nils-herrmann/clean_scirepeval huggingface dataset:
Specifying the Neurobiological Basis of Human Attachment: Brain, Hormones, and Behavior in Synchronous and Intrusive Mothers

The mother–infant bond provides the foundation for the infant's future mental health and adaptation and depends on the provision of species-typical maternal behaviors that are supported by neuroendocrine and motivation-affective neural systems. Animal research has demonstrated that natural variations in patterns of maternal care chart discrete profiles of maternal brain–behavior relationships that uniquely shape the infant's lifetime capacities for stress regulation and social affiliation. Such patterns of maternal care are mediated by the neuropeptide Oxytocin and by stress- and reward-related neural systems. Human studies have similarly shown that maternal synchrony—the coordination of maternal behavior with infant signals—and intrusiveness—the excessive expression of maternal behavior—describe distinct and stable maternal styles that bear long-term consequences for infant well-being. To integrate brain, hormones, and behavior in the study of maternal–infant bonding, we examined the fMRI responses of synchronous vs intrusive mothers to dynamic, ecologically valid infant videos and their correlations with plasma Oxytocin. In all, 23 mothers were videotaped at home interacting with their infants and plasma OT assayed. Sessions were micro-coded for synchrony and intrusiveness. Mothers were scanned while observing several own and standard infant-related vignettes. Synchronous mothers showed greater activations in the left nucleus accumbens (NAcc) and intrusive mothers exhibited higher activations in the right amygdala. Functional connectivity analysis revealed that among synchronous mothers, left NAcc and right amygdala were functionally correlated with emotion modulation, theory-of-mind, and empathy networks. Among intrusive mothers, left NAcc and right amygdala were functionally correlated with pro-action areas. Sorting points into neighborhood (SPIN) analysis demonstrated that in the synchronous group, left NAcc and right amygdala activations showed clearer organization across time, whereas among intrusive mothers, activations of these nuclei exhibited greater cross-time disorganization. Correlations between Oxytocin with left NAcc and right amygdala activations were found only in the synchronous group. Well-adapted parenting appears to be underlay by reward-related motivational mechanisms, temporal organization, and affiliation hormones, whereas anxious parenting is likely mediated by stress-related mechanisms and greater neural disorganization. Assessing the integration of motivation and social networks into unified neural activity that reflects variations in patterns of parental care may prove useful for the study of optimal vs high-risk parenting.
The Development of Indonesian Online Game Addiction Questionnaire

Online game is an increasingly popular source of entertainment for all ages, with relatively prevalent negative consequences. Addiction is a problem that has received much attention. This research aims to develop a measure of online game addiction for Indonesian children and adolescents. The Indonesian Online Game Addiction Questionnaire draws from earlier theories and research on the internet and game addiction. Its construction is further enriched by including findings from qualitative interviews and field observation to ensure appropriate expression of the items. The measure consists of 7 items with a 5-point Likert Scale. It is validated by testing 1,477 Indonesian junior and senior high school students from several schools in Manado, Medan, Pontianak, and Yogyakarta. The validation evidence is shown by item-total correlation and criterion validity. The Indonesian Online Game Addiction Questionnaire has good item-total correlation (ranging from 0.29 to 0.55) and acceptable reliability (α = 0.73). It is also moderately correlated with the participant's longest time record to play online games (r = 0.39; p<0.01), average days per week in playing online games (ρ = 0.43; p<0.01), average hours per days in playing online games (ρ = 0.41; p<0.01), and monthly expenditure for online games (ρ = 0.30; p<0.01). Furthermore, we created a clinical cut-off estimate by combining criteria and population norm. The clinical cut-off estimate showed that the score of 14 to 21 may indicate mild online game addiction, and the score of 22 and above may indicate online game addiction. Overall, the result shows that Indonesian Online Game Addiction Questionnaire has sufficient psychometric property for research use, as well as limited clinical application.
IR-UWB Radar Demonstrator for Ultra-Fine Movement Detection and Vital-Sign Monitoring

