nils-herrmann/clean_scirepeval huggingface dataset:
The Tokuhashi score: significant predictive value for the life expectancy of patients with breast cancer with spinal metastases.

Retrospective study of 55 consecutive patients with spinal metastases secondary to breast cancer who underwent surgery.



To evaluate the predictive value of the Tokuhashi score for life expectancy in patients with breast cancer with spinal metastases.



The score, composed of 6 parameters each rated from 0 to 2, has been proposed by Tokuhashi and colleagues for the prognostic assessment of patients with spinal metastases.



A total of 55 patients surgically treated for vertebral metastases secondary to breast cancer were studied. The score was calculated for each patient and, according to Tokuhashi, the patients were divided into 3 groups with different life expectancy according to their total number of scoring points. In a second step, the grouping for prognosis was modified to get a better correlation of the predicted and definitive survival.



Applying the Tokuhashi score for the estimation of life expectancy of patients with breast cancer with vertebral metastases provided very reliable results. However, the original analysis by Tokuhashi showed a limited correlation between predicted and real survival for each prognostic group. Therefore, our patients were divided into modified prognostic groups regarding their total number of scoring points, leading to a higher significance of the predicted prognosis in each group (P < 0.0001), and a better correlation of the predicted and real survival.



The modified Tokuhashi score assists in decision making based on reliable estimators of life expectancy in patients with spinal metastases secondary to breast cancer.
Semantic Expansion of Hashtags for Enhanced Event Detection in Twitter

In this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and event detection performance in comparison to word-based vector generation methods. By analyzing the contexts of hashtags and their co-occurrence statistics with other words, we identify their paradigmatic relationships and similarities. We make use of this information while applying a lexico-semantic expansion on tweet contents before clustering the tweets based on their similarities. Our aim is to tolerate spelling errors and capture statements which actually refer to the same concepts. We evaluate our enhancement solution on a three-day dataset of tweets with Turkish content. In our evaluations, we observe clearer clusters, improvements in accuracy, and earlier event detection times.
Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors

There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.
Variation in perceptions of physical dominance and trustworthiness predicts individual differences in the effect of relationship context on women's preferences for masculine pitch in men's voices.

Several studies have found that women tend to demonstrate stronger preferences for masculine men as short-term partners than as long-term partners, though there is considerable variation among women in the magnitude of this effect. One possible source of this variation is individual differences in the extent to which women perceive masculine men to possess antisocial traits that are less costly in short-term relationships than in long-term relationships. Consistent with this proposal, here we show that the extent to which women report stronger preferences for men with low (i.e., masculine) voice pitch as short-term partners than as long-term partners is associated with the extent to which they attribute physical dominance and low trustworthiness to these masculine voices. Thus, our findings suggest that variation in the extent to which women attribute negative personality characteristics to masculine men predicts individual differences in the magnitude of the effect of relationship context on women's masculinity preferences, highlighting the importance of perceived personality attributions for individual differences in women's judgments of men's vocal attractiveness and, potentially, their mate preferences.
Sprache und Raum: Natürlichsprachlicher Zugang zu visuellen Daten

Der vorliegende Beitrag befaßt sich mit der Repräsentation und Verarbeitung räumlich-zeitlichen Wissens im Hinblick auf den natürlichsprachlichen Zugang zu visuellen Daten. Im Kontext des Projektes Vitra, das sich mit der Entwicklung wissensbasierter Systeme zur Integration von maschinellem Sehen und der Verarbeitung natürlicher Sprache auseinandersetzt, soll diese Problemstellung näher diskutiert werden. Zunächst wird am Beispiel des Bildfolgenanalysesystems Actions demonstriert, auf welche Weise Trajektorien bewegter Objekte aus Realweltbildfolgen extrahiert werden können. Daran anschließend wird die in Vitra zugrundegelegte Schnittstelle zur Bildanalyse, die geometrische Szenenbeschreibung, vorgestellt. Am Beispiel der lokalen orientierungsabhängigen Relationenrechts, links, vor und hinter wird dann gezeigt, wie sich eine rein geometrische Darstellung in eine propositionale Beschreibung der räumlichen Anordnung überführen läßt. Schließlich geht es um die Interpretation und Repräsentation von Ereignissen, d.h. räumlich-zeitlichen Konzepten. Im Gegensatz zu bisherigen Ansätzen soll dabei jedoch nicht von einer bereits vollständig analysierten Bildfolge ausgegangen werden; vielmehr sind Ereignisse so zu repräsentieren, daß sie simultan zu ihrem Auftreten in der Szene detektiert und natürlichsprachlich beschrieben werden können.
An extensible formal semantics for UML activity diagrams

