nils-herrmann/clean_scirepeval huggingface dataset:
A picture's worth: partner photographs reduce experimentally induced pain.

Social support is associated with reduced pain experience across several domains (Cogan & Spinnato, 1988; Kulik & Mahler, 1989; Zaza & Baine, 2002); intriguingly, a handful of experimental studies suggest that this connection may reflect a causal relationship. Participants who received interactive support during a cold pressor task reported less pain than participants who completed the task alone or engaged in nonsupportive interactions (Brown, Sheffield, Leary, & Robinson, 2003; Jackson, Iezzi, Chen, Ebnet, & Eglitis, 2005). Moreover, the mere presence of another supportive individual (vs. being alone) reduced pain ratings in a cold pressor task (Brown et al., 2003; but see McClelland & McCubbin, 2008) and reduced pain ratings among fibromyalgia patients following stimulation to a painful body site (Montoya, Larbig, Braun, Preissl, & Birbaumer, 2004). Could the same pain-attenuating effects of social support be observed by merely activating the mental representation of a supportive other? Previous work has shown that activating mental representations of important others can produce effects similar to those created by the actual presence of these individuals (Fitzsimons & Bargh, 2003; Mikulincer & Shaver, 2001). Building on this research, the current study examined whether simply viewing a photograph of one’s romantic partner could reduce physical-pain experience. We examined how this condition compared with one that is more consistent with previous conceptualizations of social support—one in which the participant held her partner’s hand.
Comparative analysis of Role Base and Attribute Base Access Control Model in Semantic Web

To control the unauthorized access to the resources and services is an emerging security issue in Semantic Web (SW). There are various existing access control models such as Role base, Attribute base, Credential base, Concept level access control models. They all have some strengths and weaknesses with them. In this paper we first take an overview of history of access control models and the need of access control models in semantic web. This paper has a discussion of strengths and weaknesses of the RBAC and ABAC. Than we have a comparative analysis of RBAC and ABAC with some points of issue
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

Multiresolution representations are very effective for analyzing the information content of images. We study the properties of the operator which approximates a signal at a given resolution. We show that the difference of information between the approximation of a signal at the resolutions 2’ + ’ and 2jcan be extracted by decomposing this signal on a wavelet orthonormal basis of L*(R”). In LL(R ), a wavelet orthonormal basis is a family of functions ( @ w (2’~ n)) ,,,“jEZt, which is built by dilating and translating a unique function t+r (xl. This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror lilters. For images, the wavelet representation differentiates several spatial orientations. We study the application of this representation to data compression in image coding, texture discrimination and fractal analysis.
Parasitic-Aware Optimization and Retargeting of Analog Layouts: A Symbolic-Template Approach

Layout parasitics can significantly affect the performance of analog integrated circuits (ICs). In this paper, a systematic method of optimizing an existing analog layout considering parasitics is presented for technology migration and performance retargeting. This method represents the locations of layout rectangle edges as variables and extracts circuit and layout integrity such as device symmetry, matching, and design rules as constraints. To ensure the desired circuit performance, bounds of layout parasitics are determined first. These bounds are used to constrain the layout geometries while retargeting existing high-quality layouts across technologies and specification sets. The problem is then solved by a graph-based algorithm combined with nonlinear optimization. The proposed method has been implemented in a parasitic-aware automatic layout optimization and retargeting tool (intellectual property reuse-based analog IC layout). Its efficiency and effectiveness are demonstrated by successfully retargeting operational amplifiers within 1 min of CPU time.
Social collaborative filtering for cold-start recommendations

We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.
What's in it for Me? Conceptualizing the Perceived Value of Knowledge Sharing

