nils-herrmann/clean_scirepeval huggingface dataset:
Experiences with Selecting Search Engines Using Metasearch

Search engines are among the most useful and high-profile resources on the Internet. The problem of finding information on the Internet has been replaced with the problem of knowing where search engines are, what they are designed to retrieve, and how to use them. This article describes and evaluates SavvySearch, a metasearch engine designed to intelligently select and interface with multiple remote search engines. The primary metasearch issue examined is the importance of carefully selecting and ranking remote search engines for user queries. We studied the efficacy of SavvySearch's incrementally acquired metaindex approach to selecting search engines by analyzing the effect of time and experience on performance. We also compared the metaindex approach to the simpler categorical approach and showed how much experience is required to surpass the simple scheme.
A Tool for Automatic Hammersmith Infant Neurological Examination

Hammersmith Infant Neurological Examination (HINE) is a popular method to estimate the neurological development of infants aged less than two years. Using HINE, especially for preterm or premature babies, the risk of neural disorder can be minimized through proper preventive measures. This paper presents the design of a semi-automatic application that can be used as an aid to doctors for efficiently conducting the examinations listed in the Hammersmith chart. The user friendly version of the examination interface provides a platform for quantitative neurological assessment of the infants. It includes various simplified video and image based schemes that are suited to inexperienced staff. It provides an interface to go through the previous records of patients. Ten examinations are enlisted in the Hammersmith chart for neonatal babies. This paper examines a semi-automatic approach for posture estimation examination. For post neonatal infants, a follow-up management interface is designed that can be used to fetch / consult past records of the patients for better diagnosis. The application is currently in operation at Neonatal Intensive Care Unit (NICU) of Institute of Post-Graduate Medical Education & Research (IPGME & R) and Seth Sukhlal Karnani Memorial (SSKM) Hospital, Kolkata, India.
Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

Dynamic decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -aka quality propertiesand the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-time. In this paper we develop further our ideas on re-assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage.
Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.
Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.
Design of a Novel Wideband and Dual-Polarized MagnetoElectric Dipole Antenna

A wideband antenna with ±45° dual polarization is proposed. The antenna is mainly comprised of four printed trapezoidal metal walls standing on a ground plane that forms a 3-D open square aperture cavity structure with tapered cut on the four wall edges. The metal walls are fed by two orthogonally positioned (±45°) feeding networks, forming two pairs of dipole arms. Because of the excited magnetic-electric resonances, wide bandwidth can be achieved. The proposed antenna is fabricated, and the measured results show that it has an impedance bandwidth of 56% (1.62–2.87 GHz) for VSWR < 1.5. In addition, isolation of larger than 30 dB is also exhibited across the bandwidth between the two feeding ports. Because the proposed antenna has a hollow inside structure, it is a good candidate for multiband sharing aperture antenna element. Furthermore, it has a simplified fabrication process and is suitable for outdoor base station antennas that require VSWR < 1.5.
gSLIC: a real-time implementation of SLIC superpixel segmentation

We introduce a parallel implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Our implementation uses GPU and the NVIDIA CUDA framework. Using a single graphic card, our implementation achieves speedups of 10x∼20x from the sequential implementation. This allow us to use the superpixel segmentation method in real-time performance. Our implementation is compatible with the standard sequential implementation. Finally, the software is now online and is open source.
Protective action of green tea catechins in neuronal mitochondria during aging.

Mitochondria are central players in the regulation of cell homeostasis. They are essential for energy production but at the same time, reactive oxygen species accumulate as byproducts of the electron transport chain causing mitochondrial damage. In the central nervous system, senescence and neurodegeneration occur as a consequence of mitochondrial oxidative insults and impaired electron transfer. The accumulation of several oxidation products in neurons during aging prompts the idea that consumption of antioxidant compounds may delay neurodegenerative processes. Tea, one of the most consumed beverages in the world, presents benefits to human health that have been associated to its abundance in polyphenols, mainly catechins, that possess powerful antioxidant properties in vivo and in vitro. In this review, the focus will be placed on the effects of green tea catechins in neuronal mitochondria. Although these compounds reach the brain in small quantities, there are several possible targets, signaling pathways and molecular machinery impinging in the mitochondria that will be highlighted. Accumulated evidence thus far seems to indicate that catechins help prevent neurodegeneration and delay brain function decline.
Scalable and Crash-Tolerant Load Balancing Based on Switch Migration for Multiple Open Flow Controllers

