nils-herrmann/clean_scirepeval huggingface dataset:
Options for Formulating a Digital Transformation Strategy

Integrating and exploiting new digital technologies is one of the biggest challenges that companies currently face. No sector or organization is immune to the effects of digital transformation. The market-changing potential of digital technologies is often wider than products, business processes, sales channels or supply chains—entire business models are being reshaped and frequently overturned.2 As a result, digital transformation has become a high priority on leadership agendas, with nearly 90% of business leaders in the U.S. and U.K. expecting IT and digital technologies to make an increasing strategic contribution to their overall business in the coming decade.3 The question is no longer when companies need to make digital transformation a strategic priority—this tipping point has passed—but how to embrace it and use it as a competitive advantage. Faced with the digital transformation challenge and the need to remain competitive in their industries, business leaders must formulate and execute strategies that embrace the implications of digital transformation and drive better operational performance. Unfortunately, there are many recent examples of organizations that have been unable to keep pace with the new digital reality. Prominent examples include the bankruptcy of the movie-rental company Blockbuster and the sale of the Washington Post to Jeff Bezos, founder of Amazon—largely resulting from those firms’
Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.
An Accurate Power Control Strategy for Power-Electronics-Interfaced Distributed Generation Units Operating in a Low-Voltage Multibus Microgrid

In this paper, a power control strategy is proposed for a low-voltage microgrid, where the mainly resistive line impedance, the unequal impedance among distributed generation (DG) units, and the microgrid load locations make the conventional frequency and voltage droop method unpractical. The proposed power control strategy contains a virtual inductor at the interfacing inverter output and an accurate power control and sharing algorithm with consideration of both impedance voltage drop effect and DG local load effect. Specifically, the virtual inductance can effectively prevent the coupling between the real and reactive powers by introducing a predominantly inductive impedance even in a low-voltage network with resistive line impedances. On the other hand, based on the predominantly inductive impedance, the proposed accurate reactive power sharing algorithm functions by estimating the impedance voltage drops and significantly improves the reactive power control and sharing accuracy. Finally, considering the different locations of loads in a multibus microgrid, the reactive power control accuracy is further improved by employing an online estimated reactive power offset to compensate the effects of DG local load power demands. The proposed power control strategy has been tested in simulation and experimentally on a low-voltage microgrid prototype.
Integrated MIMO Slot Antenna on Laptop Computer for Eight-Band LTE/WWAN Operation

This paper proposes a two-element multi-input multi-output (MIMO) open-slot antenna implemented on the display ground plane of a laptop computer for eight-band long-term evolution/wireless wide-area network operations. The metal surroundings of the antennas have been well integrated as a part of the radiation structure. In the single-element open-slot antenna, the nearby hinge slot (which is bounded by two ground planes and two hinges) is relatively large as compared with the open slot itself and acts as a good radiator. In the MIMO antenna consisting of two open-slot elements, a T slot is embedded in the display ground plane and is connected to the hinge slot. The T and hinge slots when connected behave as a radiator; whereas, the T slot itself functions as an isolation element. With the isolation element, simulated isolations between the two elements of the MIMO antenna are raised from 8.3–11.2 to 15–17.1 dB in 698–960 MHz and from 12.1–21 to 15.9–26.7 dB in 1710–2690 MHz. Measured isolations with the isolation element in the desired low- and high-frequency ranges are 17.6–18.8 and 15.2–23.5 dB, respectively. Measured and simulated efficiencies for the two-element MIMO antenna with either element excited are both larger than 50% in the desired operating frequency bands.
Rule extraction: Using neural networks or for neural networks?

In the research of rule extraction from neural networks,fidelity describes how well the rules mimic the behavior of a neural network whileaccuracy describes how well the rules can be generalized. This paper identifies thefidelity-accuracy dilemma. It argues to distinguishrule extraction using neural networks andrule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.
Asynchronous Accelerated Stochastic Gradient Descent