Social theories suggest that individuals expect something in return when they decide to share knowledge. These expectations of benefits vary from tangible incentives provided by organizations to positive feelings aroused by altruistic behavior. The theory of consumption values (TCV) allows a specification of distinct types of benefits (functional, social, emotional, epistemic and conditional) which individuals consider before they decide to share knowledge. Furthermore, the perceived costs of sharing can reduce the perceived value of knowledge sharing. These costs are non-monetary sacrifices (effort and risks) reducing the perceived value of sharing. In this work we develop the perceived value of knowledge sharing as a multidimensional construct, grounded in assumptions of social exchange theory, consumer research and knowledge sharing literature. This conceptualization is intended to serve as a basis for the operationalization of perceived knowledge value in a future study on knowledge sharing intentions.
Applying the Waek Learning Framework to Understand and Improve C4.5

There has long been a chasm between theoretical mod els of machine learning and practical machine learn ing algorithms For instance empirically successful algorithms such as C and backpropagation have not met the criteria of the PAC model and its vari ants Conversely the algorithms suggested by com putational learning theory are usually too limited in various ways to nd wide application The theoreti cal status of decision tree learning algorithms is a case in point while it has been proven that C and all reasonable variants of it fails to meet the PAC model criteria other recently proposed decision tree algo rithms that do have non trivial performance guaran tees unfortunately require membership queries Two recent developments have narrowed this gap between theory and practice not for the PAC model but for the related model known as weak learning or boosting First an algorithm called Adaboost was proposed that meets the formal criteria of the boosting model and is also competitive in practice Second the basic algorithms underlying the popular C and CART programs have also very recently been shown to meet the formal criteria of the boosting model Thus it seems plausible that the weak learning frame work may provide a setting for interaction between formal analysis and machine learning practice that is lacking in other theoretical models Our aim in this paper is to push this interaction further in light of these recent developments In par ticular we perform experiments suggested by the for mal results for Adaboost and C within the weak learning framework We concentrate on two particu larly intriguing issues First the theoretical boosting results for top down decision tree algorithms such as C suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual information gain We con rm this suggestion experimentally Second a super cial interpretation of the theo retical results suggests that Adaboost should vastly outperform C This is not the case in practice and we argue through experimental results that the theory must be understood in terms of a measure of a boosting algorithm s behavior called its advantage sequence We compare the advantage sequences for C and Adaboost in a number of experiments We nd that these sequences have qualitatively dif ferent behavior that explains in large part the dis crepancies between empirical performance and the theoretical results Brie y we nd that although C and Adaboost are both boosting algorithms Adaboost creates successively harder ltered dis tributions while C creates successively easier ones in a sense that will be made precise
Three-Dimensional Object Recognition from Single Two-Dimensional Images

A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision. This paper has been published in Artificial Intelligence, 31, 3 (March 1987), pp. 355–395.
Awareness 2.0: staying aware of projects, developers and tasks using dashboards and feeds

Software development teams need to maintain awareness of various different aspects ranging from overall project status and process bottlenecks to current tasks and incoming artifacts. Currently, there is a lack of theoretical foundations to guide tool selection and tool design to best support awareness tasks. In this paper, we explore how the combination of highly configurable project, team and contributor dashboards along with individual event feeds is used to accomplish extensive awareness. Our results stem from an empirical study of several large development teams, with a detailed study of a team of 150 developers and additional data from another four project teams. We present how dashboards become pivotal to task prioritization in critical project phases and how they stir competition while feeds are used for short term planning. Our findings indicate that the distinction between high-level and low-level awareness is often unclear and that integrated tooling could improve development practices.
A multi-rater framework for studying personality: The trait-reputation-identity model.