As the size of networks continues to increase, the scalability of the centralized controller becomes increasingly issues in Software Defined Networking. Distributed controllers have been proposed to solve the problem that the centralized controllers such as NOX and Floodlight suffer from. That logically centralized, but physically distributed architecture divide the network into zones with separate multiple controllers to achieve a better scalability control plane. However, Such distributed architecture introduces a new challenge to the load rebalancing of controllers when uneven load distribution among the controllers due to the statically configured mapping between a switch and a controller. Therefore, under variable traffic conditions in real networks, keeping load balance dynamically among the controller clusters is essential for realizing a high performance and scalability control plane. To address these issues, we propose a dynamic load rebalancing method based on switch migration mechanism for clustered controllers. The multiple controllers use Groups to coordinate actions for switch migration. The whole network is divided into several groups and each group is set up one controller cluster. Our proposed method can dynamically shift the load across the multiple controllers through switch migration. The mechanism support controller failover without switch disconnection avoiding the single point of failure problem. We also implemented a prototype system based on Open Day light Hydrogen controller to evaluated the performance of our design. Our preliminary result shows that the method enables controllers to relieve the overload via switch migration and can improve throughput and reduce the response time of the control plane.
Round Robin Classification

In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results show that the technique is very likely to improve Ripper’s classification accuracy without having a high risk of decreasing it. More importantly, we give a general theoretical analysis of the complexity of the approach and show that its run-time complexity is below that of the commonly used one-against-all technique. These theoretical results are not restricted to rule learning but are also of interest to other communities where pairwise classification has recently received some attention. Furthermore, we investigate its properties as a general ensemble technique and show that round robin classification with C5.0 may improve C5.0’s performance on multi-class problems. However, this improvement does not reach the performance increase of boosting, and a combination of boosting and round robin classification does not produce any gain over conventional boosting. Finally, we show that the performance of round robin classification can be further improved by a straight-forward integration with bagging.
Recovering pulse rate during motion artifact with a multi-imager array for non-contact imaging photoplethysmography

Photoplethysmography relies on characteristic changes in the optical absorption of tissue due to pulsatile (arterial) blood flow in peripheral vasculature. Sensors for observing the photoplethysmographic effect have traditionally required contact with the skin surface. Recent advances in non-contact imaging photoplethysmography have demonstrated that measures of cardiopulmonary system state, such as pulse rate, pulse rate variability, and respiration rate, can be obtained from a participant by imaging their face under relatively motionless conditions. A critical limitation in this method that must be resolved is the inability to recover these measures under conditions of head motion artifact. To investigate the adequacy of channel space dimensionality for the use of blind source separation in this context, nine synchronized, visible spectrum imagers positioned in a semicircular array centered on the imaged participant were used for data acquisition in a controlled lighting environment. Three-lead electrocardiogram and finger-tip reflectance photoplethysmogram were also recorded as ground truth signals. Controlled head motion artifact trial conditions were compared to trials in which the participant remained stationary, with and without the aid of a chinrest. Bootstrapped means of one-minute, non-overlapping trial segments show that, for situations involving little to no head motion, a single imager is sufficient for recovering pulse rate with an average absolute error of less than two beats per minute. However, error in the recovered pulse rate measurement for the single imager can be as high as twenty-two beats per minute when head motion artifact is severe. This increase in measurement error during motion artifact was mitigated by increasing the dimensionality of the imager channel space with multiple imagers in the array prior to applying blind source separation. In contrast to single-imager results, the multi-imager channel space resulted in an absolute error in the recovered pulse rate measurement that is comparable with pulse rate measured via fingertip reflectance photoplethysmography. These results demonstrate that non-contact, imaging photoplethysmography can be accurate in the presence of head motion artifact when a multi-imager array is implemented to increase the dimensionality of the decomposed channel space.
Finding a Shortest Solution for the N × N Extension of the 15-PUZZLE Is Intractable

The g-puzzle and the 15puzzle have been used for many years as a domain for testing heuristic search techniques. From experience it is known that these puzzles are “difficult” and therefore useful for testing search techniques. In this paper we give strong evidence that these puzzles are indeed good test problems. We extend the 8puzzle and the Epuzzle to a nxn board and show that finding a shortest solution for the extended puzzle is NP-hard and thus computationally infeasible. We also present an approximation algorithm for transforming boards that is guaranteed to use no more than c%(V) moves, where L(SP) is the length of the shortest solution and c is a constant which is independent of the given boards and their size n .
Global optimization for artificial neural networks: A tabu search application

