nils-herrmann/clean_scirepeval huggingface dataset:
CNN-based Segmentation of Medical Imaging Data

Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of medical images. Though closely related to semantic segmentation, medical image segmentation includes specific challenges that need to be addressed, such as the scarcity of labelled data, the high class imbalance found in the ground truth and the high memory demand of three-dimensional images. In this work, a CNN-based method with three-dimensional filters is demonstrated and applied to hand and brain MRI. Two modifications to an existing CNN architecture are discussed, along with methods on addressing the aforementioned challenges. While most of the existing literature on medical image segmentation focuses on soft tissue and the major organs, this work is validated on data both from the central nervous system as well as the bones of the hand.
Robotics to promote elementary education pre-service teachers' STEM engagement, learning, and teaching

We report a research project with a purpose of help ing teachers learn how to design and implement science, technology, engineering, and mat hematics (STEM) lessons using robotics. Specifically, pre-service teachers’ STEM engagement, learning, and teaching via robotics were investigated in an elementary tea ch r preparation course. Data were collected from surveys, classroom observations, int erviews, and lesson plans. Both quantitative and qualitative data analyses indicate d that pre-service teachers engaged in robotics activities actively and mindfully. Their S TEM engagement improved overall. Their emotional engagement (e.g., interest, enjoyme nt) in STEM significantly improved and in turn influenced their behavioral and cogniti ve engagement in STEM. Their lesson designs showed their STEM teaching was developing i n productive directions although further work was needed. These findings suggest tha t robotics can be used as a technology in activities designed to enhance teache rs’ STEM engagement and teaching through improved attitudes toward STEM. Future rese arch and teacher education recommendations are also presented.
LiDAR Point Clouds to 3-D Urban Models$:$ A Review

Three-dimensional (3-D) urban models are an integral part of numerous applications, such as urban planning and performance simulation, mapping and visualization, emergency response training and entertainment, among others. We consolidate various algorithms proposed for reconstructing 3-D models of urban objects from point clouds. Urban models addressed in this review include buildings, vegetation, utilities such as roads or power lines and free-form architectures such as curved buildings or statues, all of which are ubiquitous in a typical urban scenario. While urban modeling, building reconstruction, in particular, clearly demand specific traits in the models, such as regularity, symmetry, and repetition; most of the traditional and state-of-the-art 3-D reconstruction algorithms are designed to address very generic objects of arbitrary shapes and topology. The recent efforts in the urban reconstruction arena, however, strive to accommodate the various pressing needs of urban modeling. Strategically, urban modeling research nowadays focuses on the usage of specialized priors, such as global regularity, Manhattan-geometry or symmetry to aid the reconstruction, or efficient adaptation of existing reconstruction techniques to the urban modeling pipeline. Aimed at an in-depth exploration of further possibilities, we review the existing urban reconstruction algorithms, prevalent in computer graphics, computer vision and photogrammetry disciplines, evaluate their performance in the architectural modeling context, and discuss the adaptability of generic mesh reconstruction techniques to the urban modeling pipeline. In the end, we suggest a few directions of research that may be adopted to close in the technology gaps.
A Review on Internet of Things for Defense and Public Safety

The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide beneﬁts similar to those in industry. It identiﬁes scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warﬁghter or ﬁrst responders, while reducing costs and increasing operation efﬁciency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military ﬁeld and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.
BUILDING INFORMATION MODELLING AND HERITAGE DOCUMENTATION

Despite the widespread adoption of building information modelling (BIM) for the design and lifecycle management of new buildings, very little research has been undertaken to explore the value of BIM in the management of heritage buildings and cultural landscapes. To that end, we are investigating the construction of BIMs that incorporate both quantitative assets (intelligent objects, performance data) and qualitative assets (historic photographs, oral histories, music). Further, our models leverage the capabilities of BIM software to provide a navigable timeline that chronicles tangible and intangible changes in the past and projections into the future. In this paper, we discuss three projects undertaken by the authors that explore an expanded role for BIM in the documentation and conservation of architectural heritage. The projects range in scale and complexity and include: a cluster of three, 19th century heritage buildings in the urban core of Toronto, Canada; a 600 hectare village in rural, south-eastern Ontario with significant modern heritage value, and a proposed web-centered BIM database for materials and methods of construction specific to heritage conservation.
Smart City as a Service (SCaaS): A Future Roadmap for E-Government Smart City Cloud Computing Initiatives