Personality and social psychology have historically been divided between personality researchers who study the impact of traits and social-cognitive researchers who study errors in trait judgments. However, a broader view of personality incorporates not only individual differences in underlying traits but also individual differences in the distinct ways a person's personality is construed by oneself and by others. Such unique insights are likely to appear in the idiosyncratic personality judgments that raters make and are likely to have etiologies and causal force independent of trait perceptions shared across raters. Drawing on the logic of the Johari window (Luft & Ingham, 1955), the Self-Other Knowledge Asymmetry Model (Vazire, 2010), and Socioanalytic Theory (Hogan, 1996; Hogan & Blickle, 2013), we present a new model that separates personality variance into consensus about underlying traits (Trait), unique self-perceptions (Identity), and impressions conveyed to others that are distinct from self-perceptions (Reputation). We provide three demonstrations of how this Trait-Reputation-Identity (TRI) Model can be used to understand (a) consensus and discrepancies across rating sources, (b) personality's links with self-evaluation and self-presentation, and (c) gender differences in traits. We conclude by discussing how researchers can use the TRI Model to achieve a more sophisticated view of personality's impact on life outcomes, developmental trajectories, genetic origins, person-situation interactions, and stereotyped judgments.
DNW-Controllable triggered voltage of the integrated diode triggered SCR (IDT-SCR) ESD protection device

A novel Integrated Diode Triggered SCR (IDT-SCR), with low capacitance (<200fF), and a controllable VT1 is proposed for 1.8V application. The triggering is determined by the DNW biasing plus one integrated diode. The fail-current density is 16 times higher, and leakage 20 times lower, compared to a traditional capacitive-triggered bigFET.
On Networking of Internet of Things: Explorations and Challenges

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.
Wideband Circularly Polarized Patch Antennas on Reactive Impedance Substrates

A reduced-size wideband single-feed circularly polarized patch antenna is introduced for telemetry applications in S-band around 2300 MHz. The proposed structure consists of a slot-loaded patch antenna printed over an optimized metamaterial-inspired reactive impedance substrate (RIS). We demonstrate, step by step, the main role of each antenna element by comparing numerically and experimentally the performance of various antenna configurations: antenna over a single- or dual-layer substrate, standard patch or slot-loaded patch, antenna with or without RIS. The final optimized structure exhibits an axial-ratio bandwidth of about 15% and an impedance bandwidth better than 11%, which is much wider than the conventional printed antenna on the same materials.
Ratio estimators in simple random sampling

This study proposes ratio estimators by adapting the estimators type ofRay and Singh  to traditional and the other ratio-type estimators in simple random sampling in literature. Theoretically, mean square error (MSE) equations of all proposed ratio estimators are obtained and compared with each other. By these comparisons the conditions, which make each proposed estimator more efficient than the others, are found. The theoretical results are supported by a numerical illustration.
Multi-scale Improves Boundary Detection in Natural Images

In this work we empirically study the multi-scale boundary detection problem in natural images. We utilize local boundary cues including contra st, localization and relative contrast, and train a classifier to integrate them acro ss scales. Our approach successfully combines strengths from both larg e-scale detection (robust but poor localization) and small-scale detection (detail-pre serving but sensitive to clutter). We carry out quantitative evaluations on a variety of boundary and object datasets with human-marked groundtruth. We sho w that multi-scale boundary detection offers large improvements, ranging fro m 20% to 50%, over single-scale approaches. This is the first time that multi-scale is demonstrated to improve boundary detection on large datasets of natural images.
Accidental death due to complete autoerotic asphyxia associated with transvestic fetishism and anal self-stimulation - case report.

A case is reported of a 36-year-old male, found dead in his locked room, lying on a bed, dressed in his mother's clothes, with a plastic bag over his head, hands tied and with a barrel wooden cork in his rectum. Two pornographic magazines were found on a chair near the bed, so that the deceased could see them well. Asphyxia was controlled with a complex apparatus which consisted of two elastic luggage rack straps, the first surrounding his waist, perineum, and buttocks, and the second the back of his body, and neck. According to the psychological autopsy based on a structured interview (SCID-I, SCID-II) with his father, the deceased was single, unemployed and with a part college education. He had grown up in a poor family with a reserved father and dominant mother, and was indicative of fulfilling DSM-IV diagnostic criteria for alcohol dependence, paraphilia involving hypoxyphilia with transvestic fetishism and anal masturbation and a borderline personality disorder. There was no evidence of previous psychiatric treatment. The Circumstances subscale of Beck's Suicidal Intent Scale (SIS-CS) pointed at the lack of final acts (thoughts or plans) in anticipation of death, and absence of a suicide note or overt communication of suicidal intent before death. Integration of the crime scene data with those of the forensic medicine and psychological autopsy enabled identification of the event as an accidental death, caused by neck strangulation, suffocation by a plastic bag, and vagal stimulation due to a foreign body in the rectum.
Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations

