nils-herrmann/clean_scirepeval huggingface dataset:
WordSeer: a knowledge synthesis environment for textual data

We describe WordSeer, a tool whose goal is to help scholars and analysts discover patterns and formulate and test hypotheses about the contents of text collections, midway between what humanities scholars call a traditional "close read'' and the new "distant read" or "culturomics" approach. To this end, WordSeer allows for highly flexible "slicing and dicing" (hence "sliding") across a text collection. The tool allows users to view text from different angles by selecting subsets of data, viewing those as visualizations, moving laterally to view other subsets of data, slicing into another view, expanding the viewed data by relaxing constraints, and so on. We illustrate the text sliding capabilities of the tool with examples from a case study in the field of humanities and social sciences -- an analysis of how U.S. perceptions of China and Japan changed over the last 30 years.
Detecting psychopathy from thin slices of behavior.

This study is the first to demonstrate that features of psychopathy can be reliably and validly detected by lay raters from "thin slices" (i.e., small samples) of behavior. Brief excerpts (5 s, 10 s, and 20 s) from interviews with 96 maximum-security inmates were presented in video or audio form or in both modalities combined. Forty raters used these excerpts to complete assessments of overall psychopathy and its Factor 1 and Factor 2 components, various personality disorders, violence proneness, and attractiveness. Thin-slice ratings of psychopathy correlated moderately and significantly with psychopathy criterion measures, especially those related to interpersonal features of psychopathy, particularly in the 5- and 10-s excerpt conditions and in the video and combined channel conditions. These findings demonstrate that first impressions of psychopathy and related constructs, particularly those pertaining to interpersonal functioning, can be reasonably reliable and valid. They also raise intriguing questions regarding how individuals form first impressions and about the extent to which first impressions may influence the assessment of personality disorders.
Principles of neural ensemble physiology underlying the operation of brain–machine interfaces

Research on brain–machine interfaces has been ongoing for at least a decade. During this period, simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons have been used for direct, real-time control of various artificial devices. Brain–machine interfaces have also added greatly to our knowledge of the fundamental physiological principles governing the operation of large neural ensembles. Further understanding of these principles is likely to have a key role in the future development of neuroprosthetics for restoring mobility in severely paralysed patients.
Relationship Quality as a Predictor of B 2 B Customer loyalty

This study aims to provide a picture of how relationship quality can influence customer loyalty or loyalty in the business-to-business context. Building on prior research, we propose relationship quality as a higher construct comprising trust, commitment, satisfaction and service quality. These dimensions of relationship quality can reasonably explain the influence of relationship quality on customer loyalty. This study follows the composite loyalty approach providing both behavioural aspects (purchase intentions) and attitudinal loyalty in order to fully explain the concept of customer loyalty. A literature search is undertaken in the areas of customer loyalty, relationship quality, perceived service quality, trust, commitment and satisfaction. This study then seeks to address the following research issues: Does relationship quality influence both aspects of customer loyalty? Which relationship quality dimensions influence each of the components of customer loyalty? This study was conducted in a business-to-business setting of the courier and freight delivery service industry in Australia. The survey was targeted to Australian Small to Medium Enterprises (SMEs). Two methods were chosen for data collection: mail survey and online survey. The total number of usable respondents who completed both survey was 306. In this study, a two step approach (Anderson and Gerbing 1988) was selected for measurement model and structural model. The results also show that all measurement models of relationship dimensions achieved a satisfactory level of fit to the data. The hypothesized relationships were estimated using structural equation modeling. The overall goodness of fit statistics shows that the structural model fits the data well. As the results show, to maintain customer loyalty to the supplier, a supplier may enhance all four aspects of relationship quality which are trust, commitment, satisfaction and service quality. Specifically, in order to enhance customer’s trust, a supplier should promote the customer’s trust in the supplier. In efforts to emphasize commitment, a supplier should focus on building affective aspects of commitment rather than calculative aspects. Satisfaction appears to be a crucial factor in maintaining purchase intentions whereas service quality will strongly enhance both purchase intentions and attitudinal loyalty.
Negative comparisons about one's appearance mediate the relationship between Facebook usage and body image concerns.