In this paper an impulse-radio ultra-wideband (IR-UWB) hardware demonstrator is presented, which can be used as a radar sensor for highly precise object tracking and breath-rate sensing. The hardware consists of an impulse generator integrated circuit (IC) in the transmitter and a correlator IC with an integrating baseband circuit as correlation receiver. The radiated impulse is close to a fifth Gaussian derivative impulse with σ = 51 ps, efficiently using the Federal Communications Commission indoor mask. A detailed evaluation of the hardware is given. For the tracking, an impulse train is radiated by the transmitter, and the reflections of objects in front of the sensor are collected by the receiver. With the reflected signals, a continuous hardware correlation is computed by a sweeping impulse correlation. The correlation is applied to avoid sampling of the RF impulse with picosecond precision. To localize objects precisely in front of the sensor, three impulse tracking methods are compared: Tracking of the maximum impulse peak, tracking of the impulse slope, and a slope-to-slope tracking of the object's reflection and the signal of the static direct coupling between transmit and receive antenna; the slope-to-slope tracking showing the best performance. The precision of the sensor is shown by a measurement with a metal plate of 1-mm sinusoidal deviation, which is clearly resolved. Further measurements verify the use of the demonstrated principle as a breathing sensor. The breathing signals of male humans and a seven-week-old infant are presented, qualifying the IR-UWB radar principle as a useful tool for breath-rate determination.
An Improved S-Transform for Time-Frequency Analysis

The time-frequency representation (TFR) has been used as a powerful technique to identify, measure and process the time varying nature of signals. In the recent past S-transform gained a lot of interest in time-frequency localization due to its superiority over all the existing identical methods. It produces the progressive resolution of the wavelet transform maintaining a direct link to the Fourier transform. The S-transform has an advantage in that it provides multi resolution analysis while retaining the absolute phase of each frequency component of the signal. But it suffers from poor energy concentration in the time-frequency domain. It gives degradation in time resolution at lower frequency and poor frequency resolution at higher frequency. In this paper we propose a modified Gaussian window which scales with the frequency in a efficient manner to provide improved energy concentration of the S-transform. The potentiality of the proposed method is analyzed using a variety of test signals. The results of the study reveal that the proposed scheme can resolve the time-frequency localization in a better way than the standard S-transform.
Medical Document Classification Based on MeSH

One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics from the Medical Subject Headings (MeSH). I collected a corpus of 50 full-text journal articles (N=50) from MEDLINE, which were already indexed by experts based on MeSH. Using natural language processing (NLP), my algorithm classifies the collected articles under MeSH subject headings. I evaluated the algorithm's outcome by measuring its precision and recall of resulting subject headings from the algorithm, comparing results to the actual documents' subject headings. The algorithm classified the articles correctly under 45% to 60% of the actual subject headings and got 40% to 53% of the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.
Configurable NOR gate arrays from Belousov-Zhabotinsky micro-droplets.

We investigate the Belousov-Zhabotinsky (BZ) reaction in an attempt to establish a basis for computation using chemical oscillators coupled via inhibition. The system consists of BZ droplets suspended in oil. Interdrop coupling is governed by the non-polar communicator of inhibition, Br2. We consider a linear arrangement of three droplets to be a NOR gate, where the center droplet is the output and the other two are inputs. Oxidation spikes in the inputs, which we define to be TRUE, cause a delay in the next spike of the output, which we read to be FALSE. Conversely, when the inputs do not spike (FALSE) there is no delay in the output (TRUE), thus producing the behavior of a NOR gate. We are able to reliably produce NOR gates with this behavior in microfluidic experiment.
Style as a Choice of Blending Principles

This paper proposes a new approach to style, arising from our work on computational media using structural blending, which enriches the conceptual blending of cognitive linguistics with structure building operations in order to encompass syntax and narrative as well as metaphor. We have implemented both conceptual and structural blending, and conducted initial experiments with poetry, although the approach generalizes to other media. The central idea is to analyze style in terms of principles for blending, based on our £nding that very different principles from those of common sense blending are needed for some creative works.
High-frequency oscillations and the neurobiology of schizophrenia

Neural oscillations at low- and high-frequency ranges are a fundamental feature of large-scale networks. Recent evidence has indicated that schizophrenia is associated with abnormal amplitude and synchrony of oscillatory activity, in particular, at high (beta/gamma) frequencies. These abnormalities are observed during task-related and spontaneous neuronal activity which may be important for understanding the pathophysiology of the syndrome. In this paper, we shall review the current evidence for impaired beta/gamma-band oscillations and their involvement in cognitive functions and certain symptoms of the disorder. In the first part, we will provide an update on neural oscillations during normal brain functions and discuss underlying mechanisms. This will be followed by a review of studies that have examined high-frequency oscillatory activity in schizophrenia and discuss evidence that relates abnormalities of oscillatory activity to disturbed excitatory/inhibitory (E/I) balance. Finally, we shall identify critical issues for future research in this area.
Transferring responsibility and accountability in maternity care: clinicians defining their boundaries of practice in relation to clinical handover

This exploratory study reports on maternity clinicians' perceptions of transfer of their responsibility and accountability for patients in relation to clinical handover with particular focus transfers of care in birth suite.