We would like to learn a representation of the data that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a set of face images grouped by identity. We wish to anchor the semantics of the grouping into a disentangled representation that we can exploit. However, existing deep probabilistic models often assume that the samples are independent and identically distributed, thereby disregard the grouping information. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of grouped data. The ML-VAE separates the latent representation into semantically relevant parts by working both at the group level and the observation level, while retaining efficient test-time inference. We experimentally show that our model (i) learns a semantically meaningful disentanglement, (ii) enables control over the latent representation, and (iii) generalises to unseen groups.
Advances in High-Voltage Modulators for Applications in Pulsed Power and Plasma-Based Ion Implantation

Modern pulsed power technology has its roots in the late 1950s and early 1960s, and it was driven overwhelmingly by applications in national defense carried out by several countries, especially the U.S., U.K., Russia, and China. The following decades, particularly the early 1990s, witnessed an increased interest in compact systems with pulse repetition rate that could be used in nondefense applications such as treatment of material surfaces by plasma and beam interactions, treatment of pollutants, food sterilization, medical applications, etc. This spawned a new generation of pulsed power components (solid-state switches) that led to completely solid-state modulators. This paper describes how the pulsed power technology used originally in beam sources and cathodic arcs has converged to produce power sources for plasma-based ion implantation (PBII) and related technologies. The present state of the art is reviewed, and prospects for future advances are described, especially for PBII.
Imaging Breathing Rate in the CO2Absorption Band

Following up on our previous work, we have developed one more non-contact method to measure human breathing rate. We have retrofitted our mid-wave infra-red (MWIR) imaging system with a narrow band-pass filter in the CO2 absorption band (4.3 mum). This improves the contrast between the foreground (i.e., expired air) and background (e.g., wall). Based on the radiation information within the breath flow region, we get the mean dynamic thermal signal. This signal is quasi-periodic due to the interleaving of high and low intensities corresponding to expirations and inspirations respectively. We sample the signal at a constant rate and then determine the breathing frequency through Fourier analysis. We have performed experiments on 9 subjects at distances ranging from 6 - 8 ft. We compared the breathing rate computed by our novel method with ground-truth measurements obtained via a traditional contact device (PowerLab/4SP from ADInstruments with an abdominal transducer). The results show high correlation between the two modalities. For the first time, we report a Fourier based breathing rate computation method on a MWIR signal in the CO2 absorption band. The method opens the way for desktop, unobtrusive monitoring of an important vital sign, that is, breathing rate. It may find widespread applications in preventive medicine as well as sustained physiological monitoring of subjects suffering from chronic ailments
Shedding Light on the Dark Triad of Personality : Narcissism , Machiavellianism , and Psychopathy

Of the offensive yet non-pathological personalities in the literature, three are especially prominent: Machiavellianism, subclinical narcissism, and subclinical psychopathy. We evaluated the recent contention that, in normal samples, this Dark Triad of constructs are one and the same. In a sample of 245 students, we measured the three constructs with standard measures and examined a variety of laboratory and self-report correlates. The measures were moderately inter-correlated, but certainly were not equivalent. Their only common Big Five correlate was disagreeableness. Subclinical psychopaths were distinguished by low neuroticism; Machiavellians, and psychopaths were low in conscientiousness; narcissism showed small positive associations with cognitive ability. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. We conclude that the Dark Triad of personalities, as currently measured, are overlapping but distinct constructs.