. The ultralight graphene aerogels possessing high elasticity are promising as compliant and energy-absorbing materials.
Structural analysis of chat messages for topic detection

This paper studies the characteristics of chat messages from analyzing a collection of 33,121 sample messages gathered from 1700 sessions of conversations of 72 pairs of MSN Messenger users over 4-month duration from June to September of 2005. The primary objective of chat message characterization is to understand the properties of chat messages for effective message analysis such as message topic detection.  From the study on chat message characteristics, an indicative term-based categorization approach for chat topic detection is proposed. In the proposed approach, different techniques such as sessionalization of chat messages and extraction of features from icon texts and URLs are incorporated for message pre-processing. And Näıve Bayes, Associative Classification, and Support Vector Machine are employed as classifiers for categorizing topics from chat sessions.  Indicative term-based approach is superior than the traditional document frequency based approach for feature selection in chat topic categorization.  This paper studies the characteristics of chat messages and proposes an indicative term-based categorization approach for chat topic detection. The proposed approach has been incorporated into an instant message analysis system for both online and offline chat topic detection.  May 2006
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities

Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error.
 Our algorithm is evaluated on Twitter hashtags extracted from a dataset of more than 400 million tweets. We analyze the contribution and the limitations of the various feature types to the spread of information, demonstrating that content aspects can be used as strong predictors thus should not be disregarded. We also study the dependencies between global features such as graph topology and content features.
Design of a Low-Torque-Ripple Fractional-Slot Interior Permanent-Magnet Motor

Despite a strong nonlinear behavior and a complex design, the interior permanent-magnet (IPM) machine is proposed as a good candidate among the PM machines owing to its interesting peculiarities, i.e., higher torque in flux-weakening operation, higher fault tolerance, and ability to adopt low-cost PMs. A second trend in designing PM machines concerns the adoption of fractional-slot (FS) nonoverlapped coil windings, which reduce the end winding length and consequently the Joule losses and the cost. Therefore, the adoption of an IPM machine with an FS winding aims to combine both advantages: high torque and efficiency in a wide operating region. However, the combination of an anisotropic rotor and an FS winding stator causes some problems. The interaction between the magnetomotive force harmonics due to the stator current and the rotor anisotropy causes a very high torque ripple. This paper illustrates a procedure in designing an IPM motor with the FS winding exhibiting a low torque ripple. The design strategy is based on two consecutive steps: at first, the winding is optimized by taking a multilayer structure, and then, the rotor geometry is optimized by adopting a nonsymmetric structure. As an example, a 12-slot 10-pole IPM machine is considered, achieving a torque ripple lower than 1.5% at full load.
Scene Segmentation with DAG-Recurrent Neural Networks

In this paper, we address the challenging task of scene segmentation. In order to capture the rich contextual dependencies over image regions, we propose Directed Acyclic Graph-Recurrent Neural Networks (DAG-RNN) to perform context aggregation over locally connected feature maps. More specifically, DAG-RNN is placed on top of pre-trained CNN (feature extractor) to embed context into local features so that their representative capability can be enhanced. In comparison with plain CNN (as in Fully Convolutional Networks-FCN), DAG-RNN is empirically found to be significantly more effective at aggregating context. Therefore, DAG-RNN demonstrates noticeably performance superiority over FCNs on scene segmentation. Besides, DAG-RNN entails dramatically less parameters as well as demands fewer computation operations, which makes DAG-RNN more favorable to be potentially applied on resource-constrained embedded devices. Meanwhile, the class occurrence frequencies are extremely imbalanced in scene segmentation, so we propose a novel class-weighted loss to train the segmentation network. The loss distributes reasonably higher attention weights to infrequent classes during network training, which is essential to boost their parsing performance. We evaluate our segmentation network on three challenging public scene segmentation benchmarks: Sift Flow, Pascal Context and COCO Stuff. On top of them, we achieve very impressive segmentation performance.