Stochastic gradient descent (SGD) is a widely used optimization algorithm in machine learning. In order to accelerate the convergence of SGD, a few advanced techniques have been developed in recent years, including variance reduction, stochastic coordinate sampling, and Nesterov’s acceleration method. Furthermore, in order to improve the training speed and/or leverage larger-scale training data, asynchronous parallelization of SGD has also been studied. Then, a natural question is whether these techniques can be seamlessly integrated with each other, and whether the integration has desirable theoretical guarantee on its convergence. In this paper, we provide our formal answer to this question. In particular, we consider the asynchronous parallelization of SGD, accelerated by leveraging variance reduction, coordinate sampling, and Nesterov’s method. We call the new algorithm asynchronous accelerated SGD (AASGD). Theoretically, we proved a convergence rate of AASGD, which indicates that (i) the three acceleration methods are complementary to each other and can make their own contributions to the improvement of convergence rate; (ii) asynchronous parallelization does not hurt the convergence rate, and can achieve considerable speedup under appropriate parameter setting. Empirically, we tested AASGD on a few benchmark datasets. The experimental results verified our theoretical findings and indicated that AASGD could be a highly effective and efficient algorithm for practical use.
Comparison of Two Optimal Rotary Transformer Designs for Highly Constrained Applications

The lack of reliability of gliding contacts in highly constrained environments induces manufacturers to develop contactless transmission power systems such as rotary transformers. The following paper proposes an optimal design methodology for rotary transformers supplied from a low-voltage source at high temperatures. The method is based on an accurate multidisciplinary analysis model divided into magnetic, thermal and electrical parts, optimized thanks to a sequential quadratic programming method. The technique is used to discuss the design particularities of rotary transformers. Two optimally designed structures of rotary transformers : an iron silicon coaxial one and a ferrite pot core one, are compared.
Reliability and Validity of the Korean Version of the Connor-Davidson Resilience Scale

The Connor-Davidson Resilience Scale (CD-RISC) measures various aspects of psychological resilience in patients with posttraumatic stress disorder (PTSD) and other psychiatric ailments. This study sought to assess the reliability and validity of the Korean version of the Connor-Davidson Resilience Scale (K-CD-RISC).



In total, 576 participants were enrolled (497 females and 79 males), including hospital nurses, university students, and firefighters. Subjects were evaluated using the K-CD-RISC, the Beck Depression Inventory (BDI), the Impact of Event Scale-Revised (IES-R), the Rosenberg Self-Esteem Scale (RSES), and the Perceived Stress Scale (PSS). Test-retest reliability and internal consistency were examined as a measure of reliability, and convergent validity and factor analysis were also performed to evaluate validity.



Cronbach's alpha coefficient and test-retest reliability were 0.93 and 0.93, respectively. The total score on the K-CD-RISC was positively correlated with the RSES (r=0.56, p<0.01). Conversely, BDI (r=-0.46, p<0.01), PSS (r=-0.32, p<0.01), and IES-R scores (r=-0.26, p<0.01) were negatively correlated with the K-CD-RISC. The K-CD-RISC showed a five-factor structure that explained 57.2% of the variance.



The K-CD-RISC showed good reliability and validity for measurement of resilience among Korean subjects.
Cognitive Developmental Robotics: A Survey

Cognitive developmental robotics (CDR) aims to provide new understanding of how human's higher cognitive functions develop by means of a synthetic approach that developmentally constructs cognitive functions. The core idea of CDR is ldquophysical embodimentrdquo that enables information structuring through interactions with the environment, including other agents. The idea is shaped based on the hypothesized development model of human cognitive functions from body representation to social behavior. Along with the model, studies of CDR and related works are introduced, and discussion on the model and future issues are argued.
The digital agenda of virtual currencies: Can BitCoin become a global currency?

This paper identifies and analyzes BitCoin features which may facilitate Bitcoin to become a global currency, as well as characteristics which may impede the use of BitCoin as a medium of exchange, a unit of account and a store of value, and compares BitCoin with standard currencies with respect to the main functions of money. Among all analyzed BitCoin features, the extreme price volatility stands out most clearly compared to standard currencies. In order to understand the reasons for such extreme price volatility, we attempt to identify drivers of BitCoin price formation and estimate their importance econometrically. We apply time-series analytical mechanisms to daily data for the 2009-2014 period. Our estimation results suggest that BitCoin attractiveness indicators are the strongest drivers of BitCoin price followed by market forces. In contrast, macro-financial developments do not determine BitCoin price in the long-run. Our findings suggest that as long as BitCoin price will be mainly driven by speculative investments, BitCoin will not be able to compete with standard currencies.
Autopilot System for Kiteplane