This paper presents an operational semantics for UML activity diagrams. The purpose of this semantics is three-fold: to give a robust basis for verifying model correctness; to help validate model transformations; and to provide a well-formed basis for assessing whether a proposed extension/interpretation of the modeling language is consistent with the standard. The challenges of a general formal framework for UML models include the semi-formality of the semantics specification, the extensibility of the language, and (sometimes deliberate, sometimes accidental) under-specification of model behavior in the standard. Our approach is based on structural operational semantics, which can be extended according to domain-specific needs. The presented semantics has been implemented and tested.
From Cybernetics to Second-Order Cybernetics : A Comparative Analysis of Their Central Ideas

> Context • The enactive paradigm in the cognitive sciences is establishing itself as a strong and comprehensive alternative to the computationalist mainstream. However, its own particular historical roots have so far been largely ignored in the historical analyses of the cognitive sciences. > Problem • In order to properly assess the enactive paradigm’s theoretical foundations in terms of their validity, novelty and potential future directions of development, it is essential for us to know more about the history of ideas that has led to the current state of affairs. > Method • The meaning of the disappearance of the field of cybernetics and the rise of second-order cybernetics is analyzed by taking a closer look at the work of representative figures for each of the phases – Rosenblueth, Wiener and Bigelow for the early wave of cybernetics, Ashby for its culmination, and von Foerster for the development of the second-order approach.  It is argued that the disintegration of cybernetics eventually resulted in two distinct scientific traditions, one going from symbolic AI to modern cognitive science on the one hand, and the other leading from second-order cybernetics to the current enactive paradigm.  We can now understand that the extent to which the cognitive sciences have neglected their cybernetic parent is precisely the extent to which cybernetics had already carried the tendencies that would later find fuller expression in second-order cybernetics.
Conversational agents in healthcare: a systematic review

Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.



We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement.



The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies.



The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting.
Control of autonomous vehicles at an unsignalized intersection

Safety and optimal traffic flow are important priorities in intelligent transportation systems. This paper presents a collaborative method for collision avoidance between autonomous vehicles at an unsignalized intersection. The collision conditions are expressed in terms of the vehicles' speed ratios. The problem is then formulated and solved as an optimization problem with the safety constraints represented by the speed ratios. The cost function is defined in terms of the deviation from the current speeds. Both pairwise and three vehicle formulations are discussed. The proposed formulation allows to easily scale the solution to higher number of vehicles. The method is highly effective in resolving conflicts and achieving smooth flow. The results are formally proven and illustrated using simulation.
An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. However, most of the existing studies have a shortage of real data for analysis and focus on a very limited version of the problem by covering only a specific actor, healthcare service, or disease. The purpose of this study is to implement and evaluate a novel framework to detect fraudulent and abusive cases independently from the actors and commodities involved in the claims and an extensible structure to introduce new fraud and abuse types. Interactive machine learning that allows incorporating expert knowledge in an unsupervised setting is utilized to detect fraud and abusive cases in healthcare. In order to increase the accuracy of the framework, several well-known methods are utilized, such as the pairwise comparison method of analytic hierarchical processing (AHP) for weighting the actors and attributes, expectation maximization (EM) for clustering similar actors, two-stage data warehousing for proactive risk calculations, visualization tools for effective analyzing, and z-score and standardization in order to calculate the risks. The experts are involved in all phases of the study and produce six different abnormal behavior types using storyboards. The proposed framework is evaluated with real-life data for six different abnormal behavior types for prescriptions by covering all relevant actors and commodities. The Area Under the Curve (AUC) values are presented for each experiment. Moreover, a cost-saving model is also presented. The developed framework, i.e., the eFAD suite, is actorand commodity-independent, configurable (i.e., easily adaptable in the dynamic environment of fraud and abusive behaviors), and effectively handles the fragmented nature of abnormal behaviors. The proposed framework combines both proactive and retrospective analysis with an enhanced visualization tool that significantly reduces the time requirements for the fact-finding process after the eFAD detects risky claims. This system is utilized by a company to produce monthly reports that include abnormal behaviors to be evaluated by the insurance company.
RF Characterization and Modeling of 10 µm Fine-Pitch Cu-Pillar on a High Density Silicon Interposer

This work aims at providing a RLCG modeling ofthe 10 µm fine-pitch microbump type interconnects in the 100 MHz-40 GHz frequency band based on characterization approach. RF measurements are performed on two-port test structures within a short-loop with chip to wafer assembly using the fine pitch 10 µm Cu-pillar on a 10 Ohm.cm substrate resistivity silicon interposer. Accuracy is obtained thanks to a coplanar transmission line using 44 Cu-pillar transitions. To the author knowledge, it is the first time that RLCG modeling of fine-pitch Cu-pillar is extracted from experimental results. Another goal of this work is to get a better understanding of the main physical effects over a wide frequency range, especially concerning the key parameter of fine pitch Cu-pillar, i.e. the resistance. Finally, analysis based on the proposed RLCG modeling are performed to optimize over frequency the resistive interposer-to-chip link thanks to process modifications mitigating high frequency parasitic effects.
Singing Voice Separation and Pitch Extraction from Monaural Polyphonic Audio Music via DNN and Adaptive Pitch Tracking