Use of social media, such as Facebook, is pervasive among young women. Body dissatisfaction is also highly prevalent in this demographic. The present study examined the relationship between Facebook usage and body image concerns among female university students (N=227), and tested whether appearance comparisons on Facebook in general, or comparisons to specific female target groups (family members, close friends, distant peers [women one may know but do not regularly socialize with], celebrities) mediated this relationship. Results showed a positive relationship between Facebook usage and body image concerns, which was mediated by appearance comparisons in general, frequency of comparisons to close friends and distant peers, and by upward comparisons (judging one's own appearance to be worse) to distant peers and celebrities. Thus, young women who spend more time on Facebook may feel more concerned about their body because they compare their appearance to others (especially to peers) on Facebook.
A 3T Gain Cell Embedded DRAM Utilizing Preferential Boosting for High Density and Low Power On-Die Caches

Circuit techniques for enabling a sub-0.9 V logic-compatible embedded DRAM (eDRAM) are presented. A boosted 3T gain cell utilizes Read Word-line (RWL) preferential boosting to increase read margin and improve data retention time. Read speed is enhanced with a hybrid current/voltage sense amplifier that allows the Read Bit-line (RBL) to remain close to VDD. A regulated bit-line write scheme for driving the Write Bit-line (WBL) is equipped with a steady-state storage node voltage monitor to overcome the data `1' write disturbance problem of the PMOS gain cell without introducing another boosted supply for the Write Word-line (WWL) over-drive. An adaptive and die-to-die adjustable read reference bias generator is proposed to cope with PVT variations. Monte Carlo simulations compare the 6-sigma read and write performance of proposed eDRAM against conventional designs. Measurement results from a 64 kb eDRAM test chip implemented in a 65 nm low-leakage CMOS process show a 1.25 ms data retention time with a 2 ns random cycle time at 0.9 V, 85°C, and a 91.3 μW per Mb static power dissipation at 1.0 V, 85°C.
A Hybrid Algorithm for Tracking of GMPP Based on P&O and PSO With Reduced Power Oscillation in String Inverters

Maximum power point tracking (MPPT) is essential for photovoltaic (PV) string inverter systems. Partially shaded PV strings with bypass diodes exhibit multiple peaks in the power-voltage characteristic. Under partial shading conditions, conventional algorithms get trapped in a local maximum power point, and fail to track the global MPP (GMPP). To overcome this problem, global search algorithms such as particle swarm optimization (PSO) have been proposed. However, these can cause excessive oscillations in the output power before converging onto the GMPP. In this paper, a new GMPPT technique combining the PSO and Perturb and Observe (P&O) algorithms has been presented. The P&O technique is used to track the MPP under uniform irradiance, and the same is used to detect the occurrence of partial shading. Only on the onset of partial shading conditions, PSO is employed. Furthermore, the search space of PSO is reduced by using a window-based search in order to reduce the power oscillations and convergence time. The effectiveness of the proposed algorithm in tracking the GMPP, both under uniform and nonuniform irradiance conditions, is demonstrated experimentally.
Towards a Technique for Extracting Microservices from Monolithic Enterprise Systems

The idea behind microservices architecture is to develop a single large, complex, application as a suite of small, cohesive, independent services. On the other way, monolithic systems get larger over the time, deviating from the intended architecture, and becoming tough, risky, and expensive to evolve. This paper describes a technique to identify and define microservices on a monolithic enterprise system. As the major contribution, our evaluation demonstrate that our approach could identify good candidates to become microservices on a 750 KLOC banking system, which reduced the size of the original system and took the benefits of microservices architecture, such as services being developed and deployed independently, and technology independence.
Single switch reconfigurable bandpass filter with variable bandwidth using a dual-mode triangular patch resonator

A reconfigurable bandpass filter is demonstrated using a dual-mode microstrip triangular patch resonator. The proposed circuit uses a single switch to control its fractional bandwidth while keeping a fixed center frequency of 10 GHz. The switching mechanism is accomplished by a p-i-n diode that connects and isolates a tuning stub from the rest of the circuit. The on and off state of the diode effectively controls the resonance frequencies of the two poles, produced by the dual-mode behavior of the resonator. The filter achieves a measured 1.9:1 tunable passband ratio. The circuit presented here represents the first single switch bandwidth reconfigurable filter that requires no size compensation to maintain a fixed center frequency.
Two axes orthogonal drive transmission for omnidirectional crawler with surface contact