The concept of a smart city has been identified as not only representing a crucible for technological innovation, a medium for realizing global integration but also as an exemplar response for addressing current and impending global issues (societal, environmental economic and governance). There is now general consensus that emerging information communication technologies (ICT) such as cloud computing can be deployed as a vehicle for catalyzing smart city innovation. There is also evidence to suggest that cloud computing has become a strategic direction for many e-government initiatives as evidenced by the emergence of global government clouds (G-Cloud). In this paper, we delineate the concept of a smart city and explore the propitious potential of cloud computing to enable the development of smart cities. We review the current state of the art pertaining to a selection of cloud schemes currently being operationalized by international governments aimed at capitalizing on the nascent innovation capabilities of the technological paradigm, and present a future research roadmap for cloud facilitated smart city development.
A Raspberry Pi-Based Platform for Signal Processing Education [SP Education]

One of the most important application areas of signal processing (SP) is, without a doubt, the software-defined radio (SDR) field [1]-[3]. Although their introduction dates back to the 1980s, SDRs are now becoming the dominant technology in radio communications, thanks to the dramatic development of SP-optimized programmable hardware, such as field-programmable gate arrays (FPGAs) and digital signal processors (DSPs). Today, the computational throughput of these devices is such that sophisticated SP tasks can be efficiently handled, so that both the baseband and intermediate frequency (IF) sections of current communication systems are usually implemented, according to the SDR paradigm, by the FPGA's reconfigurable circuitry (e.g., [4]-[6]), or by the software running on DSPs.
Envy on Facebook: A Hidden Threat to Users' Life Satisfaction?

The wealth of social information presented on Facebook is astounding. While these affordances allow users to keep up-to-date, they also produce a basis for social comparison and envy on an unprecedented scale. Even though envy may endanger users’ life satisfaction and lead to platform avoidance, no study exists uncovering this dynamics. To close this gap, we build on responses of 584 Facebook users collected as part of two independent studies. In study 1, we explore the scale, scope, and nature of envy incidents triggered by Facebook. In study 2, the role of envy feelings is examined as a mediator between intensity of passive following on Facebook and users’ life satisfaction. Confirming full mediation, we demonstrate that passive following exacerbates envy feelings, which decrease life satisfaction. From a provider’s perspective, our findings signal that users frequently perceive Facebook as a stressful environment, which may, in the long-run, endanger platform sustainability.
The Effect of the General Data Protection Regulation on Medical Research

The enactment of the General Data Protection Regulation (GDPR) will impact on European data science. Particular concerns relating to consent requirements that would severely restrict medical data research have been raised.



Our objective is to explain the changes in data protection laws that apply to medical research and to discuss their potential impact.



Analysis of ethicolegal requirements imposed by the GDPR.



The GDPR makes the classification of pseudonymised data as personal data clearer, although it has not been entirely resolved. Biomedical research on personal data where consent has not been obtained must be of substantial public interest.



The GDPR introduces protections for data subjects that aim for consistency across the EU. The proposed changes will make little impact on biomedical data research.
Marginal Structural Models versus Structural nested Models as Tools for Causal inference

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs). This paper describes an alternative new class of causal modelsthe (non-nested) marginal structural models (MSMs). We will then describe a class of semiparametric estimators for the parameters of these new models under a sequential randomization (i.e., ignorability) assumption. We then compare the strengths and weaknesses of MSMs versus SNMs for causal inference from complex longitudinal data with time-dependent treatments and confounders. Our results provide an extension to continuous treatments of propensity score estimators of an average treatment effect.
A synthesis of the effects of occupational therapy for persons with stroke, Part II: Remediation of impairments.