A qualitative study of semistructured interviews and focus groups of maternity clinicians was undertaken in 2007. De-indentified data were transcribed and coded using the constant comparative method. Multiple themes emerged but only those related to responsibility and accountability are reported in this paper.



One tertiary Australian maternity hospital.



Maternity care midwives, nurses (neonatal, mental health, bed managers) and doctors (obstetric, neontatology, anaesthetics, internal medicine, psychiatry).



Primary outcome measures were the perceptions of clinicians of maternity clinical handover.



The majority of participants did not automatically connect maternity handover with the transfer of responsibility and accountability. Once introduced to this concept, they agreed that it was one of the roles of clinical handover. They spoke of complete transfer, shared and ongoing responsibility and accountability. When clinicians had direct involvement or extensive clinical knowledge of the patient, blurring of transition of responsibility and accountability sometimes occurred. A lack of 'ownership' of a patient and their problems were seen to result in confusion about who was to address the clinical issues of the patient. Personal choice of ongoing responsibility and accountability past the handover communication were described. This enabled the off-going person to rectify an inadequate handover or assist in an emergency when duty clinicians were unavailable.



There is a clear lack of consensus about the transition of responsibility and accountability-this should be explicit at the handover. It is important that on each shift and new workplace environment clinicians agree upon primary role definitions, responsibilities and accountabilities for patients. To provide system resilience, secondary responsibilities may be allocated as required.
Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification.

We propose a novel computer-aided detection (CAD) framework of breast masses in mammography. To increase detection sensitivity for various types of mammographic masses, we propose the combined use of different detection algorithms. In particular, we develop a region-of-interest combination mechanism that integrates detection information gained from unsupervised and supervised detection algorithms. Also, to significantly reduce the number of false-positive (FP) detections, the new ensemble classification algorithm is developed. Extensive experiments have been conducted on a benchmark mammogram database. Results show that our combined detection approach can considerably improve the detection sensitivity with a small loss of FP rate, compared to representative detection algorithms previously developed for mammographic CAD systems. The proposed ensemble classification solution also has a dramatic impact on the reduction of FP detections; as much as 70% (from 15 to 4.5 per image) at only cost of 4.6% sensitivity loss (from 90.0% to 85.4%). Moreover, our proposed CAD method performs as well or better (70.7% and 80.0% per 1.5 and 3.5 FPs per image respectively) than the results of mammography CAD algorithms previously reported in the literature.
On building secure SCADA systems using security patterns

Critical infrastructure systems are important systems that support our daily lives. Protecting these systems is attracting attention from the research community. The key component of a critical infrastructure system is the process control system, also known as the supervisory, control, and data acquisition (SCADA) system. On the other hand, security patterns are a well established concept that are used to analyze, construct and evaluate secure systems. This paper aims to propose methods to build a secure SCADA system using security patterns. In this paper, we study the architecture of a general SCADA system and analyze the potential attacks against it. Also, we use security patterns as a tool to design a secure SCADA system that is resistant to these attacks. We believe our research work lays a new direction for future research on secure SCADA systems.
Security Risks in Deep Learning Implementations

Advances in deep learning algorithms overshadow their security risk in software implementations. This paper discloses a set of vulnerabilities in popular deep learning frameworks including Caffe, TensorFlow, and Torch. Contrary to the small code size of deep learning models, these deep learning frameworks are complex, and they heavily depend on numerous open source packages. This paper considers the risks caused by these vulnerabilities by studying their impact on common deep learning applications such as voice recognition and image classification. By exploiting these framework implementations, attackers can launch denial-of-service attacks that crash or hang a deep learning application, or control-flow hijacking attacks that lead to either system compromise or recognition evasions. The goal of this paper is to draw attention to software implementations and call for community collaborative effort to improve security of deep learning frameworks.
Flow-Based Propagators for the SEQUENCE and Related Global Constraints

We propose new filtering algorithms for the S EQUENCE constraint and some extensions of the S EQUENCEconstraint based on network flows. We enforce domain consistency on the S EQUENCEconstraint inO(n) time down a branch of the search tree. This improves upon the best exist ing domain consistency algorithm by a factor of O(log n). The flows used in these algorithms are derived from a linear program. Some of them differ from th e flows used to propagate global constraints like G CC since the domains of the variables are encoded as costs on the edges rather than capacities. Such flows are efficient for maintaining bounds consistency over large domains and may b e useful for other global constraints.
Query-Driven OnThe-Fly Knowledge Base Construction Dat