In this paper, we propose an omnidirectional mobile mechanism with surface contact. This mechanism is expected to perform on rough terrain and weak ground at disaster sites. In the discussion on the drive mechanism, we explain how a two axes orthogonal drive transmission system is important and we propose a principle drive mechanism for omnidirectional motion. In addition, we demonstrated that the proposed drive mechanism has potential for omnidirectional movement on rough ground by conducting experiments with prototypes.
An effective and fast iris recognition system based on a combined multiscale feature extraction technique

The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and the various sources of intra-class variations result in the difficulty of iris representation. Although, a number of iris recognition methods have been proposed, it has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. In this paper and after a thorough analysis and summarization, a multiscale edge detection approach has been employed as a pre-processing step to efficiently localize the iris followed by a new feature extraction technique which is based on a combination of some multiscale feature extraction techniques. This combination uses special Gabor filters and wavelet maxima components. Finally, a promising feature vector representation using moment invariants is proposed. This has resulted in a compact and efficient feature vector. In addition, a fast matching scheme based on exclusive OR operation to compute bits similarity is proposed where the result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner and comparable to the best iris recognition algorithm found in the current literature.
Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set

Basketball team formation Ranking Multi-criteria decision-making (MCDM) TOPSIS a b s t r a c t Basketball is one of the most popular sports games in the world. Professional basketball has become a significant contributor to global economics and business. Considerable funds attracted by the game motivate participants of the sporting process (players, coaches, club owners, administration and etc.) to strive for better athletic results, this way promoting internal and external rivalry. A large number of players and the desire of teams to attract better team members as well as improve the quality of the already available athletes, boost the use of assessment and rating processes. The most popular and widely used player rating systems are based on performance statistics, which reflect situational factors of the game. Most specialists believe that such systems lack objectivity. Meanwhile, the Authors suggest a systematic solution, i.e. an adjusted well-known TOPSIS method and principles for the design of the algorithm based on the method. As a consistent problem solving system, algorithms based on multi-criteria decision-making are regarded to be simple and clear, suitable to substantiate solutions as well as easily applied in practise. Methodologies used by the Authors will help ensuring a greater efficiency of player and team rating, more accurate prognoses of sports results, team formation, and optimisation of the training process considering individualism of team players and encouraging their versatility, i.e. conformity to general physical preparedness norms of the team. The suggested research methods could be used in other sports. Furthermore , these principles could be used in business management for team formation. Some authors argue that basketball requires reciprocal coaching with leadership and decision-making based on sharing (Erculj & Supej, 2009) as well as on a collaborative and facilitating environment , which is rather different from dominant and controlling basketball coaching described by (Adler & Adler, 1988). Basketball involves many continuous motor skills. However, shooting is a relatively discreet skill (Stec, 2012). Thus, basketball requires a tremendous amount of gross motor skills that involve the movement of the whole body. In basketball, taking a foul shot is a relatively closed environment when compared to the movement required to execute an offensive set. The sequence of plays is unpredictable and dependant on a situation. To a large extent, success depends on the ability to effectively execute discrete tasks. This does not imply that basketball has nothing to do with continuous motor …
iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

Clustering plays an important role in many large-scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widelyused topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph-based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways.
Short Segment Screw Fixation without Fusion for Unstable Thoracolumbar and Lumbar Burst Fracture : A Prospective Study on Selective Consecutive Patients

The purpose of this prospective study was to evaluate the efficacy and safety of screw fixation without bone fusion for unstable thoracolumbar and lumbar burst fracture.



Nine patients younger than 40 years underwent screw fixation without bone fusion, following postural reduction using a soft roll at the involved vertebra, in cases of burst fracture. Their motor power was intact in spite of severe canal compromise. The surgical procedure included postural reduction for 3 days and screw fixations at one level above, one level below and at the fractured level itself. The patients underwent removal of implants 12 months after the initial operation, due to possibility of implant failure. Imaging and clinical findings, including canal encroachment, vertebral height, clinical outcome, and complications were analyzed.