The ability of neural networks to closely approximate unknown functions to any degree of desired accuracy has generated considerable demand for Neural Network research in Business. The attractiveness of neural network research stems from researchers’ need to approximate models within the business environment without having a priori knowledge about the true underlying function. Gradient techniques, such as backpropagation, are currently the most widely used methods for neural network optimization. Since these techniques search for local solutions, a global search algorithm is warranted. In this paper we examine a recently popularized optimization technique, Tabu Search, as a possible alternative to the problematic backpropagation. A Monte Carlo study was conducted to test the appropriateness of this global search technique for optimizing neural networks. Holding the neural network architecture constant, 530 independent runs were conducted for each of seven test functions, including a production function that exhibits both increasing and diminishing marginal returns and the Mackey-Glass chaotic time series, were used for a comparison of Tabu Search and backpropagation optimized neural networks. Tabu Search derived significantly superior solutions for in-sample, interpolation, and extrapolation test data for all seven test functions. It was also shown that fewer function evaluations were needed to find these optimal values.
Price Prediction of Share Market using Artificial Neural Network (ANN)

Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network. General Terms Artificial Neural Network, Machine Learning, Back propagation Algorithms, Share Market.
A Public Domain Dataset for Human Activity Recognition using Smartphones

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.
Continuous and fine-grained breathing volume monitoring from afar using wireless signals

In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.
A critical evaluation of the complex PTSD literature: implications for DSM-5.

Complex posttraumatic stress disorder (CPTSD) has been proposed as a diagnosis for capturing the diverse clusters of symptoms observed in survivors of prolonged trauma that are outside the current definition of PTSD. Introducing a new diagnosis requires a high standard of evidence, including a clear definition of the disorder, reliable and valid assessment measures, support for convergent and discriminant validity, and incremental validity with respect to implications for treatment planning and outcome. In this article, the extant literature on CPTSD is reviewed within the framework of construct validity to evaluate the proposed diagnosis on these criteria. Although the efforts in support of CPTSD have brought much needed attention to limitations in the trauma literature, we conclude that available evidence does not support a new diagnostic category at this time. Some directions for future research are suggested.
Pharmacist-managed medication therapy adherence clinics: The Malaysian experience.

In Malaysia, the first pharmacist-managed clinic was established in 2004. This type of clinics is locally known as a medication therapy adherence clinic (MTAC).1e3 In fact, these clinics were introduced by the Pharmaceutical Services Division (PSD), Ministry of Health Malaysia (MOH) as part of the clinical pharmacy services to improve the quality, safety, and cost-effectiveness of patient care through better medicines management at ambulatory care settings in hospitals and MOH clinics.2 The major aims of the clinics include optimization of medication therapy, improvement of medication adherence, and prevention or reduction of adverse drug events and other medication related  Over a decade, the number of MTACs has increased tremendously to 660 by the end of 2013.1 Moreover, currently, there are 13 types of clinics being operated in Malaysia that include neurology (stroke), diabetes, heart failure, respiratory, psoriasis, anticoagulation, rheumatology, nephrology, hepatitis, geriatrics, retroviral disease, hemophilia, and psychiatry.1,3,4 The pharmacists operate the clinics and provide pharmaceutical care services to the patients in collaboration with physicians and other healthcare professionals according to specific protocols. The specific roles of the pharmacist include providing drug information, patient education and counseling, identifying and solving drugrelated problems, monitoring patients' drug therapy and treatment outcomes, offering feedback on patient progress, making appropriate recommendations to physicians to individualize the patients' drug regimens according to patient-related factors including clinical, humanistic, and economic factors.1e4 Several studies have been conducted to evaluate the impact of pharmacist-managed clinics on patient care in Malaysia.5e10 These studies have demonstrated that the clinics are beneficial in optimizing medication therapy, improving medication adherence, and giving better clinical outcomes. Moreover, some studies have shown that these clinics are more cost-effective than standard medical care.11,12 In addition, it has been shown that the patients are satisfied with the pharmaceutical care services provided by these clinics.13,14 Therefore, the current evidence supports further expansion of these clinics in the ambulatory care system in Malaysia. We believe there are several factors that have led to the success of the pharmacist-managed clinics in Malaysia. These include the tremendous support and guidance from the PSD, Ministry of Health Malaysia to help in the establishment of these clinics. As an example of this support, the PSD has established a protocol for each clinic to help the pharmacists run the clinic, and to enable standardization of practice and the expansion of the clinics throughout the country. These protocols are written by specialized clinical pharmacy committees (e.g. respiratory subspecialty,