With the explosive growth of audio music everywhere over the Internet, it is becoming more important to be able to classify or retrieve audio music based on their key components, such as vocal pitch for common popular music. This paper proposes a novel and effective two-stage approach to singing pitch extraction, which involves singing voice separation and pitch tracking for monaural polyphonic audio music. The first stage extracts singing voice from the songs by using deep neural networks in a supervised setting. Then the second stage estimates the pitch based on the extracted singing voice in a robust manner. Experimental results based on MIR-1K showed that the proposed approach outperforms a previous state-of-the-art approach in raw-pitch accuracy. Moreover, the proposed approach has been submitted to the singing voice separation and audio melody extraction tasks of Music Information Retrieval Evaluation eXchange (MIREX) in 2015. The results of the competition shows that the proposed approach is superior to other submitted algorithms, which demonstrates the feasibility of the method for further applications in music processing.
Large-scale agile transformation at Ericsson: a case study

Many large organizations are adopting agile software development as part of their continuous push towards higher flexibility and shorter lead times, yet few reports on large-scale agile transformations are available in the literature. In this paper we report how Ericsson introduced agile in a new R&D product development program developing a XaaS platform and a related set of services, while simultaneously scaling it up aggressively. The overarching goal for the R&D organization, distributed to five sites at two continents, was to achieve continuous feature delivery. This single case study is based on 45 semi-structured interviews during visits at four sites, and five observation sessions at three sites. We describe how the organization experimented with different set-ups for their tens of agile teams aiming for rapid end-to-end development: from component-based virtual teams to totally cross-functional, cross-component, cross-site teams. Moreover, we discuss the challenges the organization faced and how they mitigated them on their journey towards continuous and rapid software engineering. We present four lessons learned for large-scale agile transformations: 1) consider using an experimental approach to transformation, 2) consider implementing the transformation step-wise in complex large-scale settings, 3) team inter-changeability can be limited in a complex large-scale product — specialization might be needed, and 4) not using a common agile framework for the whole organization, in combination with insufficient common trainings and coaching may lead to a lack of common direction in the agile implementation. Further in-depth case studies on large-scale agile transformations, on customizing agile to large-scale settings, as well as on the use of scaling frameworks are needed.
Sensor Network Infrastructure for a Home Care Monitoring System

This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus.
Diplophthalmia versus cyclopia and synophthalmia

In historical and many scientific papers of the 20th century double formation of eyes has been described as cyclopia, which develops as a variably marked fusion of both eye anlagen into a single eye in median position. In contrast with mythology, no cases were viable, as cyclopia is always accompanied by other severe deformities, such as cerebral defects and proboscis. For the first time a double eye on one side - a unilateral diplophthalmia - in a viable child born in Austria is presented after a reviewal of fundamentals of general and experimental embryology, cyclopia and unilateral synophthalmia. Unlike cases of cyclopia with two fused eyes from two anlagen in one single orbit, we found a total of three eyes from two anlagen in two orbits; a normally developed eye in the right orbit and a double eye in the left with no proboscis. In addition porencephaly, a double canine tooth, and a lateral branchial cyst were also present on the left side. An examination of this unique case partially corroberated the findings of experimental embryology as to the normal formation of the lens and brought to light new findings concerning the formation of the vitreous body and the retina, because in the ‘extra’ left eye a hyaloid artery did not develop. There was only one optic nerve with a normal optic disc as a guiding structure for this artery in the ‘normal’ left eye.
Tunable CMOS Integrated Duplexer With Antenna Impedance Tracking and High Isolation in the Transmit and Receive Bands

A hybrid transformer-based integrated tunable duplexer is demonstrated. High isolation between the transmit and receive ports is achieved through electrical balance between the antenna and balance network impedances. A novel high-power-tolerant balance network, which can be tuned at both the transmit and receive frequencies, allows high isolation in both the transmit and receive bands even under realistic antenna impedance frequency dependence. To maintain high isolation despite antenna impedance variation, a feedback loop is employed to measure the transmitter leakage and correct the impedance of the balance network. An isolation > 50 dB in the transmit and receive bands with an antenna voltage standing-wave ratio within 2:1 was achieved. The duplexer, along with a cascaded direct-conversion receiver, achieves a noise figure of 5.3 dB, a conversion gain of 45 dB, and consumes 51 mW of power. The insertion loss in the transmit path was less than 3.8 dB. Implemented in a 65-nm CMOS process, the chip occupies an active area of 2.2 mm2.
Inpainting of Long Audio Segments With Similarity Graphs

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e., the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.