Today’s openly available knowledge bases, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts about the world’s entities. However, even the largest among these (i) are still limited in up-to-date coverage of what happens in the real world, and (ii) miss out on many relevant predicates that precisely capture the wide variety of relationships among entities. To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner. Our system, called QKBfly, supports analysts and journalists as well as question answering on emerging topics, by dynamically acquiring relevant facts as timely and comprehensively as possible. QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight and integrated manner. In contrast to Open IE, our output is canonicalized. In contrast to traditional IE, we capture more predicates, including ternary and higher-arity ones. Our experiments demonstrate that QKBfly can build high-quality, on-the-fly knowledge bases that can readily be deployed, e.g., for the task of ad-hoc question answering. PVLDB Reference Format: D. B. Nguyen, A. Abujabal, N. K. Tran, M. Theobald, and G. Weikum. Query-Driven On-The-Fly Knowledge Base Construction. PVLDB, 11 (1): 66-7 , 2017.
Timing Jitter in Symmetric Load Ring Oscillators and the Estimation of Aperture Uncertainty in A-D Converters

I am submitting herewith a thesis written by Venkatesh Srinivasan entitled “Timing Jitter in Symmetric Load Ring Oscillators and the Estimation of Aperture Uncertainty in A-D Converters.” I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Electrical Engineering.
Tumor Detection and Classification using Decision Tree in Brain MRI

The main focus of image mining is concerned with the classification of brain tumor in the CT scan brain images. The major steps involved in the system are: pre-processing, feature extraction, association rule mining and classification. Here, we present some experiments for tumor detection in MRI images. The pre-processing step has been done using the median filtering process and features have been extracted using texture feature extraction technique. The extracted features from the CT scan images are used to mine the association rules. The proposed method is used to classify the medical images for diagnosis. In this system we are going to use Decision Tree classification algorithm. The proposed method improves the efficiency than the traditional image mining methods. Here, results which we get are compared with Naive Bayesian classification algorithm.
INFLUENCE OF EXTRINSIC AND INTRINSIC MOTIVATION ON EMPLOYEES ’ PERFORMANCE

The study investigated the influence of extrinsic and intrinsic motivation on employees performance. Subjects for the study consisted of one hundred workers of Flour Mills of Nigeria PLC, Lagos. Data for the study were gathered through the administration of a self-designed questionnaire. The data collected were subjected to appropriate statistical analysis using Pearson Product Moment Correlation Coefficient, and all the findings were tested at 0.05 level of significance. The result obtained from the analysis showed that there existed relationship between extrinsic motivation and the performance of employees, while no relationship existed between intrinsic motivation and employees performance. On the basis of these findings, implications of the findings for future study were stated.
Psychometric Properties of the Family Support Scale Adapted for African American Women with Type 2 Diabetes Mellitus.

The purpose of our study was two-fold: 1) adapt and test a social support measure specific to the experiences of African American women with type 2 diabetes mellitus (T2DM); 2) examine its relationship to psychosocial measures.



200 rural African American women with uncontrolled T2DM participating in a randomized controlled trial completed surveys at baseline on their social support, empowerment, self-care, self-efficacy, depression and diabetes distress. Exploratory factor analysis and correlation analysis were conducted to test the psychometric properties of the Dunst Family Support Scale adapted for AA women with T2DM (FSS-AA T2DM) and its relationship with other psychosocial measures.



The 16 items of the FSS-AA T2DM loaded onto three distinct factors: parent and spouse/partner support, community and medical support, and extended family and friends support. Reliability for the entire scale was good (Cronbach's α = .90) and was acceptable to high across these three factors (Cronbach's α of .86, .83, and .83 respectively). All three factors were significantly correlated with self-reported empowerment, self-care, self-efficacy, depression and diabetes distress, although the pattern was different for each factor. FSS-AA-T2DM showed good concurrent validity when compared with similar items on the Diabetes Distress Scale.



The FSS-AA T2DM, a 16-item scale measuring social support among rural African American women with T2DM, is internally consistent and reliable. Findings support the utility of this screening tool in this population, although additional testing is needed with other groups in additional settings.
Classification Techniques for Speech Recognition: A Review

Speech Processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speech recognition is the process of automatically recognizing the spoken words of person based on information content in speech signal. This paper introduces a brief survey on Automatic Speech Recognition and discusses the various classification techniques that have been accomplished in this wide area of speech processing. The objective of this review paper is to summarize some of the well-known methods that are widely used in several stages of speech recognition system. Feature Extraction, Acoustic phonetic Approach, Pattern Recognition,Artificial intelligence Approach, Speech Recognition.