This paper proposes an autopilot system for a small and light unmanned air vehicle called Kiteplane. The Kiteplane has a large delta-shaped main wing that is easily disturbed by the wind, which was minimized by utilizing trim flight with drift. The proposed control system for autonomous trajectory following with a wind disturbance included fuzzy logic controllers, a speed controller, a wind disturbance attenuation block, and low-level feedback controllers. The system was implemented onboard the aircraft. Experiments were performed to test the performance of the proposed system and the Kiteplane nearly succeeded in following the desired trajectory, under the wind disturbance. Although the path was not followed perfectly, the airplane was able to traverse the waypoints by utilizing a failsafe waypoint updating rule
Optimising Learning Using Flashcards : Spacing Is More Effective Than Cramming

The spacing effect—that is, the benefit of spacing learning events apart rather than massing them together—has been demonstrated in hundreds of experiments, but is not well known to educators or learners. I investigated the spacing effect in the realistic context of flashcard use. Learners often divide flashcards into relatively small stacks, but compared to a large stack, small stacks decrease the spacing between study trials. In three experiments, participants used a web-based study programme to learn GRE-type word pairs. Studying one large stack of flashcards (i.e. spacing) was more effective than studying four smaller stacks of flashcards separately (i.e. massing). Spacing was also more effective than cramming—that is, massing study on the last day before the test. Across experiments, spacing was more effective than massing for 90% of the participants, yet after the first study session, 72% of the participants believed that massing had been more effective than spacing.
Virtual Reality for Training and Lifelong Learning

This article covers the application of virtual reality (VR) to training and lifelong learning. A number of considerations concerning the design of VR applications are included. The introduction is dedicated to the more general aspects of applying VR to training. From multiple perspectives, we will provide an overview of existing applications with their main purposes and go into more depth on certain learning areas. Recent developments of virtual environments for training and lifelong learning are analyzed, followed by an analytical viewpoint on design, advocating more explicit paradigmatic considerations and development of generic design methods. These approaches and proposals are aimed at better exploiting the uniqueness of VR and designing more effective virtual environments. Finally, a number of conclusions will be drawn for future technology-enhanced training and for lifelong learning using VR.
Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients.

A decrease in the abundance and biodiversity of intestinal bacteria within the dominant phylum Firmicutes has been observed repeatedly in Crohn disease (CD) patients. In this study, we determined the composition of the mucosa-associated microbiota of CD patients at the time of surgical resection and 6 months later using FISH analysis. We found that a reduction of a major member of Firmicutes, Faecalibacterium prausnitzii, is associated with a higher risk of postoperative recurrence of ileal CD. A lower proportion of F. prausnitzii on resected ileal Crohn mucosa also was associated with endoscopic recurrence at 6 months. To evaluate the immunomodulatory properties of F. prausnitzii we analyzed the anti-inflammatory effects of F. prausnitzii in both in vitro (cellular models) and in vivo [2,4,6-trinitrobenzenesulphonic acid (TNBS)-induced] colitis in mice. In Caco-2 cells transfected with a reporter gene for NF-kappaB activity, F. prausnitzii had no effect on IL-1beta-induced NF-kappaB activity, whereas the supernatant abolished it. In vitro peripheral blood mononuclear cell stimulation by F. prausnitzii led to significantly lower IL-12 and IFN-gamma production levels and higher secretion of IL-10. Oral administration of either live F. prausnitzii or its supernatant markedly reduced the severity of TNBS colitis and tended to correct the dysbiosis associated with TNBS colitis, as demonstrated by real-time quantitative PCR (qPCR) analysis. F. prausnitzii exhibits anti-inflammatory effects on cellular and TNBS colitis models, partly due to secreted metabolites able to block NF-kappaB activation and IL-8 production. These results suggest that counterbalancing dysbiosis using F. prausnitzii as a probiotic is a promising strategy in CD treatment.
Core affect and the psychological construction of emotion.