This article is the second of a two-part synthesis of research regarding the effects of occupational therapy to improve activity and participation and to reduce impairment for persons with stroke. Part I synthesized research findings for restoration of role participation and activity performance. Part II synthesizes research findings regarding the effects of occupational therapy to remediate psychosocial, cognitive-perceptual, and sensorimotor impairments. Only 29 studies involving 832 participants (mean age = 64.3 years) addressed these goals. No studies directly researched the effects of occupational therapy on depression after stroke. Eight studies addressed cognitive-perceptual abilities. The findings indicated that homemaking tasks resulted in greater improvement of cognitive ability than paper-and-pencil drills and that tasks that forced awareness of neglected space, including movement of the opposite limb into that space, improved unilateral neglect. Fifteen studies examined the effect of occupational therapy on various motor capacities after stroke. Coordinated movement improved under these conditions: (a) following written and illustrated guides for movement exercises, (b) using meaningful goal objects as targets, (c) practicing movements with specific goals, (d) moving both arms simultaneously but independently, and (e) imagining functional use of the affected limb. Research on inhibitory splinting was inconclusive. Based on these few studies and lack of replication, we could make only tentative recommendations for practice. Further definitive research is needed.
OPTIMIZATION OF CASH MANAGEMENT FOR ATM NETWORK

This paper presents an approach to cash management for automatic teller machine (ATM) network. This approach is based on an artificial neural network to forecast a daily cash demand for every ATM in the network and on the optimization procedure to estimate the optimal cash load for every ATM. During the optimization procedure, the most important factors for ATMs maintenance were considered: cost of cash, cost of cash uploading and cost of daily services. Simulation studies show, that in case of higher cost of cash (interest rate) and lower cost for money uploading, the optimization procedure allows to decrease the ATMs maintenance costs around 15-20 %. For practical implementation of the proposed ATMs’ cash management procedure, further experimental investigations are necessary.
Review of Literature on Heat Transfer Enhancement in Compact Heat Exchangers

This paper features a broad discussion on the application of enhanced heat transfer surfaces to compact heat exchangers. The motivation for heat transfer enhancement is discussed, and the principles behind compact heat exchangers are summarized. Next, various methods for evaluating and comparing different types of heat transfer enhancement devices using ftrst and/or second law analysis are presented. Finally, the following plate-fm enhancement geometries are discussed: rectangular and triangular plain ftns, offset strip ftns, louvered fms, and vortex generators.  The study of enhanced heat transfer has gained serious momentum during recent years, however, due to increased demands by industry for heat exchange equipment that is less expensive to build and operate than standard heat exchange devices. Savings in materials and energy use also provide strong motivation for the development of improved methods of enhancement. When designing cooling systems for automobiles and spacecraft, it is imperative that the heat exchangers are especially compact and lightweight. Also, enhancement devices are necessary for the high heat duty exchangers found in power plants (i. e. air-cooled condensers, nuclear fuel rods). These applications, as well as numerous others, have led to the development of various enhanced heat transfer surfaces. In general, enhanced heat transfer surfaces can be used for three purposes: (1) to make heat exchangers more compact in order to reduce their overall volume, and possibly their cost, (2) to reduce the pumping power required for a given heat transfer process, or (3) to increase the overall UA value of the heat exchanger. A higher UA value can be exploited in either of two ways: (1) to obtain an increased heat exchange rate for ftxed fluid inlet temperatures, or (2) to reduce the mean temperature difference for the heat exchange; this increases the thermodynamic process efficiency, which can result in a saving of operating costs. Enhancement techniques can be separated into two categories: passive and active. Passive methods require no direct application of external power. Instead, passive techniques employ special surface geometries or fluid additives which cause heat transfer enhancement. On the other hand, active schemes such as electromagnetic ftelds and surface vibration do require external power for operation . The majority of commercially interesting enhancement techniques are passive ones. Active techniques have attracted little commercial interest because of the costs involved, and the problems that are associated with vibration or acoustic noise . This paper deals only with gas-side heat transfer enhancement using special surface geometries.
Movie Revenue Prediction Using Regression and Clustering

Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy. Specifically, two major steps were employed. Initially, linear regression, polynomial regression and support vector regression (SVR) were applied on the entire movie data to predict the movie revenue. Then, clustering techniques, such as by genre, using Expectation Maximization (EM) and using K-means were applied to divide data into groups before regression analyses are executed. To compare accuracy among different techniques, R-square and the root-mean-square error (RMSE) were used as a performance indicator. Our study shows that generally linear regression without clustering offers the model with the highest R-square, while linear regression with EM clustering yields the lowest RMSE.
On the power of quantum computation