Prior to surgery, the mean pain score (visual analogue scale) was 8.2, which decreased to 2.2 at 12 months after screw fixation. None of the patients complained of worsening of pain during 6 months after implant removal. All patients were graded as having excellent or good outcomes at 6 months after implant removal. The proportion of canal compromise at the fractured level improved from 55% to 35% at 12 months after surgery. The mean preoperative vertebral height loss was 45.3%, which improved to 20.6% at 6 months after implant removal. There were no neurological deficits related to neural injury. The improved vertebral height and canal compromise were maintained at 6 months after implant removal.



Short segment pedicle screw fixation, including fractured level itself, without bone fusion following postural reduction can be an effective and safe operative technique in the management of selected young patients suffering from unstable burst fracture.
A novel ternary half adder and multiplier based on carbon nanotube field effect transistors

A lot of research has been done on multiple-valued logic (MVL) such as ternary logic in these years. MVL reduces the number of necessary operations and also decreases the chip area that would be used. Carbon nanotube field effect transistors (CNTFETs) are considered a viable alternative for silicon transistors (MOSFETs). Combining carbon nanotube transistors and MVL can produce a unique design that is faster and more flexible. In this paper, we design a new half adder and a new multiplier by nanotechnology using a ternary logic, which decreases the power consumption and chip surface and raises the speed. The presented design is simulated using CNTFET of Stanford University and HSPICE software, and the results are compared with those of other studies.
Connecting Two (or Less) Dots: Discovering Structure in News Articles

Finding information is becoming a major part of our daily life. Entire sectors, from Web users to scientists and intelligence analysts, are increasingly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture.
 In this article, we investigate methods for automatically connecting the dots---providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events starting with the decline of home prices (January 2007), and ending with the health care debate (2009).
 We formalize the characteristics of a good chain and provide a fast search-driven algorithm to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. We also provide a method to handle partially-specified endpoints, for users who do not know both ends of a story. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate that the objective we propose captures the users’ intuitive notion of coherence, and that our algorithm effectively helps users understand the news.
Using gamification to incentivize sustainable urban mobility

Sustainable urban mobility is an important dimension in a Smart City, and one of the key issues for city sustainability. However, innovative and often costly mobility policies and solutions introduced by cities are liable to fail, if not combined with initiatives aimed at increasing the awareness of citizens, and promoting their behavioural change. This paper explores the potential of gamification mechanisms to incentivize voluntary behavioural changes towards sustainable mobility solutions. We present a service-based gamification framework, developed within the STREETLIFE EU Project, which can be used to develop games on top of existing services and systems within a Smart City, and discuss the empirical findings of an experiment conducted in the city of Rovereto on the effectiveness of gamification to promote sustainable urban mobility.
From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records

Inferring phenotypic patterns from population-scale clinical data is a core computational task in the development of personalized medicine. One important source of data on which to conduct this type of research is patient Electronic Medical Records (EMR). However, the patient EMRs are typically sparse and noisy, which creates significant challenges if we use them directly to represent patient phenotypes. In this paper, we propose a data driven phenotyping framework called Pacifier (PAtient reCord densIFIER), where we interpret the longitudinal EMR data of each patient as a sparse matrix with a feature dimension and a time dimension, and derive more robust patient phenotypes by exploring the latent structure of those matrices. Specifically, we assume that each derived phenotype is composed of a subset of the medical features contained in original patient EMR, whose value evolves smoothly over time. We propose two formulations to achieve such goal. One is Individual Basis Approach (IBA), which assumes the phenotypes are different for every patient. The other is Shared Basis Approach (SBA), which assumes the patient population shares a common set of phenotypes. We develop an efficient optimization algorithm that is capable of resolving both problems efficiently. Finally we validate Pacifier on two real world EMR cohorts for the tasks of early prediction of Congestive Heart Failure (CHF) and End Stage Renal Disease (ESRD). Our results show that the predictive performance in both tasks can be improved significantly by the proposed algorithms (average AUC score improved from 0.689 to 0.816 on CHF, and from 0.756 to 0.838 on ESRD respectively, on diagnosis group granularity). We also illustrate some interesting phenotypes derived from our data.