At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated. These states--called core affect--influence reflexes, perception, cognition, and behavior and are influenced by many causes internal and external, but people have no direct access to these causal connections. Core affect can therefore be experienced as free-floating (mood) or can be attributed to some cause (and thereby begin an emotional episode). These basic processes spawn a broad framework that includes perception of the core-affect-altering properties of stimuli, motives, empathy, emotional meta-experience, and affect versus emotion regulation; it accounts for prototypical emotional episodes, such as fear and anger, as core affect attributed to something plus various nonemotional processes.
DART: Dense Articulated Real-Time Tracking

This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree. DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators. To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization. We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past.
Inkjet-Printed Humidity Sensor for Passive UHF RFID Systems

This paper presents a novel inkjet-printed humidity sensor tag for passive radio-frequency identification (RFID) systems operating at ultrahigh frequencies (UHFs). During recent years, various humidity sensors have been developed by researchers around the world for HF and UHF RFID systems. However, to our best knowledge, the humidity sensor presented in this paper is one of the first passive UHF RFID humidity sensor tags fabricated using inkjet technology. This paper describes the structure and operation principle of the sensor tag as well as discusses the method of performing humidity measurements in practice. Furthermore, measurement results are presented, which include air humidity-sensitivity characterization and tag identification performance measurements.
Efficient 3D shape matching and retrieval using a concrete radialized spherical projection representation

We present a 3D shape retrieval methodology based on the theory of spherical harmonics. Using properties of spherical harmonics, scaling and axial flipping invariance is achieved. Rotation normalization is performed by employing the continuous principal component analysis along with a novel approach which applies PCA on the face normals of the model. The 3D model is decomposed into a set of spherical functions which represents not only the intersections of the corresponding surface with rays emanating from the origin but also points in the direction of each ray which are closer to the origin than the furthest intersection point. The superior performance of the proposed methodology is demonstrated through a comparison against state-of-the-art approaches on standard databases.
A model for measuring e-learning systems success in universities

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.03.028 ⇑ Corresponding author.  E-mail addresses: ar_hassanzadeh@modares.ac.ir com (A. Hassanzadeh), ftmh_kanani@yahoo.com (F. K (S. Elahi). 1 Measuring e-learning systems success. In the era of internet, universities and higher education institutions are increasingly tend to provide e-learning. For suitable planning and more enjoying the benefits of this educational approach, a model for measuring success of e-learning systems is essential. So in this paper, we try to survey and present a model for measuring success of e-learning systems in universities. For this purpose, at first, according to literature review, a conceptual model was designed. Then, based on opinions of 33 experts, and assessing their suggestions, research indicators were finalized. After that, to examine the relationships between components and finalize the proposed model, a case study was done in 5 universities: Amir Kabir University, Tehran University, Shahid Beheshti University, Iran University of Science & Technology and Khaje Nasir Toosi University of Technology. Finally, by analyzing questionnaires completed by 369 instructors, students and alumni, which were e-learning systems user, the final model (MELSS Model).
The aerodynamics of insect flight.

The flight of insects has fascinated physicists and biologists for more than a century. Yet, until recently, researchers were unable to rigorously quantify the complex wing motions of flapping insects or measure the forces and flows around their wings. However, recent developments in high-speed videography and tools for computational and mechanical modeling have allowed researchers to make rapid progress in advancing our understanding of insect flight. These mechanical and computational fluid dynamic models, combined with modern flow visualization techniques, have revealed that the fluid dynamic phenomena underlying flapping flight are different from those of non-flapping, 2-D wings on which most previous models were based. In particular, even at high angles of attack, a prominent leading edge vortex remains stably attached on the insect wing and does not shed into an unsteady wake, as would be expected from non-flapping 2-D wings. Its presence greatly enhances the forces generated by the wing, thus enabling insects to hover or maneuver. In addition, flight forces are further enhanced by other mechanisms acting during changes in angle of attack, especially at stroke reversal, the mutual interaction of the two wings at dorsal stroke reversal or wing-wake interactions following stroke reversal. This progress has enabled the development of simple analytical and empirical models that allow us to calculate the instantaneous forces on flapping insect wings more accurately than was previously possible. It also promises to foster new and exciting multi-disciplinary collaborations between physicists who seek to explain the phenomenology, biologists who seek to understand its relevance to insect physiology and evolution, and engineers who are inspired to build micro-robotic insects using these principles. This review covers the basic physical principles underlying flapping flight in insects, results of recent experiments concerning the aerodynamics of insect flight, as well as the different approaches used to model these phenomena.