1 Abstract This paper surveys the use of thèhybrid argument' to prove to that quantum computations are insensitive to small perturbations. This property of quantum computations is used to establish that quantum circuits are robust against inaccuracy in the implementation of its elementary quantum gates. The insensitivity to small perturbations is also used to establish lower-bounds, including showing that relative to an oracle, the class NP requires exponential time on a quantum computer; and that quantum algorithms are polynomially related to deterministic algorithms in the black box model. 2  Quantum computation is an exciting area that lies at the foundations of both quantum physics and computer science. Quantum computers appear to violate the modern form of the Church-Turing thesis which states that Supported by a JSEP grant.
Layerwise Interweaving Convolutional LSTM

A deep network structure is formed with LSTM layer and convolutional layer interweaves with each other. The Layerwise Interweaving Convolutional LSTM(LIC-LSTM) enhanced the feature extraction ability of LSTM stack and is capable for versatile sequential data modeling. Its unique network structure allows it to extract higher level features with sequential information involved. Experiment results show the model achieves higher accuracy and shoulders lower perplexity on sequential data modeling tasks compared with state of art LSTM models.
Gated Self-Matching Networks for Reading Comprehension and Question Answering

In this paper, we present the gated selfmatching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3% on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9%. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.
DNS-based email sender authentication mechanisms: A critical review

We describe and compare three predominant email sender authentication mechanisms based on DNS: SPF, DKIM and Sender-ID Framework (SIDF). These mechanisms are designed mainly to assist in filtering of undesirable email messages, in particular spam and phishing emails. We clarify the limitations of these mechanisms, identify risks, and make recommendations. In particular, we argue that, properly used, SPF and DKIM can both help improve the efficiency and accuracy of email filtering.
A Method for Learning From Hints

We address the problem of learning an unknown function by pu tting together several pieces of information (hints) that we know about the function. We introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated for new types of hints. All the hints are represented to the learning process by examples, and examples of the function are treated on equal footing with the rest of the hints. During learning, examples from different hints are selected for processing according to a given schedule. We present two types of schedules; fixed schedules that specify the relative emphasis of each hint, and adaptive schedules that are based on how well each hint has been learned so far. Our learning method is compatible with any descent technique that we may choose to use.
Design of a compliant constant force gripper mechanism based on buckled fixed-guided beam

This paper presents the design of a compliant constant force output gripper mechanism. The function of constant force output is achieved by using the negative stiffness effect of a buckled fixed-guided beam. One advantage is that it can eliminate the needs of complicated force-displacement combined control algorithm. Details of the nonlinear design process have been demonstrated. ANSYS APDL and MATLAB are used to solve this nonlinear problem. The structural design of the gripper is performed based on the theoretical model. An experimental study is carried out to verify the theoretical model. A force sensor and a displacement sensor are used to test the performance of the constant force output in the experiments. Results shows that the gripper can provide 1.1 N near constant force output in 200 μm range.
Cross-modal Retrieval with Correspondence Autoencoder

The problem of cross-modal retrieval, e.g., using a text query to search for images and vice-versa, is considered in this paper. A novel model involving correspondence autoencoder (Corr-AE) is proposed here for solving this problem. The model is constructed by correlating hidden representations of two uni-modal autoencoders. A novel optimal objective, which minimizes a linear combination of representation learning errors for each modality and correlation learning error between hidden representations of two modalities, is used to train the model as a whole. Minimization of correlation learning error forces the model to learn hidden representations with only common information in different modalities, while minimization of representation learning error makes hidden representations are good enough to reconstruct input of each modality. A parameter $\alpha$ is used to balance the representation learning error and the correlation learning error. Based on two different multi-modal autoencoders, Corr-AE is extended to other two correspondence models, here we called Corr-Cross-AE and Corr-Full-AE. The proposed models are evaluated on three publicly available data sets from real scenes. We demonstrate that the three correspondence autoencoders perform significantly better than three canonical correlation analysis based models and two popular multi-modal deep models on cross-modal retrieval tasks.